characterising the relationship between fatigue and depression · 2017-09-04 · depression and...

CHARACTERISING THE RELATIONSHIP

BETWEEN FATIGUE AND DEPRESSION

Elizabeth Corfield

BSc (Honours I)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Institute of Health and Biomedical Innovation

Faculty of Health

Queensland University of Technology

2017

Characterising the Relationship between Fatigue and Depression i

Keywords

Candidate gene, chronic fatigue syndrome, comorbidity, depression, familiality,

fatigue, gene-based analysis, genetic relationship, genetics, genetic association,

genome-wide association, heritability, major depression, minor depression, major

depressive disorder, minor depressive disorder, population genetics,

symptomatology, and twin study.

ii Characterising the Relationship between Fatigue and Depression

Abstract

Fatigue is a common symptom, which is represented by a spectrum and associated

with numerous disorders, including major depressive disorder. Fatigue associated

with major depressive disorder further reduces the quality of life and increased

functional impairment in patients. However, little is known about the etiology of

fatigue and major depressive disorder. Additionally, the underlying mechanisms that

facilitate the high prevalence of comorbid fatigue and depression are poorly

understood. The objective of this dissertation was to increase our knowledge and

understanding of the comorbidity and genetics of fatigue and depression.

The majority of this project has been conducted utilising an older, Australian

twin cohort (comprising 2,281 twin pairs) with a self-report measure of fatigue and

depression. Microarray genotyping data was available for a subset of this population

(307 fatigue cases and 744 non-fatigued controls). Phenotypic characterisation was

undertaken to determine the fatigue and depression status of individuals within the

cohort. Additionally, a small chronic fatigue syndrome cohort (comprising 47

patients and 55 healthy controls) was utilised in Chapter 7. Microarray genotyping

data was available for all individuals in the chronic fatigue syndrome cohort.

A symptomatic analysis was conducted to determine if the depression symptom

profile differed between fatigued and non-fatigued individuals. Similarly, differences

in the fatigue symptom profile were investigated in individuals with major depressive

disorder, minor depressive disorder, and non-depressed individuals. This analysis

was conducted utilising logistic regression modelling. Results from this analysis

indicated fatigued individuals experienced significantly increased depression

symptomatology and prevalence. The most significant finding from this analysis was

the identification that the overlapping symptomatology between fatigue and

depression was not driving the association between the two phenotypes.

The familiality and heritability of fatigue were then investigated to determine

the relative importance of genetic and environmental factors in the total phenotypic

variation. Relative risks and structural equation modelling were utilising within this

analysis, which was replicated for major depressive disorder and minor depressive

disorder. Additionally, the liability threshold model was fitted to determine if major

Characterising the Relationship between Fatigue and Depression iii

depressive disorder and minor depressive disorder exist on a continuum. A larger

Australian depression cohort was utilised to determine whether broadening the

depression case phenotype, to include minor depressive disorder, in genome-wide

association analyses, will facilitate the elucidation of the molecular mechanisms of

major depressive disorder. Results from these analyses indicated fatigue, minor

depressive disorder, and major depressive disorder are all familial and have

significant additive genetic contributions. The most important finding from these

analyses is that minor depressive disorder and major depressive disorder exist on a

genetic continuum and that utilisation of a broad depression phenotype (which

includes both minor depressive disorder and major depressive disorder cases) should

facilitate further elucidation of the underlying genetic architecture of major

depressive disorder.

Expansion of the familiality and heritability analyses was utilised to assess the

magnitude of shared heritability between depression and fatigue. Furthermore, the

co-twin control method was utilised to determine whether the association between

depression and fatigue is explained by a causal, non-causal, non-causal shared

environment, or non-causal genetic model. Results from these analyses indicated a

significant additive genetic correlation of 0.71 (95% confidence interval = 0.51-0.92)

and bivariate heritability of 21% (95% confidence interval = 10-35%) exist between

depression and fatigue. Additionally, the association between depression and fatigue

is likely explained by a non-causal genetic relationship. The most important finding

from this analysis was that the contribution of shared genetic factors remained

significant independently of the overlapping symptomatology of the traits.

A genome-wide association analysis and gene-based investigation of fatigue

were then conducted, utilising linear mixed modelling, including a genetic

relationship matrix, to account for the relatedness within the data. While, an

evaluation of previous genetic findings associated with CFS was conducted, utilising

a chi-squared allelic test and gene-based analysis was conducted in a chronic fatigue

syndrome cohort. Results from this analysis indicated previously implicated genes

and risk loci are likely false positives and are unlikely to be associated with fatigue

or chronic fatigue syndrome. The most important finding from this analysis was the

identification of six genomic locations of interest, which are potentially associated

with fatigue.

iv Characterising the Relationship between Fatigue and Depression

Overall, these results provided evidence supporting a substantial additive

genetic overlap between fatigue and depression, which is independent of their

overlapping symptoms.

Characterising the Relationship between Fatigue and Depression v

Table of Contents

Keywords .................................................................................................................................. i

Abstract .................................................................................................................................... ii

Table of Contents ......................................................................................................................v

List of Figures ....................................................................................................................... viii

List of Tables .......................................................................................................................... ix

List of Abbreviations ............................................................................................................. xii

List of Publications ............................................................................................................... xiv

List of Presentations ................................................................................................................xv

Statement of Original Authorship ......................................................................................... xvi

Acknowledgements .............................................................................................................. xvii

Chapter 1: Introduction ...................................................................................... 1

1.1 Background and Significance .........................................................................................1

1.2 Purpose ...........................................................................................................................2 1.2.1 Aims .....................................................................................................................2 1.2.2 Hypotheses ...........................................................................................................3

1.3 Thesis Outline .................................................................................................................4

Chapter 2: Literature Review ............................................................................. 5

2.1 Fatigue Classifications ....................................................................................................5

2.2 Classification of CFS, ME/CFS, ME, and SEID ............................................................6 2.2.1 CFS Definition .....................................................................................................6 2.2.2 ME/CFS Definition ..............................................................................................7 2.2.3 ME Definition .......................................................................................................8 2.2.4 SEID Definition ....................................................................................................9 2.2.5 Differences between CFS, ME/CFS, and ME ......................................................9

2.3 Epidemiology of Fatigue ..............................................................................................11

2.4 Pathophysiology of CFS, ME/CFS and ME .................................................................13 2.4.1 Infection and Immune Dysfunction ....................................................................13 2.4.2 Endocrine and Metabolic Dysfunction ...............................................................14 2.4.3 Cardiovascular and Neurologic Dysfunction .....................................................15 2.4.4 Psychiatric Disorders ..........................................................................................15 2.4.5 Genetics ..............................................................................................................16

2.5 Heritability of Fatigue...................................................................................................16

2.6 Molecular Genetics of Fatigue ......................................................................................18

2.7 Classification of MDD and MiDD................................................................................25

2.8 Epidemiology of MDD and MiDD ...............................................................................26

2.9 Pathophysiology of MDD .............................................................................................27 2.9.1 Endocrine and Neurologic Dysfunction .............................................................27 2.9.2 Genetics ..............................................................................................................27 2.9.3 Environmental Factors .......................................................................................28

vi Characterising the Relationship between Fatigue and Depression

2.10 Heritability of MDD and MiDD .................................................................................. 28

2.11 Molecular Genetics of MDD ........................................................................................ 32

2.12 Comorbidity between MDD and Fatigue ..................................................................... 36 2.12.1 Heritability Links between Fatigue and Depression .......................................... 37

Chapter 3: Co-occurrence and Symptomatology of Fatigue and Depression49

3.1 Abstract ........................................................................................................................ 51

3.2 Introduction .................................................................................................................. 52

3.3 Methods ........................................................................................................................ 55 3.3.1 Sample and Questionnaires ................................................................................ 55 3.3.2 Statistical Analysis ............................................................................................. 59

3.4 Results .......................................................................................................................... 61 3.4.1 Study Population ................................................................................................ 61 3.4.2 Fatigued Individuals Report a Higher Proportion of Depression

Symptoms .......................................................................................................... 62 3.4.3 Depressed Individuals Report Higher Proportions of Fatigue Symptoms ......... 64

3.5 Discussion .................................................................................................................... 67

Chapter 4: Familiality and Heritability of Fatigue in an Australian Twin

Sample 72

4.1 Abstract ........................................................................................................................ 74

4.2 Introduction .................................................................................................................. 75

4.3 Methods ........................................................................................................................ 79 4.3.1 Study Cohort and Fatigue Classification ........................................................... 79 4.3.2 Statistical Analysis ............................................................................................. 80

4.4 Results .......................................................................................................................... 81

4.5 Discussion .................................................................................................................... 84

Chapter 5: A Continuum of Genetic Liability for Minor and Major

Depression 87

5.1 Abstract ........................................................................................................................ 89

5.2 Introduction .................................................................................................................. 90

5.3 Materials and Methods ................................................................................................. 92 5.3.1 Study Cohorts .................................................................................................... 92 5.3.2 Statistical Analysis ............................................................................................. 93

5.4 Results .......................................................................................................................... 95

5.5 Discussion .................................................................................................................. 102

Chapter 6: Shared Genetic Factors in the Co-Occurrence of Depression and

Fatigue 107

6.1 Abstract ...................................................................................................................... 109

6.2 Introduction ................................................................................................................ 110

6.3 Materials and Methods ............................................................................................... 111 6.3.1 Study Cohort .................................................................................................... 111 6.3.2 Diagnosis of Depression and Fatigue .............................................................. 111 6.3.3 Familial Clustering .......................................................................................... 112 6.3.4 Genetic Analysis .............................................................................................. 113

Characterising the Relationship between Fatigue and Depression vii

6.3.5 Relationship Analysis .......................................................................................114

6.4 Results ........................................................................................................................116 6.4.1 Relative Risks ...................................................................................................116 6.4.2 Polychoric Correlations ....................................................................................119 6.4.3 Bivariate Heritability Estimates .......................................................................119 6.4.4 Co-twin Control ................................................................................................120

6.5 Discussion ...................................................................................................................121

Chapter 7: Systematic Evaluation of Risk Loci from Candidate Gene and

Genome-wide Association Studies of Fatigue ...................................................... 126

7.1 Abstract .......................................................................................................................126

7.2 Introduction ................................................................................................................127

7.3 Methods ......................................................................................................................129 7.3.1 Previously Implicated Genes ............................................................................129 7.3.2 Study Cohorts, Genotyping Data and Quality-control .....................................145 7.3.3 Statistical Analysis ...........................................................................................147

7.4 Results ........................................................................................................................148 7.4.1 Previously Implicated SNPs and Genes ...........................................................148 7.4.2 Genome-wide association results .....................................................................150

7.5 Discussion ...................................................................................................................155

Chapter 8: General Discussion ....................................................................... 159

8.1 Summary of Findings .................................................................................................159

8.2 Limitations ..................................................................................................................161

8.3 Future Directions ........................................................................................................162

8.4 Conclusions ................................................................................................................163

Bibliography ........................................................................................................... 164

Appendices .............................................................................................................. 191

viii Characterising the Relationship between Fatigue and Depression

List of Figures

Figure 6.1. Expected outcomes of the co-twin control method under the causal,

non-causal, non-causal shared environment, and non-causal genetic

models within the general population (light grey), discordant DZ twin

pairs (grey) who share 50% of their genetics and 100% of their

common environment, and discordant MZ twin pairs (dark grey) who

share 100% of their genetics and common environment. Under a

causal model an association is expected within all three groups. Under

a non-causal model, an association is expected within the general

population, discordant DZ cohort will have a small association, and

discordant MZ cohort will have no association. Similarly, under the

non-causal shared environmental model, discordant DZ and MZ twin

pairs have a small, equal association. Finally, under the non-causal

genetic model, discordant DZ twin pairs have an association, whereas

discordant MZ twin pairs have a smaller association. ............................... 115

Figure 6.2. Path diagram of the bivariate Cholesky model variance estimates

(with their 95% confidence intervals) for two-category depression and

fatigue. The observed traits are shown in the rectangles. Similarly, the

latent variables (additive genetic factors: A, and unique environmental

factors: E) are depicted by circles. The arrows depict the relationship

between the variables. ................................................................................ 120

Figure 6.3. Left: The observed odds ratios (OR) for a current diagnosis of

fatigue given a current diagnosis of depression in the general

population (1,266 unrelated twin singles), 99 discordant DZ twin

pairs, and 96 discordant MZ twin pairs. Right: The observed OR for a

current diagnosis of depression given a current diagnosis of fatigue in

the general population (1,266 unrelated twin singles), 200 discordant

DZ twin pairs, and 215 discordant MZ twin pairs. In both situations,

the observed OR patterns are consistent with a non-causal genetic

model. ......................................................................................................... 121

Figure 7.1. Manhattan plot of the chronic fatigue syndrome (CFS) cohort

genome-wide association raw p-values. The horizontal dashed line

corresponds to the genome-wide significance threshold (p < 5 × 10-8).

The three genes suggestively associated (p < 1 × 10-4) with CFS in

gene-based analyses are indicated in green (CDCP2), pink (EMCN),

and blue (CAPRIN1). ................................................................................. 152

Figure 7.2. Manhattan plot of the fatigue cohort genome-wide association raw

p-values. The horizontal dashed line corresponds to the genome-wide

significance threshold (p < 5 × 10-8). The two genes suggestively

associated (p < 1 × 10-4) with fatigue in gene-based analyses are

indicated in pink (TBCA) and blue (PLXDC2). ......................................... 153

Characterising the Relationship between Fatigue and Depression ix

List of Tables

Table 2.1. Comparison of patients meeting the Canadian consensus criteria and

International consensus criteria compared to those diagnosed using the

Centres for Disease Control criteria for differences in demographics,

heart rate, cognitive measures, and responses to the 36-item Short-

form health survey and World Health Organisation disability

adjustment schedule 2.0 items. .................................................................... 10

Table 2.2. Population prevalence estimates for prolonged fatigue, chronic

fatigue, idiopathic chronic fatigue, chronic fatigue syndrome, and

myalgic encephalomyelitis/chronic fatigue syndrome. ................................ 12

Table 2.3. List of the pathogens investigated as potential triggering agents in

chronic fatigue syndrome onset. .................................................................. 14

Table 2.4. Heritability estimates (and their 95% confidence intervals) of the

unique additive genetic factors (A), common environmental factors

(C), and unique environmental factors (E) contributing to fatigue

severity, interfering fatigue, short-duration fatigue, abnormal

tiredness, abnormal fatigue, prolonged fatigue, chronic fatigue,

idiopathic chronic fatigue, and chronic fatigue syndrome. .......................... 18

Table 2.5. Candidate genes and implicated single nucleotide polymorphisms

associated with chronic fatigue syndrome. .................................................. 20

Table 2.6. List of reported genome-wide significant (7.5 × 10-8) risk loci

associated with chronic fatigue syndrome from a genome-wide

association study of 42 cases and 38 controls by Schlauch and

colleagues (2016). ........................................................................................ 23

Table 2.7. List of genes associated (p < 2.77 × 10-6) or suggestively associated

(p < 1.00 × 10-4) with self-reported tiredness. ............................................. 25



(C), and unique environmental factors (E) contributing to major

depressive disorder (MDD).......................................................................... 30

Table 2.9. Candidate genes associated with major depressive disorder in meta-

analysis studies............................................................................................. 33

Table 2.10. Summary of the genome-wide association studies conducted for

major depressive disorder (MDD). .............................................................. 34

Table 2.11. List of genome-wide significant (5 × 10-8) risk loci associated with

depression from a genome-wide association studies by the

CONVERGE consortium (2015), Hek and colleagues (2013), and

Direk and colleagues (2016). ....................................................................... 36



(C), and unique environmental factors (E) from previous trivariate and

x Characterising the Relationship between Fatigue and Depression

multivariate common factor twin models, which include a fatigue and

depression phenotype. .................................................................................. 45



(C), and unique environmental factors (E) from previous bivariate,

trivariate, and multivariate Cholesky twin models, which include a

fatigue and depression phenotype. ............................................................... 46

Table 2.13. Continued Heritability estimates (and their 95% confidence

intervals) of the unique additive genetic factors (A), common

environmental factors (C), and unique environmental factors (E) from

previous bivariate, trivariate, and multivariate Cholesky twin models,

which include a fatigue and depression phenotype. ..................................... 47

Table 3.1. Questionnaire items used to assess fatigue. .............................................. 56

Table 3.2. Questionnaire items used to assess the criteria of a major depressive

episode. ........................................................................................................ 58

Table 3.3. Prevalence ratios of fatigue and depression. ............................................. 63

Table 3.4. Logistic regression, unadjusted and and adjusted for, relatedness,

comparing the depression symptoms exhibited by fatigued individuals

(N = 766) to non-fatigued (N = 1,849) individuals. ..................................... 63

Table 3.5. Logistic regression, both unadjusted and adjusted for relatedness, of

fatigue symptoms exhibited by depressed (N = 275) and non-

depressed (N = 2,340) individuals................................................................ 65

Table 3.6. Logistic regression of fatigue symptoms exhibited by individuals

with major depressive disorder (N = 50), minor depressive disorder (N

= 225), and are non-depressed (N = 2,340). ................................................. 66

Table 4.1. Previously published variance estimates (with their 95% confidence

intervals) for varying fatigue classifications, in adults, from univariate

structural equation modelling. ...................................................................... 78

Table 4.2. Relative riska of fatigue within complete monozygotic (MZ), same-

sex dizygotic (DZss), and opposite-sex dizygotic (DZos) twin pairs. ........... 82

Table 4.3. Tetrachoric correlations (r) with their 95% confidence intervals (CI)

for fatigue according to zygosity. ................................................................ 83

Table 4.4. Fit statistics and variance estimates (with their 95% confidence

intervals) from univariate structural equation modelling. ............................ 84

Table 5.1. Relative riska of depression and fatigue within monozygotic (MZ),

same-sex dizygotic (DZss), and opposite-sex dizygotic (DZos) twin

pairs. ............................................................................................................. 96

Table 5.2. Liability threshold model fit p-values. ...................................................... 97

Table 5.3. Polychoric correlations with their 95% confidence intervals for

depression according to zygosity. ................................................................ 98

Table 5.4. Fit statistics and variance estimates (with their 95% confidence

intervals) from univariate structural equation modelling. .......................... 100

Characterising the Relationship between Fatigue and Depression xi

Table 6.1. Cross-tabulationa of two-category depression and fatigue status

within twin pairs. ....................................................................................... 116

Table 6.2. Relative riska of two-category depression and fatigue within

monozygotic (MZ), same-sex dizygotic (DZss), and opposite-sex

dizygotic (DZos) twin pairs. ....................................................................... 118

Table 6.3. Polychoric correlations with their 95% confidence intervals for two-

category depression and fatigue according to zygosity. ............................ 119

Table 6.4. Bivariate heritability model fits. ............................................................. 120

Table 7.1. Summary of genes from candidate gene association studies for

fatigue traits. .............................................................................................. 131

Table 7.2. Summary of SNPs from genome-wide association studies for

chronic fatigue syndrome. .......................................................................... 136

Table 7.3. Summary of SNPs from genome-wide association study for self-

reported tiredness.a ..................................................................................... 144

Table 7.4. Summary of genes from gene-based association analysis of self-

reported tiredness. ...................................................................................... 144

Table 7.5. Summary of SNPs from genome-wide association studies of

depression phenotypes, in Europeans. ....................................................... 145

Table 7.6. Summary of SNPs reaching suggestive significance thresholds for

chronic fatigue syndrome and fatigue. ....................................................... 154

Table 7.7. Summary of genes reaching suggestive significance thresholds from

gene-based association analysis for chronic fatigue syndrome and

fatigue. ....................................................................................................... 154

xii Characterising the Relationship between Fatigue and Depression

List of Abbreviations

χ2 Chi-squared test

Δ df Difference in degrees of freedom

-2LL Minus two log-likelihood

A Additive genetic factors

AIC Akaike information criterion

bp Base pair

C Common environmental factors

CCC Canadian consensus criteria

CDC Centres for Disease Control

CF Chronic fatigue

CFS Chronic fatigue syndrome

CGA Candidate gene association

Chr Chromosome

CI Confidence interval

D Non-additive (dominance) genetic factors

DSM Diagnostic and statistical manual of mental disorders

DSSI/sAD Delusions-symptoms states inventory, states of anxiety and depression

DZ Dizygotic

E Unique environmental factors

F Female

Freq Frequency

GC Genotyping call

GHQ General Health Questionnaire

GWA Genome-wide association

h2 Narrow-sense heritability

HR Heart rate

HRC Haplotype reference consortium

HREC Human Research Ethics Committee

HWE Hardy-Weinberg equilibrium

ICC International consensus criteria

ICF Idiopathic chronic fatigue

IOM Institute of Medicine

ins/del Insertion/deletion

M Male

MAF Minor allele frequency

matSpD Matrix spectral decomposition

ME Myalgic encephalomyelitis

ME/CFS Myalgic encephalomyelitis/chronic fatigue syndrome

MDD Major depressive disorder

MD Major depression

Characterising the Relationship between Fatigue and Depression xiii

MiDD Minor depressive disorder

MiD Minor depression

MZ Monozygotic

N Number

NC Not calculable

NS Non-significant

OA Other allele

OR Odds ratio

os Opposite sex

p p-value

PR Prevalence ratio

RR Relative risk

QIMRB QIMR Berghofer Medical Research Institute

r Correlation

RA Risk allele

rc Common environmental correlation

re Unique environmental correlation

rg Genetic correlation

SE Standard error

SEID Systematic exertion intolerance disease

SNP Single nucleotide polymorphism

SOFA Schedule of Fatigue and Anergia

ss Same-sex

UTR Untranslated region

xiv Characterising the Relationship between Fatigue and Depression

List of Publications

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2016). Co-occurrence and

symptomatology of fatigue and depression. Comprehensive Psychiatry, 71, 1-10.

doi:10.1016/j.comppsych.2016.08.004

The content outlined in this paper relates to Chapter 3.

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (In Press, accepted 20March 2017).

Familiality and heritability of fatigue in an Australian twin sample. Twin Research

and Human Genetics


Corfield, E. C., Yang Y.. Martin, N. G., & Nyholt, D. R. (In Press, accepted 4 April

2017). A continuum of genetic liability for minor and major depressive disorder.

Translational Psychiatry


Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2016). Shared Genetic Factors in

the Co-Occurrence of Depression and Fatigue. Twin Research and Human Genetics,

1-9. doi:10.1017/thg.2016.79


Characterising the Relationship between Fatigue and Depression xv

List of Presentations

ORAL

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2016) Shared genetics of depression

and fatigue. Behavior Genetics Association forty-sixth Annual Meeting. Brisbane,

Australia.

POSTER

Corfield, E. C., Marshall-Gradisnik, S.M., Martin, N. G., & Nyholt, D. R. (2016)

Systematic evaluation of risk loci from candidate gene and genome-wide association

studies of fatigue. American Society of Human Genetics sixty-sixth Annual Meeting.

Vancouver, Canada.

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2015) Genetic heritability of minor

and major depressive disorder. GeneMappers eleventh sesquiennial Conference.

Perth, Australia.

This poster won best student poster presentation.

xvi Characterising the Relationship between Fatigue and Depression

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date: 28/08/2017

QUT Verified Signature

Characterising the Relationship between Fatigue and Depression xvii

Acknowledgements

Firstly, to my supervisor, Dale, thank you for your continuous support, patience, and

guidance throughout the last three years. My PhD experience has been incomparable

with my honours year and I doubt this dissertation would be complete without your

help. To Nick, thank you for providing feedback and comments on my manuscripts

and to everyone from level 7 of the Bancroft building at QIMR Berghofer and the

members of SGEL, both past and present, thank you for answering my plethora of

questions. Finally, to my family, thank you for your support throughout this journey.

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 BACKGROUND AND SIGNIFICANCE

Abnormal tiredness or fatigue are multidimensional symptoms, which exist on a

continuum (Wessely et al., 1997). The fatigue symptom spectrum ranges from non-

specific to essential; with disorders such as chronic fatigue syndrome (CFS)

requiring specified durations and severities before a diagnosis can be made (Griffith

& Zarrouf, 2008; Hadzi-Pavlovic et al., 2000). Little is known about the underlying

mechanisms of fatigue or CFS. Although, throughout the entire fatigue continuum

high levels of comorbidity with depression are observed. Furthermore, individuals

with medically unexplained fatigue have an increased risk of a lifetime major

depressive disorder (MDD) diagnosis compared to individuals who have never been

fatigued (Addington et al., 2001). The comorbidity between fatigue and depression

could be accountable to overlapping symptoms. With fatigue or loss of energy

representing the second highest depression symptom reported, in a community-based

MDD outpatient population (Zimmerman et al., 2015). However, a number of

prescribed antidepressants do not treat fatigue symptoms or result in fatigue and

drowsiness. After antidepressant treatment, unresolved or residual fatigue is highly

prevalent in partial responders and remitted MDD patients (Fava et al., 2014).

Additionally, the severity of a major depressive episode has been identified as an

independent predictor of residual fatigue in remitted or partially remitted MDD

patients (Chung et al., 2015). However, currently available antidepressant therapies

inadequately treat residual fatigue, which leads to higher functional impairment and

MDD relapse.

Considerable economic burden and reduction in quality of life are associated

with fatigue and depression. As a depression symptom, fatigue is associated with

higher health care utilisation, increased medication use, 10-20% greater annual

health care cost, and lower quality of life (Robinson et al., 2015). CFS has an

estimated annual cost, in the United States, of approximately 18.7 to 24.0 billion US

dollars (Jason et al., 2008). In 2010, MDD had an estimated annual cost, in the

United States, of approximately 210.5 billion US dollars (Greenberg et al., 2015);

meanwhile, minor depressive disorder (MiDD), a less severe form of MDD (Ayuso-

2 Chapter 1: Introduction

Mateos et al., 2010; Fils et al., 2010), has approximately two-thirds the annual cost

(Cuijpers et al., 2007). Therefore, fatigue, CFS, MDD, and MiDD represent

significant health burdens.

Heritability estimates indicate both fatigue and MDD have moderate genetic

contributions. Fatigue experienced for at least one month has an estimated

heritability of 18-51% (Schur et al., 2007; Sullivan et al., 2005). Similarly, CFS, in

females, has an estimated heritability of 51% (Schur et al., 2007). Meanwhile, MDD

has an estimated heritability of 37% (Sullivan et al., 2000). However, to date, the

heritability of MiDD has not been investigated. An underlying genetic relationship

has been implicated between fatigue and MDD by the identification of genetic

factors which contribute to the heritability of both traits. Numerous genetic

association analyses have been conducted for MDD while few have been conducted

for CFS. To date, no genes have been consistently associated with CFS. However, in

August 2016, the first risk loci robustly associated with MDD, in Europeans, were

reported.

1.2 PURPOSE

The overall objective of this project is to increase our knowledge and understanding

of the comorbidity and genetics of fatigue and depression. In silico phenotypic and

genotypic analysis will be conducted within this project, which will be divided into

four sections. Initially, a symptomatic analysis of fatigue and depression will be

conducted. Next, the familiality and heritability of fatigue and depression will be

characterised. The genetic relationship between fatigue and depression will then be

investigated. Finally, the molecular genetics of fatigue and CFS will be investigated.

The overall hypothesis of this project was that both fatigue and depression will have

a significant genetic contribution, with shared genetic effects driving the comorbidity

between the traits.

1.2.1 Aims

The symptomatic analysis aims to:

1. Determine if the depression symptom profiles of fatigued and non-fatigued

individuals are quantitatively different.

2. Determine if the fatigue symptom profiles of MDD, MiDD, and non-

depressed individuals are quantitatively different.

Chapter 1: Introduction 3

The familiality and heritability analysis aims to:

1. Assess the familiality of fatigue experienced over the past few weeks.

2. Identify the heritability of fatigue.

3. Assess the familiality of minor and major depression.

4. Investigate whether minor and major depression lie on a single genetic

continuum.

The genetic relationship analysis aims to:

1. Determine the genetic correlation between fatigue and depression (MDD

plus MiDD).

2. Determine if the heritability of fatigue is different in MDD compared to

MiDD and non-depressed controls.

3. Characterise the type of relationship between fatigue and depression.

The molecular genetic analysis aims to:

1. Characterise the genetic risk associated with fatigue and CFS.

1.2.2 Hypotheses

The hypotheses of the symptomatic analysis were that:

1. Fatigued individuals report an increased preponderance of depression

symptoms compared to non-fatigued individuals.

2. Depressed (MDD plus MiDD) individuals will report an increased

preponderance of fatigue symptoms compared to non-depressed

individuals; with MDD cases reporting more fatigue symptoms that MiDD

cases.

The hypotheses of the familiality and heritability analysis were that:

1. Fatigue experienced over the past few weeks has a familialial contribution,

with a significant additive genetic contribution.

2. Minor and major depression are familialial and lie on a single genetic

continuum.

The hypotheses of the genetic relationship analysis were that:

4 Chapter 1: Introduction

1. A non-causal genetic relationship, driven by a significant genetic

correlation, explains the association between fatigue and depression (MDD

plus MiDD); with fatigue exhibiting a similar genetic correlation with

MDD and MiDD cases.

The hypotheses of the molecular genetic analysis were that:

1. Genetic risk variants will be associated with fatigue and CFS, with similar

biological pathways contributing to the etiology of both traits.

1.3 THESIS OUTLINE

In order to address the project objectives and aims a literature review was initially

conducted, which is detailed in Chapter 2. The following five chapters comprise

articles which are in preparation, currently under review or have been published after

peer-review. To reduce repetition within the dissertation a separate methods chapter

has not been included, rather the methodology utilised and results obtained are

detailed within the appropriate chapter. For ease of reading the formatting of

individual manuscripts has been standardised within this thesis. As such, the

abstracts have been included as a single paragraph and the numbering of figures and

tables has been updated to contain the chapter number followed by the individual

figure or table number. Additionally, to reduce repetition, a single reference list,

containing all references utilised throughout this thesis, is provided at the end of the

dissertation rather than providing a reference list after each manuscript.

A prologue has been included before each of the results chapters to maintain

the logical flow of the dissertation. Chapter 3 details the findings of the symptomatic

analysis of fatigue and depression. The familiality and heritability analyses are

detailed in two separate chapters. Chapter 4 details the findings of the first two

aims—which relate to fatigue, while Chapter 5 details the findings of the last two

aims—which relate to MDD and MiDD. Chapter 6 details the findings of the

analysis investigating the genetic relationship between fatigue and depression.

Chapter 7 details the findings of the molecular genetics analysis of fatigue and CFS.

Finally, Chapter 8 contains a general discussion which briefly describes the main

findings of each study, illustrates the relationship between the results of the

individual chapters, and explains the implications and future directions of this

project.

Chapter 2: Literature Review 5

Chapter 2: Literature Review

Abnormal tiredness or fatigue is a highly prevalent symptom, which is difficult to

quantify, and associated with numerous medical diagnoses. As a diagnostic

symptom, fatigue has been significantly associated with a number of illnesses,

including mental health, gut/stomach problems, arthritis/rheumatism, heart

conditions, breathing difficulties, cancer, skin disease, back problems, diabetes, and

blood pressure (Williamson et al., 2005). Fatigue has an estimated prevalence of

25.6% in general practice, representing the main complaint in a quarter of all

consultations (Cullen et al., 2002). Symptoms of fatigue exist on a continuum and

can be classified into physical, mental, and emotional dimensions (Lewis & Wessely,

1992; Wessely et al., 1997). The fatigue symptom spectrum ranges from non-specific

to essential; with disorders such as CFS requiring specified severities and durations

before a diagnosis can be made (Griffith & Zarrouf, 2008; Hadzi-Pavlovic et al.,

2000).

2.1 FATIGUE CLASSIFICATIONS

Quantifiable difficulties associated with fatigue has resulted in classification based

on arbitrary durations and severities by either self-report or clinical evaluation

(David et al., 1990). Prolonged fatigue and chronic fatigue (CF) are classified as self-

reported fatigue experienced for at least one month and at least six months,

respectively (Fukuda et al., 1994). Classification of prolonged fatigue requires

persistent fatigue; however, relapsing fatigue is acceptable for classification of CF.

After clinical evaluation, individuals exhibiting unexplainable CF can be classified

with idiopathic chronic fatigue (ICF), CFS, or myalgic encephalomyelitis/chronic

fatigue syndrome (ME/CFS). Furthermore, individuals can be diagnosed with

myalgic encephalomyelitis (ME), which has a highly similar classification to both

CFS and ME/CFS; however, presentation with unexplained CF is not required.

Finally, systemic exertion intolerance disease (SEID) has been defined as a result of

the recent investigation into chronic fatigue syndrome or myalgic encephalomyelitis

classifications (Institute of Medicine (IOM). 2015).

6 Chapter 2: Literature Review

2.2 CLASSIFICATION OF CFS, ME/CFS, ME, AND SEID

CFS is a complex, multisystem disorder, of unknown aetiology. Attempts to describe

the underlying pathology of CFS have resulted in numerous nomenclature changes,

based on speculation of the disorders aetiology. Generally, the terms CFS and ME

are used interchangeably to describe the complex disorder. Furthermore, numerous

case definitions have been published based on aetiological research; with each

definition representing increased knowledge and understanding about the disorder. A

common feature of all the definitions is the requirement for diagnosis to occur based

on exclusion of any medical condition which explains the symptoms experienced by

the individual.

2.2.1 CFS Definition

In 1988, the first definition for CFS was published by the Centres for Disease

Control (CDC) (Holmes et al., 1988). The original case definition requires the

presence of new onset unexplained CF and either at least six symptom criteria and

two physical criteria, or at least eight of the symptom criteria. The eleven symptom

criteria are: mild fever or chills, sore throat, painful lymph nodes (cervical or

axillary), muscle weakness, muscle discomfort or myalgia, post-exertional fatigue (at

least 24 hours), headaches, migratory arthralgia, neuropsychologic complaints, sleep

disturbance, and acute onset (Holmes et al., 1988). The three physical symptom

criteria of the original CFS definition are: low grade fever, nonexudative pharyngitis,

and palpable or tender lymph nodes (cervical or axillary).

In 1994, the CDC released a revised version of the CFS definition, primarily to

provide a research framework and standardise the definition used worldwide (Fukuda

et al., 1994). The CDC definition requires the presence of unexplained CF and

concurrent occurrence of at least four of the eight diagnostic symptoms, which have

not predated the fatigue but have been persistent or relapsing for at least six months.

The diagnostic symptoms are: sore throat, tender lymph nodes (cervical or axillary),

headaches, cognitive difficulties, unrefreshing sleep, multijoint pain, muscle pain,

and post-exertional malaise (for at least 24 hours). ICF is diagnosed when an

individual exhibits unexplained CF but does not meet the diagnostic criteria for CFS.


2.2.2 ME/CFS Definition

In 2003, the Canadian consensus criteria (CCC) for ME/CFS was published to aid

clinicians with the diagnosis and treatment of CFS (Carruthers et al., 2003). The key

distinguishing features of the CCC definition compared to the CDC definition are the

addition of extra compulsory symptoms and the grouping of symptoms based on

their region of pathogenesis. The CCC criteria has six symptom groups of which the

first 4 are compulsory for diagnosis (Carruthers et al., 2003). Furthermore, at least

two of the symptoms from category five and one symptom from two of the

subgroups of category six are required. The categorical definition of ME/CFS is:

1. Fatigue: CF which is unexplained and of a new onset, presenting with

both physical and mental fatigue.

2. Post-exertional malaise and/or fatigue: loss of physical and mental

stamina, post-exertional malaise, fatigue or pain, with a recovery period

exceeding 24 hours.

3. Sleep dysfunction: unrefreshing sleep or sleep quantity.

4. Pain: muscle or joint pain and headaches

5. Neurological/cognitive manifestations: confusion, impaired

concentration and short-term memory, disorientation, and perceptual and

sensory disturbances.

6. a) Autonomic manifestations: orthostatic intolerance, light-headedness,

extreme pallor, nausea and irritable bowel syndrome, bladder dysfunction,

palpitations with/without cardiac arrhythmias, and exertional dyspnea.

b) Neuroendocrine manifestations: loss of thermostatic stability,

intolerance to temperature change, weight change, loss of adaptability and

worsening of symptoms with stress.

c) Immune manifestation: Tender lymph nodes, recurrent sore throat,

recurrent flu-like symptoms, general malaise, and new sensitivities to food,

medication and/or chemicals.

Diagnosis with ME/CFS requires symptom persistence for at least 6 months.

The CCC definition also endorses the classification of ICF when individuals do not

meet the criteria for ME/CFS but have unexplained CF.


2.2.3 ME Definition

In 2011, the international consensus criteria (ICC) for myalgic encephalomyelitis

(ME) was published (Carruthers et al., 2011). The CCC was used as the basis for

construction of the ICC. However, there are a number of differences between the

definitions. The ICC has increased stringency and enables diagnosis without

requiring 6 months of symptom persistence. Diagnosis with ME can be further

classified based on symptom severity, with mild cases exhibiting a 50% reduction in

activity, moderate cases mostly housebound, severe cases mostly bedridden, and very

severe cases totally bedridden, requiring help with basic functions (Carruthers et al.,

2011). The ICC definition separates the symptom criteria into four impairment types.

The first category is compulsory and at least one symptom must be exhibited from

each of the other three impairment types for diagnosis with ME (Carruthers et al.,

2011). The four categories used for diagnosis of ME are:

1. Post-exertional neuroimmune exhaustion: post-exertional physical

and/or cognitive fatigability, post-exertional symptom exacerbation, post-

exertional exhaustion, prolonged post-exertional recovery period, and a

low threshold of physical and mental fatigability.

2. Neurological impairments: neurocognitive impairments (cognitive

difficulties and short-term memory loss), pain (headaches, and non-

inflammatory pain), sleep disturbance, and neurosensory, perceptual and

motor disturbances.

3. Immune, gastro-intestinal, and genitourinary impairments: flu-like

symptoms, susceptibility to viral infections, gastro-intestinal tract,

genitourinary, and sensitivities to food medicine, or chemicals.

4. Energy production/transportation impairments: cardiovascular,

respiratory, loss of thermostatic stability, and intolerance of extremes in

temperatures.

Individuals can be diagnosed with typical ME if they do not meet the full

criteria for ME, but satisfy the post-exertional neuroimmune exhaustion criterion and

exhibit at least one symptom from the three impairment types (Carruthers et al.,

2011).


2.2.4 SEID Definition

In 2015, the Institute of Medicine (IOM) published a definition for SEID (Institute of

Medicine (IOM). 2015). Evidence-based consensus of the investigating committee

was used to construct the report (Clayton, 2015). The definition for SEID requires

individuals to exhibit: CF accompanied by impairment, post-exertional malaise, and

unrefreshing sleep (Institute of Medicine (IOM). 2015). Furthermore, cognitive

impairment or orthostatic intolerance must also be exhibited.

2.2.5 Differences between CFS, ME/CFS, and ME

Comparison of patients diagnosed using the CCC and ICC definitions compared to

the CDC criteria has revealed a number of demographic and symptomatic differences

(Table 2.1) (Brown et al., 2013; Jason et al., 2012; Jason et al., 2013; Johnston et al.,

2014). However, the set of symptoms examined in each study varies, making

comparison difficult, particularly when some symptoms are split into varying

components in one study but not others. Differences in fatigue, sleep, pain,

neurological/neurocognitive, autonomic, neuroendocrine, and immune symptoms

have been identified between ME/CFS and CFS patients in all four independent

samples studied (Jason et al., 2012; Jason et al., 2013). Similarly, differences in post-

exertional malaise, neurological impairments, immune, gastrointestinal, and

genitourinary impairments, and energy production/transportation impairments have

been identified between ME and CFS patients (Brown et al., 2013). Indicating,

patients diagnosed with ME or ME/CFS represent a subset of CFS patients, which

experience more severe clinical symptoms.


Table 2.1. Comparison of patients meeting the Canadian consensus criteria and International consensus criteria compared to those diagnosed using the Centres for Disease

Control criteria for differences in demographics, heart rate, cognitive measures, and responses to the 36-item Short-form health survey and World Health Organisation

disability adjustment schedule 2.0 items.

ME/CFS vs. CFS ME vs. CFS

Cohort Chicago DePaul Solve CFS Biobank Newcastle Chicago South East Queensland

Study Jason et al. (2012) Jason et al. (2013) Brown et al. (2013) Johnston et al. (2014)

Demographic differences

Sex NS NS NS NS NS Increased number of females

Current psychiatric diagnosis Higher NS - NS Higher - Disability payment NS NS More on disability NS NS -

Higher education NS NS NS Higher NS -

Heart rate (HR) and cognition tests

HR lying down Higher - - - Higher -

HR standing 2 minutes Higher - - - Higher -

HR standing 10 minutes Higher - - - Higher - Cognition test trail making A Higher completion time - - - NS -

Cognition test trail making B Higher completion time - - - NS -

Short-form health survey

Physical functioning Worse Worse Worse Worse Worse Worse

Role physical NS NS NS NS NS Worse

Bodily pain Worse Worse Worse Worse Worse Worse General health Worse NS Worse Worse NS NS

Social functioning Worse NS Worse Worse Worse Worse

Mental health Worse NS Worse NS NS NS Role emotional Worse NS NS NS NS NS

Vitality Worse NS Worse NS Worse NS

World Health Organisation disability adjustment schedule 2.0

Cognition - - - - - Worse

Mobility - - - - - Worse

Self-care - - - - - Worse Getting along - - - - - Worse

Life activities - - - - - Worse

Participation - - - - - Worse

CFS: chronic fatigue syndrome: ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome; ME: myalgic encephalomyelitis; NS: not significant


2.3 EPIDEMIOLOGY OF FATIGUE

Epidemiological studies investigating prolonged fatigue, CF, ICF, CFS, ME/CFS,

and ME have been conducted in varying populations, resulting in a range of

population prevalence estimates and overall incidence rates due to differing

ethnicities and study designs (Table 2.2). The population prevalence estimations

range from: 6.16-28.00% for prolonged fatigue (EvengÅRd et al., 2005; Hamaguchi

et al., 2011; Jason et al., 1999; Kim et al., 2005; Njoku et al., 2007), 2.00-12.20% for

CF (Bierl et al., 2004; Cho et al., 2009; EvengÅRd et al., 2005; Friedberg et al.,

2015; Hamaguchi et al., 2011; Jason et al., 1995; Jason et al., 1999; Kim et al., 2005;

Loge et al., 1998; Njoku et al., 2007; Patel et al., 2005; Steele et al., 1998; Wessely et

al., 1995; Wessely et al., 1997; Wong & Fielding, 2010), 1.00-9.00% for ICF

(Hamaguchi et al., 2011; Kim et al., 2005; Wessely et al., 1997), 0.07-2.60% for CFS

(Cho et al., 2009; Hamaguchi et al., 2011; Jason et al., 1999; Kawakami et al., 1998;

Kim et al., 2005; Lindal et al., 2002; Nacul et al., 2011; Njoku et al., 2007; Reyes et

al., 2003; Vincent et al., 2012; Wessely et al., 1995; Wessely et al., 1997), and 0.11

for ME/CFS (Nacul et al., 2011). Population prevalence estimates for ME and SEID

have not been established due to the recent publication of the diagnostic definitions.

Investigations into the variation observed in population prevalence estimations for

CFS have established the differences may be partially explained by differences in

study design (Johnston et al., 2013). Higher population prevalence estimations were

obtained for CFS patients diagnosed by self-report (3.28%) compared to clinical

assessment (0.76%). Similarly, studies using community based cohorts (0.87%)

reported lower estimations than investigations conducted within primary care settings

(1.72%) (Johnston et al., 2013).


Table 2.2. Population prevalence estimates for prolonged fatigue, chronic fatigue, idiopathic chronic

fatigue, chronic fatigue syndrome, and myalgic encephalomyelitis/chronic fatigue syndrome.

Prevalence (%) Population Age range Study

Prolonged fatigue

7.70 Chicago, USA ≥ 18 Jason et al. (1999) 21.10 South Korea ≥ 18 Kim et al. (2005)

6.16 Nigeria ≥ 18 Njoku et al. (2007)

28.00 Japan 20-78 Hamaguchi et al. (2011) 12.33 Sweden 42-64 EvengÅRd et al. (2005)

Chronic fatigue

5.00 Chicago, USA ≥ 18 Jason et al. (1995) 10.78 Southern England, UK 18-45 Wessely et al. (1995)

11.30 Southern England, UK 18-45 Wessely et al. (1997)

11.40 Norway 19-80 Loge et al. (1998) 2.00 San Francisco, USA ≥ 18 Steele et al. (1998)

4.20 Chicago, USA ≥ 18 Jason et al. (1999)

12.19 USA 18-69 Bierl et al. (2004)

8.40 South Korea ≥ 18 Kim et al. (2005)

12.10 India 18-50 Patel et al. (2005)

9.48 Nigeria ≥ 18 Njoku et al. (2007) 12.20 São Paulo, Brazil 18-45 Cho et al. (2009)

10.30 London, England, UK 18-45 Cho et al. (2009)

10.70 Hong Kong ≥ 18 Wong and Fielding (2010) 7.30 Japan 20-78 Hamaguchi et al. (2011)

8.26 Sweden 42-64 EvengÅRd et al. (2005) 3.70 USA ≥ 18 Friedberg et al. (2015)

5.20 Ukraine ≥ 18 Friedberg et al. (2015)

Idiopathic chronic fatigue



1.30 Japan 20-78 Hamaguchi et al. (2011) Chronic fatigue syndrome


2.60 Southern England, UK 18-45 Wessely et al. (1997) 1.50 Japan ≥ 18 Kawakami et al. (1998)

0.42 Chicago, USA ≥ 18 Jason et al. (1999)

2.20 Iceland 19-75 Lindal et al. (2002) 2.35 Wichita, USA 18-69 Reyes et al. (2003)


0.68 Nigeria ≥ 18 Njoku et al. (2007) 1.60 São Paulo, Brazil 18-45 Cho et al. (2009)

2.10 England, UK 18-45 Cho et al. (2009)

1.00 Japan 20-78 Hamaguchi et al. (2011) 0.19 England, UK 18-64 Nacul et al. (2011)

0.07 Minnesota, USA ≥ 18 Vincent et al. (2012)

Myalgic encephalomyelitis/chronic fatigue syndrome

0.11 England, UK 18-64 Nacul et al. (2011)

The combined overall incidence rate of CFS and ME/CFS has been estimated

at 0.03-0.09% within primary care, hospitals, and outpatient clinics (Bakken et al.,

2014; Nacul et al., 2011). Overall incidence rate is higher in females, at 0.04-0.14%,

compared to males, at 0.01-0.04%, which concurs with the predominance of females

diagnosed with CFS, who represent ≥ 75% of all cases (Bakken et al., 2014; Capelli

et al., 2015; Prins et al., 2006). Onset of CFS predominantly occurs between twenty

and fifty years of age (Collin et al., 2017). The highest incidence of CFS occurs in

the age ranges of 30-34 and 35-39 at 0.04% (Bakken et al., 2014), with no significant

difference in age of onset between males and females (Capelli et al., 2015).

Understanding the change in incidence with age could be insightful in terms of


hormonal changes occurring at particular life stages, which may contribute to the

development of CFS.

2.4 PATHOPHYSIOLOGY OF CFS, ME/CFS AND ME

Aetiological investigations into CFS have thus far been unsuccessful, despite the

substantial number of proposed causes. Dysfunction of the immune, endocrine,

nervous, cardiovascular, and digestive systems have been implicated in the

pathophysiology of CFS.

2.4.1 Infection and Immune Dysfunction

Numerous viral and bacterial pathogens have been investigated in relation to CFS

onset. Seasonality investigations have shown onset of CFS and ICF is higher in

winter (in the northern hemisphere), with a 5-fold increase observed in January

(Jason et al., 2001; Zhang et al., 2000). The implication of increased case onset in

winter provides some evidence for a pathogenic cause. However, despite persistent

attempts and numerous links to CFS onset a causal pathogen has not consistently

been identified (Table 2.3) (Armstrong et al., 2014; Devanur & Kerr, 2006; Ortega-

Hernandez & Shoenfeld, 2009).

The immune dysfunction often exhibited by CFS patients has been frequently

studied, with numerous investigations of B cells, T cells, natural killer cells,

interferons, interleukins, and immunoglobulins. However, the conflicting results

between studies raise questions about the clinical significance of the findings (Lyall

et al., 2003; Natelson et al., 2002). Particularly considering none of the abnormalities

have been identified as a biomarker. Although, the multitude of abnormalities

identified implies the immune system has a pivotal role. The subsets of immune

dysfunctions identified could imply that different subgroups of CFS cases are

associated with specific abnormalities. However, the specific role of the immune

system will be indeterminable without larger sample sizes and reduced heterogeneity

within study cohorts.


Table 2.3. List of the pathogens investigated as potential triggering agents in chronic fatigue

syndrome onset.

Pathogen Associated illness Studies

RNA virus

Enteroviruses (Coxsackie A & B,

echoviruses, polioviruses)

Acute respiratory and gastrointestinal infections,

aseptic meningitis, and polio

Chia and Chia (2003); Chia (2005); Clements et al. (1995); Dalakas (2003); Gow et al. (1991); Lane et al. (2003); Ortega-

Hernandez and Shoenfeld (2009)

Dengue virus Dengue fever Seet et al. (2007) Hepatitis C virus Hepatitis C Chia and Chia (2003); Ortega-Hernandez and Shoenfeld (2009)

Ross river virus Ross river fever Hickie et al. (2006)

Murine Leukaemia virus-related virus

No evidence that MLRV can infect humans

Erlwein et al. (2010); Knox et al. (2011); Lombardi et al. (2009); Mikovits et al. (2010)

DNA virus

Cytomegalovirus Infectious mononucleosis Beqaj et al. (2008); Chia and Chia (2003)

Epstein-Barr virus Infectious mononucleosis

Glaser et al. (2005); Holmes et al. (1987); Ikuta et al. (2003);

Jones et al. (1991); Katz (2002); Kawai and Kawai (1992); Koo

(1989); Lerner et al. (2004); Ortega-Hernandez and Shoenfeld

(2009); Vernon et al. (2006); White et al. (1998); White et al.

(2001)

Herpes simplex virus Herpes simplex Bond (1993)

Hepatitis B virus Hepatitis B Canadian Laboratory Center for Disease Control (LCDC).

(1993); Nancy and Shoenfeld (2008)

Human herpesvirus-6 Roseola Ablashi et al. (2000); Chapenko et al. (2006); Chia and Chia (2003); Di Luca et al. (1995); Komaroff (2006); Lum et al.

(2014); Nicolson et al. (2003) Human herpesvirus-7 Roseola Chapenko et al. (2006); Di Luca et al. (1995)

Parvovirus B19 Fifth disease

Jacobson et al. (1997); Kerr et al. (2001); Kerr et al. (2002); Kerr

and Mattey (2008); Matano et al. (2003); Ortega-Hernandez and Shoenfeld (2009); Seishima et al. (2008)

Varicella zoster virus Chickenpox Shapiro (2009)

Bacteria

Chlamydia pneumonia Pneumonia Chia and Chia (1999); Chia and Chia (2003); Nicolson et al.

(2003)

Coxiella burnetii Q fever Arashima et al. (2004); Hickie et al. (2006); Ikuta et al. (2003); Ledina et al. (2007); Strauss et al. (2012); Wildman et al. (2002)

Mycoplasma spp. Pneumonia, fever Choppa et al. (1998); Endresen (2003); Nicolson et al. (2003);

Nijs et al. (2002); Vernon et al. (2003); Vojdani et al. (1998)

2.4.2 Endocrine and Metabolic Dysfunction

Endocrine-metabolic dysfunction is one of the most consistent findings in CFS

aetiological research. Particularly, the presence of alterations in the hypothalamic-

pituitary-adrenal (HPA) axis (Cleare, 2004; Sorenson & Jason, 2013). Dysfunctions

identified in the HPA axis of CFS patients includes: mild hypocortisolism, attenuated

diural variation and enhanced corticosteroid-induced negative feedback (Tomas et

al., 2013). The abnormalities in the HPA axis also affect the serotonergic and

noradrenergic pathways (Armstrong et al., 2014). However, the findings suggest the

alterations in the HPA axis are not a predisposing factor to CFS and occur after onset

(Cleare, 2004).

Energy metabolism has also been implicated in the pathophysiology of CFS,

with evidence suggesting mitochondrial dysfunction plays a key role (Booth et al.,

2012; Myhill et al., 2009; Vernon et al., 2006). Although recent investigations have

established mitochondrial DNA mutations are unlikely to be associated with CFS

aetiology (Billing-Ross et al., 2016; Schoeman et al., 2017). However, investigation


of mitochondrial dysfunction in CFS patients has identified decreased levels of

ubiquinone and increased levels of lipid peroxidation in peripheral blood

mononuclear cells compared to controls (Castro-Marrero et al., 2013). Furthermore,

CFS cases exhibit lower mitochondrial ATP production than healthy controls. The

level of effect on mitochondrial function induced by the differing levels of

ubiquinones and lipid peroxidation is unknown. However, mitochondrial function

and oxidative stress are obviously affected which can be observed by the lower ATP

production and increased levels of isoprostanes in CFS cases compared to controls

(Armstrong et al., 2014; Castro-Marrero et al., 2013; Kennedy et al., 2005). It is

unknown if these dysfunctions are predisposing factors or occur after CFS onset.

2.4.3 Cardiovascular and Neurologic Dysfunction

Abnormalities associated with the nervous and cardiovascular system have been

identified in CFS cases. Symptoms of cardiac dysfunction have been frequently

identified in CFS cases (Miwa & Fujita, 2009b). A small left ventricle with a low

cardiac output (small heart) has been identified in a subset of CFS cases (Miwa &

Fujita, 2008, 2009a, 2009b). Furthermore, the small hearts have been associated with

orthostatic intolerance in CFS cases (Miwa & Fujita, 2011). Investigations into the

type of the orthostatic intolerance exhibited by CFS cases revealed increased rates of

neurally mediated hypotension, postural orthostatic tachycardia syndrome, and

orthostatic hypocapnia compared to controls (Bou-Holaigah et al., 1995; Hoad et al.,

2008; Natelson et al., 2007).

Brain volume abnormalities have been identified in CFS patients, with

decreased grey matter, increased white matter, and brainstem dysfunctions observed

using magnetic resonance imaging scans (Barnden et al., 2011; Barnden et al., 2015;

de Lange et al., 2005; Natelson et al., 1993; Okada et al., 2004). However, the role

these abnormalities play in CFS pathophysiology or symptomatology is unknown.

2.4.4 Psychiatric Disorders

The possibility that CFS is a psychiatric disorder or a subtype of MDD is

controversial leading to confusion and unwillingness to diagnose patients with CFS.

The high levels of MDD observed in CFS cohorts fuels the debate for classification

as a psychiatric disorder. However, there are key differences between MDD and

CFS. In particular, the diagnostic criteria for CFS which are not associated with


depression and the differences in depressive symptoms experienced (Afari &

Buchwald, 2003).

2.4.5 Genetics

Recently, studies have started to investigate the genetic contribution of CFS (Narita

et al., 2003; Ortega-Hernandez et al., 2009; Sommerfeldt et al., 2011). However,

conflicting results have been obtained from gene expression analyses (Grans et al.,

2005; Kaushik et al., 2005; Powell et al., 2003). In 2006, the Critical Assessment of

Microarray Data Analysis conference challenge dataset (Wichita clinical study) was

a cohort of CFS (N = 68) and ICF (N = 81) cases and non-fatigued (N = 73) controls

(Nisenbaum et al., 2003; Reeves et al., 2005). Multiple biostatistical approaches,

ranging from gene expression and association analyses to gene-gene interactions,

have been utilised to analyse the data available within the Wichita clinical study.

Analyses using the Wichita clinical study have identified genes of potential

functional significance for CFS (Chung et al., 2007; Goertzel et al., 2006; Lin &

Huang, 2008; Lin & Hsu, 2009; Smith et al., 2006; Smith et al., 2009).

2.5 HERITABILITY OF FATIGUE

Limited studies have been conducted on the genetic basis of fatigue, prolonged

fatigue, CF, ICF, or CFS. However, a number of familial studies have indicated that

genetic and/or environmental factors are associated with predisposition to CF and

CFS within first and second degree relatives (Albright et al., 2011; Bell et al., 1991;

Buchwald et al., 2001; Levine et al., 1998; van de Putte et al., 2006; Walsh et al.,

2001). The importance of both genetic and common environmental factors was

revealed by the shared symptom complex identified between adolescents with CFS

and their mothers, which was not exhibited by the fathers (van de Putte et al., 2006).

Convincing evidence for the contribution of genetic factors to the development or

onset of CFS was provided in a population based study investigating familial

clustering in first, second, and third degree relatives (Albright et al., 2011).

Heritability predictions have been conducted in a number of twin studies,

which utilised structural equation modelling to investigate the additive genetic

factors (A), common environmental factors (C), and unique environmental factors

(E) involved in the heritability of varying levels of fatigue (Table 2.4) (Ball et al.,

2010b; Buchwald et al., 2001; Farmer et al., 1999; Schur et al., 2007; Sullivan et al.,


2003; Sullivan et al., 2005). Interestingly, males are predicted to have a higher

percentage of genetic components associated with abnormal tiredness, prolonged

fatigue, and CF than females (Schur et al., 2007; Sullivan et al., 2005). Fatigue

severity (a continuous measure of the 11 core fatigue and 2 muscle pain items of the

Chalder Fatigue Questionnaire (Chalder et al., 1993)) has an estimated heritability of

30% (Ball et al., 2010b). The heritability of interfering fatigue (tiredness or fatigue

experienced for at least five days) is estimated at 6% in males and 26% in females

(Sullivan et al., 2003). Short-duration fatigue (fatigue experienced for at least one

week) has an estimated heritability of 42% (Farmer et al., 1999). The heritability of

abnormal tiredness is estimated at 30% in males and 26% in females (Sullivan et al.,

2005). Abnormal fatigue (assessed by the 11 core fatigue items of the Chalder

Fatigue Questionnaire) has an estimated heritability of 39% (Ball et al., 2010b). The

heritability of prolonged fatigue is estimated to range from 34-51% in males and 18-

27% in females (Buchwald et al., 2001; Schur et al., 2007; Sullivan et al., 2005).

Additionally, the heritability of prolonged fatigue, in a cohort of males and females,

has been estimated at 54% (Farmer et al., 1999). Furthermore, the heritability of CF

is estimated to range from 34-51% and 30-47% in males and 12-32% in females

(Buchwald et al., 2001; Schur et al., 2007; Sullivan et al., 2005). Finally, within

females ICF and CFS have both been estimated to have 51% heritability (Buchwald

et al., 2001; Schur et al., 2007).


Table 2.4. Heritability estimates (and their 95% confidence intervals) of the unique additive genetic

factors (A), common environmental factors (C), and unique environmental factors (E) contributing to

fatigue severity, interfering fatigue, short-duration fatigue, abnormal tiredness, abnormal fatigue,

prolonged fatigue, chronic fatigue, idiopathic chronic fatigue, and chronic fatigue syndrome.

Population Sex

Number

of twin

pairs

(MZ / DZ)

Mean age ±

standard

deviation

(Age range)

A (%) C (%) E (%) Study

Fatigue severity

Sri Lanka F & M 816 / 1,080 (≥ 15) 30 (24-35) 0 (0-0) 70 (65-76) Ball et al. (2010b)

Interfering fatigue USA M 1,299 /

1,964

Case: 34.9 ± 9.3

Control: 35.1 ± 9.2

6 (0-46) 21 (0-25) 73 (54-90) Sullivan et al. (2003)

F 26 (0-44) 1 (0-30) 73 (56-92)

Short-duration fatigue South Wales M & F 278 / 378 (5-17) 42 38 20 Farmer et al. (1999)

Abnormal tiredness

Sweden M 3,229 / 8,824

(42-64) 30 (11-40) 0 (0-14) 70 (60-80) Sullivan et al. (2005)

F 26 (8-33) 0 (0-14) 74 (67-82)

Abnormal fatigue

Sri Lanka M & F 816 / 1,080 (≥ 15) 39 (29-49) 0 (0-0) 61 (51-71) Ball et al. (2010b) Prolonged fatigue

South Wales M & F 278 / 378 (5-17) 54 19 26 Farmer et al. (1999)

Sweden M 3,229 / 8,824

(42-64) 34 (3-45) 0 (0-25) 66 (55-79) Sullivan et al. (2005)

F 27 (6-35) 0 (0-16) 73 (65-82)

USA M 1,042 / 828

32.4 ± 14.7

(18-90)

51 (13-69) 0 (0-33) 49 (31-71) Schur et al. (2007)

F 18 (0-54) 23 (0-48) 59 (46-74) Chronic fatigue

Sweden M 3,229 /

8,824 46

30 (2-44) 0 (0-23) 70 (56-86) Sullivan et al. (2005)

F 32 (11-41) 0 (0-16) 68 (59-78) USA M

1,042 / 828 (42-64) 47 (0-68) 0 (0-39) 52 (32-79) Schur et al. (2007)

F 12 (0-48) 26 (0-48) 62 (47-78)

USA F 106 / 40 32.4 ± 14.7

(18-90) 19 (0-56) 69 (32-89) 12 (7-19) Buchwald et al. (2001)

Idiopathic chronic fatigue USA F 77 / 22 46 51 (7-96) 42 (0-85) 8 (4-13) Buchwald et al. (2001)

Chronic fatigue syndrome

USA F 648 / 258 32.4 ± 14.7

(18-90) 51 (0-82) 12 (0-72) 36 (18-65) Schur et al. (2007)

M: male; F: female; M & F: male and female.

2.6 MOLECULAR GENETICS OF FATIGUE

Few studies have investigated the molecular genetics of fatigue associated with

cancer, hepatitis C, multiple sclerosis, and hypothyroidism (Landmark-Hoyvik et al.,

2010). However, the majority of research conducted has investigated the molecular

genetics of CFS (and occasionally ICF). Candidate gene association (CGA) studies,

genome-wide association (GWA) analyses, various bioinformatic data-mining

approaches, and genetic interaction investigations have been conducted to identify

the molecular genetic contribution of CFS.

CFS CGA studies have implicated genotypic associations between CFS and

ADRB2, CHRM3, CHRNA2, CHRNA3, CHRNA4, CHRNB1, CHRNB4, CHRNE,

COMT, DCP1, HLA-DQA1, SLC6A4, TRPC2, TRPC4, TRPC6, TRPM3, TRPM3,

and TRPM8 (Table 2.5) (Marshall-Gradisnik et al., 2016a; Marshall-Gradisnik et al.,


2016b; Narita et al., 2003; Smith et al., 2005; Sommerfeldt et al., 2011; Vladutiu &

Natelson, 2004). Similarly, CFS CGA haplotypic analyses have identified an

association between CFS and four DRB1 and RAGE haplotypes (Table 2.5) (Carlo-

Stella et al., 2009). Finally, CFS CGA have implicated allelic associations between

CFS and BMP2K, CHRM1, CHRM2, CHRM3, CHRM5, CHRNA2, CHRNA3,

CHRNA4, CHRNA5, CHRNA9, CHRNA10, CHRNB1, CHRNB4, CHRND, CHRNE,

CHRNG, DISC1, EIF3A, FAM126B, HTR2A, IL6ST, IL-17F, INFG., METTL3,

NR3C1, SORL1, TCF3, TNF, TRPA1, TRPC4, TRPC2, TRPC4, TRPC6, TRPM3,

TRPM4, TRPM8, TRPV2, TRPV3, UBTF, and PEX16 (Table 2.5) (Carlo-Stella et al.,

2006; Fukuda et al., 2010; Marshall-Gradisnik et al., 2015a; Marshall-Gradisnik et

al., 2016a; Marshall-Gradisnik et al., 2016b; Marshall-Gradisnik et al., 2015b;

Metzger et al., 2008; Rajeevan et al., 2007; Shimosako & Kerr, 2014; Smith et al.,

2008).

To date, three CFS GWA studies have been conducted—containing very small

sample sizes. No genome-wide significant SNP loci were identified from the first

two CFS GWA analyses (Rajeevan et al., 2015; Smith et al., 2011). Meanwhile, the

latest CFS GWA study identified 92 SNP loci reaching the studies Bonferroni

adjusted genome-wide threshold of 7.5 × 10-8 (Table 2.6) (Schlauch et al., 2016).

However, methodological concerns associated with this study raise questions about

the accuracy of these associations.


Table 2.5. Candidate genes and implicated single nucleotide polymorphisms associated with chronic

fatigue syndrome.

Gene

symbol Variant

Cases /

Controls RA OR (95% CI) p-value Study

Genotypic associations

SLC6A4 5-HTTLPR 78 / 50

long and

extra-

long variants

3.04 (1.36-4.65) 0.0310 Narita et al. (2003)

DCP1 ACE 59 / 44 I allele 5.55 (1.75-17.52) 0.0200 Vladutiu and Natelson

(2004) HLA-DQ A1 49 / 102 01 allele 1.93 (1.20-3.30) 0.0080 Smith et al. (2005)

ADRB2 rs1042714 53 / 33 GG 2.48 (1.01-6.14) 0.0440 Sommerfeldt et al. (2011)

COMT rs4680 GG &

AG 2.00 (1.01-3.96) 0.0460

CHRNA2 rs891398 39 / 30 CC 11.39 (1.38-94.16) 0.0069 Marshall-Gradisnik et al.

(2016a) rs2741343 CC 11.39 (1.38-94.16) 0.0069

CHRNA3 rs12914385 TT 6.22 (1.27-30.44) 0.0136

CHRNB4 rs12441088 TT 3.57 (1.31-9.73) 0.0113

CHRNE rs33970119 GG 4.36 (1.05-18.22) 0.0328 TRPC2 rs7108612 GT 4.06 (1.18-13.96) 0.0205

TRPC4 rs655207 GG 6.22 (1.27-30.44) 0.0136

rs1570612 GG 3.81 (1.35-10.71) 0.0095 rs2985167 AA 4.21 (1.41-12.56) 0.0079

TRPM3 rs1106948 TT 4.06 (1.18-13.96) 0.0205 rs1891301 TT 3.64 (1.05-12.57) 0.0343

rs6560200 CC 5.63 (1.45-21.83) 0.0076

rs11142822 GG 5.14 (1.25-21.13) 0.0154 rs12350232 TT 3.13 (0.98-9.94) 0.0479

TRPM8 rs11563204 GA 7.19 (2.27-22.76) 0.0004

rs17865678 AG 3.56 (1.27-9.94) 0.0135 CHRM3 rs1867264 11 / 11 TA 7.11 (1.09-46.44) 0.0330 Marshall-Gradisnik et al.

(2016b) rs6688537 CA 7.11 (1.09-46.44) 0.0330

CHRNA4 rs11698563 CC 12.00 (1.12-128.84) 0.0221 CHRNB1 rs2302767 TT 17.50 (1.60-191.90) 0.0078

rs3829603 CC 26.67 (2.31-308.01) 0.0024

rs4151134 TT 17.50 (1.60-191.90) 0.0078 rs7210231 CA 7.88 (1.10-56.12) 0.0301

TRPC6 rs10791504 GG 7.88 (1.10-56.12) 0.0301

TRPM3 rs7038646 AG 7.88 (1.10-56.12) 0.0301 Haplotypic associations

DRB1

RAGE

374 75 / 141

04

T 2.70 (1.02-7.18) 0.0400 Carlo-Stella et al. (2009)

DRB1

RAGE

374

09

T NC 0.0040

DRB1 RAGE

374

11 T

2.27 (1.05-4.89) 0.0390

DRB1

RAGE

374

13

A 8.41 (1.52-46.65) 0.0150

Allelic associations

IFNG rs2430561 47 / 140 T 1.43 (0.89-2.28) 0.0440 Carlo-Stella et al. (2006)

TNF rs1799724 80 / 224 T 1.80 (1.20-2.72) 0.0040 NR3C1 rs6188 40 / 55 C 1.76 (0.83-3.73) 0.0383 Rajeevan et al. (2007)

rs852977 A 1.76 (0.83-3.73) 0.0365

rs860458 G 2.10 (0.87-5.07) 0.0180 rs1866388 A 1.85 (0.87-3.93) 0.0335

rs2918419 T 2.10 (0.87-5.07) 0.0164

HTR2A rs6311 40 / 42 A 2.52 (1.00-6.30) 0.0065 Smith et al. (2008) rs6313 T 2.32 (0.93-5.78) 0.0150

rs1923884 C 2.51 (0.95-6.67) 0.0100

IL-17F rs763780 89 / 56 T 4.07 (1.70-9.71) 0.0018 Metzger et al. (2008) DISC1 rs821616 155 / 502 T 1.50 (1.02-2.19) 0.0370 Fukuda et al. (2010)

BMP2K rs1426139 108 / 68 A 1.10 (0.28-4.29) 0.0091 Shimosako and Kerr (2014)

rs3775516 G 1.11 (0.28-4.35) 0.0025 EIF3A rs10787901 A 1.22 (0.67-2.25) < 0.0001

FAM126B rs11895568 G NC 0.0110

IL6ST rs1373998 T 1.55 (0.54-4.41) 0.0130 METTL3 rs3752411 A 2.40 (0.76-7.62) 0.0310

PEX16 rs3802758 C 3.50 (1.51-8.11) < 0.0001

SORL1 rs3737529 T 7.78 (0.40-152.39) 0.0280 TCF3 rs1860661 G 6.16 (1.80-21.13) < 0.0001

UBTF rs2071167 A 1.89 (0.92-2.25) 0.0240

See Table 2.5 footnotes on page 22.


Table 2.5. Continued Candidate genes and implicated single nucleotide polymorphisms associated

with chronic fatigue syndrome.

Gene

symbol Variant

Cases /


TRPA1 rs2383844 115 / 90 G 1.54 (1.04-2.29) 0.0400 Marshall-Gradisnik et al. (2015b) rs4738202 A 1.73 (1.12-2.65) 0.0180

TRPC4 rs655207 G 1.66 (1.11-2.46) 0.0180

rs6650469 T 1.66 (1.12-2.48) 0.0160 TRPM3 rs1160742 A 1.78 (1.19-2.66) 0.0080

rs1328153 C 1.99 (1.18-3.35) 0.0130

rs1504401 C 1.88 (1.06-3.36) 0.0410 rs3763619 A 1.70 (1.13-2.56) 0.0140

rs4454352 C 1.99 (1.18-3.35) 0.0130

rs7865858 A 1.65 (1.10-2.48) 0.0210 rs10115622 C 1.53 (1.02-2.29) 0.0500

rs11142508 C 1.89 (1.25-2.85) 0.0040

rs12682832 A 1.93 (1.27-2.91) 0.0030

CHRM3 rs589962 115 / 90 T 2.02 (1.32-3.09) 0.0035 Marshall-Gradisnik et al.

(2015a) rs726169 A 1.70 (1.12-2.56) 0.0235

rs1072320 G 2.12 (1.33-3.39) 0.0037 rs4463655 C 1.97 (1.32-2.96) 0.0028

rs6429157 G 1.59 (1.07-2.35) 0.0375

rs6661621 C 2.10 (1.30-3.39) 0.0054 rs6669810 C 1.66 (1.12-2.45) 0.0236

rs7520974 A 1.71 (1.15-2.53) 0.0167 rs7543259 A 2.07 (1.30-3.31) 0.0051

CHRNA10 rs2672211 C 1.85 (1.20-2.86) 0.0107

rs2672214 C 1.87 (1.21-2.88) 0.0108 rs2741862 C 1.77 (1.10-2.84) 0.0304

rs2741868 T 1.85 (1.20-2.86) 0.0119

rs2741870 G 1.83 (1.19-2.82) 0.0128 CHRNA2 rs2565048 T 2.18 (1.24-3.86) 0.0140

CHRNA5 rs951266 T 1.82 (1.19-2.79) 0.0115

rs7180002 T 1.64 (1.07-2.49) 0.0368 CHRM1 rs2075748 39 / 30 A 2.79 (1.05-7.43) 0.0369 Marshall-Gradisnik et al.

(2016a) rs11823728 C 3.45 (1.03-11.55) 0.0394

CHRM3 rs4620530 T 2.11 (1.04-4.28) 0.0381 CHRNA2 rs891398 C 2.35 (1.17-4.70) 0.0168

rs2741343 C 2.29 (1.15-4.59) 0.0186

CHRNA3 rs2869546 T 2.29 (1.13-4.66) 0.0217 rs3743074 T 2.08 (1.03-4.23) 0.0410

rs3743075 G 2.08 (1.03-4.23) 0.0410

rs4243084 G 2.12 (1.03-4.39) 0.0403 rs12914385 T 2.40 (1.17-4.92) 0.0153

CHRNA5 rs951266 T 2.22 (1.07-4.60) 0.0332

rs7180002 T 2.11 (1.02-4.36) 0.0433 CHRNB4 rs12441088 T 2.79 (1.28-6.09) 0.0090

CHRNE rs33970119 G 3.85 (0.97-15.18) 0.0414

TRPC2 rs6578398 A 2.36 (1.06-5.27) 0.0338 TRPM3 rs1106948 T 2.44 (1.22-4.87) 0.0107

rs1891301 T 2.19 (1.10-4.36) 0.0241

rs6560200 C 2.48 (1.24-4.95) 0.0100 rs11142822 G 4.41 (1.14-17.09) 0.0212

rs12350232 T 2.27 (1.14-4.52) 0.0183

TRPM8 rs6758653 G 2.54 (1.23-5.25) 0.0108 rs11563204 A 4.17 (1.67-10.41) 0.0014

rs17865678 A 4.25 (1.89-9.57) 0.0003

CHRM2 rs1424569 11 / 11 A 3.00 (0.88-10.27) 0.0300 Marshall-Gradisnik et al. (2016b) CHRM3 rs1134 C 3.15 (0.92-10.78) 0.0200

rs576386 C 3.24 (0.90-11.62) 0.0400

rs619214 T 3.67 (1.05-12.82) 0.0300 rs685550 C 6.57 (0.65-66.86) 0.0500

rs1019882 A 2.62 (0.78-8.84) 0.0500

rs1155611 C 2.62 (0.78-8.84) 0.0500 rs1155612 A 3.00 (0.88-10.27) 0.0300

rs1416789 A 2.62 (0.78-8.84) 0.0500

rs1544170 G 2.83 (0.83-9.62) 0.0400 rs1867263 G 2.71 (0.79-9.34) 0.0400

rs1867264 T 4.50 (1.26-16.08) 0.0000

rs1867265 G 2.96 (0.86-10.14) 0.0300 rs1899616 G 4.12 (1.17-14.47) 0.0100

rs2083817 T 3.05 (0.89-10.49) 0.0300



Table 2.5. Continued Candidate genes and implicated single nucleotide polymorphisms associated

with chronic fatigue syndrome.

Gene

symbol Variant

Cases /


rs2163546 G 3.11 (0.88-10.95) 0.0400

rs2165872 C 3.05 (0.89-10.49) 0.0300

rs3738436 C 2.62 (0.78-8.84) 0.0500

rs6429147 G 2.81 (0.81-9.71) 0.0400 rs6684622 G 3.50 (1.01-12.12) 0.0200

rs6688537 C 2.91 (0.85-9.94) 0.0300

rs6694220 A 2.73 (0.80-9.29) 0.0500 rs6700643 T 2.81 (0.81-9.71) 0.0400

rs7511970 G 2.62 (0.78-8.84) 0.0500

rs7513746 A 2.62 (0.78-8.84) 0.0500 rs7551001 A 2.96 (0.86-10.14) 0.0300

rs10754677 A 2.67 (0.79-9.01) 0.0500

rs10802795 T 2.62 (0.78-8.84) 0.0500

rs10802802 G 3.33 (0.97-11.49) 0.0200

rs10925941 G 2.81 (0.81-9.71) 0.0400

rs10925964 T 2.62 (0.78-8.84) 0.0500 rs11585281 C 3.14 (0.92-10.79) 0.0200

rs12029701 T 3.14 (0.92-10.79) 0.0200

rs12093821 G 3.32 (0.96-11.57) 0.0200 rs12743042 T 2.98 (0.87-10.14) 0.0300

rs16838637 A 2.96 (0.86-10.14) 0.0300 CHRM5 rs511422 C 3.06 (0.81-11.57) 0.0400

rs603152 A 3.29 (0.87-12.42) 0.0300

rs646950 T 3.26 (0.86-12.28) 0.0300 CHRNA2 rs2741341 C 4.08 (1.08-15.38) 0.0100

CHRNA4 rs11698563 C 5.99 (1.63-22.02) 0.0000

CHRNA9 rs4861065 C 4.53 (0.98-20.84) 0.0200 rs4861323 A 3.89 (0.98-15.41) 0.0100

rs7669882 A 4.53 (0.98-20.84) 0.0200

rs10009228 G 4.60 (1.16-18.18) 0.0000 rs10015231 C 2.92 (0.76-11.28) 0.0400

CHRNB1 rs2302767 T 2.6 (0.74-9.10) 0.0500

rs3829603 C 2.86 (0.80-10.15) 0.0500 rs4151134 T 3.52 (1.02-12.20) 0.0100

CHRNB4 rs12440298 T 11.00 (0.39-313.06) 0.0100

CHRND rs2767 T 3.60 (1.04-12.49) 0.0100 rs2853457 A 3.00 (0.84-10.75) 0.0400

rs3762529 T 3.15 (0.92-10.78) 0.0200

rs3791729 C 3.00 (0.88-10.24) 0.0300 rs3828246 C 3.41 (0.85-13.73) 0.0200

rs4973537 A 3.00 (0.88-10.24) 0.0300

rs11674608 C 6.79 (1.78-25.88) 0.0000 rs12463989 T 3.60 (1.04-12.49) 0.0100

rs12466358 T 3.41 (0.85-13.73) 0.0200

rs13026409 C 3.29 (0.83-13.08) 0.0200 rs67583510 G 3.67 (0.94-14.34) 0.0200

rs112001880 I 3.60 (1.04-12.49) 0.0100

CHRNE rs33970119 G 5.40 (0.44-66.84) 0.0400 CHRNG rs13018423 C 3.29 (0.83-13.08) 0.0200

TRPC6 rs11224816 T 4.10 (1.09-15.46) 0.0100

TRPM3 rs1317103 C 5.97 (1.04-34.27) 0.0100 rs3812532 C 2.83 (0.83-9.62) 0.0400

rs4620343 T 3.48 (0.85-14.22) 0.0300

rs10780950 T 4.46 (0.76-26.21) 0.0500 TRPM4 rs11083963 A 3.00 (0.86-10.48) 0.0300

TRPV2 rs3514 G 4.17 (0.65-26.90) 0.0300

rs2075763 C 5.20 (0.44-62.13) 0.0500 rs7222754 T 3.01 (0.80-11.39) 0.0500

rs12602006 A 2.71 (0.77-9.56) 0.0500

rs12942540 G 4.17 (0.65-26.90) 0.0300 rs35400274 G 4.33 (0.66-28.62) 0.0300

TRPV3 rs4790519 C 3.75 (1.04-13.49) 0.0100

RA: risk allele; OR: odds ratio; CI: confidence interval; NC: not calculable due to an allele frequency of 0 in controls.


Table 2.6. List of reported genome-wide significant (7.5 × 10-8) risk loci associated with chronic

fatigue syndrome from a genome-wide association study of 42 cases and 38 controls by Schlauch and

colleagues (2016).

SNP Risk allele OR (95% CI) p-value

rs12235235 T 10.61 (4.15-27.08) 5.76 × 10-16 rs10144138 T 27.75 (6.38-120.63) 6.99 × 10-14

rs17120254 A NC 5.20 × 10-13

rs41493945 A 48.53 (6.43-366.21) 6.25 × 10-13 rs3788079 C NC 3.42 × 10-12

rs41378447 T 10.84 (4.46-26.34) 1.06 × 10-11

rs3913434 T 46.15 (6.11-348.47) 1.26 × 10-11 rs5967529 A 18.75 (8.46-41.56) 1.69 × 10-11

rs254577 C 11.04 (5.28-23.07) 2.35 × 10-11

rs270838 C 7.73 (3.31-18.05) 3.61 × 10-11 rs1523773 T NC 4.73 × 10-11

rs16827966 T 41.67 (5.51-315.00) 5.32 × 10-11

rs2249954 G 10.11 (3.96-25.82) 5.47 × 10-11 rs8029503 T 8.10 (3.47-18.93) 5.66 × 10-11

rs3095598 C 18.25 (5.32-62.62) 1.02 × 10-10

rs7010471 G 12.24 (4.09-36.66) 2.49 × 10-10 rs6757577 A 11.18 (4.10-30.51) 2.77 × 10-10

rs11157573 G 5.09 (2.40-10.77) 2.97 × 10-10

rs16987633 A 6.21 (3.10-12.43) 3.46 × 10-10 rs12312259 C 6.46 (3.05-13.69) 3.60 × 10-10

rs948440 C 6.92 (3.14-15.26) 3.92 × 10-10

rs6445832 G 8.75 (3.42-22.38) 4.36 × 10-10 rs9585049 T 15.75 (4.58-54.13) 5.25 × 10-10

rs7220341 G 4.97 (2.53-9.75) 5.41 × 10-10

rs2816751 C 5.11 (2.48-10.51) 5.43 × 10-10 rs2200706 T 5.91 (2.98-11.74) 5.48 × 10-10

rs17255510 C 10.23 (4.82-21.71) 6.61 × 10-10

rs6892217 T 8.19 (4.01-16.73) 6.61 × 10-10 rs17112444 A 21.64 (4.96-94.39) 8.02 × 10-10

rs7849492 C 5.91 (2.74-12.75) 9.95 × 10-10

rs686190 G 11.65 (3.88-34.92) 1.11 × 10-9 rs16826918 G 10.11 (3.96-25.82) 1.13 × 10-9

rs12317807 T 4.71 (2.13-10.43) 1.47 × 10-9 rs5974598 T 4.71 (2.13-10.43) 1.55 × 10-9

rs1932556 T 54.13 (7.15-409.98) 1.63 × 10-9

rs6797416 G NC 1.71 × 10-9 rs2733416 G NC 1.71 × 10-9

rs17035358 A 9.66 (3.53-26.41) 1.72 × 10-9

rs17368935 G 9.66 (3.53-26.41) 1.72 × 10-9 rs6679280 T 9.66 (3.53-26.41) 1.72 × 10-9

rs3867246 T 5.20 (2.35-11.48) 1.88 × 10-9

rs689462 C 8.54 (3.52-20.76) 2.08 × 10-9 rs9285128 A 4.00 (2.04-7.86) 2.15 × 10-9

rs822027 A 35.53 (4.69-269.28) 2.52 × 10-9

rs11168709 T 35.53 (4.69-269.28) 2.52 × 10-9 rs12055682 G 4.79 (2.43-9.41) 2.99 × 10-9

rs17047694 T 3.07 (1.60-5.90) 3.66 × 10-9

rs10978470 G 5.59 (2.64-11.85) 4.32 × 10-9

rs890527 T 3.12 (1.62-6.01) 4.60 × 10-9

rs11062852 C 4.07 (2.00-8.26) 4.84 × 10-9

rs16992281 A NC 5.22 × 10-9 rs6675622 T 9.20 (4.28-19.77) 5.94 × 10-9

rs6863118 G 7.37 (3.15-17.22) 6.22 × 10-9

rs10121299 C 4.42 (2.27-8.61) 6.62 × 10-9 rs12014391 A 9.38 (4.53-19.39) 6.66 × 10-9

rs12391243 C 8.35 (4.02-17.32) 6.68 × 10-9

rs1041296 G 4.76 (2.37-9.59) 6.89 × 10-9 rs11027583 T 7.39 (3.04-17.99) 7.03 × 10-9

rs12305678 G 4.41 (2.21-8.79) 7.87 × 10-9

rs9581771 T 7.76 (3.19-18.87) 7.96 × 10-9 rs4022211 G 7.41 (3.67-14.98) 9.09 × 10-9

rs16883408 C 5.51 (2.76-10.98) 1.06 × 10-8

rs41456945 C 18.50 (4.23-80.94) 1.07 × 10-8 rs361236 A 3.33 (1.72-6.45) 1.07 × 10-8

rs1007540 G 3.25 (1.70-6.21) 1.17 × 10-8

rs7143222 T NC 1.54 × 10-8 rs17092382 A NC 1.54 × 10-8



Table 2.6. Continued List of reported genome-wide significant (7.5 × 10-8) risk loci associated with

chronic fatigue syndrome from a genome-wide association study of 42 cases and 38 controls by

Schlauch and colleagues (2016).


rs7549528 C NC 1.54 × 10-8 rs6854376 T 10.53 (3.50-31.63) 1.71 × 10-8

rs16902672 C 5.25 (2.60-10.59) 1.77 × 10-8

rs4473594 A 8.54 (3.52-20.76) 1.81 × 10-8 rs10737169 A 5.36 (2.68-10.75) 2.51 × 10-8

rs7883119 G 7.15 (3.52-14.55) 2.57 × 10-8

rs4623336 T 4.85 (2.29-10.27) 2.68 × 10-8 rs2748997 C 9.18 (3.59-23.48) 2.76 × 10-8

rs584569 A 8.74 (3.19-23.95) 2.84 × 10-8

rs13339179 T 12.83 (3.72-44.3) 2.89 × 10-8 rs1222400 T 12.83 (3.72-44.3) 2.89 × 10-8

rs2882361 G 10.44 (4.26-25.54) 3.02 × 10-8

rs41464146 C 18.50 (4.23-80.94) 3.22 × 10-8 rs9446695 T 17.53 (4.00-76.78) 3.46 × 10-8

rs7290437 G 2.73 (1.39-5.35) 3.52 × 10-8

rs12607783 A 4.71 (2.13-10.43) 4.31 × 10-8 rs606324 A 6.48 (2.52-16.69) 4.39 × 10-8

rs2869820 T NC 4.39 × 10-8

rs6643261 A 30.57 (7.04-132.77) 4.55 × 10-8 rs17133553 A 5.53 (2.80-10.91) 4.74 × 10-8

rs2816936 A 14.99 (5.46-41.14) 4.91 × 10-8

rs1915603 G 9.73 (2.79-33.90) 5.15 × 10-8 rs17052315 A 10.00 (3.32-30.08) 5.97 × 10-8

rs6502875 G 3.90 (2.02-7.53) 5.97 × 10-8

rs10047684 A 4.51 (2.28-8.94) 7.31 × 10-8

OR: odds ratio; CI: confidence interval; NC: not calculable due to an

allele frequency of 0 in either cases or controls

In 2017, the largest GWA of a fatigue phenotype was conducted within the UK

Biobank sample. The study by Deary and colleagues (2017) investigated self-

reported tiredness experienced over the past two weeks, which had a SNP-based

heritability (i.e., the proportion of a traits variation which is explained by all SNPs

investigated within a GWA analysis dataset) of 8.4% (standard error [SE] = 0.6%).

One genome-wide significant SNP (Affymetrix ID 1:64178756_C_T in an intergenic

region on chromosome 1, p = 1.36 × 10-11) and two suggestive peaks on chromosome

1 (rs142592148 in the intron of SLC44A5, p = 5.88 × 10-8) and 17 (rs2555592 in the

intron of PAFAH1B1, p = 6.86 × 10-8) were associated with self-reported tiredness.

Within this study, a gene-based association analysis identified five genes which

reached genome-wide significance (p < 2.77 × 10-6) and 44 genes which were

suggestively associated (p < 1.00 × 10-4) with self-reported tiredness (Table 2.7)

(Deary et al., 2017).


Table 2.7. List of genes associated (p < 2.77 × 10-6) or suggestively associated (p < 1.00 × 10-4) with

self-reported tiredness.

Gene Chromosome p-value

DRD2 11 2.94 × 10-7

PRRC2C 1 1.43 × 10-6 C3orf84 3 1.45 × 10-6

ANO10 3 1.52 × 10-6

ASXL3 18 2.67 × 10-6 RHOA 3 4.07 × 10-6

CTNND1 11 4.09 × 10-6

THEM4 1 5.44 × 10-6 FBXO21 12 5.66 × 10-6

ADARB1 21 6.01 × 10-6

NAPA 19 6.06 × 10-6 KANSL1L 2 6.24 × 10-6

RHCG 15 6.90 × 10-6

PLAC8 4 6.95 × 10-6

KLF7 2 7.40 × 10-6

RPE 2 1.00 × 10-5

TMX2 11 1.36 × 10-5 SNF8 17 1.38 × 10-5

CCDC36 3 1.38 × 10-5

SSBP4 19 1.87 × 10-5 ISYNA1 19 1.93 × 10-5

RELT 11 2.37 × 10-5 CSMD3 8 2.49 × 10-5

ZDHHC5 11 2.66 × 10-5

METTL16 17 2.67 × 10-5 SRRM4 12 3.03 × 10-5

BSN 3 3.20 × 10-5

NRXN1 2 3.25 × 10-5 ZNF780A 19 3.30 × 10-5

SMC1B 22 3.33 × 10-5

TCTA 3 3.36 × 10-5 GIP 17 3.45 × 10-5

CKMT1A 15 4.09 × 10-5

NICN1 3 4.18 × 10-5 UBE2Z 17 5.11 × 10-5

DAG1 3 5.26 × 10-5

ATP11B 3 5.28 × 10-5 PSMC4 19 5.44 × 10-5

FAM168A 11 5.86 × 10-5

CCNT2 2 6.25 × 10-5 OPA1 3 6.42 × 10-5

CATSPER2 15 6.52 × 10-5

ZBTB37 1 6.67 × 10-5 ELL 19 6.91 × 10-5

SERPING1 11 7.49 × 10-5

PLGRKT 9 7.89 × 10-5 PRR12 19 8.37 × 10-5

UBA7 3 8.48 × 10-5

CAMK1D 10 9.36 × 10-5

The study by Deary and colleagues (2017)

was conducted in a UK population.

Tiredness was assessed by the question: “Over the past two weeks, how often have

you felt tired or had little energy?”; 6,948

individuals responded “nearly every day”, 6,404 individuals responded “more than half

the days”, 44,208 individuals responded

“several days”, and 51,416 individuals responded “not at all”.

2.7 CLASSIFICATION OF MDD AND MIDD

The standard definition used for diagnosis with MDD and MiDD is the Diagnostic

and Statistical Manual of Mental Disorders (DSM) (American Psychiatric

Association, 2000, 2013). To be diagnosed with MDD, an individual must have had


at least one major depressive episode. A major depressive episode is assessed over a

two-week period and contains nine symptom criteria. To meet the DSM-V criteria of

a depressive episode, symptoms from at least one of the first two criteria must be

exhibited. The criteria of a depressive episode are:

1. Depressed mood

2. Loss of interest or pleasure (anhedonia)

3. Change in weight (5% of weight within a month) or appetite

4. Insomnia or hypersomnia

5. Psychomotor agitation or retardation

6. Fatigue or loss of energy

7. Feelings of worthlessness or excessive guilt

8. Inability to concentrate or indecisiveness

9. Recurrent thoughts of death and suicidal thoughts, plans, or attempts.

Diagnosis of MDD requires endorsement of at least five of the criteria

(American Psychiatric Association, 2013). Similarly, diagnosis of MiDD is assessed

using the symptoms of a major depressive episode. However, diagnosis only requires

endorsement of two (to four) of the criteria (American Psychiatric Association,

2000).

2.8 EPIDEMIOLOGY OF MDD AND MIDD

Epidemiological studies have consistently shown females have an increased risk of

diagnosis with MDD compared to males. Mean age at first onset of MDD occurs at

age 31.7 ± 12.3 (Fernandez-Pujals et al., 2015). The prevalence of a lifetime, twelve

month, and current diagnosis of MDD is estimated at 16.2%, 6.6%, and 4.1%,

respectively (Centers for Disease Control and Prevention, 2010; Kessler et al., 2003).

Meanwhile, prevalence of a current diagnosis of MiDD is 5.1% (Centers for Disease

Control and Prevention, 2010). MDD prevalence increases from puberty, with

variation in prevalence observed at different ages (Centers for Disease Control and

Prevention, 2010; Kessler et al., 2003). In adults (aged ≥ 18 years), lifetime

prevalence of MDD gradually increases before declining at ~ 60 years (Kessler et al.,

2003). Interestingly, the 12-month prevalence of MDD is highest in the 18-29 year


age range after which it progressively decreases. Meanwhile, the prevalence of

current diagnosis of MDD exhibits a similar pattern to that of the lifetime prevalence,

gradually increasing from 18-24 years, peaking at 45-64 years after which it

decreases (Centers for Disease Control and Prevention, 2010). The pattern of current

diagnosis of MiDD is particularly interesting. The 18-24 age range has the highest

prevalence which gradually decreases until 35-44 years after which the prevalence

increases to the 65+ age group.

2.9 PATHOPHYSIOLOGY OF MDD

To date, aetiological investigations have indicated MDD is a complex, multifactorial

trait with a multitude of biological functions and environmental contributions

implicated in the pathophysiology of depression. To date, neuroendocrine

functioning, the central nervous system, genetics, and environmental factors have

been implicated in the pathophysiology of depression. However, in most instances, it

is impossible to determine if the altered biological function observed in MDD cases

is a causal factor rather than a consequence of depression.

2.9.1 Endocrine and Neurologic Dysfunction

Neurobiological changes associated with a sustained stress response, particularly

those involving the neuroendocrine, noradrenergic, and serotonergic pathways, have

been emphasized in MDD aetiological research (National Research Council (US) &

Institute of Medicine (US) Committee on Depression, 2009; Thase et al., 2014). In

particular hypercortisolism, elevated HPA-axis activity, and deficiency of the

serotonin neurotransmitters (National Research Council (US) & Institute of Medicine

(US) Committee on Depression, 2009; Thase et al., 2014; Verduijn et al., 2015).

Additionally, neuroimaging techniques have enabled identification of brain regions

associated with depression symptomatology, namely, the amydgala, anterior

cingulate cortex, dorsolateral prefrontal cortex, medial prefrontal cortex,

orbitofrontal cortex, and striatum (Treadway & Pizzagalli, 2014).

2.9.2 Genetics

A multitude of linkage and candidate gene associations studies have been conducted

for MDD (Flint & Kendler, 2014). However, conflicting results have been obtained

between investigations and the findings have not been replicated in GWA studies.

Although robustly associated risk loci for MDD have recently been identified, to


date, the proportion of the variance explained by all SNPs included in the GWA

investigations is relatively small compared to heritability estimates identified from

twin studies—indicating our understanding of the genetics underlying MDD is

limited. Additionally, gene-gene and gene-environment interactions could contribute

to the development and recurrence of MDD, however, studies which enable

identification of these mechanisms are currently vastly underpowered.

2.9.3 Environmental Factors

Stressful life events and exposure to early adversity have consistently been

associated with MDD. An increase in depression symptoms and MDD onset has

consistently been associated with triggering stressful life events (either positive or

negative), such as death, illness, or injury of a loved one (i.e., spouse, close family

member, or friend), outstanding personal achievement, or change in work, residence,

recreation, social activities, sleeping habits, and eating habits (Kendler et al., 1999;

National Research Council (US) & Institute of Medicine (US) Committee on

Depression, 2009; Shapero et al., 2014). Similarly, MDD in adolescents or adults has

been significantly associated with childhood physical, sexual, or emotional abuse

with some evidence indicating childhood adversity is predictive of chronic or

recurrent depression (Bifulco et al., 2002; Kendler et al., 2000; National Research

Council (US) & Institute of Medicine (US) Committee on Depression, 2009).

However, the mechanisms underlying the association between environmental factors

and MDD are unknown.

2.10 HERITABILITY OF MDD AND MIDD

Numerous studies have been conducted investigating the familiality of MDD and

MiDD. Familial clustering has consistently been observed with first-degree relatives

of MDD probands having increased odds of experiencing MDD (Mantel-Haenszel

odds ratio (OR) = 2.84, 95% confidence interval (CI) = 2.31-3.49) (Gershon et al.,

1982; Maier et al., 1993; Sullivan et al., 2000; Tsuang et al., 1980; Weissman et al.,

1984; Weissman et al., 1993). Furthermore, first-degree relatives and patients with

MiDD have an increased risk of experiencing MDD (Chen et al., 2000; Cuijpers et

al., 2004; Cuijpers & Smit, 2004; Judd et al., 1997; Lewinsohn et al., 2003; Rapaport

et al., 2002). These results indicate MDD has a significant additive genetic and/or

common environmental contribution, and suggests a proportion of these genetic and


environmental factors are shared with MiDD. A recent large family-based population

study in Scotland has utilised 20,198 adults aged ≥ 18 (median age of 49) to

investigate the epidemiology and heritability (modelled using the full pedigree

structure of the cohort and the phenotypic variability among family members) of

MDD (classified according to the DSM-IV criteria) (Fernandez-Pujals et al., 2015).

Within the cohort males had a lower estimated heritability for MDD at 35% (95% CI

= 8-63%; C = 8%, 95% CI = 0-20%) compared to females at 44% (95% CI = 25-

61%; C = 4%, 95% CI = 0-13%). The authors also determined recurrent MDD had a

higher heritability at 41% (95% CI = 20-60; C = 7%, 95% CI = 0-16%) compared to

single episode MDD at 28% (95% CI = 14-41; C = 3%, 95% CI = 0-8).

To date, no studies have investigated the heritability of MiDD, while numerous

twin studies have been conducted to estimate the heritability of MDD (Table 2.8)

(Bierut et al., 1999; Kendler et al., 1995; Kendler & Prescott, 1999; Kendler et al.,

2001; Kendler et al., 2006; Lyons et al., 1998; McGuffin et al., 1996; Sullivan et al.,

2000). Within clinical samples the contribution of A, C, and E have been estimated

to range from 49-58%, 0-21% and 30-42%, respectively, in males, and 17-38%, 0%,

and 62-83%, respectively, in females (Kendler et al., 1995; McGuffin et al., 1996).

Similarly, within community cohorts, the contribution of A, C, and E have been

estimated to range from 18-57%, 0-2%, and 41-81%, respectively, in males, and 36-

78%, 0%, and 22-64%, respectively, in females (Bierut et al., 1999; Kendler et al.,

1995; Kendler & Prescott, 1999; Kendler et al., 2001; Kendler et al., 2006; Lyons et

al., 1998). Finally, within a meta-analysis, the contribution of A, C, and E has been

estimated at 37%, 0%, and 63%, respectively (Sullivan et al., 2000).


Table 2.8. Heritability estimates (and their 95% confidence intervals) of the unique additive genetic factors (A), common environmental factors (C), and unique

environmental factors (E) contributing to major depressive disorder (MDD).

Diagnosis Population Sex Number of twin pairs

(MZ / DZ)

Mean age ±

standard deviation

(Age range)

A (%) C (%) E (%) Notes Study

Individual depression symptoms in a community cohort.

DSM-IV United States M & F 1,206 / 1,878 and

1,325 singleton twins

(≥ 14) 29 0 71 Depressed mood Kendler et al. (2013)

29 1 70 Loss of interest/pleasure 12 5 83 Weight or appetite change

19 4 77 Insomnia or hypersomnia

20 2 78 Psychomotor changes 38 1 61 Fatigue

29 2 69 Worthlessness/guilt

25 4 71 Problems with concentration 26 5 69 Suicidal ideation

≥ 1major depressive episode reported within the specified timeframe in a community cohort.

DSM-III-R United States F 508 / 350 (≥ 14) 41 (27-54) - 59 (46-73) 1 year Kendler and Aggen (2001) 41 (26-55) - 59 (45-74) 6 month

35 (16-52) - 65 (48-84) 3 month

34 (11-55) - 66 (45-89) 1 month Lifetime MDD in a community cohort.

DSM-III United States F 1,033 twin pairs and

97 singleton twins

30.1 ± 7.6 (17-55) 39 (34-45) - 60 Remaining variance is

explained by age correction

Kendler et al. (1992)

DSM-III-R 42 (37-47) - 58

DSM-III-R Sweden M 251 / 495 NR 57 (0-73) 2 (0-73) 41 (4-100) Included in meta-analysis Kendler et al. (1995)

F 78 (18-94) 0 (0-42) 22 (6-55)

DSM-III-R United States M 1,874 / 1,498 46.6 ± 2.8 (36-55) 36 (11-47) 0 (0-20) 64 (53-75) Included in meta-analysis Lyons et al. (1998)

DSM-III-R United States M 75 / 145 (18-60) 31 (5-41) 0 (0-22) 69 (59-79) Included in meta-analysis Kendler and Prescott (1999) F 38 (1-50) 0 (0-31) 62 (50-75)

M & F 39 (30-47) - 61

DSM-III-R Australian M 1,323 / 1,339 42 ± 11.23 (28-84) 24 (0-39) 0 (0-26) 76 (61-91) Included in meta-analysis Bierut et al. (1999)

F 44 ± 12.35 (28-89) 44 (29-53) 0 (0-12) 56 (47-65)

DSM-IV M 42 ± 11.23 (28-84) 18 (0-36) 0 (0-27) 81 (64-97)

F 44 ± 12.35 (28-89) 36 (15-46) 0 (0-16) 64 (54-75)

DSM-III-R United States M 6,763 twin pairs and

895 singleton twins

NR 44 (38-50) - 56 (50-62) Kendler et al. (2001)

F 57 (51-63) - 43 (37-49)

DSM-IV United States F 855 / 661 15.5 (13-19) 40 (24-55) - 60 (45-76) Glowinski et al. (2003) DSM-IV Sweden M 4,091 / 11,402 NR 29 (19-38) - 71 (62-81) Kendler et al. (2006)

F 42 (36-47) - 58 (53-64)

M & F 38 - 62



Table 2.8. Continued Heritability estimates (and their 95% confidence intervals) of the unique additive genetic factors (A), common environmental factors (C), and unique

environmental factors (E) contributing to major depressive disorder (MDD).

Diagnosis Population Sex Number of twin pairs

(MZ / DZ)

Mean age ±

standard deviation

(Age range)

A (%) C (%) E (%) Notes Study

DSM-IV Sri Lankan F 465 / 307 34 (≥ 15) 59 (43-72) - 41 (28-57) Ball et al. (2009)

DSM-IV United States

African-American F

254 twin pairs and 296

singleton twins (18-28) 56 (29-78) - 44 (22-72) Duncan et al. (2014)

United States

European-American

1,514 twin pairs and

1,712 singleton twins 41 (29-52) - 59 (48-71)

United States

African and European American

1,768 twin pairs and 2,008 singleton twins

43 (33-53) - 57 (47-67)

Lifetime MDD in a clinical cohort.

DSM-III-R Sweden M 23 / 64 NR 49 (0-99) 21 (0-89) 30 (1-93) Included in meta-analysis Kendler et al. (1995) F 17 (0-55) 0 (0-38) 83 (45-83)

DSM-IV United Kingdom M 68 / 109 NR 58 (4-81) 0 (0-40) 42 (19-72) Included in meta-analysis McGuffin et al. (1996)

F 38 (14-61) 0 (0-24) 62 (39-86) M & F 48 0 52

Lifetime MDD in a clinical or community cohort.

DSM-III-R Sweden M & F 274 / 559 NR 60 - 40 Included in meta-analysis Kendler et al. (1995) DSM European ancestry M & F 4,736 / 5,567 NR 37 (31-42) 0 (0-5) 63 (58-67) Meta-analysis Sullivan et al. (2000)

Note: 95% confidence intervals are reported as described in the original studies therefore, if they are absent they were not reported in the original publication. Populations are of European decent unless otherwise

specified. M: male; F: female; M & F: male and female; NR: not reported.


2.11 MOLECULAR GENETICS OF MDD

Considerable effort has gone into investigating the molecular genetics of MDD, with

numerous linkage, candidate gene, and GWA studies conducted (Flint & Kendler,

2014). Linkage studies conducted for MDD have yielded some significant findings.

However, the results of comparable studies are inconstant, leading one to question

the contribution of rare large effect genetic loci to MDD (Flint & Kendler, 2014;

Lohoff, 2010). Similarly, conflicting results have been obtained from the multitude

of CGA analyses conducted. Meta-analyses have been conducted for a minority (N =

26) of the investigated genes, which resulted in the identification of six significant

genes—based on allelic tests for individual variants (Table 2.9) (Flint & Kendler,

2014; Lopez-Leon et al., 2008). Although conflicting results have been obtained

from different meta-analyses of the same variant—which could be due to differing

sample sizes. Furthermore, to date the genes implicated by CGA studies have not

been replicated in GWA studies (Bosker et al., 2011; Flint & Kendler, 2014).

Twenty-two GWA studies have been conducted which attempted to identify

genetic risk loci associated with replication of MDD CGA study results (N = 1),

MDD (N = 3), recurrent MDD (N = 4), MDD or recurrent MDD (N = 5), MDD or

recurrent MDD under a recessive model (N = 1), MDD or recurrent MDD and age at

onset (N = 2), self-reported major depression (N = 1), depressive symptoms (N = 4),

and depressive symptoms and MDD (N = 1) (Table 2.10).


Table 2.9. Candidate genes associated with major depressive disorder in meta-analysis studies.

Gene

symbol Variant

Number of

Studies

Cases /


APOE ε2/ ε3/ ε4 7 827 / 1616 ε3 1.96 (1.03-3.72) ≤ 0.0010 Lopez-Leon et

al. (2008)

DRD4 48 bp

repeat 5 318 / 814

2

allele 1.73 (1.29-2.32) 0.00030

Lopez Leon et

al. (2005)

GNB3 rs5443 3 375 / 492 T 1.38 (1.13-1.69) ≤ 0.0500 Lopez-Leon et al. (2008)

HTR1A rs6295 13 3,199 / 4,380 G 1.15 (1.04-1.28) 0.0060 Kishi et al.

(2013)

7 1,658 / 2,046 G 1.22 (1.03-1.44) 0.0330 Kishi et al.

(2009)

4 NA G 1.16 (0.98-1.38) NS Lopez-Leon et

al. (2008)

MTHFR rs1801133 17 3,341 / 13,840 T 1.02 (0.96-1.08) 0.5790 Peerbooms et al. (2011)

10 1,280 / 10,429 T 1.14 (1.04-1.26) < 0.0500 Gilbody et al.

(2007)

6 875 / 3,859 T 1.20 (1.07-1.34) < 0.0500 Lopez-Leon et

al. (2008)

5 291 / 897 T 1.15 (0.97-1.36) > 0.1000 Zintzaras (2006)

4 1,222 / 835 T 0.96 (0.84-1.09) 0.3900 Gaysina et al.

(2008)

SLC6A4 44 bp

ins/del 39 6,836/14,903 S 1.09 (1.02-1.16) 0.0070

Clarke et al.

(2010)

22 3,752 / 5,707 S 1.11 (1.04-1.19) ≤ 0.0500 Lopez-Leon et al. (2008)

14 1,961 / 3,402 S 1.05 (0.96-1.14) 0.2800 Lasky-Su et al.

(2005)

11 941 / 2,110 S 1.08 (0.96-1.22) 0.1980 Anguelova et al.

(2003)

4 275 / 739 S 1.23 (1.01-1.52) 0.0420 Furlong et al. (1998)

RA: risk allele; OR: odds ratio; CI: confidence interval; bp: base pair; ins/del: insertion/deletion.


Table 2.10. Summary of the genome-wide association studies conducted for major depressive

disorder (MDD).

Population Number of

SNP loci Sample Cases / Controls Study

Replication of MDD candidate gene studies

Western European ancestry 2,467,430 Discovery 1,738 / 1,802 Bosker et al. (2011)

MDD

Netherlands 435,291 Discovery 1,738 / 1,802 Sullivan et al. (2009) Replication 6,079 / 5,893

Germany 491,238 Discovery 597 / 1,295 Rietschel et al. (2010)

Meta-analysis 1,006 / 1,836 Netherlands 433,556 Discovery 1,726 / 1,630 Aragam et al. (2011)

Recurrent MDD

United Kingdom 471,747 Discovery 1,636 / 1,594 Lewis et al. (2010) Meta-analysis 3,054 / 3,512

Germany and Switzerland 522,008 Discovery 926 / 866 Muglia et al. (2010)

370,697 Discovery 492 / 1,052

494,678 Meta-analysis 1,359 / 1,782

European decent 457,670 Discovery 805 / 805 Power et al. (2013)

Han Chinese Women 6,242,619 Discovery 5,303 / 5,337 CONVERGE Consortium (2015) Meta-analysis 8,534 / 8,523

MDD or recurrent MDD

United States 382,598 Discovery 1,221 / 1,636 Shyn et al. (2011) 2,391,203 Meta-analysis 3,957 / 3,428

United States 671,424 Discovery 1,020 / 1,636 Shi et al. (2011) Europe / United States 365,676 Discovery 356 / 366 Kohli et al. (2011)

Meta-analysis 4,088 / 11,001

Australia / Europe / United States

1,079,979 Discovery 2,431 / 3,673 Wray et al. (2012) 427,362 Meta-analysis 5.763 / 6.901

Australia / Europe /

United States

1,235,109 Discovery 9,240 / 9,519 Major Depressive Disorder

Working Group of the Psychiatric GWAS Consortium et al. (2013)

Replication 6,783 / 50,695

MDD or recurrent MDD under a recessive model

Australia / Europe / United States

929,138 Discovery 9,238 / 9,521 Power et al. (2014)

MDD or recurrent MDD and age of onset

European decent 471,581 Discovery 2,746 / 1,594 Power et al. (2012) Germany Replication 1,480 / 1,584

European decent 1,235,109 Discovery 8,920 / 9,519 Power et al. (2017)

Meta-analysis 22,158 / 133,749 Self-reported major depression

European 13,519,496 Discovery 75,607 / 231,747 Hyde et al. (2016)

~ 1,220,000 Meta-analysis 121,380 / 338,101 Depressive symptoms

Sardinian ~ 2,500,000 Discovery 3,972 Terracciano et al. (2010)

United States ~ 2,500,000 Discovery 839 Meta-analysis 4,811

European ~ 300,000 Discovery 4,525 Luciano et al. (2012)

Replication 527 Replication 1,383

European decent ~ 2,500,000 Discovery 34,549 Hek et al. (2013)

Meta-analysis 51,258 European decent 6,544,862 Discovery 105,739 Okbay et al. (2016)

4,661,873 Discovery 7,231 / 49,316

998,753 Discovery 9,240 / 95,19 Meta-analysis 180,866

MDD or recurrent MDD and depressive symptoms

European decent Discovery 70,017 Direk et al. (2016) 918,921 Meta-analysis 98,345

In 2011, a genome-wide significant SNP (rs1545843, p = 2.30 × 10-8) within a

gene desert on chromosome 12) was reported for a recessive model of inheritance

(Kohli et al., 2011). However, the consensus within the research community is that

this was a false positive finding (Cohen-Woods et al., 2013). In 2015, two genome-

wide significant SNP loci (rs35936514 in the intron of LHPP and rs12415800 near

the SIRT1 gene) were associated with MDD (Table 2.11) in Han Chinese Women


(CONVERGE Consortium, 2015). However, these genes have not been associated

with MDD in Europeans. In 2016, 17 SNP loci within 15 independent genomic

locations (rs10514299 in an intron of TMEM161B-AS1; rs1518395 in an intron of

VRK2, rs2179744 in an intron of L3MBTL2, rs11209948 downstream of NEGR1,

rs454214 upstream of MEF2C, rs301806 in an intron of RERE, rs1475120 in an

intron of LIN28B, rs10786831 in an intron of SORCS3, rs12552 in the 3′ untranslated

region (UTR) of OLFM4, rs6476606 in an intron of PAX5, rs8025231 in an

intergenic region between MEIS2 and TMCO5A, rs12065553 in an intergenic region

on chromosome 1, rs1656369 in the intergenic region between RSRC1 and MLF1,

rs4543289 in an intergenic region on chromosome 5, rs2125716 upstream of

SLC6A15, rs2422321 downstream of NEGR1, and rs7044150 in the intergenic region

between KIAA0020 and RFX3) were associated with self-reported major depression

(Table 2.11) , which had a SNP-based heritability of 5.9%, in Europeans (Hyde et al.,

2016). Further insights into the molecular genetics of depression, in Europeans, were

identified in 2016 through the investigation of depressive symptoms and a broad

depression phenotype (that included MDD, recurrent MDD, and depressive

symptoms). Depressive symptoms, which had a SNP-based heritability of 4.7% (SE

= 0.004), was associated with two SNPs (rs7973260 in an intron of KSR2 and

rs62100776 in an intron of DCC) (Table 2.11) (Okbay et al., 2016). Similarly, one

SNP (rs9825823 located in the intron of FHIT) was associated with a broad

depression phenotype that included MDD or recurrent MDD and depressive

symptoms, which had a SNP-based heritability of 21% (SE = 0.020), in Europeans

(Table 2.11) (Direk et al., 2016). Finally, in 2017, one SNP (rs7647854 located in an

intergenic region on chromosome 3) was associated with MDD and recurrent MDD

in adults aged over 27 years (Table 2.11) (Power et al., 2017).

Additional SNP-based heritability estimates have been calculated for MDD.

The SNP-based heritability was estimated at 32% (SE = 0.086) in the Netherlands

MDD cohort (1,620 cases and 1,625 controls) (Lubke et al., 2012). Similarly, the

SNP-based heritability was estimated at 21% (SE = 0.021) in the Psychiatric

Genomics Consortium MDD cohort (9,041 cases and 9,381 controls) (Cross-

Disorder Group of the Psychiatric Genomics Consortium, 2013). Finally, the SNP-

based heritability of MDD age of onset was estimated at 17.5% (SE = 0.104) in 3,468

unrelated MDD of European decent (Ferentinos et al., 2015).


Table 2.11. List of genome-wide significant (5 × 10-8) risk loci associated with depression from a

genome-wide association studies by the CONVERGE consortium (2015), Hek and colleagues (2013),

and Direk and colleagues (2016).


Recurrent MDD in Han Chinese women (CONVERGE Consortium, 2015) rs35936514 C 1.19 (1.13-1.25) 6.45 × 10-12

rs12415800 A 1.15 (1.10-1.20) 2.53 × 10-10

MDD and recurrent MDD in Europeans (Hyde et al., 2016)1 rs10514299 C 1.05 (1.04-1.07) 9.99 × 10-16

rs1518395 A 1.03 (1.02-1.05) 4.32 × 10-12

rs2179744 G 1.04 (1.02-1.05) 6.03 × 10-11 rs11209948 G 1.04 (1.02-1.05) 8.38 × 10-11

rs454214 T 1.03 (1.02-1.05) 1.09 × 10-9

rs301806 T 1.03 (1.02-1.04) 1.90 × 10-9 rs1475120 G 1.03 (1.02-1.04) 4.17 × 10-9

rs10786831 G 1.03 (1.02-1.04) 8.11 × 10-9

rs12552 G 1.05 (1.03-1.06) 8.16 × 10-9 rs6476606 G 1.03 (1.02-1.04) 1.20 × 10-8

rs8025231 A 1.04 (1.02-1.05) 1.23 × 10-8

rs12065553 A 1.03 (1.02-1.05) 1.32 × 10-8 rs1656369 A 1.04 (1.02-1.05) 1.34 × 10-8

rs4543289 T 1.03 (1.02-1.04) 1.36 × 10-8

rs2125716 G 1.04 (1.02-1.05) 3.05 × 10-8 rs2422321 A 1.03 (1.02-1.04) 3.18 × 10-8

rs7044150 T 1.03 (1.02-1.05) 4.31 × 10-8

MDD and age of onset > 27 years (Power et al., 2017) rs7647854 G 1.16 (1.11-1.21) 5.20 × 10-11

Depressive symptoms in Europeans (Okbay et al., 2016)

rs7973260 A 1.03 (1.02-1.04) 1.80 × 10-9 rs62100776 T 1.03 (1.02-1.03) 8.50 × 10-9

MDD and depressive symptoms in Europeans (Direk et al., 2016)

rs9825823 T NC 8.20 × 10-9

OR: odds ratio; CI: confidence interval; NC: not calculable. ap-values

reported are from the meta-analyses of the discovery and replication analyses,

while the OR and CI reported are with respect to the discovery cohort as the effect was not reported for the meta-analysis

2.12 COMORBIDITY BETWEEN MDD AND FATIGUE

Depressive disorders are present in 50-75% of patients presenting with medically

unexplainable symptoms (Kroenke et al., 1994; Kroenke, 2003). Furthermore, the

risk of diagnosis with a psychiatric disorder has been linked to presentation with

increased numbers of unexplained physical symptoms. Although the presence of

physical symptoms does not confirm a psychiatric explanation of the symptoms

(Kroenke et al., 1994). The prevalence of a current or lifetime diagnosis of MDD in

individuals with CF is 15.3% and 76.5%, respectively, and 10.2% and 67.3%,

respectively, when fatigue is excluded as a diagnostic criterion (Katon et al., 1991).

Similarly, the prevalence of current MDD is 10.7% and current MiDD or MDD is

14.5% in individuals with CFS (Cella et al., 2013; Janssens et al., 2015). The high

prevalence of comorbid fatigue and depression often results in fatigue being

perceived as a purely psychological symptom. However, numerous studies have

shown a subgroup of fatigued individuals exists which are not depressed (Harvey et

al., 2009; Hickie et al., 1999c; Van Der Linden et al., 1999).


The CDC, CCC, and ICC diagnostic criteria for CFS, ME/CFS, and ME,

respectively, list pre-existing depression as an exclusionary condition (Carruthers et

al., 2003; Carruthers et al., 2011; Fukuda et al., 1994). However, there are a few

differential symptoms between pure cases of depression, CFS, ME/CFS, and ME

compared to CFS, ME/CFS or ME with comorbid depression, which could indicate a

concomitant presentation (Brown, 2014; Griffith & Zarrouf, 2008). Notably, cases of

CFS, ME/CFS, and ME do not exhibit anhedonia and in general have a sudden onset,

while depression onset is gradual (Brown et al., 2013; Jason et al., 2005). Similarly,

exercise helps relieve depression while exacerbating CFS symptoms (Griffith &

Zarrouf, 2008). Furthermore, the fatigue experienced within a depressive episode is

not as severe and depressed cases do not exhibit a number of the diagnostic

symptoms of CFS (i.e., sore throat and tender lymph nodes). Investigation of CFS

onset has revealed a seasonal aspect to both ICF and CFS onset, which could

implicate either an infectious agent or depression (Jason et al., 2001; Zhang et al.,

2000). However, CFS patients have reduced levels of seasonal changes in symptoms

associated with seasonal major depressive disorder (Garcia-Borreguero et al., 1998).

In 2001, Addington and colleagues (2001) showed individuals with medically

unexplained fatigue are 10.9 times more likely to have a lifetime diagnosis of

depression than non-fatigued individuals, within the community. Additionally,

individuals with remitted, incident, and recurrent medically unexplained fatigue have

significantly increased risk of new onset depression (over a 13-year period)

compared to individuals who have never been fatigued (Addington et al., 2001). The

increased risk of depression in fatigued individuals indicates shared genetic factors

may contribute to the high levels of comorbidity observed between fatigue and

depression. Therefore, investigation of the genetic underpinnings of fatigue and

depression could provide insight into the observed comorbidity between the

disorders.

2.12.1 Heritability Links between Fatigue and Depression

The overlapping heritability of fatigue (or fatigue symptoms), psychological

disorders (including MDD and anxiety), disability pension due to mood and neurotic

diagnoses, chronic widespread pain, headaches, irritable bowel syndrome, and the

immune system, has been investigated within twin studies, using structural equation

modelling (Ball et al., 2010b; Ball et al., 2011; Burri et al., 2015; Fowler et al., 2006;


Hickie et al., 1999a; Hickie et al., 1999b; Hickie et al., 2001; Hur et al., 2012; Kato

et al., 2009; Narusyte et al., 2016).

Bivariate Twin Modelling of Depression and Fatigue

In 2006, the overlapping heritability of depression (assessed by the mood and

feelings questionnaire (Costello & Angold, 1988)) with short-duration fatigue

(fatigue experienced for at least one week) was investigated in English and Welsh

children (Fowler et al., 2006). The authors only investigated the full ACE model,

which included two independent A components, two independent C, and two

independent E components (Table 2.13). The first A component explained 59% (95%

CI = 37-66%) and 8% (95% CI = 0-21%) of the variance in depression and short-

duration fatigue, respectively. Similarly, the first C component explained of the

variance in 0% (95% CI = 0-17%) and 13% (0-63%) of the variance in depression

and short-duration fatigue, respectively. Similarly, the first E component explained

41% (95% CI = 34-50%) and 1% (95% CI = 0-7%) of the variance in depression and

short-duration fatigue, respectively. The remaining 52% (95% CI = 0-80%), 0%

(95% CI = 0-63%), and 26% (95% CI = 11-49%) of the variance in short-duration

fatigue was explained by the second A, C, and E component, respectively.

Bivariate twin modelling was also utilised to investigate the overlapping

heritability of depression and prolonged fatigue, in English and Welsh children

(Fowler et al., 2006). The authors only investigated the full ACE model, which

included two independent A components, two independent C, and two independent E

components (Table 2.13). The first A component explained 59% (95% CI = 37-63%)

and 11% (95% CI = 0-28%) of the variance in depression and prolonged fatigue,

respectively. Similarly, the first C component explained 0% (95% CI = 0-17%) and

34% (95% CI = 0-75%) of the variance in depression and prolonged fatigue,

respectively. Meanwhile, the first E component explained 41% (95% CI = 34-49%)

and 1% (95% CI = 0-9%) of the variance in depression and prolonged fatigue,

respectively. The remaining 30% (95% CI = 0-81%), 0% (95% CI = 0-7%), and 24%

(95% CI = 9-51%) of the variance of prolonged fatigue was explained by the second

A, C, and E component, respectively.

In 2010, the overlapping heritability of fatigue (assessed by the Chalder fatigue

questionnaire (Chalder et al., 1993)) and a lifetime indicator of MDD (assessed by

the two screening questions of the CIDI Composite International Diagnostic


Interview (World Health Organization, 1990) assessing the core symptoms of a

major depressive episode [i.e. depressed mood and anhedonia]) was investigated, in

twin pairs aged 15 years or older from Sri Lanka (Ball et al., 2010b). Although the

authors indicated the overlap in heritability was largely explained by unique

environmental factors, estimates for the specific variance components were not

provided so the bivariate genetic and environmental contribution within the study

cohort is unknown.

Trivariate twin modelling of depression, fatigue, and insomnia

In 2012, the overlap in heritability of depression, fatigue and insomnia was

investigated, in adult females from the United Kingdom (Hur et al., 2012). Common

and symptom-specific A and E components were estimated to explain the variance

within the traits (Table 2.12Table 2.12). Common A components explained 16%

(95% CI = 13-21%), 26% (95% CI = 21-32%), and 19% (95% CI = 15-24%) of the

variance in depression, fatigue, and insomnia, respectively. Similarly, common E

components explained 17% (95% CI = 13-21%), 27% (95% CI = 21-33%), and 19%

(95% CI = 15-24%) of the variance in depression, fatigue, and insomnia,

respectively. The remaining 18% (95% CI = 14-23%) and 49% (95% CI = 44-53%)

of the variation in depression was explained by symptom-specific A and E factors,

respectively. Similarly, the remaining 11% (95% CI = 6-16%) and 36% (95% CI =

30-40%) of the variation in fatigue was explained by symptom-specific A and E

factors, respectively. Finally, the remaining 11% (95% CI = 6-16%) and 51% (95%

CI = 45-50%) of the variation in insomnia was explained by symptom-specific A and

E factors, respectively.

Multivariate modelling of psychological distress, anxiety, depression, and

fatigue

In 1999, multivariate twin modelling was utilised to determine the overlap in

heritability of psychological distress, anxiety, depression, and fatigue, in adults from

Australia (Hickie et al., 1999b). Three independent A components and four

independent E components were identified which explained the variation in the traits

(Table 2.13). The first A component explained 36%, 23%, 25%, and 20% of the

variation in psychological distress, anxiety, depression, and fatigue, respectively.

Similarly, the second A component explained 11%, 9%, and 5% of the variation in

anxiety, depression, and fatigue, respectively. Meanwhile, the third A component


explained 20% of the variation in fatigue. The first E component explained 64%,

24%, and 30% of the variation in psychological distress, anxiety, and depression,

respectively. Similarly, the second E component explained 42% and 8% of the

variation in anxiety and depression, respectively. Furthermore, the third E component

explained the remaining 28% of the variation in depression. Finally, the fourth E

component explained the remaining 55% of the variation in fatigue.

Trivariate modelling of psychological symptoms, fatigue symptoms, and

somatic symptoms

In 2011, the overlap in heritability of psychological symptoms, fatigue symptoms,

and somatic symptoms was investigated in males and females separately, from a Sri

Lankan cohort aged 15-85 (Ball et al., 2011). Common and symptom-specific A and

E components were estimated to explain the variance within the traits (Table 2.12).

In males, common A components explained 5%, 14%, and 11% of the

variation in psychological symptoms, fatigue symptoms, and somatic symptoms,

respectively. Similarly, common C components explained 4%, 13%, and 10% of the


respectively. While, common E components explained 9%, 30%, and 24% of the


respectively. The remaining 4%, 19%, and 59% of the variation in psychological

symptoms was explained by specific A, C, and E components, respectively. The

remaining 4% and 39% of the variation in fatigue symptoms was explained by

specific A and E factors, respectively. Finally, remaining 10% and 45% of the

variation of somatic symptoms was explained by specific A and E factors,

respectively.

In females, common A components explained 3%, 7%, and 6% of the variation

in psychological symptoms, fatigue symptoms, and somatic symptoms, respectively.

Common C factors explained 8%, 21% and 19% of the variation in psychological

symptoms, fatigue symptoms, and somatic symptoms, respectively. Common E

components explained 8%, 21%, and 18% of the variation in psychological

symptoms, fatigue symptoms, and somatic symptoms, respectively. The remaining

14%, 3%, and 64% of the variation in psychological symptoms was explained by

specific A, C, and E factors. Specific E components explained the remaining 51% of

the variation in fatigue symptoms. Finally, the remaining 22% and 35% of the


variation in somatic symptoms was explained by specific A and E factors,

respectively.

Trivariate modelling of MDD/generalised anxiety disorder, CF and disability

pension due to neurotic diagnoses

In 2016, the overlap in heritability of MDD or generalised anxiety disorder, CF, and

disability pension due to mental diagnoses was investigated, in adult females from

Sweden (Narusyte et al., 2016). Three independent A components and three

independent E components were identified which explained the variation in the traits

(Table 2.13). The first A component explained 42% (95% CI = 41-48), 15% (95% CI

= 12-16), and 14% (95% CI = 13-27%) of the variance in MDD or generalised

anxiety disorder, CF, and disability pension due to mental diagnoses, respectively.

Similarly, the second A component explained 27% (95% CI = 14-38%) and 0%

(95% CI = 0-9%) of the variation in CF and disability pension due to mental

diagnoses, respectively. The last A component explained 31% (95% CI = 30-48) of

the variation in disability pension due to mental diagnoses. Meanwhile, the first E

component explained 58% (95% CI = 52-61%), 1% (95% CI = 0-4%), and 3% (95%

CI = 2-4%) of the variation in MDD or generalised anxiety disorder, CF, and

disability pension due to mental diagnoses, respectively. Similarly, the second E

component explained 57% (95% CI = 48-69%) and 6% (95% CI = 3-15%) of the

variation in CF and disability pension due to mental diagnoses, respectively. Finally,

the third E component explained the remaining 46% (95% CI = 30-50%) of the

variation in disability pension due to mental diagnoses.

Multivariate modelling of dehydroepiandrosterone sulfate, fatigue,

depression, and chronic widespread musculoskeletal pain


heritability of dehydroepiandrosterone sulfate (an endogenous androstane steroid

hormone that confers a reduced risk of developing depressive symptoms over a four-

year period, when high levels are observed at baseline in blood samples (Souza-

Teodoro et al., 2016)), fatigue, depression, and chronic widespread musculoskeletal

pain, was assessed in adult females from the UK (Burri et al., 2015). Three

independent A components and four independent E components were identified

which explained the variation in the traits (Table 2.13). The first A component

explained 80%, 7%, and 6% of the variation in dehydroepiandrosterone sulfate,

depression, and chronic widespread musculoskeletal pain, respectively. Similarly, the


second A component explained 40% and 40% of the variation in fatigue and chronic

widespread musculoskeletal pain, respectively. Meanwhile, the third A component

explained 31% and 20% of the variation in depression and chronic widespread

musculoskeletal pain, respectively. The first E component explained 20% and 15%

of the variation in dehydroepiandrosterone sulfate and depression, respectively.

Similarly, the second E component explained 60% and 9% of the variation in fatigue

and chronic widespread musculoskeletal pain, respectively. Furthermore, the third E

component explained the remaining 38% of the variation in depression. Finally, the

fourth E component explained the remaining 34% of the variation in chronic

widespread musculoskeletal pain.

Multivariate modelling of MDD, generalised anxiety disorder, headaches,

irritable bowel syndrome, CF, and chronic widespread pain


heritability of MDD, generalised anxiety disorder, headaches, irritable bowel

syndrome, CF, and chronic widespread pain, was assessed in adults from Sweden

(Kato et al., 2009). Within this study, it was determined that two latent

(unobservable) phenotypes were shared by the traits (Table 2.12). It was estimated

that 73% and 27% of the variation in the first latent phenotype was explained by A

and E factors, respectively. Similarly, 44% and 56% of the variation in the second

latent phenotype was explained by A and E factors, respectively. The first latent

phenotype was shared by all six traits investigated. The common A component from

the first latent phenotype explained 42%, 34%, 5%, 7%, 12%, and 5% of the

variation in MDD, generalised anxiety disorder, headaches, irritable bowel

syndrome, CF, and chronic widespread pain, respectively. Furthermore, the common

E component from the first latent phenotype explained 16%, 13%, 2%, 2%, 4%, and

2% of the variation in MDD, generalised anxiety disorder, headaches, irritable bowel

syndrome, CF, and chronic widespread pain, respectively. Meanwhile the second

latent phenotype was only shared by four of the traits. The common A component

from the second latent phenotype explained 12%, 15%, 20%, and 31% of the

variation in headaches, irritable bowel syndrome, CF, and chronic widespread pain,

respectively. Similarly, the common E component from the second latent phenotype

explained 15%, 20%, 26%, and 39% of the variation in headaches, irritable bowel

syndrome, CF, and chronic widespread pain, respectively. Additionally, symptom-

specific A and E factors explained the remaining variation within the six traits. The


remaining 42% of the variation in MDD was explained by specific E factors.

Similarly, the remaining 53% of the variation in generalised anxiety disorder was

explained by specific E factors. Headache specific A and E factors explained 24%

and 42% of the variation, respectively. Similarly, A and E factors specific to irritable

bowel syndrome specific explained 8% and 48% of the variation, respectively.

Furthermore, A and E factors specific to CF explained 9% and 29% of the variation,

respectively. Finally, A and E factors specific to chronic widespread pain explained

16% and 7% of the variation, respectively.

Trivariate modelling of psychological distress, fatigue, and immune

responsiveness

In 1999, the overlap in heritability between psychological distress, fatigue, and

immune responsiveness, was assessed in Australian adults (Hickie et al., 1999a).

Two independent A components, one C component, and three independent E

components were estimated to explain the variation between the phenotypes (Table

2.13). The first A component explained 61%, 30%, and 9% of the variation in

psychological distress, fatigue, and immune responsiveness, respectively.

Meanwhile, the second A component explained 22% of the variation in fatigue. The

C component explained 21% of the variation in immune responsiveness. The first E

component explained 39% and 8% of the variation in psychological distress, and

immune responsiveness, respectively. Similarly, the second E component explained

48% and 2% of the variation in fatigue and immune responsiveness. Finally, the

remaining 60% of the variation in immune responsiveness was explained by the third

E component.

Multivariate modelling of fatigue and immunological factors

In 2001, the overlap in heritability between fatigue and the immunological factors

IL-4, IFN-ɣ, and sCD23, was assessed in adults from Australia (Hickie et al., 2001).

Four A components, one C component, and four E components were identified

which explained the variation in the phenotypes (Table 2.13). The first A component

explained 47%, 4%, 7%, and 9% of the variation in fatigue, IL-4, IFN-ɣ, and sCD23,

respectively. Notably, the remaining three A components only explained variation

within a single phenotype. The second A component explained 28% of the variation

in IL-4. The third A component explained 1% of the variation in IFN-ɣ. The fourth A

component explained 10% of the variation in sCD23. Meanwhile, the C component


explained 35%, 39%, and 28% of the variation in IL-4, IFN-ɣ, and sCD23,

respectively. The first E component explained 53%, 2%, and 2% of the variation in

fatigue, IFN-ɣ, and sCD23, respectively. Similarly, the second E component

explained 33%, 7%, and 3% of the variation in IL-4, IFN-ɣ, and sCD23, respectively.

Furthermore, the third E component explained 44% and 20% of the variation in IFN-

ɣ and sCD23, respectively. Finally, the fourth E component explained the remaining

28% of the variation in sCD23.

In summary, results from previously published bivariate, trivariate, and

multivariate twin modelling studies indicate a genetic association likely exists

between varying fatigue classifications and depression; as well as other phenotypes,

such as immunological and psychological traits. However, differences in cohort age,

sex, ethnicity, phenotype classifications, and reported results means further

investigation is warranted. In particular, further work is required to investigate the

type of twin model that explains the comorbidity between fatigue and depression. Of

the eight previous bivariate, trivariate, and multivariate twin modelling studies, the

best-fitting model or the only model investigated was the common factor model

(Table 2.12) or the Cholesky decomposition (Table 2.13). Furthermore, the majority

of studies identified shared genetic factors between fatigue and depression, however,

some studies did not identify any genetic overlap between the traits and the

magnitude of the overlap varies between studies. One advantage of the Cholesky

decomposition is that it enables the genetic correlation between the investigated

phenotypes to be calculated. Within the previous studies which reported a Cholesky

decomposition and included a fatigue and depression phenotype the genetic

correlation range was 0-0.74 (depression and short-duration fatigue = 0.36 (Fowler et

al., 2006); depression and prolonged fatigue 0.53 (Fowler et al., 2006); depression

and fatigue = 0.74 (Hickie et al., 1999b); MDD/generalised anxiety disorder and

chronic fatigue = 0.60 (Narusyte et al., 2016); and depression and fatigue = 0 (Burri

et al., 2015)). Therefore, further investigation is required to characterise the

mechanism of the observed comorbidity between fatigue and depression.



environmental factors (E) from previous trivariate and multivariate common factor twin models, which include a fatigue and depression phenotype.

Phenotypes Population Sex Number of twin pairs

(MZ / DZ)

Mean age ±

standard

deviation

(Age range)

Common between phenotypes Specific to an individual phenotype

Latent factor 1 Latent factor 2

A C E A E A C E

Multivariate modelling of major depressive disorder (MDD), generalised anxiety disorder, headaches, irritable bowel syndrome, chronic fatigue, and chronic widespread pain (Kato et al., 2009).

MDD

Sweden M & F 3260 / 8988 & 127 twin

pairs of unknown

zygosity

53.7 ± 5.7

(41-64)

42 - 16 0 0 0 - 42 Generalised anxiety disorder 34 - 13 0 0 0 - 53

Headaches 5 - 2 12 15 24 - 42

Irritable bowel syndrome 7 - 2 15 20 8 - 48 Chronic fatigue 12 - 4 20 26 9 - 29

Chronic widespread pain 5 - 2 31 39 16 - 7

Trivariate modelling of psychological symptoms, fatigue symptoms, and somatic symptoms (Ball et al., 2011).

Psychological symptoms

Sri Lanka

M

1805 twin pairs & 137 singleton twins

33.9 ± 13.4 (15-85)

5 4 9 - - 4 19 59

Fatigue symptoms 14 13 30 - - 4 0 39

Somatic symptoms 11 10 24 - - 10 0 45 Psychological symptoms

F

3 8 8 - - 14 3 64

Fatigue symptoms 7 21 21 - - 0 0 51

Somatic symptoms 6 19 18 - - 22 0 35 Trivariate twin modelling of depression, fatigue, and insomnia (Hur et al., 2012).

Depression

UK F 893 / 884 & 204

singleton twins

50

(18-81)

16 (13-21) - 17 (13-21) - - 18 (14-23) - 49 (44-53)

Fatigue 26 (21-32) - 27 (21-33) - - 11 (6-16) - 36 (30-40) Insomnia 19 (15-24) - 19 (15-24) - - 11 (6-16) - 51 (45-50)

Note: 95% confidence intervals are reported as described in the original studies therefore, if they are absent they were not reported in the original publication. Populations are of European decent unless otherwise

specified. M: male; F: female; M & F: male and female.



environmental factors (E) from previous bivariate, trivariate, and multivariate Cholesky twin models, which include a fatigue and depression phenotype.

Phenotypes Population Sex

Number of

twin pairs

(MZ / DZ)

Mean age ±

standard

deviation

(Age range)

A1

(%)

C1

(%)

E1

(%)

A2

(%)

C2

(%)

E2

(%)

A3

(%)

C3

(%)

E3

(%)

A4

(%)

E4

(%)

Bivariate Twin Modelling of Depression and Fatigue (Fowler et al., 2006).

Depression

English and

Welsh M & F 1468 (8-17)

59 (37-66)

0 (0-17)

41 (34-50)

- - - - - - - -

Short-duration fatigue 8

(0-21)

13

(0-63)

1

(0-7)

52

(0-80)

0

(0-63)

26

(11-49) - - - - -

Depression 59

(37-63)

0

(0-17)

41

(34-49) - - - - - - - -

Prolonged fatigue 11

(0-28)

34

(0-75)

1

(0-9)

30

(0-81)

0

(0-7)

24

(9-51) - - - - -

Multivariate modelling of psychological distress, anxiety, depression, and fatigue (Hickie et al., 1999b).

Psychological distress

Australian M & F 533 / 471 61.9

(> 50)

36 - 64 - - - - - - - -

Anxiety 23 - 24 11 - 42 - - - - -

Depression 25 - 30 9 - 8 - - 28 - - Fatigue 20 - - 5 - - - - - 20 55

Trivariate modelling of major depressive disorder (MDD)/generalised anxiety disorder, CF and disability pension due to neurotic diagnoses (Narusyte et al., 2016).

MDD or generalised anxiety disorder

Sweeden F

1776 / 2358

& 1717

singleton twins

53.2 ± 5.7

(< 65)

42 (41-48)

- 58

(52-61) - - - - - -

Chronic fatigue 15

(12-16) -

1

(0-4)

27

(14-38) -

57

(48-69) - - -

Disability pension due to mental diagnoses 14

(13-27) -

3

(2-4)

0

(0-9) -

6

(3-15)

31

(30-48) -

46

(30-50)

Multivariate modelling of dehydroepiandrosterone sulfate, fatigue, depression, and chronic widespread musculoskeletal pain (Souza-Teodoro et al., 2016).

Dehydroepiandrosterone sulfate

UK F

219 / 324 &

642

singleton

twins

58.4 ± 11.1

(26-82)

80 - 20 - - - - - - - -

Fatigue - - - 40 - 60 - - - - -

Depression 7 - 15 - - - 31 - 38 - -

Chronic widespread musculoskeletal pain 6 - - 40 - 9 20 - - - 34

Trivariate modelling of psychological distress, fatigue, and immune responsiveness (Hickie et al., 1999a).

Psychological distress Australian M & F 79 / 45

46.6

(31-84)

61 - 39 - - - - - - Fatigue 30 - - 22 - 48 - - -

Immune responsiveness 9 - 8 - - 2 - 21 60

Table 2.13 footnote on page 47.


Table 2.14. Continued Heritability estimates (and their 95% confidence intervals) of the unique additive genetic factors (A), common environmental factors (C), and unique

environmental factors (E) from previous bivariate, trivariate, and multivariate Cholesky twin models, which include a fatigue and depression phenotype.

Phenotypes Population Sex

Number of

twin pairs

(MZ / DZ)

Mean age ±

standard

deviation

(Age range)

A1

(%)

C1

(%)

E1

(%)

A2

(%)

C2

(%)

E2

(%)

A3

(%)

C3

(%)

E3

(%)

A4

(%)

E4

(%)

Multivariate modelling of fatigue and immunological factors (Hickie et al., 2001).

Fatigue

Australian M & F 79 / 45 46.9

47 - 53 - - - - - - - - IL-4 4 - - 28 35 33 - - - - -

IFN-ɣ 7 - 2 - 39 7 1 - 44 - -

cSD23 9 - 2 - 28 3 - - 20 10 28

Note: 95% confidence intervals are reported as described in the original studies therefore, if they are absent they were not reported in the original publication. Populations are of European decent unless otherwise specified. M: male; F: female; M & F: male and female.

48

Although high levels of comorbidity have consistently been observed between fatigue

and depression, the presenting symptoms of the phenotypes partially overlap. To

date, the differences in depression symptoms reported by fatigued individuals and

fatigue symptoms reported by depressed individuals has never been investigated.

Additionally, the contribution of the overlapping fatigue and depression symptoms to

the high comorbidity observed between the traits is unknown. In order to determine

the quantitative differences in presenting symptoms and the role of overlapping

symptoms the following chapter aimed to investigate the co-occurrence and

symptomatology of fatigue and depression.

Chapter 3: Co-occurrence and Symptomatology of Fatigue and Depression 49

Chapter 3: Co-occurrence and

Symptomatology of Fatigue and

Depression

This chapter comprises the following published article:

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2016). Co-occurrence and

symptomatology of fatigue and depression. Comprehensive Psychiatry, 71, 1-10.

doi:10.1016/j.comppsych.2016.08.004

50 Chapter 3: Co-occurrence and Symptomatology of Fatigue and Depression




3.1 ABSTRACT

Fatigue and depression are highly comorbid phenotypes with partially overlapping

symptoms. The main aims of the present study are to: i) identify the risk of current

fatigue and depression; ii) determine if the depression symptoms experienced by

individuals who are fatigued (N = 766) and non-fatigued (N = 1,849) are different;

and iii) identify if the fatigue symptoms experienced by depressed (N = 275) and

non-depressed (N = 2,340) individuals are different, in a community-based sample of

Australian twins aged over 50. Fatigue and depression symptom profiles and

classifications were generated using the Schedule of Fatigue and Anergia (SOFA);

the General Health Questionnaire; and the Delusions-Symptoms-States Inventory,

States of Anxiety and Depression questionnaires. The association between co-

occurring fatigue and depression was assessed using prevalence ratios. Differences in

the preponderance of fatigue and depression symptoms were assessed using logistic

regression modelling. Individuals with either fatigue or depression have an

approximately two-fold increased risk for comorbid presentation of both traits,

compared to the general population. Logistic regression analysis indicated fatigued

individuals were significantly more likely to report all of the Diagnostic and

Statistical Manual of Mental Disorders (DSM) depression symptoms assessed in the

study. Similarly, depressed individuals were significantly more likely to report all

SOFA fatigue symptoms. Fatigue and depression are highly correlated traits within

the community. Depression symptomatology and prevalence are significantly

increased in fatigued individuals. Fatigue and especially the symptoms of insomnia

and poor concentration are strong predictors of depression. Notably, the association

between fatigue and depression is independent of their overlapping symptomatology.

Therefore, presentation with fatigue, and in particular the symptoms of insomnia and

poor concentration, should be considered as warning signs of depression in older

adults.


3.2 INTRODUCTION

Fatigue is a multidimensional symptom, which is highly prevalent in medical

practice, and difficult to quantify (Kroenke et al., 1988). Numerous classifications

exist for fatigue, which are based on arbitrary durations and severities, as a result of

its continuous nature (Wessely et al., 1997). Fatigue is associated with numerous

physical and psychiatric diagnoses, potentially due to the physical, cognitive, and

emotional dimensions the symptoms comprise (Arnold, 2008). Causation of fatigue

has been associated with numerous predisposing, precipitating, and perpetuating

factors (Sharpe & Wilks, 2002). A common predisposing factor is sex; with females

1.5 times as likely to experience fatigue as males (Chen, 1986). Additionally,

increased age has been associated with fatigue, in both males and females (Loge et

al., 1998). Comparison of fatigue symptoms based on sex has found females report a

higher prevalence of tiring easily and needing rest (David et al., 1990). However,

knowledge of the biological mechanisms underlying fatigue, which could account for

the differences between the sexes, is limited. Reduced health outcomes and quality of

life are associated with fatigue, which is commonly linked to psychiatric disorders,

particularly major depressive disorder (MDD) (Kroenke et al., 1994; Lyon et al.,

2014).

MDD is classified according to the Diagnostic and Statistical Manual of

Mental Disorders (DSM), which requires the presence of at least one major

depressive episode (American Psychiatric Association, 2013). The criterion for a

major depressive episode requires a two-week period where at least five of nine

symptoms are exhibited and either depressed mood or anhedonia (an inability to feel

pleasure in normally pleasurable activities) is reported. The symptoms of a major

depressive episode are: 1) depressed mood, 2) anhedonia, 3) a change in weight or

appetite, 4) insomnia (difficulty sleeping) or hypersomnia (excessive sleeping), 5)

psychomotor (i.e., thought and physical movement) agitation or retardation, 6)

fatigue or loss of energy, 7) feelings of worthlessness or excessive guilt, 8) inability

to concentrate or make decisions, and 9) thoughts about death, suicidal thoughts,

suicidal plans, or suicidal attempts (American Psychiatric Association, 2013).

thoughts about death, suicidal thoughts, suicidal plans, or suicidal attempts

(American Psychiatric Association, 2013). Minor depressive disorder (MiDD) is also

classified using the criterion for a major depressive episode (American Psychiatric


Association, 2000, 2013). However, only two to four symptoms occurring over a

two-week period are required for diagnosis, of which at least depressed mood or

anhedonia must be exhibited. Differences in the prevalence of depression occur over

the lifespan, with the prevalence increasing from puberty before declining after the

age of approximately 60 years (Centers for Disease Control and Prevention, 2010;

Kessler et al., 2003). The preponderance of depression in females has been

frequently investigated with numerous risk factors attributed to the increased

prevalence observed compared to males (Kuehner, 2003).

Investigation of differences in depression symptom prevalence (assessed using

the Composite International Diagnostic Interview) of individuals with depression in

Sri Lanka based on sex, revealed males report more hypersomnia and fewer thoughts

about death than females (Ball et al., 2010a). Furthermore, in the Netherlands, males

reported increased levels of anhedonia and psychomotor symptoms, while females

reported higher levels of mid-nocturnal insomnia, increases in weight, and somatic

complaints (the depression symptoms were assessed by the 30 item Inventory of

Depressive Symptomatology) (Schuch et al., 2014). Depression symptom profiles

have been investigated in individuals with seasonal affective disorder, and

differential symptoms have been identified among patients with unipolar, bipolar I,

and bipolar II depression (Goel et al., 2002). Finally, individuals with depression

were able to be distinguished from those with Alzheimer’s disease based on items

from three depression scales using regression modelling (Purandare et al., 2001).

Identification of differential symptoms between disorders facilitates increased

accuracy of diagnosis, thereby enabling utilisation of the most effective treatment

options. Medically unexplained symptoms are associated with depressive disorders

in 50-75% of patients (Kroenke, 2003). Furthermore, fatigue or loss of energy is the

second most frequently reported criterion of the DSM classification, experienced by

87.2% of MDD patients (Zimmerman et al., 2015). The co-occurrence of fatigue and

depression is likely due, in part, to their overlapping symptomatology. Therefore,

identification of symptoms which enable differential diagnosis would assist

physicians in distinguishing between fatigue and depression, thereby facilitating

symptom-guided management (Rosenthal et al., 2008).

Depression has a polythetic definition—whereby categorical diagnosis occurs

based on an arbitrarily defined threshold of symptoms being reached from a specified


criteria list, of which not all are required; therefore, the DSM classification is highly

heterogeneous, enabling a diagnosis of MDD in patients with entirely different

symptom profiles (Krueger & Bezdjian, 2009). The minimum requirement of 5

symptoms, of which at least one is depressed mood or anhedonia, enables 227

potential symptom profiles and allows the diagnosis of MDD in a subgroup of

individuals who are non-fatigued (Østergaard et al., 2011).

Fatigue and depression are highly comorbid, with fatigued individuals

reporting higher levels of depression than the general population (Cathébras et al.,

1992; Walker et al., 1993). Individuals with medically unexplained fatigue are

approximately 11 times (OR = 10.9) more likely to have a lifetime diagnosis of

depression than non-fatigued individuals, within the community (Addington et al.,

2001). Furthermore, the prevalence of co-occurring fatigue and psychological

distress within primary care is approximately 23% (Van Der Linden et al., 1999).

The high prevalence of comorbid fatigue and depression and idiopathic fatigue cases

often results in fatigue being perceived as a purely psychological symptom.

However, a subgroup of fatigued individuals exists which are not depressed (Harvey

et al., 2009; Hickie et al., 1999c; Van Der Linden et al., 1999). Although,

longitudinally (over a thirteen year period), individuals with remitted (relative risk

[RR] = 4.5), incident (RR = 53.2), and recurrent (RR = 28.4) medically unexplained

fatigue have significantly increased risk of new onset depression compared to

individuals who have never been fatigued (RR = 1.0) (Addington et al., 2001).

Therefore, understanding the relationship between fatigue and depression is vital to

facilitating diagnosis and enhanced treatment outcomes.

Initially, the present study will investigate the risk of co-occurring fatigue and

depression. Logistic regression modelling will then be utilised to determine if the

proportion of specific depression symptoms differs between individuals who are

fatigued and non-fatigued. Furthermore, the full symptom model will be investigated

to identify the distinguishing depression symptoms between fatigued and non-

fatigued individuals. The same approach will be utilised to assess if differential

fatigue symptoms are experienced by depressed and non-depressed individuals.

Finally, these analyses will identify the specific symptoms most strongly associated

with comorbid fatigue and depression.


3.3 METHODS

3.3.1 Sample and Questionnaires

Data from the over 50’s (aged) study conducted by the Genetic Epidemiology group

within QIMR Berghofer Medical Research Institute (QIMRB) was used in this study.

Informed written consent was obtained from each participant and the study was

approved by the Human Research Ethics Committee (HREC) of QIMRB. The study

was conducted from 1993 to 1996, with 2,281 twin pairs from the Australian Twin

Registry aged over 50 asked to complete a mailed Health and Lifestyle Questionnaire

(Bucholz et al., 1998; Mosing et al., 2012). The survey contained numerous self-

report questionnaires, of which the Schedule of Fatigue and Anergia (SOFA), the

twelve-item General Health Questionnaire (GHQ), and the fourteen-item Delusions-

Symptoms-States Inventory, States of Anxiety and Depression (DSSI/sAD), were

used throughout this study (Bedford & Deary, 1997; Goldberg & Blackwell, 1970;

Hickie et al., 1996).

The SOFA was originally designed to identify chronic fatigue syndrome cases.

Therefore, physical (i.e., muscular pain or tiredness), neurocognitive (i.e., memory

and concentration problems), and neurovegetative (i.e., sleep problems) fatigue

symptoms are assessed by the questionnaire. Consequently, the fatigued state

identified by the SOFA is distinct from the fatigue experienced within a major

depressive episode. Ten questions are contained in the SOFA; however, a shorter

eight-item version was included in the survey due to two questions being replicated

within the GHQ. The SOFA questions contained within the survey had a binary

yes/no response set, which was scored as 1-0. Throughout the GHQ there are two

response sets: 1) “not at all”, “no more than usual”, “rather more than usual”, and

“much more than usual”; and 2) “more so than usual”, “same as usual”, “less than

usual”, and “much less than usual”. Standard scoring of 0-0-1-1 was used for both

response sets of the GHQ. Responses to the DSSI/sAD questionnaire were

dichotomised, with the scores 0-0-1-1 representing the answers “not at all”, “a little”,

“a lot”, and “unbearably”, respectively.

Responses to the eight SOFA items and the two overlapping GHQ questions

(Table 3.1) were summed to give an overall score out of ten, which was used to

assess fatigue. Individuals with three or more positive self-report responses were

classified as fatigued. Previous studies have utilised shortened versions of the SOFA


scale to reliably assess fatigue in community cohorts (Bennett et al., 2004; Hickie et

al., 1999b; Kirk et al., 1999a; Kirk et al., 1999b). Furthermore, similar modifications

of the SOFA have been used and shown to have internal consistency (Tritt et al.,

2010). Although the current modified scale, utilising two questions from the GHQ

has not previously been used, 97.1% of the study cohort would have received the

same fatigue classification if the 8-item SOFA scoring had been used. The use of the

2 GHQ items enabled us to assess the full range of fatigue symptoms originally

included in the SOFA.

Table 3.1. Questionnaire items used to assess fatigue.

Abbreviated fatigue symptom Questionnaire and

question number Questiona

Muscle pain at rest SOFA 10 I get muscle pain even at rest Post-exertional muscle pain SOFA 6 I get muscle pain after physical activity

Post-exertional muscle fatigue SOFA 3 My muscles feel tired after physical activity

Post-exertional fatigue SOFA 1 I feel tired for a long time after physical activity Hypersomnia SOFA 5 I need to sleep for long periods

Insomnia GHQ 2 Lost much sleep over worry

Poor concentration GHQ 1 Been able to concentrate on what you’re doing Speech problems SOFA 8 I have problems with my speech

Poor memory SOFA 9 My memory is poor

Headaches SOFA 4 I get headaches

SOFA: Schedule of Fatigue and Anergia; GHQ: General Health Questionnaire. aParticipants were asked to respond with relation to their health, in general, over the past few weeks

MDD and MiDD were classified using the nine criteria of a major depressive

episode, as defined by the DSM (version IV) criteria (American Psychiatric

Association, 2000). A combination of questions from the GHQ and DSSI/sAD were

used to assess depression (Table 3.2), through the assignment of specific questions to

the appropriate criterion of the major depressive episode criteria. If a question did not

assess any of the criteria of a major depressive episode it was not used in the

analysis. When multiple questions assessed a criterion at least one positive response

indicated the individual exhibited a symptom from the specific criterion. Each

criterion was assessed by assigning one to the criterion if a symptom was exhibited

by the individual and zero if none of the symptoms for the criterion were met. The

survey did not contain any assessment of change in weight or appetite; therefore, the

third criterion of a major depressive episode (“a change in weight or appetite”) was

not assessed. The scores of the eight criteria assessed were summed if the individual

scored positively on criteria 1) or 2), otherwise the individual was assigned a score of

zero. Individuals were designated MDD, MiDD, or non-depressed, if they had a self-

report score of five or more, two to four, or less than two, respectively. The

combination of GHQ and DSSI/sAD, used to map the self-reported depression


symptoms to the DSM criteria has not previously been used. However, 93.4% of the

cohort would have received the same depression classification if the standard

DSSI/sAD scoring had been used (which is a valid and reliable measure of

depression). Furthermore, utilisation of DSM symptomatic criteria enabled us to

investigate minor and major depression cases which would be impossible using the

DSSI/sAD measure of depression.


Table 3.2. Questionnaire items used to assess the criteria of a major depressive episode.

DSM Major depressive episode

criteria

Questionnaire and

question number Questiona

Depressed mood GHQ 9 Been feeling unhappy and depressed

DSSI/sAD 5 Recently, I have been depressed without knowing why Anhedonia GHQ 7 Been able to enjoy your normal day-to-day activities

DSSI/sAD 12 Recently, I have lost interest in just about everything

Insomnia DSSI/sAD 2 Recently, I have been so miserable that I have had difficulty with my sleep DSSI/sAD 11 Recently, worrying has kept me awake at night

Psychomotor agitation DSSI/sAD 4 Recently, I have been so ‘worked up’ that I couldn’t sit still

Loss of energy DSSI/sAD 8 Recently, I have been so low in spirits that I have sat for ages doing absolutely nothing Feeling worthless GHQ 3 Felt that you are playing a useful part in things

GHQ 6 Felt that you couldn’t overcome your difficulties

GHQ 11 Been thinking of yourself as a worthless person DSSI/sAD 10 Recently, the future has seemed hopeless

Inability to concentrate GHQ 4 Felt capable of making decisions about things

DSSI/sAD 13 Recently, I have been so anxious that I couldn’t make up my mind about the simplest thing Suicidal thoughts DSSI/sAD 6 Recently, I have gone to bed not caring if I never woke up

DSSI/sAD 14 Recently, I have been so depressed that I have thought of doing away with myself

DSM: Diagnostic and Statistical Manual of Mental Disorders; GHQ: General Health Questionnaire; DSSI/sAD: Delusions-Symptoms-States Inventory, States of Anxiety and

Depression. aParticipants were asked to respond with relation to their health, in general, over the past few weeks.


3.3.2 Statistical Analysis

Prevalence Ratios

The association between fatigue and depression was investigated using contingency

tables to assess the prevalence of co-occurrence within the cohort. The likelihood of

a fatigued individual having comorbid depression compared to non-fatigued

individuals and the total cohort was assessed using the prevalence ratio (PR) measure

of association and its 95% confidence interval (CI). The PR has the same

interpretation as the relative risk (RR) with respect to its null value of 1 and values

greater or less than 1. The PR is the ratio of the prevalence rate in one group divided

by the prevalence rate in a second group. For example, the prevalence of depression

in fatigued individuals was divided by the prevalence of depression in non-fatigued

individuals. Similarly, the prevalence of fatigue in depressed individuals was divided

by the prevalence of fatigue in non-depressed individuals. To assist interpretation of

the numerous PR estimates, we also calculated PRs for specific groups relative to the

total sample, by dividing the prevalence in the specific group by the prevalence in

total sample.

The fatigued individuals likelihood of experiencing depression was re-

calculated in the subgroup of individuals without (screening negative for)

overlapping DSM depression symptoms (i.e., insomnia, poor concentration, and

hypersomnia) to remove the effect of overlapping symptoms. Likewise, the

likelihood of depressed individuals experiencing fatigue was re-calculated in the

subgroup of individuals without (screening negative for) fatigue symptoms (i.e.,

insomnia, inability to concentrate, and loss of energy).

Multiple Test Correction

The matrix spectral decomposition (matSpD) web-based tool

(http://neurogenetics.qimrberghofer.edu.au/matSpD/) estimates the effective number

of independent variables from a pairwise correlation matrix (Cheverud, 2001; Li &

Ji, 2005; Nyholt, 2004; R Development Core Team, 2003). To identify the effective

number of independent variables, matSpD analyses the eigenvalues of the correlation

matrix (after spectral decomposition—factorisation of a matrix into a canonical

form). Briefly, to retain an experiment-wide type I error rate of 5%, the significance

thresholds for analysing the full set of fatigue and depression symptom measures was

calculated by dividing the nominal significance threshold of p-value = 0.05, by the


effective number of independent measures estimated by matSpD analysis of the

pairwise correlation matrix calculated using R (R Core Team, 2014) for the fatigue

and depression symptom measures.

Logistic Regression Modelling

Demographic differences in age and sex, with respect to fatigue and depression

classification, were initially assessed by logistic regression in R (R Core Team,

2014).

Binomial logistic regression modelling was used to compare the depression

symptoms between the fatigued and the non-fatigued groups (R Core Team, 2014).

The depression symptoms were assessed individually (univariable analysis) and as

part of the full model (multivariable analysis) containing all eight symptoms. The

Akaike information criterion (AIC) was used to assess the parsimony of the

depression symptom model compared to the null model, with lower AIC indicating a

better fit (Akaike, 1973, 1974). To account for relatedness, an exchangeable

conditional covariance matrix was used (i.e., we allowed for correlated residuals

between members of the same family) and tests were based on the robust (sandwich-

corrected) standard errors, using the rms package in R (R Core Team, 2014).

Analysis of deviance containing the chi-squared test was used to assess statistical

differences between the logistic regression of the fatigued and non-fatigued groups.

The eight depression symptoms were compared between the fatigued and non-

fatigued groups using a two-tailed p-value and odds ratio (OR) with their 95% CI.

The approach was replicated to compare the ten fatigue symptoms between

depressed and non-depressed individuals. Additionally, ordinal logistic regression,

using rms, was utilised to compare the use of a broad, two-category depression

classification (non-depressed, MiDD/MDD) to an ordered three-category depression

classification (non-depressed, MiDD, MDD).

To obtain subgroup specific odds ratios, multinomial logistic regression

modelling was used to compare the fatigue symptoms between the MDD, MiDD, and

non-depressed groups. Relatedness was not accounted for due to its negligible effect

on the binomial logistic regression results. The fatigue symptoms were assessed

individually and as part of the full model. Multinomial regression modelling

conducted throughout the study followed the protocol defined by Morris et al.

(2010), using the mlogit package within R (Morris et al., 2010; R Core Team, 2014).


Parsimony of the model was assessed using the AIC and statistical differences

between the MDD, MiDD, and non-depressed groups were identified using analysis

of deviance containing the chi-squared test. The fatigue symptoms were compared

between the depression groupings using a two-tailed p-value and OR with their 95%

CI. Fatigue and depression symptoms which were significantly different were

identified using the thresholds obtained from matSpD.

3.4 RESULTS

3.4.1 Study Population

The over 50’s (aged) study consisted of 4,562 participants. However, 1,947

individuals returned incomplete responses to SOFA, GHQ, and/or DSSI/sAD

questionnaire items utilised to assess depression and fatigue in the present study and

were therefore excluded. The remaining 2,615 individuals with complete responses

comprised the study cohort which was used in all analyses (Table 3.3).

Supplementary Table 3.1 lists the number of individuals reporting each specific

symptom. The study cohort (including 496 complete monozygotic twin pairs, 440

complete dizygotic twin pairs, 5 complete twin pairs of unknown zygosity, and 733

unpaired twin singles), had a mean age of 60.5 years (range = 50-92), which was not

significantly different from the non-responders. As typically found, significantly

higher response rates (p < 2 × 10-16) were observed for females (71.9%) compared to

males (58.7%).

Depressed individuals had a two-fold (PR = 2.18, 95% CI = 1.96-2.43)

increase in risk of fatigue, compared to the total population sample. Stratification of

depressed individuals revealed the increased risk of fatigue was slightly (although

not significantly) higher in MDD cases (PR = 2.32, 95% CI = 1.90-2.83) compared

to individuals with MiDD (PR = 2.15, 95% CI = 1.92-2.42). Meanwhile, non-

depressed individuals had a reduced risk of fatigue (PR = 0.86, 95% CI = 0.79-0.94).

Significantly, depressed individuals risk of fatigue was significantly increased,

independent of insomnia, concentration problems, and hypersomnia (PR = 2.27, 95%

CI = 1.51-3.39). Similarly, fatigued individuals had a two-fold (PR = 2.18, 95% CI =

1.84-2.59) increased risk of depression, compared to the total population sample.

Furthermore, stratification of fatigued individuals risk of depression revealed

fatigued individuals had a slightly (although not significantly) higher risk of MDD


(PR = 2.32, 95% CI = 1.51-3.56) than MiDD (PR = 2.15, 95% CI = 1.77-2.62).

Meanwhile, non-fatigued individuals had a reduced risk of depression (PR = 0.51,

95% CI = 0.41-0.64). Notably, fatigued individuals risk of depression was

significantly increased, independent of insomnia, concentration problems and loss of

energy (PR = 2.27, 95% CI = 1.33-3.86).

Interestingly, the risk of depression (PR = 4.29, 95% CI = 3.40-5.41) in

fatigued compared to non-fatigued individuals is approximately two-fold greater than

the risk of fatigue (PR = 2.54, 95% CI = 2.27-2.84) in depressed compared to non-

depressed individuals.

3.4.2 Fatigued Individuals Report a Higher Proportion of Depression Symptoms

The matSpD analysis indicated moderate intercorrelation between the eight

depression symptom measures, and estimated them to be equivalent to six effectively

independent measures. Therefore, to keep type I error rate at 5%, the significance

threshold used for univariable analysis of the eight depression symptoms was

adjusted for six independent tests (i.e., Bonferroni adjusted experiment-wide

significant threshold, p = 0.05 / 6 = 8.3 × 10-3).

Analysis of age and sex revealed no significant differences between fatigued

and non-fatigued individuals. Therefore, the age and sex variables were not included

as covariates in the logistic regression analysis of fatigue symptoms.

Notably, all eight depression symptoms were significantly different

(univariable p < 8.3 × 10-3) between fatigued and non-fatigued individuals (Table

3.4). Furthermore, the full logistic regression model (AIC = 2960.5) comparing

fatigued versus non-fatigued individuals, was more parsimonious than the null model

(AIC = 3164.8). Therefore, the results provided (in Table 3.4) are for the more

parsimonious model. Comparison of the fatigued and non-fatigued groups (Table

3.4) revealed an overall significant difference in depression symptoms (χ2 = 220.32,

p < 2.2 × 10-16). In particular, the proportion of fatigued cases reporting anhedonia,

insomnia, and feeling worthless, were significantly higher than non-fatigued

individuals.


Table 3.3. Prevalence ratios of fatigue and depression.

Counts (%) PR (95% CI) of fatigue PR (95% CI) of depression

Non-fatigued Fatigued Total Non-depresseda Totalb Non-fatiguedc Totald

All symptoms

Non-depressed 1750 (66.9) 590 (22.6) 2340 (89.5) NA 0.86 (0.79-0.94) NA 0.51 (0.41-0.64) Depressed 99 (3.8) 176 (6.7) 275 (10.5) 2.54 (2.27-2.84) 2.18 (1.96-2.43) 4.29 (3.40-5.41) 2.18 (1.84-2.59)

Total 1849 (70.7) 766 (29.3) 2615 (100.0)

Non-overlapping symptoms

Non-depressed 1531 (83.4) 253 (13.8) 1784 (97.2) NA 0.96 (0.82-1.13) NA 0.78 (0.51-1.20)

Depressed 34 (1.9) 17 (0.9) 51 (2.8) 2.35 (1.57-3.52) 2.27 (1.51-3.39) 2.90 (1.64-5.11) 2.27 (1.33-3.86) Total 1565 (85.3) 270 (14.7) 1835 (100.0)

All symptoms: all individuals; Non-overlapping symptoms: individuals without the fatigue and depression overlapping symptoms; PR: prevalence ratio; CI: confidence interval; NA: not applicable. aPrevalence ratio of

fatigue in depressed compared to non-depressed individuals. bPrevalence ratio of fatigue in depressed individuals compared to the total cohort. cPrevalence ratio of depression in fatigued compared to non-fatigued

individuals. dPrevalence ratio of depression in fatigued individuals compared to the total cohort.

Table 3.4. Logistic regression, unadjusted and and adjusted for, relatedness, comparing the depression symptoms exhibited by fatigued individuals (N = 766) to non-fatigued

(N = 1,849) individuals.

Depression symptomsa

Univariable Multivariableb

Unadjusted for relatedness Adjusted for relatedness Unadjusted for relatedness Adjusted for relatedness

OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value

Depressed mood 3.88 (2.96-4.97) < 2.00 × 10-16 3.83 (2.96-4.98) < 2.00 × 10-16 1.42 (1.02-1.99) 0.04 1.42 (1.01-2.00) 0.04

Anhedonia 3.89 (3.04-4.98) < 2.00 × 10-16 3.89 (3.05-4.97) < 2.00 × 10-16 1.97 (1.46-2.65) 7.67 × 10-6 1.97 (1.46-2.66) 1.02 × 10-5 Insomnia 6.60 (4.36-9.98) < 2.00 × 10-16 6.60 (4.33-10.05) < 2.00 × 10-16 2.14 (1.29-3.53) 3.10 × 10-3 2.14 (1.23-3.73) 0.01

Psychomotor agitation 9.69 (4.96-18.93) 2.92 × 10-11 9.69 (5.02-18.71) 1.30 × 10-11 2.75 (1.26-6.00) 0.01 2.75 (1.23-6.16) 0.01

Loss of energy 4.47 (2.36-8.45) 4.11 × 10-6 4.47 (2.37-8.44) 3.95 × 10-6 0.72 (0.32-1.60) 0.42 0.72 (0.29-1.75) 0.47 Feeling worthless 4.25 (3.32-5.46) < 2.00 × 10-16 4.25 (3.33-5.43) < 2.00 × 10-16 2.12 (1.56-2.87) 1.22 × 10-6 2.12 (1.56-2.79) 1.24 × 10-6

Inability to concentrate 4.31 (3.00-6.20) 2.78 × 10-15 4.31 (3.02-6.16) 8.88 × 10-16 1.75 (1.14-2.68) 0.01 1.75 (1.10-2.79) 0.02

Suicidal thoughts 6.02 (3.06-11.87) 2.10 × 10-7 6.02 (3.08-11.77) 1.48 × 10-7 0.85 (0.38-1.93) 0.70 0.85 (0.35-2.05) 0.72

OR: odds ratio; CI: confidence interval. aDefined by the Diagnostic and Statistical Manual of Mental Disorders (DSM). bMultivariable model includes all 8 depression symptoms.


3.4.3 Depressed Individuals Report Higher Proportions of Fatigue Symptoms

The matSpD analysis of the ten fatigue symptom measures revealed minimal

intercorrelation being equivalent to nine effectively independent measures.

Therefore, to keep type I error rate at 5%, the significance threshold used for

analyses involving all the fatigue symptoms was 5.6 × 10-3 (p = 0.05 / 9).

Demographic analysis of the difference in age and sex revealed no significant

differences between depressed and non-depressed individuals. Therefore, the age and

sex variables were not included as covariates in the logistic regression analysis of

fatigue symptoms.

Interestingly, all ten fatigue symptoms were significantly different (univariable

p < 5.6 × 10-3) between depressed and non-depressed individual (Table 3.5).

Furthermore, the full symptom model (AIC = 1342.0) comparison of the fatigue

symptoms endorsed by depressed versus non-depressed individuals was more

parsimonious than the null model of no differences between the groups (AIC =

1760.7). The comparison revealed an overall significant difference in the depression

symptoms (χ2 = 438.77, p < 2 × 10-16) experienced by depressed and non-depressed

individuals (Table 3.5). In particular, the proportion of depression cases reporting

insomnia, poor concentration, and headaches, were significantly higher than non-

depressed individuals. Results were comparable between the binomial and ordinal

logistic regression modelling (Table 3.6).


Table 3.5. Logistic regression, both unadjusted and adjusted for relatedness, of fatigue symptoms exhibited by depressed (N = 275) and non-depressed (N = 2,340)

individuals.

Fatigue symptomsa

Univariable Multivariableb

Unadjusted for relatedness Adjusted for relatedness Unadjusted for relatedness Adjusted for relatedness

OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value

Binomial logistic regression Muscle pain at rest 2.79 (2.04-3.80) 8.40 × 10-11 2.79 (2.05-3.79) 7.70 × 10-11 1.35 (0.88-2.07) 0.17 1.35 (0.84-2.18) 0.22

Post-exertional muscle pain 2.32 (1.80-2.99) 9.91 × 10-11 2.32 (1.79-3.00) 1.54 × 10-10 1.11 (0.77-1.60) 0.57 1.11 (0.76-1.63) 0.59

Post-exertional muscle fatigue 2.48 (1.93-3.19) 1.78 × 10-12 2.48 (1.92-3.21) 5.50 × 10-12 1.05 (0.71-1.54) 0.81 1.05 (0.70-1.56) 0.82 Post-exertional fatigue 3.46 (2.66-4.49) < 2.20 × 10-16 3.46 (2.65-4.51) < 2.00 × 10-16 1.77 (1.20-2.60) 3.70 × 10-3 1.77 (1.17-2.67) 0.01

Hypersomnia 2.31 (1.74-3.07) 6.24 × 10-9 2.31 (1.73-3.09) 1.83 × 10-8 1.05 (0.72-1.53) 0.79 1.05 (0.71-1.56) 0.80

Insomnia 11.68 (8.67-15.74) < 2.20 × 10-16 11.68 (8.62-15.83) < 2.00 × 10-16 8.08 (5.78-11.29) < 2.20 × 10-16 8.08 (5.68-11.50) < 2.20 × 10-16 Poor concentration 12.12 (8.95-16.41) < 2.20 × 10-16 12.12 (9.04-16.25) < 2.00 × 10-16 6.92 (4.85-9.87) < 2.20 × 10-16 6.92 (4.79-9.99) < 2.20 × 10-16

Speech problems 2.01 (1.48-2.72) 7.08 × 10-6 2.01 (1.48-2.73) 8.15 × 10-6 1.01 (0.67-1.52) 0.97 1.01 (0.67-1.51) 0.97

Poor memory 2.38 (1.79-3.16) 2.66 × 10-9 2.38 (1.78-3.18) 4.57 × 10-9 1.09 (0.74-1.62) 0.66 1.09 (0.72-1.66) 0.68 Headaches 2.78 (2.14-3.63) 3.73 × 10-14 2.78 (2.13-3.564) 1.01 × 10-13 1.76 (1.28-2.44) 5.92 × 10-4 1.76 (1.24-2.50) 1.41 × 10-3

OR: odds ratio; CI: confidence interval. aAssessed by the Schedule of Fatigue and Anergia (SOFA). bMultivariable model includes all 10 fatigue symptoms.


The analyses comparing MDD (N = 50), MiDD (N = 225), and non-depressed

(N = 2,340) individuals exhibited comparable trends to the results of the ‘complete’

depressed cohort. All ten fatigue symptoms were significantly different between

MiDD and non-depressed individuals (Table 3.6). Similarly, all the fatigue

symptoms except post-exertional muscle pain are significantly different between

MDD and non-depressed individuals.

Table 3.6. Logistic regression of fatigue symptoms exhibited by individuals with major depressive

disorder (N = 50), minor depressive disorder (N = 225), and are non-depressed (N = 2,340).

Fatigue symptoma Univariable Multivariableb OR (95% CI) p-value OR (95% CI) p-value

MiDD versus non-depressed

Muscle pain at rest 2.77 (1.98-3.88) 2.75 × 10-9 1.33 (0.85-2.06) 0.21

Post-exertional muscle pain 2.42 (1.83-3.20) 4.62 × 10-10 1.22 (0.84-1.79) 0.30

Post-exertional muscle fatigue 2.38 (1.80-3.13) 7.75 × 10-10 0.99 (0.66-1.48) 0.95 Post-exertional fatigue 3.35 (2.52-4.45) < 2.20 × 10-16 1.80 (1.21-2.70) 3.98 × 10-3

Hypersomnia 2.15 (1.58-2.94) 1.46 × 10-6 1.00 (0.68-1.48) 0.98

Insomnia 8.50 (6.12-11.81) < 2.20 × 10-16 6.08 (4.23-8.73) < 2.20 × 10-16

Poor concentration 10.03 (7.22-13.93) < 2.20 × 10-16 6.13 (4.21-8.92) < 2.20 × 10-16

Speech problems 1.89 (1.35-2.65) 2.03 × 10-4 0.96 (0.62-1.48) 0.86

Poor memory 2.35 (1.72-3.20) 8.16 × 10-8 1.15 (0.76-1.73) 0.51 Headaches 2.89 (2.17-3.86) 4.44 × 10-13 1.87 (1.34-2.61) 2.31 × 10-4

MDD versus non-depressed

Muscle pain at rest 2.84 (1.46-5.51) 2.01 × 10-3 1.53 (0.62-3.75) 0.36

Post-exertional muscle pain 1.91 (1.08-3.39) 0.03 0.63 (0.29-1.40) 0.26

Post-exertional muscle fatigue 3.00 (1.70-5.28) 1.40 × 10-4 1.51 (0.68-3.37) 0.31 Post-exertional fatigue 3.97 (2.26-5.27) 1.78 × 10-6 1.61 (0.71, 3.65) 0.26

Hypersomnia 3.11 (2.73, 7.00) 1.54 × 10-4 1.47 (0.67, 3.20) 0.34

Insomnia 48.38 (25.10, 93.26) < 2.20 × 10-16 33.64 (16.80, 67.34) < 2.20 × 10-16 Poor concentration 27.27 (15.05, 49.41) < 2.20 × 10-16 12.98 (6.54, 25.77) 2.41 × 10-14

Speech problems 2.58 (1.37, 4.83) 3.20 × 10-3 1.33 (0.59, 2.98) 0.49

Poor memory 2.53 (1.37, 4.68) 3.13 × 10-3 0.983 (0.36, 1.90) 0.66 Headaches 2.32 (1.28, 4.21) 0.01 1.20 (0.58, 2.48) 0.61

Ordinal logistic regression Muscle pain at rest 2.77 (2.04-3.77) 8.62 × 10-11 1.30 (0.86-1.98) 0.22

Post-exertional muscle pain 2.30 (1.79-2.97) 1.35 × 10-10 0.99 (0.69-1.42) 0.97

Post-exertional muscle fatigue 2.49 (1.93-3.20) 1.44 × 10-12 1.17 (0.81-1.69) 0.41 Post-exertional fatigue 3.46 (2.66-4.49) < 2.20 × 10-16 1.66 (1.14-2.41) 0.01

Hypersomnia 2.33 (1.76-3.09) 4.25 × 10-9 1.09 (0.76-1.56) 0.64

Insomnia 12.79 (9.49-17.23) < 2.20 × 10-16 8.72 (6.31-12.05) < 2.20 × 10-16 Poor concentration 12.57 (9.32-16.95) < 2.20 × 10-16 7.10 (5.03-10.01) < 2.20 × 10-16

Speech problems 2.02 (1.49-2.74) 5.61 × 10-6 1.04 (0.70-1.55) 0.84

Poor memory 2.38 (1.79-3.16) 2.56 × 10-9 1.00 (0.68-1.48) 0.99 Headaches 2.76 (2.12-3.59) 5.55 × 10-14 1.62 (1.18-2.22) 2.68 × 10-3

MiDD versus non-depressed: results from multinomial logistic regression analysis for MiDD subgroup compared to non-depressed group;

MDD versus non-depressed: results from multinomial logistic regression analysis for MDD subgroup compared to non-depressed group; Ordinal logistic regression: results from ordinal logistic regression analysis of the three (MDD, MiDD and non-depressed) subgroups; OR:

odds ratio; CI: confidence interval. aAssessed by the Schedule of Fatigue and Anergia (SOFA). bMultivariable (“full”) model includes all 10

fatigue symptoms.

The full fatigue symptom model (AIC = 1585.3) was more parsimonious than

the null model (AIC = 2023.5), comparing MDD, MiDD, and non-depressed

individuals. Comparison of the MDD and MiDD groups (Table 3.6) to the non-

depressed group revealed an overall significant difference in fatigue symptoms (χ2 =

478.28, p < 2.2 × 10-16). In particular, the proportion of MiDD cases reporting post-

exertional fatigue, insomnia, poor concentration, and headaches was significantly

higher than non-depressed individuals. Similarly, the proportion of MDD cases


reporting insomnia and poor concentration was higher than non-depressed

individuals.

3.5 DISCUSSION

The results demonstrate that individuals presenting with either fatigue or depression

have a two-fold increase in risk for a co-occurring presentation of both traits. The

risk of depression in fatigued individuals compared to non-fatigued individuals, is

two-fold greater than the risk of fatigue in depressed individuals compared to non-

depressed individuals, indicating that fatigue could be used as a predictor to facilitate

early detection of depression. This is particularly interesting considering that fatigue

severity has been identified as a good predictor of MDD within cancer patients

(Deckx et al., 2015). Although fatigue severity is subjective, the use of specific

fatigue symptoms might facilitate more accurate prediction of depression.

Significantly, fatigued individuals reported more depression symptoms than

non-fatigued individuals. These results are consistent with previous findings showing

fatigued individuals have higher depression levels (Cathébras et al., 1992; Walker et

al., 1993). However, a proportion of the fatigued individuals will not have comorbid

depression; although pure fatigue appears to be a dynamic state with numerous cases

exhibiting symptoms of psychological distress (Harvey et al., 2009; Van Der Linden

et al., 1999). That said, the analysis comparing non-depressed individuals with

depressed cases revealed significant differences for all ten fatigue symptoms.

Therefore, although fatigue and depression symptoms were reported in individuals

who were non-depressed and non-fatigued, respectively, the increased number of

symptoms exhibited by fatigued and depressed cases suggests an underlying

association.

Heritable associations have been identified between fatigue and depression

(Hickie et al., 1999b) in a twin sample that partially overlaps the present one. The

heritability of fatigue and depression are both estimated to have unique genetic and

environmental factors but no contribution of common environmental factors.

Multivariate twin modelling estimated a common additive genetic component

explained 36.0%, 23.3%, 25.0%, and 20.3% of the variance in psychological distress,

anxiety, depression, and fatigue, respectively. Moreover, a second common additive

genetic component explained 11.0%, 9.0%, and 5.1% of the variance in anxiety,


depression, and fatigue, respectively. Additionally, a third additive genetic

component (independent of psychological distress, anxiety, and depression) was

found to explain a further 20.3% of variance in fatigue. Furthermore, depression and

fatigue were both estimated to have independent unique environmental factors which

explained 28% and 54.3% of their variance, respectively (Hickie et al., 1999b).

Therefore, the observed comorbidity between fatigue and depression may be

explained, in part, by shared underlying genetically determined disease mechanisms.

Insomnia was assessed as both a fatigue and depression symptom. Therefore,

the identification of insomnia as a distinguishing symptom between fatigued and

non-fatigued individuals is unsurprising. Although poor concentration was also

assessed as a symptom of both fatigue and depression, it is not a distinguishing

symptom between fatigued and non-fatigued individuals. However, concentration

problems may not have reached significance in the full symptom model due to

differences in the wording of the fatigue and depression questions for its assessment

potentially resulting in different responses by individuals. Therefore, insomnia is a

key indicator of co-occurring fatigue and depression. Considering depression

diagnosis is particularly difficult within older adults, insomnia and to a lesser extent

poor concentration, should be considered as warning signs of depression. Indeed,

Deckx and colleagues have previously shown fatigue to be an indicator of depression

in older cancer patients (Deckx et al., 2015); whereas, our results demonstrate the

broader applicability of fatigue, and in particular insomnia, as an indicator of

depression within older adults in the community. Evidence for overlapping molecular

mechanisms between fatigue, depression, and insomnia has been provided by

heritability estimates within females (Hur et al., 2012). Common and symptom-

specific additive genetic and unique environmental factors were identified which

explain the variance of insomnia, fatigue, and depression. Therefore, overlapping

genetic factors could explain the high levels of insomnia in fatigued and depressed

individuals and potentially account for a proportion of the high comorbidity of

fatigue and depression.

The present study is the first to investigate both fatigue and depression

symptoms experienced by depressed and fatigued individuals, respectively. A

possible limitation of our study lies in the relatively small number of individuals with

MDD and inability to assess the third DSM criterion of a major depressive episode—


change in weight or appetite. Although re-running the analysis removing the small

proportion (6.4%) of non-depressed individuals who report either depressed mood or

anhedonia (and could therefore be depression cases if they reported a change in

weight or appetite) did not change the study findings (data not shown). Also, the

large number of individuals who did not complete the questions used throughout this

study could potentially be due to a reduced likelihood of depressed individuals

completing the survey. Although the increased age of the present cohort has possibly

contributed to the lower levels of depression observed, we note that comparable

prevalence estimates have been reported for individuals over 65 in the United States.

The Centers for Disease Control and Prevention (2010) reported the prevalence of a

current diagnosis of MDD and MiDD in adults at 4.1% and 5.1%, respectively,

compared to 2.1% and 4.8%, respectively, in individuals over 65 (Centers for

Disease Control and Prevention, 2010). Furthermore, symptomatic differences have

been identified between younger and older adults with depression (Hybels et al.,

2012). Therefore, investigating fatigue and depression in older adults is clinically

significant; particularly considering the increased prevalence of fatigue in this age

group—although the age of participants increased the likelihood of medically

explainable fatigue within the cohort, thereby potentially reducing the specificity of

the study. However, fatigue and depression were assessed independently using

validated self-report questionnaires; allowing the utilisation of consistent assessment

measures throughout the complete study cohort, enabling comparable classifications

between individuals. Furthermore, utilising a current depression status was

advantageous because it enabled investigation of self-reported co-occurring fatigue

and depression. Finally, the use of a community study cohort removed potential

confounding with medical healthcare-seeking behaviour.

In summary, increased preponderance of depression and fatigue symptoms in

fatigued and depressed cases, respectively, indicates that an underlying association

exists between the two entities. Furthermore, the polythetic definition of depression

and the spectrum of fatigue symptoms imply that the underlying genetics of both

entities are heterogeneous. Therefore, utilisation of distinguishing symptoms could

facilitate the selection of more homogeneous subgroups, potentially enabling

identification of risk loci associated with varying phenotype presentations. Future

analyses should investigate the comorbidity of fatigue and depression by


characterising the type of relationship which exists between the two entities and their

underlying genetics.

71

Chapter 3 provided evidence that an underlying association likely exists between

fatigue and depression. Additionally, previous studies have indicated fatigue and

depression have genetic contributions. To determine if a shared genetic contribution

explains a proportion of the variation between fatigue and depression and

characterise the type of relationship that exists between the traits, the heritability of

the individual phenotypes was initially investigated. The following chapter aimed to

investigate the familiality and heritability of fatigue.

72 Chapter 4: Familiality and Heritability of Fatigue in an Australian Twin Sample

Chapter 4: Familiality and Heritability of

Fatigue in an Australian Twin

Sample

This chapter comprises the following prepared manuscript:

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (In press, accepted 20 March 2017).

Familiality and heritability of fatigue in an Australian twin sample. Twin Research

and Human Genetics.

Chapter 4: Familiality and Heritability of Fatigue in an Australian Twin Sample 73




4.1 ABSTRACT

Familial factors have previously been implicated in the etiology of fatigue, of which,

a significant proportion is likely attributable to genetic influences. However, family

studies have primarily focused on Chronic Fatigue Syndrome, while univariate twin

studies have investigated broader fatigue phenotypes. The results for similar fatigue

phenotypes vary between studies, particularly, with regard to sex-specific

contributions to the heritability of the traits. Therefore, the current study aims to

investigate the familiality and sex-specific effects of fatigue experienced over the

past few weeks, in an older Australian population of 660 monozygotic (MZ) twin

pairs, 190 MZ singleton twins, 593 dizygotic (DZ) twin pairs, and 365 DZ singleton

twins. Higher risks for fatigue were observed in MZ compared to DZ co-twins of

probands with fatigue. Univariate heritability analyses indicated fatigue has a

significant genetic component, with a heritability (h2) estimate of 40%. Sex-specific

effects did not significantly contribute to the heritability of fatigue, with similar

estimates for males (h2 = 41%, 95% confidence interval [CI] = 18-62%) and females

(h2 = 40%, 95% CI = 27-52%). These results indicate that fatigue experienced over

the past few weeks has a familial contribution, with additive genetic factors playing

an important role in its etiology.


4.2 INTRODUCTION

Fatigue is a highly prevalent trait with multidimensional symptoms. The broad

symptom spectrum is associated with quantifiable difficulties, resulting in fatigue

classifications based on arbitrarily defined durations and severities. Commonly

utilised classifications include prolonged fatigue, chronic fatigue (CF), idiopathic

chronic fatigue (ICF), and chronic fatigue syndrome (CFS). Prolonged fatigue is

classified as self-reported persistent or relapsing fatigue experienced for at least one

month (Fukuda et al., 1994), and has an estimated population prevalence of 6.16-

28.00% (EvengÅRd et al., 2005; Hamaguchi et al., 2011; Jason et al., 1999; Kim et

al., 2005; Njoku et al., 2007). CF is classified as self-reported persistent or relapsing

fatigue experienced for at least six months (Fukuda et al., 1994), and has an

estimated population prevalence of 2.00-12.20% (Bierl et al., 2004; Cho et al., 2009;

EvengÅRd et al., 2005; Friedberg et al., 2015; Hamaguchi et al., 2011; Jason et al.,

1995; Jason et al., 1999; Kim et al., 2005; Loge et al., 1998; Njoku et al., 2007; Patel

et al., 2005; Steele et al., 1998; Wessely et al., 1995; Wessely et al., 1997; Wong &

Fielding, 2010). ICF is classified as clinically evaluated, medically unexplained CF,

with insufficient symptom presentation for diagnosis with CFS (Fukuda et al., 1994),

and has an estimated population prevalence of 1.00-9.00% (Hamaguchi et al., 2011;

Kim et al., 2005; Wessely et al., 1997).

The original CFS classification was published in 1988, by the Centres for

Disease Control (Holmes et al., 1988). This CFS classification required the presence

of new onset unexplained CF and either six or more symptom criteria (mild fever or

chills, sore throat, painful lymph nodes, muscle weakness, muscle discomfort or

myalgia, post-exertional fatigue, headaches, migratory arthralgia, neuropsychologic

complaints, sleep disturbance, and acute onset) and two physical criteria (low grade

fever, nonexudative pharyngitis, and palpable or tender lymph nodes), or at least

eight of the symptom criteria. In 1994, the Centres for Disease Control published a

revision to the CFS classification which has become the standard definition utilised

worldwide (Fukuda et al., 1994). The 1994, CFS classification requires clinically

evaluated, medically unexplained CF, with four or more physical symptoms (sore

throat, tender lymph nodes, headaches, cognitive difficulties, unrefreshing sleep,

multijoint pain, muscle pain, and post-exertional malaise) experienced over a six-

month period, which have not pre-dated the fatigue (Fukuda et al., 1994). The


population prevalence of CFS has been estimated at 0.07-2.60% (Cho et al., 2009;

Hamaguchi et al., 2011; Jason et al., 1999; Kawakami et al., 1998; Kim et al., 2005;

Lindal et al., 2002; Nacul et al., 2011; Njoku et al., 2007; Reyes et al., 2003; Vincent

et al., 2012; Wessely et al., 1995; Wessely et al., 1997).

Familial studies of fatigue have mainly focused on CFS. In 1991, Bell and

colleagues (1991) showed, children (aged 6-17) with CFS (based on the original CFS

classification) were significantly more likely to have family members with CFS

symptoms than asymptomatic controls (relative risks [RR] = 48.60, 95% confidence

interval [CI] = 9.43-587.22). Although the degree of relatedness investigated by the

authors is unclear. In 2001, Walsh and colleagues (2001) showed first-degree

relatives of CFS cases (with a mean age of 37.6 years) have an increased risk of

prolonged fatigue (RR = 2.18, 95% CI = 0.88-3.48) and CFS (RR = 9.22, 95% CI =

7.84-10.60). Additionally, Buchwald and colleagues (2001) showed monozygotic

(MZ) twin pairs have higher concordance rates compared to dizygotic (DZ) twin

pairs for CF and ICF (within a cohort with a mean age of 46 years). In 2006,

adolescents, aged 12-18, with CFS and their mothers were shown to have shared

symptom complexes, which were not exhibited by their fathers (van de Putte et al.,

2006). Finally, in 2011, Albright and colleagues (2011) showed CFS cases’ first (RR

= 2.70, 95% CI = 1.56-4.66), second (RR = 2.34, 95% CI = 1.32-4.19), and third (RR

= 1.93, 95% CI = 1.21-3.07) degree relatives had an increased risk of CFS compared

to controls. These family studies indicate genetic and common environmental factors

likely contribute to CFS.

Univariate twin studies have been utilised to estimate the contribution of

additive genetic (also known as narrow-sense heritability [h2]), common

environmental, and unique environmental factors to the variation observed in the

population of interfering fatigue (tiredness or fatigue experienced for at least five

days), abnormal tiredness, prolonged fatigue, CF, ICF, and CFS, in adults (see Table

4.1 for a summary) (Buchwald et al., 2001; Schur et al., 2007; Sullivan et al., 2003;

Sullivan et al., 2005). Interfering fatigue has an estimated heritability of 6% in males

and 26% in females (Sullivan et al., 2003). Similarly, abnormal tiredness has an

estimated heritability of 30% in males and 26% in females (Sullivan et al., 2005).

Prolonged fatigue has an estimated heritability of 34-51% in males and 18-27% in

females (Schur et al., 2007; Sullivan et al., 2005). CF has an estimated heritability of


30-47% in males and 12-32% in females (Buchwald et al., 2001; Schur et al., 2007;

Sullivan et al., 2005). Finally, ICF and CFS both have an estimated heritability’s of

51% in females (Buchwald et al., 2001; Schur et al., 2007). Notably, the heritability

estimates for males and females were similar within the Swedish cohort, which had

an age range of 42-64 years. Meanwhile, the American cohorts with mean ages of

32.4 years and approximately 35 years have greater differences in heritability

estimates between the sexes.

To date, only two studies have been conducted which included children or

adolescents and utilised univariate twin modelling to investigate the contribution of

genetic and environmental factors in fatigue phenotypes. The first study investigated

the heritability of short-duration fatigue (fatigue experienced for at least one week)

and prolonged fatigue within children (aged 5-17) from South Wales (Farmer et al.,

1999). However, sex-specific effects were not investigated and confidence intervals

were not reported for the heritability estimates. Nonetheless, short-duration fatigue

had an estimated additive genetic, common environmental, and unique

environmental contribution of 42%, 38%, and 20%, respectively. Similarly,

prolonged fatigue had an estimated additive genetic, common environmental, and

unique environmental contribution of 54%, 19%, and 26%, respectively. Meanwhile,

the heritability of fatigue severity (a continuous scale of the 11 core fatigue items and

2 muscle pain items of the Chalder Fatigue Questionnaire (Chalder et al., 1993)) and

abnormal fatigue (assessed by the 11 core fatigue items of the Chalder Fatigue

Questionnaire) was investigated in a Sri Lankan population of adolescents and adults

(aged ≥ 15) (Ball et al., 2010b). Fatigue severity had an estimated additive genetic,

and unique environmental contribution of 30% (95% CI = 24-35%) and 70% (95%

CI = 65-76%), respectively. Similarly, abnormal fatigue had an estimated additive

genetic, and unique environmental contribution of 39% (95% CI = 29-49%) and 61%

(95% CI = 51-71%), respectively.


Table 4.1. Previously published variance estimates (with their 95% confidence intervals) for varying fatigue classifications, in adults, from univariate structural equation

modelling.

Fatigue classification Study Population Mean age ± standard deviation

(Age range)

Number of

twin pairs

Male (M) Female (F)

A C E A C E

Interfering fatigue Sullivan et al. (2003) USA Case: 34.9 ± 9.3

Control: 35.1 ± 9.2

M = 3422

F = 3104

0.06

(0.00-0.46)

0.21

(0.00-0.25)

0.73

(0.54-0.90)

0.26

(0.00-0.44)

0.01

(0.00-0.30)

0.73

(0.56-0.92)

Abnormal tiredness Sullivan et al. (2005) Swedish (42-64) M =11293

F = 12813

0.30

(0.11-0.40)

0.00

(0.00-0.14)

0.70

(0.60-0.80)

0.26

(0.08-0.33)

0.00

(0.00-0.14)

0.74

(0.67-0.82)

Prolonged fatigue Sullivan et al. (2005) Swedish (42-64) M =11293

F = 12813

0.34

(0.03-0.45)

0.00

(0.00-0.25)

0.66

(0.55-0.79)

0.27

(0.06-0.35)

0.00

(0.00-0.16)

0.73

(0.65-0.82)

Schur et al. (2007) USA 32.4 ± 14.7

(18-90) M = 1468 F = 2272

0.51 (0.13-0.69)

0.00 (0.00-0.33)

0.49 (0.31-0.71)

0.18 (0.00-0.54)

0.23 (0.00-0.48)

0.59 (0.46-0.74)

Chronic fatigue Buchwald et al. (2001) USA 46 F = 146 - - - 0.19

(0.00-0.56)

0.69

(0.32-0.89)

0.12

(0.07-0.19)

Sullivan et al. (2005) Swedish (42-64) M =11293

F = 12813

0.30

(0.02-0.44)

0.00

(0.00-0.23)

0.70

(0.56-0.86)

0.32

(0.11-0.41)

0.00

(0.00-0.16)

0.68

(0.59-0.78)

Schur et al. (2007) USA 32.4 ± 14.7

(18-90) M = 1468 F = 2272

0.47 (0.00-0.68)

0.00 (0.00-0.39)

0.53 (0.32-0.79)

0.12 (0.00-0.48)

0.26 (0.00-0.48)

0.62 (0.47-0.78)

Idiopathic chronic fatigue Buchwald et al. (2001) USA 46 F = 146 - - - 0.51

(0.07-0.96)

0.42

(0.00-0.85)

0.08

(0.04-0.13)

Chronic fatigue syndrome Schur et al. (2007) USA 32.4 ± 14.7

(18-90)

M = 1444

F = 2222 - - -

0.51

(0.00-0.82)

0.12

(0.00-0.72)

0.36

(0.18-0.65)

A: additive genetic component; C: common environmental component E: unique environmental component.


Additional studies have utilised multivariate twin modelling to investigate the

contribution of shared genetic and environmental factors to numerous traits that are

comorbid or hypothesised to be associated with fatigue. The traits investigated within

previous multivariate studies include various fatigue definitions (i.e., fatigue

symptoms, short-duration fatigue, abnormal fatigue, fatigue, prolonged fatigue, and

CF) and major depressive disorder, insomnia, psychological distress, anxiety,

depression, psychological symptoms, somatic symptoms, generalised anxiety

disorder, disability pension due to neurotic diagnoses, headaches, irritable bowel

syndrome, chronic widespread pain, immune responsiveness, and the immunological

factors IL-4, IFN-γ, and sCD23. The heritability of the various fatigue measures

ranged from 7% to 60%, and a number of these studies reported significant evidence

for shared genetic factors between fatigue and other traits, in particular strong genetic

correlations (rg) were observed between prolonged fatigue and depression (rg = 0.53),

CF and depression or anxiety (rg = 0.60), fatigue and psychological distress (rg =

0.67), and fatigue and immune responsiveness (rg = 0.76) (Ball et al., 2011; Fowler et

al., 2006; Hickie et al., 1999a; Hickie et al., 1999b; Hickie et al., 2001; Hur et al.,

2012; Kato et al., 2009; Narusyte et al., 2016).

Given the large variation in both the definition of fatigue and estimates of

heritability produced from a relatively small number of univariate twin studies

(conducted in Swedish and American cohorts), the current study aimed to investigate

the heritability of fatigue experienced over the past few weeks, in a cohort of

Australian twin pairs. While previously published family studies have focused on

CFS, we assessed the familiality of fatigue experienced over a shorter time period.

4.3 METHODS

4.3.1 Study Cohort and Fatigue Classification

The present study utilises data from the over 50’s (aged) study, conducted by the

genetic epidemiology group within the QIMR Berghofer Medical Research Institute

(QIMRB), between 1993 and 1996. The study invited 2,281 twin pairs, aged over 50,

from the Australian Twin Registry to complete a 16-page mailed Health and

Lifestyle Questionnaire (Bucholz et al., 1998; Mosing et al., 2012). Informed written

consent was obtained from each participant, and the study was approved by the

Human Research Ethics Committee (HREC) of QIMRB.


The fatigue classification utilised throughout this study was assessed by the

Schedule of Fatigue and Anergia (SOFA) (Hickie et al., 1996). Ten questions are

contained in the SOFA; however, a shorter eight-item version was used in the Health

and Lifestyle Questionnaire, due to two items being replicated within the General

Health Questionnaire (GHQ) (Goldberg & Blackwell, 1970), that was also

administered to the participants. Responses to the eight SOFA and two GHQ items

were used to assess fatigue within the cohort, as previously detailed (Corfield et al.,

2016a). Individuals were classified as fatigued if they reported three or more of the

ten fatigue symptoms (muscle pain at rest, post-exertional muscle pain, post-

exertional muscle fatigue, post-exertional fatigue, hypersomnia, insomnia, poor

concentration, speech problems, poor memory, and headaches), over the past few

weeks. Fatigue was dichotomized rather than using symptom counts because the

SOFA was originally designed to identify chronic fatigue syndrome cases and

assesses physical, neurocognitive, and neurovegetative fatigue symptoms. Hopefully

the utilisation of case-control classifications enabled us to identify fatigued

individuals with similar underlying pathophysiology as CFS cases and prevented

confounding with other traits.


Familial clustering of fatigue was investigated by calculating RR, measured by the

prevalence ratio, with their 95% CI in complete MZ and DZ twin pairs. RR were

calculated relative to non-fatigued individuals. Within MZ and same-sex DZ twin

pairs RR were calculated by averaging over using twin 1 or twin 2 as the proband.

Tetrachoric correlations were calculated for fatigue, within MZ and DZ twin

pairs and singleton twins, using the polycor package in R (R Core Team, 2014). The

tetrachoric correlation assumes that underlying the observed binary distribution of

affection status, there exists a continuous, normally distributed latent (non-

observable) liability (Kendler, 1993). That is, the tetrachoric correlation is an

estimate of the correlation between two latent variables, where each latent variable is

assumed to have a bivariate normal distribution. Comparison of the correlations

between MZ and DZ twins was used to provide information on the importance of

genetic and environmental factors contributing to the heritability of fatigue.

Correlations that are larger in MZ compared to DZ twins indicate the phenotype has


a genetic contribution. While correlations that are similar in MZ and DZ twins

indicate environmental factors explain the majority of variation in the phenotype.

Structural equation modelling (SEM), including the threshold model, was

utilised to investigate the heritability of fatigue. The threshold model posits that

distinct traits represent a single, normally distributed, severity continuum. A single

threshold was used to separate non-fatigued and fatigued individuals. SEM was used

to estimate the contribution of additive genetic (A), non-additive (dominance)

genetic (D), common environmental (C), and unique environmental (E) variance

components (Neale et al., 1992). Adjustments for (linear) age and sex effects were

included in the model. Significance of the variance components was assessed by

comparing the fit of the full model (ACE/ADE) to the nested models (AE, CE, and

E) where the effect was dropped, using OpenMx in R (Boker et al., 2011).

Additionally, sex-limitation modelling was conducted to determine if sex-specific

effects contribute to the heritability of fatigue. Initially, a non-scalar sex-limitation

model was fitted which included variance components for females (i.e., Af, Cf, and

Ef) and males (i.e., Am, Cm, and Em), as well as an additional additive genetic

component specific to males (A′m). Restricted non-scalar sex-limitation modelling

was then conducted, whereby, A′m was removed. Evidence for sex-specific genetic

effects was formally tested by determining if the genetic correlation within opposite-

sex DZ twin pairs significantly differs from 0.5. The goodness of fit parameters used

to assess the differences in the twin models were the likelihood-ratio chi-square test

(χ2) and the p-value. Additionally, model fit was compared utilising Akaike’s

Information Criteria (AIC); with the lowest AIC indicating the most parsimonious

model (Akaike, 1973, 1974).

Tetrachoric correlations and SEM were estimated using full information

maximum likelihood (FIML), whereby both complete twin pairs and incomplete twin

pairs (singleton twins) were included in the analyses. The inclusion of singleton

twins provides more accurate estimation of the thresholds and may correct for

participation bias.

4.4 RESULTS

Within the over 50’s study, 473 twin pairs and 555 singleton twins returned

incomplete responses to the SOFA and GHQ questionnaire items utilised to assess


fatigue within the present study and were therefore excluded. The remaining 1,253

complete twin pairs and 555 singleton twins with fatigue data comprised the cohort

utilised within the present study. The study cohort contained 660 MZ twin pairs (504

female-female and 156 male-male twin pairs) and 190 MZ singleton twins (109

females and 81 males) with a mean age of 61.3 ± 8.9 (range = 50-92), and 593 DZ

twin pairs (272 female-female, 76 male-male, 137 female-male, and 108 male-female

twin pairs) and 365 DZ singleton twins (260 females and 105 males) with a mean age

of 61.0 ± 8.5 (range = 50-94). The prevalence of fatigue defined as above was 30.7%

(31.7% of females and 28.3% of males).

An increased risk of fatigue in co-twins of fatigued probands was observed,

indicating a significant familial contribution. Strong evidence for a genetic

contribution to fatigue is provided by the higher RR observed in MZ compared to DZ

twin pairs (Table 4.2). In particular, the risk of fatigue in co-twins of fatigued

probands was 2.20 (95% CI = 1.77-2.75) in MZ twin pairs compared to 1.32 (95% CI

= 1.01-1.73) in DZ twin pairs (applicable to first-degree relatives in the general

population). Analysis of familial clustering within males and females indicated a

similar pattern of risks.

Table 4.2. Relative riska of fatigue within complete monozygotic (MZ), same-sex dizygotic (DZss),

and opposite-sex dizygotic (DZos) twin pairs.

Zygosity Number of complete twin pairs RR (95% CI)

MZ 660 2.20 (1.77-2.75)

MZ [F-F] 504 2.14 (1.68-2.74)

MZ [M-M] 156 2.28 (1.38-3.78) DZ total 593 1.32 (1.01-1.73)

DZss 348 1.16 (0.83-1.62)

DZss [F-F] 272 1.14 (0.78-1.66) DZss [M-M] 76 1.23 (0.61-2.49)

DZos 241 1.59 (1.03-2.45)

F: female; M: male; aRelative risks and 95% confidence intervals were calculated with respect to non-depressed or non-fatigued status in twin 1.

Same-sex twin pair tables were made symmetrical by averaging over

using twin 1 or twin 2 as the proband.

The tetrachoric correlations for fatigue were approximately three times larger

in MZ compared to DZ twin pairs (Table 4.3). Overall, the observed MZ > DZ

correlations, indicate additive genetic factors contribute to the variation in fatigue.


Table 4.3. Tetrachoric correlations (r) with their 95% confidence intervals (CI) for fatigue according

to zygosity.

Zygosity r (95% CI)

MZ 0.43 (0.32-0.54)

MZ [F-F] 0.43 (0.30-0.56) MZ [M-M] 0.41 (0.17-0.66)

DZ total 0.14 (0.01-0.28)

DZss 0.08 (-0.10-0.26) DZss [F-F] 0.07 (-0.14-0.27)

DZss [M-M] 0.12 (-0.26-0.51)

DZos 0.24 (0.02-0.46)

F: female; M: male.

Initially, full univariate ACE and ADE models were fitted, however,

systematic dropping of A, C, and D effects was used to determine if the effect of the

individual variance components was significant (Table 4.4). Dropping C (i.e., AE

model), from the ACE model, did not worsen the model fit. However, dropping A

(i.e., CE model) or both A and C (i.e., E model) was significant (p = 4.76 × 10-3 and

8.91 × 10-11, respectively)—indicating A is an important source of variance in the

heritability of fatigue. Meanwhile, dropping D (i.e., AE model), from the ADE

model, did not worsen the model fit. However, dropping both A and D (i.e., E model)

was significant (p = 6.46 × 10-11)—indicating genetic factors play an essential role in

the heritability of fatigue. Therefore, the AE model was selected as the most

parsimonious model based on fit statistics. No differences in threshold distributions

were observed within twin pairs and singleton twins, or across zygosity and sex

groups.

Additive genetic factors were estimated to explain approximately 40% of the

heritability of fatigue. No significant evidence for sex-specific genetic effects was

observed within the cohort. Results of the non-scalar sex-limitation modelling

indicated the restricted model was the most parsimonious (AIC = -2405.41) with

similar heritability estimates for fatigue in males, at 41% (95% CI = 18-62%; E =

59%, 95% CI = 38-82%), compared to females, at 40% (95% CI = 27-52%; E =

60%, 95% CI = 48-73%).


Table 4.4. Fit statistics and variance estimates (with their 95% confidence intervals) from univariate

structural equation modelling.

Model -2LL p-value (ACE) p-value (ADE) AIC A C (or D) E

ACE 3704.60 NA NA -2407.40 0.40

(0.15-0.50)

0.00

(0.00-0.20)

0.60

(0.50-0.71)

ADE 3703.96 NA NA -2408.05 0.17

(0.00-0.50)

0.25

(0.00-0.53)

0.58

(0.47-0.70)

AE 3704.60 1.00 0.42 -2409.40 0.40

(0.29-0.50) -

0.60

(0.50-0.71)

CE 3712.57 4.76 × 10-3 NA -2401.43 - 0.30

(0.21-0.39)

0.71

(0.61-0.79)

E 3750.88 8.91 × 10-11 6.46 × 10-11 -2365.12 - - 1.00

(1.00-1.00)

-2LL: minus two log-likelihood; A: additive genetic component; C: common environmental component E: unique environmental component. The best-fitting model is indicated in bold. p-value compares -2LL for the full ACE or ADE model

to the reduced (AE, CE, DE, and E) models.

4.5 DISCUSSION

Findings from the present study indicate fatigue, in older adults, is familial, and has a

genetic contribution with no significant sex-specific effects.

The familial clustering analysis revealed co-twins of fatigued probands were at

an increased risk of fatigue. These results indicate the familial contribution of fatigue

is not specific to CFS. Although, in 2001, first-degree relatives of CFS cases were

shown to have an increased risk of prolonged fatigue and MZ twin pairs were shown

to have higher concordance rates than DZ twin pairs for CF and ICF (Buchwald et

al., 2001; Walsh et al., 2001). However, to our knowledge, this is the first study to

characterise the familial clustering of fatigue experienced for less than six months.

The higher risk observed in MZ twin pairs compared to DZ twin pairs indicates

genetic factors likely contribute to the etiology of fatigue. These results are reflective

of the conclusions drawn from previous family studies of CF, ICF, and CFS

(Buchwald et al., 2001; van de Putte et al., 2006). However, the similar pattern of

risks observed within males and females indicates the underlying etiology of fatigue

is likely independent of sex. This finding opposes the results of van de Putte and

colleagues (2006), who found an increase of CFS symptoms in mothers of children

with CFS, but not fathers. Indicating the etiology of fatigue may differ with age.

The differences in heritability between males and females identified within

previous twin studies are larger in the cohorts comprised of younger adults compared

to cohorts of older cohorts. In contrast, results from the present study indicate that

the underlying etiology of fatigue is independent of sex in older adults. Sex-

limitation modelling revealed males (h2 = 41%, 95% CI = 18-62%) and females (h2 =

40% (95% CI = 27-52%) had very similar heritability estimates. Furthermore, based


on fit statistics the most parsimonious model was the univariate AE twin model—

which did not include sex-specific effects. These results support the suggestion of

Sullivan and colleagues (2005), that females and males have similar genetic and

environmental contributions for varying fatigue classifications—despite the higher

prevalence of fatigue in females. In comparison, Schur and colleagues (2007)

suggested further investigations are required to understand the differences in fatigue

etiology between the sexes. Within the present study higher tetrachoric correlations

were observed in opposite-sex DZ twin pairs compared to same-sex twin pairs. The

same pattern of correlations has been reported for other complex phenotypes (Vink et

al., 2012). One possible explanation is that males and females respond differently on

self-report questionnaires (Sigmon et al., 2005). Based on our results and previous

findings we suggest further investigation into the heritability of fatigue across the

lifespan is required.

A possible limitation of our study is the utilisation of self-report rather than

interview based data. However, considering prolonged fatigue and CF classifications

are based on self-report the utilisation of questionnaire-based data is valid.

Additionally, this prevented confounding within the study by healthcare seeking

behaviour, due to the population-based structure of the cohort. Another potential

limitation of the study was the utilisation of a non-standard fatigue duration—due to

the ambiguity of the questionnaire, which assessed fatigue symptoms experienced

“over the past few weeks”. However, considering the SOFA was designed to assess

CFS symptoms, fatigue is representative of a spectrum, and previous studies have

looked at similar fatigue definitions—our findings still offer valid insights into the

underlying etiology of fatigue.

In summary, we have shown that fatigue experienced over the past few weeks

is familial, with additive genetic factors explaining a substantial proportion of its

variance in older adults. Future research aimed at identifying the specific genes and

risk loci associated with fatigue (e.g., via genome-wide association studies), will

increase our understanding of its underlying biological mechanisms.

86

In Chapter 4, fatigue experienced over the past few weeks, in older adults, was

shown to have a familial component, with additive genetic factors explaining 40% of

the traits’ variation. A similar analysis was conducted within Chapter 5 to determine

if the familiality and heritability of MDD within the study cohort was similar to

previously published analyses. Additionally, Chapter 5 aimed to investigate the

familiality and heritability of MiDD and determine the validity of utilising a broad

depression phenotype comprising MDD and MiDD cases.

Chapter 5: A Continuum of Genetic Liability for Minor and Major Depression 87

Chapter 5: A Continuum of Genetic

Liability for Minor and Major

Depression

This chapter comprises the following submitted article:

Corfield, E. C., Yang, Y., Martin, N. G., & Nyholt, D. R. (In Press, accepted 4 April

2017). A continuum of genetic liability for minor and major depression.

Translational Psychiatry.

88 Chapter 5: A Continuum of Genetic Liability for Minor and Major Depression




5.1 ABSTRACT

The recent success of a large genome-wide association (GWA) study—analysing

130,620 major depression cases and 347,620 controls—in identifying the first single

nucleotide polymorphism (SNP) loci robustly associated with major depression in

Europeans, confirms that immense sample sizes are required to identify risk loci for

depression. Given the phenotypic similarity between major depressive disorder

(MDD) and the less severe minor depressive disorder (MiDD), we hypothesised that

broadening the case definition to include MiDD may be an efficient approach to

increase sample sizes in GWA studies of depression. By analysing two large twin

pair cohorts, we show that minor depression and major depression lie on a single

genetic continuum, with major depression more severe but not etiologically distinct

from minor depression. Furthermore, we estimate heritabilities of 37% for minor

depression, 46% for major depression, and 48% for ‘minor or major depression’ in a

cohort of older adults (aged 50-92). While, the heritability of ‘minor or major

depression’ was estimated at 40% in a cohort of younger adults (aged 23-38).

Moreover, two robust major depression risk SNPs nominally associated with major

depression in our Australian GWA dataset, produced more significant evidence for

association with ‘minor or major depression’. Hence, broadening the case phenotype

in GWA studies to include sub-threshold definitions such as MiDD, should facilitate

the identification of additional genetic risk loci for depression.


5.2 INTRODUCTION

Major depressive disorder (MDD) is a common, complex trait with an estimated

heritability of approximately 40% (Sullivan et al., 2000). However, until recently,

genome-wide association (GWA) studies in large European samples have failed to

robustly identify genetic variants contributing to MDD (Bosker et al., 2011; Kohli et

al., 2011; Lewis et al., 2010; Major Depressive Disorder Working Group of the

Psychiatric GWAS Consortium et al., 2013; Muglia et al., 2010; Rietschel et al.,

2010; Shi et al., 2011; Shyn et al., 2011; Sullivan et al., 2009; Wray et al., 2012). In

July of 2015, two genome-wide significant (p < 5 × 10-8) single nucleotide

polymorphism (SNP) loci (rs12415800 near the SIRT1 gene, p = 2.53 × 10-10 and

rs35936514 in the intron of LHPP, p = 6.45 × 10-12) were reported to be associated

with severe and recurrent MDD, in a sample of Han Chinese women (5,282 cases,

5,220 controls) (CONVERGE Consortium, 2015); however, these SNPs were not

associated in the Psychiatric Genomics Consortium (PGC) GWA study of 9,240

European MDD cases and 9,519 controls (Major Depressive Disorder Working

Group of the Psychiatric GWAS Consortium et al., 2013).

In August 2016, the first SNP loci robustly associated with major depression in

Europeans were reported (Hyde et al., 2016). This landmark study analysed a

combined cohort of 130,620 self-reported and clinically evaluated lifetime major

depression cases and 347,620 controls, and identified 17 genome-wide significant

SNPs within 15 independent genomic regions. The implicated SNP risk loci

comprise rs10514299 in an intron of TMEM161B-AS1 (p = 9.99 × 10-16), rs1518395

in an intron of VRK2 (p = 4.32 × 10-12), rs2179744 in an intron of L3MBTL2 (p =

6.03 × 10-11), rs11209948 downstream of NEGR1 (p = 8.38 × 10-11), rs454214

upstream of MEF2C (p = 1.09 × 10-9), rs301806 in an intron of RERE (p = 1.90 × 10-

9), rs1475120 in an intron of LIN28B (p = 4.17 × 10-9), rs10786831 in an intron of

SORCS3 (p = 8.11 × 10-9), rs12552 in the 3′ UTR of OLFM4 (p = 8.16 × 10-9),

rs6476606 in an intron of PAX5 (p = 1.20 × 10-8), rs8025231 in an intergenic region

between MEIS2 and TMCO5A (p = 1.23 × 10-8), rs12065553 in an intergenic region

on chromosome 1 (p = 1.32 × 10-8), rs1656369 in the intergenic region between

RSRC1 and MLF1 (p = 1.34 × 10-8), rs4543289 in an intergenic region on

chromosome 5 (p = 1.36 × 10-8), rs2125716 upstream of SLC6A15 (p = 3.05 × 10-8),

rs2422321 downstream of NEGR1 (p = 3.18 × 10-8), and rs7044150 in the intergenic


region between KIAA0020 and RFX3 (p = 4.31 × 10-8). An important implication of

this study is that immense sample sizes are required to identify a relatively modest

number of MDD risk loci in Europeans (compared to other traits of similar

prevalence and heritability) (Visscher et al., 2012).

Additional insights into the molecular mechanisms of depression, in

Europeans, were identified in 2016 through the investigation of depressive symptoms

and a broad depression phenotype. In April, rs7973260 in an intron of KSR2 (p = 1.8

× 10-9) and rs62100776 in an intron of DCC (p = 8.5 × 10-9) were associated with

depressive symptoms (Okbay et al., 2016). Meanwhile, in December, rs9825823

located in the intron of FHIT (p = 8.2 × 10-9) was associated with a broad depression

phenotype—including MDD and depressive symptoms (Direk et al., 2016). Most

recently, MDD in adults aged over 27 years was associated with the intergenic SNP

rs7647854, located on chromosome 3 (p = 5.2 × 10-11) (Power et al., 2017).

Given the phenotypic similarity between MDD and the less severe minor

depressive disorder (MiDD), we hypothesised that broadening the case definition to

include sub-threshold definitions such as MiDD may provide an efficient means to

increase sample sizes in GWA studies of depression.

In contrast to MDD, the heritability and molecular genetics of MiDD have not

been well investigated. The only difference in diagnosis between MDD and MiDD is

the number of presenting symptoms of the Diagnostic and Statistical Manual of

Mental Disorders (DSM-IV) criteria (NB, within the DSM-V, MiDD individuals

would be diagnosed as ‘Unspecified Depressive Disorder’); with MDD requiring at

least five symptoms and MiDD requiring two to four symptoms (American

Psychiatric Association, 2000, 2013). This phenotypic similarity coupled with a

reported increased risk of MDD in first degree relatives and patients with MiDD

(Chen et al., 2000; Cuijpers et al., 2004; Cuijpers & Smit, 2004; Judd et al., 1997;

Lewinsohn et al., 2003; Rapaport et al., 2002) suggests that a depression continuum

exists and that MiDD may be a relevant trait which could be utilised to elucidate the

underlying mechanisms associated with MDD. Therefore, the present study utilises

relative risks (RR) to ensure a similar pattern of familiality exists within the study

cohorts. In addition, heritability estimates and the liability threshold model were

utilised to investigate whether minor and major depression lie on a single genetic

continuum. Finally, the association signal of the 17 SNPs robustly associated with


major depression in Europeans was examined utilising both a narrow major

depression case phenotype and a broader depression phenotype including minor

depression (i.e., ‘minor or major depression’ cases).

5.3 MATERIALS AND METHODS

5.3.1 Study Cohorts

Two independent, community-based cohorts of Australian twin pairs were analysed

within the current study. Initially, the analysis was conducted within an older adult

cohort, the over 50’s (aged) study, before being replicated in a young adult cohort,

the Twin 89 (TE) study. Informed written consent was obtained from each

participant, and the study was approved by the Human Research Ethics Committee

(HREC) of the QIMR Berghofer Medical Research Institute (QIMRB).

The over 50’s cohort (Bucholz et al., 1998; Mosing et al., 2012) contained

1,220 twin pairs with complete self-report depression classifications (non-depressed,

minor depression, major depression). Current depression classifications were

obtained utilising a combination of responses from the twelve-item General Health

Questionnaire (GHQ) (Goldberg & Blackwell, 1970) and the fourteen-item

Delusions-Symptoms-States Inventory, States of Anxiety and Depression

(DSSI/sAD) (Bedford & Deary, 1997) questionnaires. As previously detailed

(Corfield et al., 2016a), specific questions from the GHQ and DSSI/sAD were

assigned to the appropriate DSM-IV major depressive episode criteria (American

Psychiatric Association, 2000). If an individual exhibited at least five of the DSM-IV

symptom criteria, of which either depressed mood or anhedonia was reported, they

were assessed as suffering major depression. Similarly, if an individual exhibited two

to four of the DSM-IV symptom criteria (depressed mood, anhedonia, a change in

weight or appetite, insomnia or hypersomnia, psychomotor agitation or retardation,

fatigue or loss of energy, feelings of worthlessness or excessive guilt, inability to

concentrate or make decisions, and thoughts about death, suicidal thoughts, suicidal

plans, or suicidal attempts), of which either depressed mood or anhedonia was

reported, they were assessed as suffering minor depression. The remaining

individuals were assessed as non-depressed. Meanwhile, the Twin 89 cohort (Heath

et al., 2001) contained 2,363 twin pairs, with complete lifetime self-report depression

classifications. Minor depression and major depression classifications required an


individual to report depressed mood and/or anhedonia. In addition, individuals

reporting a total of two to four and five or more depression symptoms for a period of

two or more weeks, across their lifespan, were classified as minor depression and

major depression, respectively. In depth explanation of depression assessment

utilised within the Twin 89 cohort is provided by Yang and colleagues (Yang et al.,

2016). Depression can be conceptualised as the extreme of a trait that is widely

distributed in the general population. Utilisation of depression symptom count is one

way to investigate the genetic architecture of the complete distribution. However, the

DSM diagnosis for depression requires the presence of depressed mood or

anhedonia, which would not be accounted for if symptom-counts were used.

Therefore, within the present study a three-category depression classification was

used to focus on traits which were as close as possible to clinical diagnoses.


Familial clustering of major depression, minor depression, and depression (minor or

major depression) was investigated by calculating RR with their 95% confidence

intervals (CI) in monozygotic (MZ) and dizygotic (DZ) twin pairs. RR were

calculated relative to non-depressed individuals. Within MZ and same-sex DZ twin

pairs RR were calculated by averaging over using twin 1 or twin 2 as the proband

(Nyholt et al., 2004).

A major goal of the genetic analysis was to test the multiple threshold model,

which asserts that different syndromes reflect different levels of severity on a single

dimension, rather than distinct etiologies (Reich et al., 1972). The fit of the multiple

threshold model was tested by calculating the polychoric correlation for the three-

category depression (non-depressed, minor depression, major depression)

classification using POLYCORR (Uebersax J.S., 2007). The polychoric correlation,

assumes that underlying the observed polychotomous distribution of affection status,

there exists a continuous, normally distributed latent (non-observable) liability

(Kendler, 1993). That is, the polychoric correlation is an estimate of the correlation

between two latent variables, where each latent variable is assumed to have a

bivariate normal distribution. A 2 goodness-of-fit test is used to test whether the

multiple threshold model provides a good fit to the observed data (i.e., compares the

observed frequencies to those predicted by the model). Additionally, polychoric

correlations were calculated for minor depression (excluding major depression


cases), major depression (excluding minor depression cases), two-category

depression (non-depressed, minor or major depression), and three-category

depression within MZ and DZ twin pairs. Comparisons of the correlations between

MZ and DZ twin pairs was used to provide information on the importance of genetic

and environmental factors contribution to the heritability of depression.

Structural equation modelling (SEM) was utilised to investigate the heritability

of minor depression (excluding major depression cases), major depression (excluding

minor depression cases), two-category depression, and three-category depression.

SEM was used to estimate the contribution of additive genetic (A), non-additive

(dominance) genetic (D), common environmental (C), and unique environmental (E)

variance components (Neale et al., 1992). Adjustments for (linear) age and sex

effects were included in the model. Significance of the variance components was

assessed by comparing the fit of the full model (ACE/ADE) to the nested models

(AE, CE, and E) where the effect was dropped, using OpenMx in R (Boker et al.,

2011). Evidence for sex-specific genetic effects was formally tested by determining

if the genetic correlation within opposite-sex DZ twin pairs significantly differs from

0.5. The goodness of fit parameters used to assess the differences in the twin models

were the likelihood-ratio chi-square test (χ2) and the p-value. Additionally, model fit

was compared utilising Akaike’s Information Criteria (AIC); with the lowest AIC

indicating the most parsimonious model (Akaike, 1973, 1974).

An association analysis was conducted for the 17 genome-wide significant loci

associated with major depression, in Europeans (Hyde et al., 2016), within the

Australian GWA dataset. The Australian GWA dataset is a community cohort which

contained 3,664 unrelated major depression cases, 620 unrelated minor depression

cases, and 7,113 unrelated controls, of European ancestry. In depth explanation of

the genotyping and quality control methods utilised within the GWA cohort have

previously been detailed by Medland and colleagues (Medland et al., 2009). Briefly,

standard quality control measures were utilised, whereby SNPs with BeadStudio

GenCall scores < 0.7, call rate < 0.95, Hardy-Weinberg equilibrium p-values < 1 ×

10-6, and minor allele frequencies < 0.01 were excluded. Imputation was then

conducted utilising HapMap samples of European ancestry. If multiple cases were

present within a family the most severe case was selected based on the number of

reported DSM depression symptoms, or an individual was randomly selected if


numerous individuals reported the same number of depression symptoms. Similarly,

if multiple controls were available within a family the single ‘best’ control was

selected based on the lowest number of depression symptoms reported or an

individual was randomly selected if numerous individuals within a family reported

the same number of depression symptoms. Finally, a single population control was

randomly selected from the remaining families for whom genotyping data but no

depression phenotype data was available.

The association analysis was conducted on 3,664 major depression cases and

7,113 controls (2,381 unaffected + 4,732 population-based controls), using logistic

regression with sex as a covariate, using PLINK (Purcell et al., 2007). The

association analysis was then repeated using a broad depression phenotype, of 4,284

cases (3,664 major depression + 620 minor depression cases). The results were then

compared to ascertain if the evidence for association was increased by the addition of

minor depression cases; thus reflecting an increase in power.

5.4 RESULTS

The over 50’s (aged) study cohort consisted of 643 MZ twin pairs (491 female-

female [F-F] and 152 male-male [M-M]) and 577 DZ twin pairs (263 F-F, 73 M-M,

136 female-male [F-M], and 105 male-female [M-F]), with a mean age of 61.30 ±

8.60 (range = 50-92). The prevalence of minor depression, major depression, and

two-category depression was 8.98%, 2.05%, and 11.02%, respectively (9.61%,

2.12%, 11.72% in females; 7.38%, 1.88%, 9.26% in males). Meanwhile, the Twin 89

(TE) cohort consisted of 1,005 MZ twin pairs (609 F-F and 396 M-M) and 1,358 DZ

twin pairs (455 F-F, 349 M-M, 301 F-M, and 253 M-F) with a mean age of 29.80 ±

2.49 (range = 23-38). The prevalence of minor depression, major depression, and two

category depression was 7.70%, 37.41%, and 45.11%, respectively (7.23%, 42.80%,

50.04% in females; 8.32%, 30.33%, 38.65% in males).

Familial clustering of minor depression and major depression cases was

observed (Table 5.1), with co-twins of minor depression probands having an

increased risk of major depression, and vice versa, within both cohorts.


Table 5.1. Relative riska of depression and fatigue within monozygotic (MZ), same-sex dizygotic (DZss), and opposite-sex dizygotic (DZos) twin pairs.

Aged TE

Probandco-twin MZ (643 pairs) DZss (336 pairs) DZos F-M (241 pairs) DZos M-F (241 pairs) MZ (1,005 pairs) DZss (804 pairs) DZos F-M (554 pairs) DZos M-F (554 pairs)

MiDMiD 2.85 (1.62-5.00) 2.67 (1.09-6.51) 1.13 (0.27-4.66) 1.15 (0.29-4.51) 0.91 (0.37-2.23) 1.71 (0.79-3.72) 1.40 (0.49-3.98) 1.05 (0.38-2.85)

MiDMD 7.32 (2.47-21.67) 2.04 (0.25-16.54) 4.24 (0.82-21.99) 4.02 (0.44-36.69) 1.64 (1.22-2.22) 1.17 (0.82-1.68) 1.06 (0.66-1.70) 1.40 (1.04-1.88)

MiDMiD/MD 3.48 (2.19-5.54) 2.54 (1.15-5.61) 1.79 (0.66-4.83) 1.51 (0.50-4.53) 1.48 (1.14-1.92) 1.27 (0.95-1.69) 1.12 (0.75-1.66) 1.34 (1.04-1.71)

MDMiD 5.45 (2.69-11.03) 2.22 (0.35-14.09) 3.53 (0.60-20.66) 3.44 (1.03-11.46) 1.17 (0.75-1.83) 1.06 (0.63-1.80) 1.72 (0.95-3.11) 0.99 (0.53-1.82)

MDMD 6.02 (0.79-45.56) - - - 2.16 (1.82-2.56) 1.46 (1.22-1.76) 1.16 (0.89-1.50) 1.17 (0.95-1.45)

MDMiD/MD 5.53 (2.99-10.22) 1.76 (0.28-11.05) 2.79 (0.48-16.07) 3.01 (0.91-9.94) 1.94 (1.68-2.24) 1.39 (1.19-1.63) 1.26 (1.01-1.56) 1.14 (0.95-1.36)

MiD/MDMiD 3.32 (2.04-5.42) 2.58 (1.12-5.94) 1.46 (0.45-4.75) 1.72 (0.64-4.60) 1.12 (0.73-1.73) 1.17 (0.72-1.91) 1.67 (0.94-2.97) 1.00 (0.57-1.76)

MiD/MDMD 7.09 (2.50-20.05) 1.64 (0.20-13.42) 3.66 (0.70-19.08) 3.01 (0.33-27.85) 2.07 (1.75-2.45) 1.41 (1.18-1.69) 1.14 (0.89-1.46) 1.22 (1.01-1.48)

MiD/MDMiD/MD 3.85 (2.54-5.84) 2.39 (1.12-5.06) 1.92 (0.78-4.76) 1.88 (0.79-4.48) 1.86 (1.62-2.14) 1.37 (1.18-1.60) 1.23 (1.00-1.52) 1.18 (1.00-1.39) aRelative risks and 95% confidence intervals were calculated with respect to non-depressed or non-fatigued status in twin 1. Same-sex twin pair tables were made symmetrical by averaging over using twin 1 or twin 2 as the proband. MiD: minor depression; MD: major depression.


No differences in threshold liability distributions were observed within twin

pairs, and across sex and zygosity groups, in either study cohort. Importantly, none

of the multiple-threshold model goodness-of-fit tests (one for each zygosity group)

were significant at the 5% level within the over 50’s study cohort (Table 5.2).

Similarly, only one multiple-threshold model goodness-of-fit test was nominally

significant (M-M DZ twin pairs, p = 0.01) in the Twin 89 cohort (Table 5.2);

however, considering goodness-of-fit tests were performed for each of the 5 zygosity

groups and 4 additional combined groupings, this finding is not considered study-

wide significant. Therefore, these results support the validity of the multiple

threshold model for the DSM-IV classifications for minor and major depression, and

indicate that they can be conceptualised as different levels of severity on a single

dimension of liability.

Table 5.2. Liability threshold model fit p-values.

Twin pair Aged TE

Complete pairs 0.41 0.53

MZ 0.73 0.75 MZf 0.64 0.81

MZm 0.62 0.92

DZ 0.46 0.43 DZss 0.66 0.66

DZf 0.34 0.21

DZm 0.64 0.01 DZos 0.46 0.46

Aged: over 50’s (aged) cohort; TE: Twin 89

(TE) cohort; MZ: monozygotic; MZf: MZ female; MZm: MZ male; DZ: dizygotic; DZss:

DZ same sex; DZf: DZ female; DZm: DZ male;

DZos: DZ opposite-sex.

The polychoric correlations for the varying depression classifications were

approximately two times larger in MZ compared to DZ twin pairs within the over

50’s cohort (Table 5.3). Similarly, with the exception of the MZ non-depressed–

minor depression correlation (due to small cell counts), the polychoric correlations

for the major depression, two-category depression, and three-category depression

were at least two to three times higher in MZ compared to DZ twin pairs within the

Twin 89 cohort (Table 5.3). The observed MZ > DZ correlations, indicate additive

genetic factors contribute to the heritability of depression.


Table 5.3. Polychoric correlations with their 95% confidence intervals for depression according to zygosity.

Aged TE

Depression classification MZ DZ MZ DZ

non-depressed, minor depression 0.37 (0.17-0.56) 0.21 (-0.04-0.45) 0.05 (-0.22-0.31) 0.17 (-0.04-0.39)

non-depressed, major depression 0.46 (-0.01-0.93) - 0.49 (0.41-0.58) 0.19 (0.10-0.28)

non-depressed, minor or major depression 0.49 (0.33-0.64) 0.25 (0.05-0.46) 0.43 (0.34-0.51) 0.18 (0.10-0.27)

non-depressed, minor depression, major depression 0.48 (0.33-0.62) 0.24 (0.04-0.43) 0.43 (0.35-0.51) 0.17 (0.09-0.25)

Aged: over 50’s (aged) cohort; TE: Twin 89 (TE) cohort; MZ: monozygotic; DZ: dizygotic


The best-fitting model for all depression classifications in the over 50’s cohort

was the AE model (Table 5.4). Similarly, the best-fitting model for major depression,

two-category depression, and three-category depression was the AE model, in the

Twin 89 cohort.

Within the over 50’s cohort unique additive genetic factors were estimated to

explain approximately 37% of the heritability of minor depression. Similarly, unique

additive genetic factors were estimated to explain approximately 46% and 45% of

the heritability of major depression, within the over 50’s and Twin 89 cohorts,

respectively (Table 5.4). Significantly, the heritability of the two-category model

(combining minor depression and major depression) was estimated at 48% (95% CI:

33–62%), which was almost indistinguishable to the three-category model estimate

of 47% (95% CI: 33–60%), in the over 50’s cohort. The observed indistinguishability

of the estimates for unique additive genetic factors between two-category depression

(A: 40%, 95% CI: 23-48%) and three-category depression (A: 40%, 95% CI: 32-

47%) was replicated within the Twin 89 cohort. No significant evidence for sex-

specific genetic effects was observed within the over 50’s or Twin 89 cohorts.

Of the 17 loci robustly associated with major depression in Europeans (Hyde et

al., 2016), two were nominally (p < 0.1) associated with major depression in our

Australian dataset, SNP rs10514299 between TMEM161B and MEF2C (allele T:

odds ratio [OR] = 1.10, 95% CI = 1.03-1.17; p = 0.006) and SNP rs11209948 near

NEGR1 (allele T: OR = 1.07, 95% CI = 1.01-1.12; p = 0.05). Broadening our case

phenotype to include an additional 620 unrelated minor depression cases (providing a

total of 4,284 unrelated cases), increased the evidence for association with depression

at both loci, producing more significant p-values of 0.003 and 0.03, respectively.

Comparable results were observed utilising a quantitative, three-category depression

classification with p-values of 0.005 and 0.04, respectively.


Table 5.4. Fit statistics and variance estimates (with their 95% confidence intervals) from univariate structural equation modelling.

Aged TE

Model -2LL p-value

(ACE)

p-value

(ADE) AIC A C (or D) E -2LL

p-value

(ACE)

p-value

(ADE) AIC A C (or D) E

non-depressed, minor depression

ACE 1446.58 - - -3323.42 0.32

(0.00-0.54)

0.05

(0.00-0.43)

0.63

(0.46-0.84) 2203.82 - - -3702.18

0.00

(0.00-0.35)

0.14

(0.00-0.30)

0.87

(0.65-1.00)

AE 1446.61 0.87 1.00 -3325.40 0.37

(0.18-0.54) -

0.63

(0.46-0.82) 2204.58 0.39 1.00 -3703.43

0.15

(0.00-0.37) -

0.85

(0.63-1.00)

CE 1447.58 0.32 - -3324.42 - 0.30

(0.13-0.45)

0.70

(0.46-0.82) 2203.82 1.00 - -3704.18 -

0.14

(0.00-0.30)

0.87

(0.70-1.00)

E 1460.62 8.90 × 10-4 9.00 × 10-4 -3313.38 - - 1.00

(1.00-1.00) 2206.15 0.31 0.46 -3703.85 - -

1.00

(1.00-1.00)

ADE 1446.61 - - -3323.40 0.37

(0.00-0.54) 0.00

(0.00-0.54) 0.63

(0.45-0.82) 2204.58 - - -3701.43

0.15 (0.00-0.37)

0.00 (0.00-0.36)

0.85 (0.63-1.00)

non-depressed, major depression

ACE 475.73 - - -3956.27 0.46

(0.00-0.83) 0.00

(0.00-0.62) 0.54

(0.17-1.00) 5717.52 - - -2996.48

0.45 (0.29-0.53)

0.00 (0.00-0.12)

0.55 (0.47-0.63)

AE 475.73 1.00 0.49 -3958.27 0.46

(0.00-0.83) -

0.54

(0.17-1.00) 5717.517 1.00 0.28 -2998.48

0.45

(0.37-0.53) -

0.55

(0.47-0.63)

CE 476.73 0.32 - -3957.27 - 0.29

(0.00-0.66)

0.71

(0.34-1.00) 5735.63 2.1 × 10-5 - -2980.37 -

0.31

(0.24-0.38)

0.69

(0.62-0.76)

E 478.03 0.32 0.25 -3957.97 - - 1.00

(1.00-1.00) 5813.19 1.7 × 10-21 9.4 × 10-22 -2904.81 - -

1.00 (1.00-1.00)

ADE 475.27 - - -3956.74 0.00

(0.00-0.81)

0.51

(0.00-0.85)

0.49

(0.15-1.00) 5716.37 - - -2997.64

0.25

(0.00-0.52)

0.23

(0.00-0.55)

0.52

(0.44-0.62) Two-category depression (non-depressed, minor depression or major depression)

ACE 1655.41 - - -3214.59 0.44

(0.00-0.61)

0.04

(0.00-0.45)

0.52

(0.38-0.69) 6353.91 - - -3088.09

0.40

(0.22-0.48)

0.00

(0.00-0.14)

0.60

(0.52-0.68)

AE 1655.44 0.88 1.00 -3216.56 0.48

(0.33-0.62) -

0.52

(0.38-0.67) 6353.91 1.00 0.56 -3090.09

0.40

(0.32-0.48) -

0.50

(0.52-0.68)

CE 1658.31 0.09 - -3213.69 - 0.51

(0.39-0.49) 0.61

(0.49-0.74) 6368.07 2.0 × 10-4 - -3075.93 -

0.28 (0.22-0.34)

0.72 (0.66-0.78)

E 1690.12 2.90 × 10-8 2.90 × 10-8 -3183.88 - - 1.00

(1.00-1.00) 6445.22 1.5 × 10-20 1.3 × 10-20 -3000.78 - -

1.00

(1.00-1.00)

ADE 1655.44 - - -3214.56 0.48

(0.00-0.62)

0.00

(0.00-0.60)

0.52

(0.38-0.67) 6353.57 - - -3088.43

0.31

(0.00-0.48)

0.11

(0.00-0.47)

0.58

(0.50-0.67)

Table 5.4 footnote on page 101.


Table 5.4. Continued Fit statistics and variance estimates (with their 95% confidence intervals) from univariate structural equation modelling.

Aged TE

Model -2LL p-value

(ACE)

p-value

(ADE) AIC A C (or D) E -2LL

p-value

(ACE)

p-value

(ADE) AIC A C (or D) E

Three-category depression (non-depressed, minor depression, major depression)

ACE 1912.56 - - -2955.44 0.47

(0.00-0.60)

0.01

(0.00-0.41)

0.53

(0.40-0.69) 8281.43 - - -1158.57

0.40

(0.24-0.47)

0.00

(0.00-0.00)

0.60

(0.53-0.68)

AE 1912.56 0.99 1.00 -2957.44 0.47

(0.32-0.60) -

0.53

(0.40-0.67) 8281.43 1.00 0.42 -1160.57

0.40

(0.32-0.47) -

0.60

(0.53-0.68)

CE 1916.14 0.06 - -2953.86 - 0.37

(0.25-0.49)

0.53

(0.51-0.75) 8298.23 4.2 × 10-5 - -1143.78 -

0.28

(0.22-0.33)

0.72

(0.67-0.78)

E 1948.85 1.30 × 10-8 1.30 × 10-8 -2923.15 - - 1.00

(1.00-1.00) 8381.43 1.9 × 10-22 1.4 × 10-22 -1062.57 - -

1.00

(1-00-1.00)

ADE 1912.56 - - -2955.44 0.47

(0.00-0.60) 0.01

(0.00-0.59) 0.53

(0.40-0.67) 8280.79 - - -1159.21

0.27 (0.00-0.46)

0.14 (0.00-0.14)

0.59 (0.51-0.67)

Aged: over 50’s (aged) cohort; TE: Twin 89 (TE) cohort; -2LL: minus two log likelihood; A: additive genetic component; C: common environmental component E: unique environmental component


5.5 DISCUSSION

Findings from the present study of two independent twin cohorts, indicate the

heritability of minor depression has a genetic contribution. Although, the heritability

of minor depression appears larger in the over 50’s cohort compared to the Twin 89

cohort. In contrast, the heritability estimates for major depression were comparable at

approximately 46% in individuals over 50 and 45% in 23-38 year olds. Within each

cohort the contribution of additive genetic factors was comparable between the two-

category and three-category depression classification. However, the heritability

estimates were larger at 47-48% in the over 50’s cohort compared to 40% in the

Twin 89 cohort. The differences in heritability estimates between the cohorts are

potentially attributable to the time periods assessed within each study cohort (i.e.,

current depression in the over 50’s study compared to lifetime depression in the Twin

89 cohort). Additionally, the difference in depression classifications between the

study cohorts explains the higher major depression prevalence within the Twin 89

cohort. Kendler and colleagues have reported a comparable elevation in lifetime

major depression prevalence within an independent cohort (Kendler et al., 1992).

The authors postulated the higher prevalence of major depression was likely

attributable to a lower average cohort age than national population cohorts and use of

self-report rather than highly structured psychiatric interview—which may

underestimate population rates of major depression. In 2016, Zeng and colleagues

showed self-declared depression is a valid alternative to MDD in genetic studies,

reporting common genetic effects were highly correlated with significant genetic

contributions associated with both classifications (Zeng et al., 2016).

The near identical heritability estimates of the two-category and three-category

depression classifications and the results of the liability threshold model indicate

minor depression and major depression lie on a single genetic liability continuum,

with major depression more severe but not etiologically distinct from minor

depression. Although, we note that the evidence for a genetic contribution to the

heritability of minor depression in younger adults is weak; this is likely due to a

relative lack of power (i.e., low number of minor depression cases) within the Twin

89 cohort. Indeed, the heritability estimate for the broad, two-category depression

classification indicates the applicability of a broad depression phenotype is not

specific to the over 50’s cohort. Therefore, broadening the depression phenotype in


genetic studies by including individuals with a diagnosis of ‘minor depression or

major depression’ should facilitate the identification of genetic risk factors associated

with depression due to improved power via increased sample size. Such improved

power will be readily provided through re-analysis of existing GWA datasets (which

currently exclude minor depression-like cases from analysis), and more cost-

effective collection of depression cases in future studies. As previously outlined by

Sullivan (Sullivan, 2012) GWA studies will continue to be of great importance for

identification of the underlying biology and genetic architecture of psychiatric

disorders. Indeed, the MDD working group of the PGC have previously emphasised

the absence of reference to the underlying biology or pathophysiology within the

MDD diagnosis (Major Depressive Disorder Working Group of the Psychiatric

GWAS Consortium et al., 2013).

Previous MDD GWA analyses have discussed possible approaches to increase

power and enable identification of genetic risk loci associated with MDD (Wray et

al., 2012). The first approach involves utilising more homogenous MDD case

samples. In 2015, the CONVERGE consortium utilised this method, by selecting

5,303 Han Chinese women with recurrent MDD (of which 85% have the severe

melancholic subtype) and 5,337 Han Chinese female controls screened to exclude

MDD, to identify the first SNP loci robustly associated with severe recurring MDD

(CONVERGE Consortium, 2015). Furthermore, in 2017, Power and colleagues

utilised additional phenotypic data to stratify cases and thereby reduce heterogeneity,

which enabled the identification of a genetic risk locus associated with MDD onset in

adults aged over 27 years (Power et al., 2017). Stratification of MDD cases based on

symptom dimensions represents an alternative method of utilising phenotypic data to

reduce heterogeneity within GWA studies; with Pearson and colleagues (Pearson et

al., 2016) showing common SNPs explain varying proportions of the variation in the

depression symptom dimensions of core depression symptoms, insomnia, appetite,

and anxiety symptoms (SNP-based heritability = 14.3%, 30.3%, 29.6%, and 4.7%,

respectively). Meanwhile, a complementary approach is to obtain larger sample sizes

which are more representative of the general population. This approach can be

achieved by broadly defining depression, to detect the common variation of small

effect given the relatively high prevalence and low heritability of MDD (Wray et al.,

2012).


To demonstrate the utility of using a broader depression phenotype, we

examined the association signal of the 17 SNPs reported by Hyde and colleagues

(Hyde et al., 2016), in Table 2, reaching genome-wide significant association with

major depression, utilising our Australian GWA dataset. In our Australian sample of

3,664 unrelated major depression cases and 7,113 unrelated controls, SNP

rs10514299 between TMEM161B and MEF2C and SNP rs11209948 near NEGR1,

were nominally associated with major depression (p ≤ 0.05).

TMEM161B encodes the transmembrane protein 161B and is expressed in the

brain (cortex, hypothalamus, anterior cingulate cortex (BA24), and cerebellum).

Similarly, MEF2C encodes myocyte enhancer factor 2C and has been associated

with phenotypes involved in the central nervous system (mental retardation,

stereotypic movements, epilepsy and cerebral malformations). Meanwhile, NERG1

encodes neuronal growth regulator 1, is expressed in the brain (cerebellar

hemisphere, cerebellum, and pituitary), and associated with body mass index,

subcutaneous adipose tissue, weight, and age-at -onset of menarche. Our current

knowledge of the functional role of TMEM161B, MEF2C, and NEGR1 substantiates

the central nervous systems involvement in the pathophysiology of depression.

Broadening our case phenotype to include an additional 620 unrelated minor

depression cases (providing a total of 4,284 unrelated cases), increased the statistical

evidence for association with depression at both loci. Although a subset (1,450 cases

and 1,711 controls) of the 3,664 Australian major depression cases and 7,113

controls were part of the PGC MDD GWA (Major Depressive Disorder Working

Group of the Psychiatric GWAS Consortium et al., 2013) that was meta-analysed in

Hyde et al (Hyde et al., 2016), these results provide proof-of-principle for using a

broader depression phenotype to increase power in genetic association studies of

depression. In addition, the study by Hyde and colleagues, provides evidence that

utilising large self-report depression data, which broadens the MDD phenotype due

to the lack of restriction to clinically validated MDD cases, is an effective strategy

for overcoming the large heterogeneity of depression (Hyde et al., 2016). Further

evidence for the utility of broad depression phenotypes in genetic studies is provided

by the investigation of ‘depression symptoms’ conducted by Okbay and colleagues

(2016) and ‘MDD or depression symptoms’ by Direk and colleagues (2016).


Continued use of broad depression phenotypes and large cohorts without

detailed clinical evaluation, such as from large ongoing commercial (e.g., 23andMe

and Kaiser Permanente) and public (e.g., UK Biobank and Generation Scotland)

datasets should therefore identify additional genetic risk factors, and provide the

crucial clues to further elucidating the complex molecular pathways underlying

MDD—which can then be characterised with respect to particular features of

depression via the study of specific patient subgroups in deeply-phenotyped clinical

cohorts.

106

In Chapter 5, minor depression and major depression, were shown to lie on a single

genetic continuum. Additionally, minor depression, major depression, and a broad

depression phenotype (minor or major depression) were all shown to have

significant, additive genetic contributions. Similarly, fatigue was shown to have a

significant additive genetic contribution in Chapter 4. Building on the results from

Chapter 4 and Chapter 5, the following chapter aims to determine if shared genetic

factors explain a proportion of the variation in depression and fatigue and

characterise the type of relationship that exists between the traits, in older adults.

Chapter 6: Shared Genetic Factors in the Co-Occurrence of Depression and Fatigue 107

Chapter 6: Shared Genetic Factors in the

Co-Occurrence of Depression

and Fatigue

This chapter comprises the following published article:

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2016). Shared Genetic Factors in the

Co-Occurrence of Depression and Fatigue. Twin Research and Human Genetics, 1-9.

doi:10.1017/thg.2016.79

108 Chapter 6: Shared Genetic Factors in the Co-Occurrence of Depression and Fatigue




6.1 ABSTRACT

Depression and fatigue have previously been suggested to share an underlying

genetic contribution. The present study aims to investigate and characterise the

familiality and genetic relationship between depression and fatigue. The familiality

of depression and fatigue was assessed by calculating relative risks, measured by the

prevalence ratio, within 643 monozygotic (MZ) and 577 dizygotic (DZ) twin pairs.

Bivariate twin modelling was utilised to assess the magnitude of shared heritability

between depression and fatigue. Finally, the relationship between depression and

fatigue was investigated using the co-twin control method, to determine whether the

association is explained by causal or non-causal models. We observed an increased

risk of fatigue in co-twins of probands with depression and increased risk of

depression in co-twins of probands with fatigue. Higher risks were observed in MZ

compared to DZ twin pairs and bivariate heritability analyses indicated significant

genetic components for depression and fatigue, with heritability estimates of 48%

and 41%, respectively. Importantly, a significant additive genetic correlation of 0.71

(95% confidence interval [CI] = 0.51-0.92) and bivariate heritability of 21% (95% CI

= 10-35%) was observed between depression and fatigue. Furthermore, results from

the co-twin control method indicate a non-causal genetic relationship likely explains

the association between depression and fatigue. Notably, the contribution of shared

genetic factors remained significant independent of the overlapping symptoms,

indicating the relationship between co-occurring depression and fatigue is primarily

due to shared genetic factors rather than overlapping symptomatology.


6.2 INTRODUCTION

Depression and fatigue are highly prevalent traits and are associated with a

considerable reduction in quality of life. The heritability of depression and fatigue

has been estimated to range from 17-78% and 18-51%, respectively (Schur et al.,

2007; Sullivan et al., 2000; Sullivan et al., 2005). The wide ranges are likely

attributable to differences in ethnicity and gender distribution within the study

populations. Shared genetic aetiologies have been implicated in studies investigating

the heritability of psychological distress, anxiety, depression, and fatigue, and

insomnia, fatigue, and depression, respectively (Hickie et al., 1999b; Hur et al.,

2012). However, the underlying mechanisms associated with depression and fatigue

which could explain the high levels of comorbidity are poorly understood.

Two studies have tested for a shared genetic influence to depression and

fatigue. In the first study, the heritability of lifetime-ever disabling fatigue (assessed

by parental report using the disabling fatigue measure (Farmer et al., 1999)) and

depression within the past three months (assessed by the mood and feelings

questionnaire in individuals over 11 (Costello & Angold, 1988)) was investigated in

children (aged 8-17) (Fowler et al., 2006). The second study examined the genetic

relationship between abnormal fatigue (assessed by the Chalder Fatigue

Questionnaire (Chalder et al., 1993)) and an indicator of lifetime-ever depression

(assessed by two screening questions of the Composite International Diagnostic

Interview (World Health Organization, 1990) regarding depressed mood and loss of

interest—the two core symptoms of a major depressive episode, as defined by the

Diagnostic and Statistical Manual of Mental Disorders (DSM) (American Psychiatric

Association, 2013))—in a Sri Lankan population (aged ≥ 15) (Ball et al., 2010b).

Although both studies indicated depression and fatigue have a shared genetic

contribution, the genetic relationship between co-occurring depression and fatigue

was not well characterised and their results are not readily comparable due to the fact

that risk for depression differs by age and sex (Bijl et al., 2002; Centers for Disease

Control and Prevention, 2010; Kessler et al., 2003).

Differences in familial and genetic risk for depression have been identified

with age. Older adults exhibit the lowest prevalence of a current depression diagnosis

and a comparable risk of onset between males and females (Bebbington et al., 1998;

Faravelli et al., 2013). Additionally, symptomatology differences have been observed


between depression patients in different age ranges (Hybels et al., 2012; Wilkowska-

Chmielewska et al., 2013). However, little is known about the genetic relationship

between co-occurring depression and fatigue in older adults. Therefore, the present

study utilises relative risks and twin modelling to investigate the familiality and

heritability of depression and fatigue within older adults. Furthermore, the co-twin

control method was utilised to investigate whether the association between

depression and fatigue is explained by a causal model or shared underlying aetiology.

6.3 MATERIALS AND METHODS

6.3.1 Study Cohort

The present study was conducted using data from the over 50’s (aged) study

conducted by the genetic epidemiology group within QIMR Berghofer. The study

invited 2,281 twin pairs from the Australian twin registry to complete a mailed

Health and Lifestyle Questionnaire (Bucholz et al., 1998; Mosing et al., 2012). The

present study utilised responses to the Schedule of Fatigue and Anergia (SOFA), the

twelve-item General Health Questionnaire (GHQ), and the fourteen-item Delusions

Symptoms-States Inventory, States of Anxiety and Depression (DSSI/sAD)

questionnaires (Bedford & Deary, 1997; Goldberg & Blackwell, 1970; Hickie et al.,

1996). The study cohort utilised here, overlaps the cohort used by Hickie et al.

(1999b) to investigated the multivariate heritability of psychological distress,

anxiety, depression, and fatigue. However, the present study focuses on depression

and fatigue, including looking at MDD and MiDD.

6.3.2 Diagnosis of Depression and Fatigue

MDD and MiDD were classified using the nine criteria of a major depressive episode

(depressed mood, anhedonia, a change in weight or appetite, insomnia or

hypersomnia, psychomotor agitation or retardation, fatigue or loss of energy, feelings

of worthlessness or excessive guilt, inability to concentrate or make decisions, and

thoughts about death, suicidal thoughts, suicidal plans, or suicidal attempts), as

defined by the DSM-IV criteria (American Psychiatric Association, 2000). A

combination of questions from the GHQ and DSSI/sAD were used to assess

depression, through assignment of specific questions to the appropriate criterion of

the major depressive episode criteria. When multiple questions assessed a criterion at

least one positive response indicated the individual exhibited a symptom from the


specific criterion. Each criterion was assessed by assigning one to the criterion if a

symptom was exhibited by the individual and zero if none of the symptoms for the

criterion were met. The survey did not contain any assessment of change in weight or

appetite; therefore, this criterion of a major depressive episode was not assessed. The

scores of the remaining eight criteria assessed were summed if the individual

screened positive (score > 0) for depressed mood and/or anhedonia, otherwise the

individual was assigned a total score of zero. Individuals were classified as MDD,

MiDD, or non-depressed, if they had a score of five or more, two to four, or less than

two, respectively.

The SOFA was originally designed to identify chronic fatigue syndrome cases.

Therefore, physical, neurocognitive, and neurovegetative fatigue symptoms are

assessed by the questionnaire. Consequently, the fatigued state identified by the

SOFA is distinct from the fatigue experienced within a major depressive episode.

Ten questions are contained in the SOFA; however, a shorter eight-item version was

included in the survey due to two questions being replicated within the GHQ.

Individuals were classified as fatigued if they reported three or more of the ten

fatigue symptoms (muscle pain at rest, post-exertional muscle pain, post-exertional

muscle fatigue, post-exertional fatigue, hypersomnia, insomnia, poor concentration,

speech problems, poor memory, and headaches).

6.3.3 Familial Clustering

Familiality between depression and fatigue was assessed by calculation of relative

risks (RR), assessed by the prevalence ratio, with their 95% confidence intervals

(CI). Initially, cross-tabulation was utilised to assess the depression and fatigue status

within twin pairs based on zygosity groupings. The method by Nyholt and colleagues

(2004) was utilised to estimate the risk within the complete cohort and same-sex twin

pairs, where the cross-tabulations from using twin 1 or twin 2 as the proband were

averaged. RR were also calculated from the averaged cross-tabulations within same-

sex monozygotic (MZ) and dizygotic (DZ) twin pairs and opposite-sex DZ twin pairs

relative to non-depressed or non-fatigued status. Initially, the risk of fatigue in co-

twins of depressed probands was calculated. Similarly, the risk of depression in co-

twins of fatigued probands was calculated.

To assess the familiality of depression and fatigue independent of their

overlapping symptoms, the risk of fatigue in co-twins was also estimated in the


subgroup of depressed individuals without overlapping DSM depression symptoms

(i.e., insomnia, poor concentration, and hypersomnia). Similarly, the risk of

depression in co-twins was also estimated in the subgroup of fatigued individuals

without overlapping fatigue symptoms (i.e., insomnia, inability to concentrate, and

loss of energy).

6.3.4 Genetic Analysis

The association between depression and fatigue was assessed by looking at twin,

phenotypic, and cross-twin cross-trait correlations within MZ and DZ twin pairs.

Polychoric correlations were calculated (due to the binary coding utilised within the

cohort) using the polycor package in R (R Core Team, 2014). Twin correlations

assessed the association of a single trait across a twin pair, phenotypic correlations

assessed the association of two traits within individuals, and cross-twin cross-trait

correlations assessed the association of two traits across a twin pair. Twin and cross-

twin cross-trait correlations which are larger in MZ compared to DZ twin pairs

indicate the aetiology of the traits has a genetic contribution.

Bivariate twin models were calculated to estimate the relative contribution of

genetic and environmental factors on the covariation of depression and fatigue. The

bivariate twin modelling was conducted utilising the Cholesky decomposition which

allowed the genetic and environmental factors of the first trait to load onto the

second trait (Neale et al., 1992). The model contains another set of genetic and

environmental factors which are unique to the second trait. Such twin modelling

partitions the observed phenotypic variance into specific components. Briefly,

phenotypic differences between MZ and DZ twin pairs (MZ twin pairs have the same

genotype and common environment while DZ twin pairs only share 50% of their

genes but have the same common environment) are used to estimate the contribution

of additive genetic (A), dominant (non-additive) genetic (D), common environmental

(C), and unique environmental (E) variance components (Neale et al., 1992).

Additionally, the genetic (rg) and environmental (re) correlation between depression

and fatigue was calculated as a measure of the overlap in gene and environmental

sets, respectively.

Heritability estimates were calculated utilising structural equation modelling,

including the threshold model. The threshold model posits that distinct traits

represent a single, normally distributed, severity continuum. Initially, a single


threshold was utilised for depression, whereby individuals were separated into a

broad, two-category (non-depressed, MiDD/MDD) depression definition.

Additionally, two thresholds were used to assess the three-category (non-depressed,

MiDD, MDD) depression definition. A single threshold was used within all models

to separate non-fatigued and fatigued individuals. Corrections for (linear) age and

sex effects were included in all models which were fitted using the OpenMx package

in R (Boker et al., 2011; R Core Team, 2014). The significance of the variance

components was assessed by comparing the fit of the full model (ACE/ADE) to the

nested submodels (AE, CE, and E) where individual variance components were

dropped from the model. The goodness of fit parameters used to assess the

differences in the twin models were the likelihood-ratio chi-square test (χ2), the

difference in degrees of freedom (Δ df), and p-value. Additionally, model fit was

compared utilising Akaike’s Information Criteria (AIC); with the lowest AIC

indicating the most parsimonious model (Akaike, 1973, 1974).

Polychoric correlations and bivariate heritability estimates were also estimated

for depression and fatigue in the subgroup of twins without overlapping symptoms

(i.e., insomnia, concentration problems, hypersomnia, and loss of energy).

6.3.5 Relationship Analysis

The co-twin control method was utilised to determine the type of relationship that

exists between depression and fatigue (Kendler et al., 1993; Kendler et al., 1999). A

causal relationship exists when a risk factor directly causes a phenotype, without

familial confounding. Meanwhile, a non-causal model exists when familial (A and C)

factors completely explain the correlation between the risk factor and the trait.

Furthermore, non-causal relationships exist, where the association between the risk

factor and the trait is mediated by familial factors (A or C) (Kendler et al., 1993;

Kendler et al., 1999; McGue et al., 2010).

The co-twin control method conducted throughout the study followed the

protocol outlined by Ligthart et al (2010), where the odds ratio (OR) of the trait is

calculated based on the presence or absence of the risk factor within three cohorts:

MZ and DZ twin pairs with the trait that are discordant for the risk factor and a

general population sample. The over 50’s (aged) study contained 200 MZ twin pairs

and 215 DZ twin pairs with a measure of depression which were discordant for

fatigue. Similarly, 99 MZ twin pairs and 96 DZ twin pairs with a measure of fatigue


were discordant for depression. The general population sample of 1,266 individuals

was obtained by selecting all unpaired twin singles and randomly selecting a single

individual from each complete twin pair (i.e., one individual from each family)

excluding those discordant for fatigue or depression.

If a causal relationship exists between the risk factor and the trait of interest the

three cohorts are expected to have comparably elevated ORs (Figure 6.1). Similarly,

under non-causal models, the general population is expected to show increased odds

of exhibiting the trait given the presence of the risk factor. However, MZ and DZ

cohorts are expected to exhibit varying OR patterns, although the association should

always be smaller than within the general population. If the relationship between the

risk factor and the trait is non-causal no association is expected in the MZ cohort

while the DZ cohort is expected to exhibit a small association (Figure 6.1). Similarly,

if a non-causal relationship exists between the risk factor and the trait which is

mediated by shared environment both the MZ and DZ cohorts are expected to exhibit

a similar association (Figure 6.1). Finally, under a non-causal model mediated by

genetic factors the MZ cohort is expected to exhibit a smaller association than the

DZ cohort (Figure 6.1).

Figure 6.1. Expected outcomes of the co-twin control method under the causal, non-causal, non-

causal shared environment, and non-causal genetic models within the general population (light grey),

discordant DZ twin pairs (grey) who share 50% of their genetics and 100% of their common

environment, and discordant MZ twin pairs (dark grey) who share 100% of their genetics and

common environment. Under a causal model an association is expected within all three groups. Under

a non-causal model, an association is expected within the general population, discordant DZ cohort

will have a small association, and discordant MZ cohort will have no association. Similarly, under the

non-causal shared environmental model, discordant DZ and MZ twin pairs have a small, equal

association. Finally, under the non-causal genetic model, discordant DZ twin pairs have an

association, whereas discordant MZ twin pairs have a smaller association.


6.4 RESULTS

The over 50’s (aged) study was a population-based cohort of 2,281 Australian twin

pairs. A total of 1,061 twin pairs were removed due to incomplete depression and

fatigue classifications for one or both twins. The remaining 1,220 twin pairs

consisted of 643 MZ twin pairs (491 female-female and 152 male-male twin pairs)

with a mean age of 61.5 ± 8.9 (range = 50-92) and 577 DZ twin pairs (263 female-

female, 73 male-male, 136 female-male, and 105 male-female twin pairs) with a

mean age of 61.2 ± 8.2 (range = 50-90). The prevalence of depression (either MDD

or MiDD) and fatigue was 11.0% (11.7% of females, 9.3% of males) and 29.6%

(32.5% of females, 24.7% of males), respectively.

6.4.1 Relative Risks

Initially, all individuals who participated in the over 50’s (aged) study were assessed

for both MDD and MiDD (Supplementary Table 6.1). The present study focused on a

two-category, broad definition of depression defined as either MDD or MiDD.

Cross-tabulation within MZ, same-sex DZ, and opposite-sex DZ twin pairs was

based on depressed or non-depressed and fatigued or non-fatigued classifications

(Table 6.1).

Table 6.1. Cross-tabulationa of two-category depression and fatigue status within twin pairs.

Non-depressed Depressed Total

MZ

Non-fatigued 411 35 446

Fatigued 157.5 39.5 197

Total 568.5 74.5 643

DZss

Non-fatigued 212 22.5 234.5

Fatigued 90.5 11 101.5

Total 302.5 33.5 336

DZos [F-M]


Fatigued 54 8 62

Total 217 24 241

DZos [M-F]


Fatigued 53 9 61

Total 212 29 241

MZ: monozygotic; DZss: same-sex dizygotic; DZos: opposite-sex dizygotic; F-M: female-male; M-F: male-female. aTables were made symmetrical in same-sex twin pairs by averaging over using either twin 1 or twin 2 as proband. For example,

within the complete twin pairs there were 155 twin pairs where twin 1 was fatigued and twin 2 was non-depressed and 160

twin pairs where twin 2 was fatigued and twin 1 was non-depressed. Therefore, the cross-tabulation averaging over twin 1 or twin 2 as proband is (155+160)/2=157.5.

We observed an increased risk of fatigue in co-twins of probands with

depression and increased risk of depression in co-twins of probands with fatigue,

indicating a significant familial association between the traits (Table 6.2). Strong

evidence for a genetic contribution is provided by the higher risk observed in MZ

compared to DZ twin pairs. In particular, the risk of fatigue in co-twins of depressed

probands was 1.91 (95% CI = 1.49-2.46) in MZ twin pairs compared to 1.10 (95% CI


= 0.66-1.84) in same-sex DZ twin pairs. Similarly, the risk of depression in co-twins

of fatigued probands was 2.56 (95% CI = 1.67-3.90) in MZ twin pairs compared to

1.13 (95% CI = 0.57-2.23) in same-sex DZ twin pairs. Analysis of familial clustering

within males and females indicated a similar pattern of risks (Supplementary Table

6.2). A comparable pattern of MZ to DZ RR was also observed for the separate

MiDD and MDD cases, but with MDD producing further increased RR

(Supplementary Table 6.3). Importantly, a similar pattern of risks was observed

when they were estimated independently of depression and fatigue overlapping

symptoms (Supplementary Table 6.7 and Supplementary Table 6.8).


Table 6.2. Relative riska of two-category depression and fatigue within monozygotic (MZ), same-sex dizygotic (DZss), and opposite-sex dizygotic (DZos) twin pairs.

Proband - co-twin MZ

(643 twin pairs)

DZss

(336 twin pairs)

DZos [F-M]

(241 twin pairs)

DZos [M-F]

(241 twin pairs)

Depressed -non-fatigued 0.65 (0.51-0.83) 0.96 (0.75-1.23) 0.92 (0.71-1.19) 0.89 (0.66-1.19)

Depressed - fatigued 1.91 (1.49-2.46) 1.10 (0.66-1.84) 1.24 (0.69-2.24) 1.34 (0.73-2.47)

Fatigued - non-depressed 0.87 (0.80-0.94) 0.99 (0.91-1.07) 0.96 (0.86-1.06) 0.96 (0.86-1.08)

Fatigued - depressed 2.56 (1.67-3.90) 1.13 (0.57-2.23) 1.44 (0.65-3.21) 1.30 (0.62-2.70)

MZ: monozygotic; DZss: same-sex dizygotic; DZos: opposite-sex dizygotic; F-M: female-male; M-F: male-female. aRelative risks were

calculated with respect to non-depressed or non-fatigued status in twin 1.


6.4.2 Polychoric Correlations

The twin correlations for depression and fatigue were approximately two and three

times larger in MZ compared to DZ twin pairs, respectively (Table 6.3). Similarly,

the cross-twin cross-trait correlations for depression and fatigue were over twice the

magnitude in MZ compared to DZ twin pairs. This observed MZ > DZ correlations,

indicate additive genetic factors contribute to the association between depression and

fatigue. A similar pattern of polychoric correlations was observed when the analyses

were repeated independently of the overlapping symptoms (Supplementary Table

6.9). Furthermore, comparable patterns of polychoric correlations were observed for

MiDD, MDD, and the three-category depression classification (non-depressed,

MiDD, MDD) (Supplementary Table 6.4).

Table 6.3. Polychoric correlations with their 95% confidence intervals for two-category depression

and fatigue according to zygosity.

Twin 1 Twin 2

Depression Fatigue Depression Fatigue

Monozygotic twin pairs (N = 643)

Twin 1 Depression 1.00

Fatigue 0.49 (0.35-0.62)a 1.00

Twin 2 Depression 0.49 (0.33-0.64)b 0.35 (0.21-0.50)c 1.00

Fatigue 0.33 (0.18-0.48)c 0.43 (0.31-0.54)b 0.49 (0.36-0.62)a 1.00

Dizygotic twin pairs (N = 577)

Twin 1 Depression 1.00 Fatigue 0.51 (0.37-0.65)a 1.00

Twin 2 Depression 0.25 (0.05-0.46)b 0.09 (-0.09-0.27)c 1.00

Fatigue 0.05 (-0.13-0.23)c 0.14 (0.001-0.28)b 0.58 (0.45-0.71)a 1.00 aPhenotypic correlation between depression and fatigue. bTwin correlation. cCross-twin cross-trait correlation.

6.4.3 Bivariate Heritability Estimates

Bivariate model fitting for depression and fatigue indicated that the AE model is the

most parsimonious (Table 6.4). No differences in depression or fatigue threshold

distributions were observed within twin pairs, and across zygosity and sex groups.

Overall, 48% (95% CI = 32-61%) and 41% (95% CI =0.30 – 0.51%) of the variance

in depression and fatigue, respectively, was explained by genetic factors. Also, 52%

(95% CI = 39-68%) and 59% (95% CI = 0.49 - 0.70%) of the variance in depression

and fatigue was explained by unique environmental factors, respectively (Figure 6.2).

Notably, 21% (95% CI = 10-35%) of the variance in depression due to genetic

factors also contributes to the heritability of fatigue (i.e., bivariate heritability of

21%). Also, 7% of the variance in depression due to unique environmental factors

also contributes to the variance in fatigue. The overlap in genetic and unique

environmental factors was supported by the rg of 0.71 (95% CI = 0.51-0.92) and re


of 0.35 (95% CI = 0.17-0.51) between depression and fatigue. Similar results were

obtained for bivariate heritability estimates between MDD or MiDD and fatigue

(Supplementary Table 6.5, Supplementary Figure 6.1, and Supplementary Figure

6.2). Almost identical bivariate heritability estimates were obtained between three-

category depression and fatigue (Supplementary Table 6.5 and Supplementary Figure

6.3). Importantly, the shared genetic contribution to depression and fatigue remained

significant independent of the overlapping symptoms (Supplementary Figure 6.4).

Table 6.4. Bivariate heritability model fits.

Model Minus two log-likelihood χ2 Δ df p-value AIC

ACE 4405.65 -5324.35 AE 4406.74 1.09 3 0.78 -5329.26

CE 4419.35 13.70 3 3.34 × 10-3 -5316.65

E 4483.87 78.22 6 8.35 × 10-15 -5258.13 ADE 4405.20 -0.46 0 NA -5324.80

Note: Fit statistics are compared to ACE model and the best-fitting model is indicated in bold. χ2: likelihood-ratio chi-squared

test; Δ df: difference in degrees of freedom.

Figure 6.2. Path diagram of the bivariate Cholesky model variance estimates (with their 95%

confidence intervals) for two-category depression and fatigue. The observed traits are shown in the

rectangles. Similarly, the latent variables (additive genetic factors: A, and unique environmental

factors: E) are depicted by circles. The arrows depict the relationship between the variables.

6.4.4 Co-twin Control

Assessment of depression as a risk factor for fatigue revealed the ORs in the general

population, discordant DZ twin pairs, and discordant MZ twin pairs were 7.20 (95%

CI: 4.49-11.56), 6.29 (95% CI: 3.35-11.81), and 1.92 (95% CI: 1.09-3.39),

respectively (Figure 6.3). Similarly, assessment of fatigue as a risk factor for

depression revealed the ORs in the general population, discordant DZ twin pairs, and


discordant MZ twin pairs were 7.20 (95% CI: 4.49-11.56), 5.21 (95% CI: 2.75-9.88),

and 1.98 (95% CI: 1.10-3.55), respectively (Figure 6.3). The pattern of OR exhibited

when fatigue was a risk factor for depression was comparable to when depression

was a risk factor for fatigue. The observed OR pattern indicates that a non-causal

genetic model best describes the relationship between depression and fatigue. A non-

causal genetic relationship was also indicated between fatigue and MiDD

(Supplementary Table 6.6). However, the co-twin control analysis could not be

replicated for fatigue and MDD due to lack of power in the smaller sub-sample.

Figure 6.3. Left: The observed odds ratios (OR) for a current diagnosis of fatigue given a current

diagnosis of depression in the general population (1,266 unrelated twin singles), 99 discordant DZ

twin pairs, and 96 discordant MZ twin pairs. Right: The observed OR for a current diagnosis of

depression given a current diagnosis of fatigue in the general population (1,266 unrelated twin

singles), 200 discordant DZ twin pairs, and 215 discordant MZ twin pairs. In both situations, the

observed OR patterns are consistent with a non-causal genetic model.

6.5 DISCUSSION

Three key findings were identified from the present study. Firstly, depression and

fatigue exhibit a familial component. Secondly, co-occurring depression and fatigue

have considerable genetic overlap. Finally, a non-causal genetic model likely

explains the association between depression and fatigue.

The familial clustering analysis revealed depression and fatigue likely have

shared underlying aetiologies. These results are supported by previous studies which

have reported higher levels of depression in fatigued individuals than the general

population (Cathébras et al., 1992; Walker et al., 1993). However, to our knowledge,

this is the first study to assess and characterise the familial clustering of depression


and fatigue, with regard to the risk of depression in fatigued individuals and the risk

of fatigue in depressed individuals.

Overlap in genetic factors between depression and fatigue has been indicated in

previous twin studies (Ball et al., 2010b; Fowler et al., 2006; Hickie et al., 1999b;

Hur et al., 2012). Previous multivariate twin studies have identified genetic and

environmental factors which are unique to fatigue. Hur et al (2012) established

common and symptom-specific genetic and environmental factors contributed to the

heritability of self-reported insomnia, fatigue, and depression experienced within a

twelve month period. Similarly, Hickie et al (1999b) determined 44% of the

heritability and 100% of the environmental contribution of fatigue is independent of

psychological distress, anxiety, and depression. Although, the proportion of genetic

factors contributing to fatigue which are independent of depression appears to be

smaller in older adults than in children. Bivariate modelling reported by Fowler et al

(2006) established that 87% and 73% of the heritability for lifetime ever short-

duration fatigue (fatigue experienced for at least one week) and lifetime ever

prolonged fatigue (fatigue experienced for at least one month), respectively, was

independent of depression within the last three months in children. The high

proportion of heritability specific to short-duration fatigue and prolonged fatigue was

substantiated by rg of 0.36 and 0.53, respectively. However, Ball et al (2010b)

indicated unique environmental variance components explained a larger proportion

of the overlap in heritability between fatigue and an indicator of lifetime depression

than familial factors, in Sri Lanka.

Bivariate twin modelling results from the current study indicated 50% of the

heritability and 12% of the environmental contribution of fatigue is shared with

depression. The rg of 0.71 between co-occurring depression and fatigue within adults

(aged over 50) was considerably higher than previous bivariate studies, indicating

larger genetic overlap exists between co-occurring depression and fatigue in older

adults than depression within the past three months and lifetime-ever disabling

fatigue in children. A small environmental overlap between co-occurring depression

and fatigue was also observed (re = 0.35). Differences in the contribution of genetic

and environmental factors to the shared heritability of depression and fatigue

between adults in Australia and Sri Lanka are likely attributable to variation in

phenotypic classification, ethnicity, age, and cultural differences. In particular, the


measure of depression, utilised within the Sri Lankan population, only assessed the

two core symptoms of the DSM criteria for a major depressive episode. Therefore,

the genetic correlation of depression and fatigue potentially increases with the

number of depression symptoms.

Skapinakis and colleagues (2004) described four possible explanations for the

association between depression and unexplained fatigue observed within their

international study: the causal (depression causes fatigue), reverse causality

(depression is caused by an unknown, disabling illness, such as chronic fatigue

syndrome), common etiology (common risk factors explain the comorbidity between

depression and fatigue), and overlapping criteria (the observed comorbidity between

depression and fatigue is a result of overlapping symptomatology) hypotheses. The

bivariate twin modelling and co-twin control method used within this study enabled

us to investigate which of the four proposed hypotheses drives the association

between depression and fatigue. Our results substantiate the common aetiology

hypothesis, whereby depression and fatigue share common risk factors. In particular,

our results indicate shared genetic factors explain the majority of the correlation. The

larger overlap in heritability between co-occurring depression and fatigue is also

supported by the results of the co-twin control analysis. That is, the determined non-

causal genetic relationship between depression and fatigue adds further support to the

comorbidity between depression and fatigue being primarily due to shared genetic

factors.

Our findings lead us to suggest that overlapping genetic factors could also

underlie the relationship between depression relapse and residual fatigue. Residual

fatigue has a prevalence of 63-98% and 22-49% in partial responders and remitted

patients, respectively, after antidepressant treatment (Fava et al., 2014). Additionally,

fatigue as a symptom of depression has been associated with higher health care

utilisation, 10-20% greater annual health care cost, increased medication uses, and

lower quality of life (Robinson et al., 2015). Furthermore, residual fatigue has been

shown to lead to higher levels of functional impairment and depression relapse.

Considering that currently available antidepressant therapies have been shown to

inadequately treat residual fatigue, we believe research should focus on

understanding the shared mechanisms of depression and fatigue. Such research holds

great potential to facilitate the development of enhanced treatment outcomes, which


are targeted to shared mechanisms of depression and fatigue. The effective treatment

of depression, focussing on symptomatic treatment of residual fatigue could lower

remission levels, thereby reducing the global burden of depression.

The present study is the first to investigate the type of relationship between co-

occurring depression and fatigue, utilising a two-category and three-category

depression status. Additionally, it is the only study which determined whether a

causal relationship exists between depression and fatigue. A possible limitation of

the study is that depression and fatigue were assessed by self-report not interview

based. However, this allowed depression and fatigue to be assessed independently

without introducing interviewer bias. Furthermore, the study was not confounded by

healthcare seeking behaviour due to the population-based structure of the cohort.

Additionally, the current study focused on an older age group for which some

evidence suggests the risk of depression is similar between the sexes (Bebbington et

al., 1998; Faravelli et al., 2013).

In summary, our results indicate depression and fatigue are familial with shared

genetic factors explaining a substantial proportion of the comorbidity between the

traits in adults. Research focusing on the underlying pathways which are shared by

depression and fatigue will facilitate the elucidation of the mechanisms driving the

association.

125

In Chapter 6, a non-causal genetic relationship was implicated to explain the

association between depression and fatigue. This result was analogous with the

significant additive genetic correlation of 0.71 and bivariate heritability of 21%

identified between depression and fatigue. Importantly, the contribution of shared

genetic factors to the heritability of depression and fatigue was independent of their

overlapping presenting symptoms. Therefore, the relationship between depression

and fatigue is primarily due to shared genetic factors.

As detailed in Chapter 4, the first SNP loci robustly associated with MDD in

Europeans was identified in 2016. However, the underlying mechanisms associated

with fatigue remain unknown. Therefore, the following chapter evaluates the

previously implicated genes and risk loci from candidate gene association analyses

and genome-wide association analyses of CFS.

126 Chapter 7: Systematic Evaluation of Risk Loci from Candidate Gene and Genome-wide Association Studies of Fatigue

Chapter 7: Systematic Evaluation of Risk

Loci from Candidate Gene and

Genome-wide Association

Studies of Fatigue

7.1 ABSTRACT

Chronic fatigue syndrome (CFS) is a complex, neurological disorder of unknown

pathophysiology. However, a genetic contribution has been implicated by a limited

number of candidate gene association (CGA) and genome-wide association (GWA)

studies, the majority of which involved very small sample sizes. Replication studies

are required to confirm these exploratory findings. Therefore, the present study aims

to evaluate the findings from CGA and GWA studies. Additionally, the selected

genes will be evaluated in a more general fatigue phenotype. Thirty-nine genes with

nominal evidence for association were selected from published CGA studies while

524 SNPs and 319 genes were selected from published CFS GWA studies. Finally, 3

SNPs and 51 genes from a GWA and gene-based analysis of tiredness were selected.

Previously implicated SNPs and genes were investigated within GWA and gene-

based analyses, respectively. Initially, the analysis was conducted in a CFS cohort of

47 cases and 55 controls. The analysis was replicated in a fatigue cohort of 307 cases

and 744 controls. Bonferroni-corrected significance thresholds were calculated to

assess the association of SNPs with CFS and fatigue. Limited evidence was

identified supporting previously implicated SNPs or genes association with CFS or

fatigue. However, within the CFS cohort 3 SNPs and 3 genes of interest were

identified. Similarly, 6 genomic locations of interest which are likely associated with

fatigue were identified. Although encouraging, these results indicate GWA studies of

CFS and fatigue require larger sample sizes to identify robustly associated SNPs and

genes.

Chapter 7: Systematic Evaluation of Risk Loci from Candidate Gene and Genome-wide Association Studies of Fatigue 127

7.2 INTRODUCTION

Chronic fatigue syndrome (CFS) is a complex, neurological disorder of unknown

pathophysiology. Diagnosis occurs based on medical exclusion and requires

presentation with persistent or relapsing fatigue experienced over a six-month period

(Fukuda et al., 1994). Additionally, concurrent occurrence of at least four physical

symptoms (sore throat, tender lymph nodes, muscle pain, multi-joint pain, headaches,

unrefreshing sleep, cognitive difficulties, and post-exertional malaise), over a six-

month period which have not predated the fatigue are required. If insufficient

physical symptoms are present individuals can be diagnosed with idiopathic chronic

fatigue (ICF).

Although little is known about the underlying etiology of CFS previous studies

have indicated molecular genetics may contribute to the pathophysiology. The

additive heritability of CFS has been estimated at 51% in females, using classical

twin studies. Furthermore, the underlying genetic contribution of CFS has been

implicated through candidate gene association (CGA) studies and genome-wide

association (GWA) studies. However, to date, the association studies conducted have

comprised small sample sizes. Genes from the immune, nervous, and endocrine

systems have been implicated from CGA studies. Candidate genes which have

previously been associated with CFS from CGA studies include: BMP2K, CHRM1,

CHRM2, CHRM3, CHRM5, CHRNA10, CHRNA2, CHRNA3, CHRNA4, CHRNA5,

CHRNA9, CHRNB1, CHRNB4, CHRND, CHRNE, CHRNG, DISC1, EIF3A, FAM126B,

HTR2A, IFNG, IL-17F, IL6ST, METTL3, NR3C1, PEX16, SORL1, TCF3, TNF, TRPA1,

TRPC2, TRPC4, TRPC6, TRPM3, TRPM4, TRPM8, TRPV2, TRPV3, and UBTF (Carlo-

Stella et al., 2006; Fukuda et al., 2010; Marshall-Gradisnik et al., 2015a; Marshall-

Gradisnik et al., 2016a; Marshall-Gradisnik et al., 2016b; Marshall-Gradisnik et al.,

2015b; Metzger et al., 2008; Rajeevan et al., 2007; Shimosako & Kerr, 2014; Smith

et al., 2008). These genes were selected based on the current understanding and

hypotheses about the pathophysiology of CFS.

The first GWA study of CFS was conducted in 2011 and investigated 116,204

SNPs in 40 cases and 40 non-fatigued controls (Smith et al., 2011). No genome-wide

significant associations were identified, however, the authors suggested GRIK2 and

NPAS2 were candidate genes which warranted further investigation. The second

GWA study of CFS was conducted in 2015 and investigated 11,000 immune and


inflammation SNPs, in 50 CFS cases and 121 non-fatigued controls (Rajeevan et al.,

2015). No genome-wide significant associations were identified, however, the

authors identified 32 SNPs of potential functional importance. Finally, in February

2016, a GWA study of 42 CFS cases and 38 healthy controls investigated 659,094

SNPs (Schlauch et al., 2016). Ninety-two genome-wide significant SNPs were

reported using a Bonferroni adjusted threshold of 7.5 × 10-8 (of which 88 reached the

standard genome-wide significance threshold of 5 × 10-8). However, methodological

concerns within this study brings to question the validity of the reported results. In

particular, 288 of the 442 SNPs (407 autosomal SNPs with p < 3.3 × 10-5 and 35 X-

chromosome SNPs with p < 1.0 × 10-5) reported with suggestive associations did not

meet the minor allele frequency (MAF) > 5% or Hardy-Weinberg equilibrium

(HWE) χ2 p-value > 8 × 10-4 thresholds, reported by the authors, in cases, controls, or

the complete cohort separately (Supplementary Table 7.1). Furthermore, of the 92

reported genome-wide significant SNPs only 18 meet the described MAF and HWE

thresholds (rs254577, p = 2.35 × 10-11; rs2200706, p = 5.48 × 10-10; rs17255510, p =

6.61 × 10-10; rs6892217, p = 6.61 × 10-10; rs16826918, p = 1.13 × 10-9; rs5974598, p

= 1.55 × 10-9; rs689462, p = 2.08 × 10-9; rs6675622, p = 5.94 × 10-9; rs12391243, p =

6.68 × 10-9; rs4022211, p = 9.09 × 10-9; rs16883408, p = 1.06 × 10-8; rs16902672, p

= 1.77 × 10-8; rs4473594, p = 1.81 × 10-8; rs10737169, p = 2.51 × 10-8; rs2748997, p

= 2.76 × 10-8; rs2882361, p = 3.02 × 10-8; rs17133553, p = 4.74 × 10-8; and

rs2816936, p = 4.91 × 10-8).

In 2017, the most powerful genetic analysis of a fatigue phenotype was

conducted in the UK Biobank sample (Deary et al., 2017). Self-reported tiredness

was analysed as a quantitative trait with four levels of classification based on

individuals response to the question “Over the last two weeks, how often have you

felt tired or had little energy?”; 6,948 individuals responded “nearly every day”,

6,404 individuals responded “more than half the days”, 44,208 individuals responded

“several days”, and 51,416 individuals responded “not at all”. The investigation

identified one SNP reaching genome-wide significance (Affymetrix ID

1:64178756_C_T, p = 1.36 × 10-11) and two suggestive peaks on chromosome 1 and

17 (top SNPs within each peak: rs142592148, p = 5.88 × 10-8 and rs2555592, p =

6.86 × 10-8). Within the study a gene-based analysis was also conducted which

identified five genome-wide significant (p < 2.768 × 10-6) genes (DRD2, p = 2.94 ×


10-7; PRRC2C, p = 1.43 × 10-6; C3orf84, p = 1.45 × 10-6; ANO10, p = 1.52 × 10-6;

and ASXL3, p = 2.67 × 10-6) (Deary et al., 2017).

Although only a few studies with limited power have attempted to identify

genetic risk loci associated with CFS, immense effort has gone into the elucidation of

the molecular genetics of depression. Depression and fatigue commonly co-occur,

with 10.7-14.5% of CFS patients diagnosed with major depressive disorder (MDD)

(Cella et al., 2013; Janssens et al., 2015). Furthermore, twin studies indicate shared

genetic factors likely contribute to the high levels of comorbidity observed between

varying levels of fatigue and depression (Burri et al., 2015; Corfield et al., 2016b;

Fowler et al., 2006; Hickie et al., 1999b; Hur et al., 2012; Kato et al., 2009; Narusyte

et al., 2016). In 2016 and 2017, 17 SNPs within 15 independent genomic locations

which are robustly associated with major depression (Hyde et al., 2016), one SNP

was associated with MDD and age of onset ≥ 27 years (Power et al., 2017), two

SNPs were associated with depressive symptoms (Okbay et al., 2016), and one SNP

was associated with a broad depression phenotype (MDD and depressive symptoms)

(Direk et al., 2016), in Europeans. However, to date nothing is known about the

molecular genetics underlying the comorbidity between fatigue and depression.

Considering the limited power and methodological concerns associated with

previous CFS CGA and GWA analyses, replication studies are required to confirm

the exploratory findings. Therefore, the present study aims to evaluate the findings

from CGA and GWA studies of fatigue phenotypes. Additionally, the association

signal of robustly associated depression risk loci will be investigated to determine if

they contribute to fatigue phenotypes. These results will be evaluated in a CFS and

more general fatigue phenotype.

7.3 METHODS

7.3.1 Previously Implicated Genes

A literature search was conducted to identify CGA and GWA studies were the

primary phenotype investigated was fatigue. Only autosomal SNP markers and genes

were investigated within the present study. Thirty-nine genes (and 151 SNPs within

these genes) with nominal evidence (p < 0.05) for an allelic association were selected

from published CFS CGA studies (Table 7.1) (Carlo-Stella et al., 2006; Fukuda et

al., 2010; Marshall-Gradisnik et al., 2015a; Marshall-Gradisnik et al., 2016a;


Marshall-Gradisnik et al., 2016b; Marshall-Gradisnik et al., 2015b; Metzger et al.,

2008; Rajeevan et al., 2007; Shimosako & Kerr, 2014; Smith et al., 2008;

Sommerfeldt et al., 2011). While 524 SNPs (and their 319 assigned genes) were

selected from published CFS GWA studies (Table 7.2) (Rajeevan et al., 2015;

Schlauch et al., 2016; Smith et al., 2011). Additionally, one SNP reaching genome-

wide significance and the two top SNPs (and the two associated genes) from

suggestive peaks in a GWA study of self-reported tiredness, within the UK biobank

sample were investigated (Table 7.3) (Deary et al., 2017). Finally, five genes

reaching genome-wide significance (p < 2.768 × 10-6) and 44 genes suggestively

associated (p < 1 × 10-4) with self-reported tiredness in a gene-based association

analysis, within the UK biobank sample were investigated (Table 7.4) (Deary et al.,

2017).

In addition, a literature search was conducted to identify SNPs (and their

assigned genes) which have been robustly associated with a depression phenotype

from GWA analyses, in Europeans. Twenty-one SNPs (and their eight assigned

genes) were selected for investigation within the present study (Table 7.5).


Table 7.1. Summary of genes from candidate gene association studies for fatigue traits.

Gene SNP Chr SNP position RA OA Frequency of RA

OR (95% CI) Allelic p-value Powerb

Case Control 1000 Genomesa

Study by Carlo-Stella and colleagues (2006) in an Italian cohort of 47 CFS cases and 104 controls.

IFNG rs2430561 12 68552522 T A 0.550 0.630 0.538 1.43 (0.89-2.28) 0.0440 0.9771

Study by Carlo-Stella and colleagues (2006) in an Italian cohort of 80 CFS cases and 224 controls.

TNF rs1799724 6 31542482 T C 0.300 0.190 0.094 1.80 (1.20-2.72) 0.0040 0.9816

Study by Rajeevan and colleagues (2007) in a European cohort of 40 CFS and 55 ICF cases and 42 controls.

NR3C1 rs6188 5 142680344 C A 0.700 0.570 0.682 1.76 (0.83-3.73) 0.0383 0.9999 rs852977 5 142687494 A G 0.700 0.570 0.680 1.76 (0.83-3.73) 0.0365 0.9999

rs860458 5 142696036 G A 0.850 0.730 0.855 2.10 (0.87-5.07) 0.0180 0.9997 rs1866388 5 142759785 A G 0.710 0.570 0.681 1.85 (0.87-3.93) 0.0335 1.0000

rs2918419 5 142722353 T C 0.850 0.730 0.854 2.10 (0.87-5.07) 0.0164 0.9997

Study by Smith and colleagues (2008) in a European cohort of 40 CFS cases and 42 controls.

HTR2A rs6311 13 47471478 A G 0.487 0.274 0.437 2.52 (1.00-6.30) 0.0065 1.0000

rs6313 13 47469940 T C 0.475 0.281 0.436 2.32 (0.93-5.78) 0.0150 1.0000

rs1923884 13 47421836 C T 0.787 0.595 0.874 2.51 (0.95-6.67) 0.0100 1.0000 Study by Metzger and colleagues (2008) in a European cohort of 89 CFS cases and 56 controls.

IL-17F rs763780 6 52101739 T C 0.955 0.839 0.942 4.07 (1.70-9.71) 0.0018 0.9997

Study by Fukuda and colleagues (2010) in a Japanese cohort of 108 CFS cases and 68 controls.

DISC1 rs821616 1 232144598 T A 0.140 0.100 0.287 (0.076)c 1.50 (1.02-2.19) 0.0370 0.9885

Study by Shimosako and Kerr (2014) in a UK cohort of 108 CFS cases and 68 controls.

BMP2K rs1426139 4 79766677 A T 0.056 0.051 0.046 1.10 (0.28-4.29) 0.0091 0.0723 rs3775516 4 79744066 G A 0.951 0.946 0.955 1.11 (0.28-4.35) 0.0025 0.0768

EIF3A rs10787901 10 120819453 A G 0.561 0.511 0.189 1.22 (0.67-2.25) < 0.0001 0.4032

FAM126B rs11895568 2 201847877 G A 0.009 0.000 0.018 NC 0.0110 NC IL6ST rs1373998 5 55255565 T C 0.12 0.081 0.133 1.55 (0.54-4.41) 0.0130 0.9282

METTL3 rs3752411 14 21968876 A G 0.131 0.059 0.147 2.40 (0.76-7.62) 0.0310 1.0000

PEX16 rs3802758 11 45936035 C T 0.319 0.118 0.085 3.50 (1.51-8.11) < 0.0001 1.0000 SORL1 rs3737529 11 121477816 T C 0.052 0.007 0.025 7.78 (0.40-152.39) 0.0280 0.9737

TCF3 rs1860661 19 1650134 G A 0.225 0.045 0.415 6.16 (1.80-21.13) < 0.0001 1.0000

UBTF rs2071167 17 42287519 A G 0.319 0.199 0.260 1.89 (0.92-2.25) 0.0240 1.0000 Study by Marshall-Gradisnik and colleagues (2015b) in an Australian cohort of 115 CFS cases and 90 controls.

TRPA1 rs2383844 8 72961252 G A 0.505 0.398 0.427 1.54 (1.04-2.29) 0.0400 0.9981

rs4738202 8 72940861 A G 0.369 0.253 0.311 1.73 (1.12-2.65) 0.0180 0.9999 TRPC4 rs655207 13 38368012 G T 0.505 0.381 0.39 1.66 (1.11-2.46) 0.0180 0.9999

rs6650469 13 38367949 T C 0.505 0.380 0.607 1.66 (1.12-2.48) 0.0160 0.9998

TRPM3 rs1160742 9 73314011 A G 0.470 0.333 0.442 1.78 (1.19-2.66) 0.0080 1.0000 rs1328153 9 73416062 C T 0.240 0.137 0.207 1.99 (1.18-3.35) 0.0130 1.0000

rs1504401 9 73916953 C T 0.900 0.827 0.905 1.88 (1.06-3.36) 0.0410 0.9645

rs3763619 9 73225802 A C 0.440 0.316 0.422 1.70 (1.13-2.56) 0.0140 1.0000

Table 7.1 footnotes on page 135.


Table 7.1. Continued Summary of genes from candidate gene association studies for fatigue traits.


OR (95% CI) Allelic p-value Powerb Case Control 1000 Genomesa

rs4454352 9 73410410 C T 0.240 0.137 0.207 1.99 (1.18-3.35) 0.0130 1.0000

rs7865858 9 73204431 A G 0.450 0.331 0.445 1.65 (1.10-2.48) 0.0210 0.9998

rs10115622 9 73306551 C A 0.665 0.565 0.661 1.53 (1.02-2.29) 0.0500 0.9903

rs11142508 9 73231662 C T 0.445 0.298 0.423 1.89 (1.25-2.85) 0.0040 1.0000

rs12682832 9 73220691 A G 0.444 0.293 0.424 1.93 (1.27-2.91) 0.0030 1.0000

Study by Marshall-Gradisnik and colleagues (2015a) in an Australian cohort of 115 CFS cases and 90 controls.

CHRM3 rs589962 1 239989964 T C 0.758 0.608 0.694 2.02 (1.32-3.09) 0.0035 1.0000

rs726169 1 239794277 A G 0.717 0.599 0.669 1.70 (1.12-2.56) 0.0235 0.9998 rs1072320 1 239982376 G A 0.324 0.184 0.272 2.12 (1.33-3.39) 0.0037 1.0000

rs4463655 1 239984294 C T 0.692 0.533 0.570 1.97 (1.32-2.96) 0.0028 1.0000

rs6429157 1 239981643 G A 0.522 0.408 0.457 1.59 (1.07-2.35) 0.0375 0.9991 rs6661621 1 239984803 C G 0.302 0.171 0.255 2.10 (1.30-3.39) 0.0054 1.0000

rs6669810 1 240068629 C G 0.579 0.453 0.521 1.66 (1.12-2.45) 0.0236 0.9999

rs7520974 1 240067260 A G 0.580 0.447 0.522 1.71 (1.15-2.53) 0.0167 1.0000 rs7543259 1 239979186 A G 0.319 0.184 0.269 2.07 (1.30-3.31) 0.0051 1.0000

CHRNA10 rs2672211 11 3690278 C T 0.374 0.243 0.339 1.85 (1.20-2.86) 0.0107 1.0000

rs2672214 11 3691512 C T 0.371 0.240 0.339 1.87 (1.21-2.88) 0.0108 0.9999

rs2741862 11 3687985 C T 0.286 0.184 0.257 1.77 (1.10-2.84) 0.0304 0.9999

rs2741868 11 3690183 T A 0.369 0.240 0.661 1.85 (1.20-2.86) 0.0119 1.0000

rs2741870 11 3690109 G C 0.371 0.243 0.661 1.83 (1.19-2.82) 0.0128 1.0000 CHRNA2 rs2565048 8 27330132 T C 0.901 0.807 0.693 2.18 (1.24-3.86) 0.0140 1.0000

CHRNA5 rs951266 15 78878541 T C 0.394 0.263 0.366 1.82 (1.19-2.79) 0.0115 1.0000

rs7180002 15 78873993 T A 0.385 0.276 0.366 1.64 (1.07-2.49) 0.0368 0.9998 Study by Marshall-Gradisnik and colleagues (2016a) in an Australian cohort of 39 CFS cases and 30 controls.

CHRM1 rs2075748 11 62688269 A G 0.244 0.103 0.230 2.79 (1.05-7.43) 0.0369 1.0000

rs11823728 11 62676802 C T 0.947 0.839 0.970 3.45 (1.03-11.55) 0.0394 0.9471 CHRM3 rs4620530 1 240063821 T G 0.474 0.300 0.418 2.11 (1.04-4.28) 0.0381 1.0000

CHRNA2 rs891398 8 27324822 C T 0.553 0.345 0.512 2.35 (1.17-4.70) 0.0168 1.0000

rs2741343 8 27326127 C T 0.553 0.350 0.514 2.29 (1.15-4.59) 0.0186 1.0000 CHRNA3 rs2869546 15 78907345 T C 0.724 0.533 0.650 2.29 (1.13-4.66) 0.0217 1.0000

rs3743074 15 78909480 T C 0.718 0.550 0.657 2.08 (1.03-4.23) 0.0410 1.0000

rs3743075 15 78909452 G A 0.718 0.550 0.659 2.08 (1.03-4.23) 0.0410 1.0000 rs4243084 15 78911672 G C 0.436 0.267 0.625 2.12 (1.03-4.39) 0.0403 1.0000

rs12914385 15 78898723 T C 0.487 0.283 0.405 2.40 (1.17-4.92) 0.0153 1.0000

CHRNA5 rs951266 15 78878541 T C 0.436 0.259 0.366 2.22 (1.07-4.60) 0.0332 1.0000 rs7180002 15 78873993 T A 0.434 0.267 0.366 2.11 (1.02-4.36) 0.0433 1.0000

CHRNB4 rs12441088 15 78928264 T G 0.821 0.621 0.745 2.79 (1.28-6.09) 0.0090 1.0000

CHRNE rs33970119 17 4804902 G A 0.962 0.867 0.938 3.85 (0.97-15.18) 0.0414 0.9997






TRPC2 rs6578398 11 3638061 A G 0.346 0.183 0.285 2.36 (1.06-5.27) 0.0338 1.0000

rs7108612 11 3650086 T G 0.192 0.067 0.118 3.33 (1.04-10.63) 0.0337 1.0000

TRPC4 rs2985167 13 38230542 A G 0.718 0.500 0.610 2.54 (1.26-5.16) 0.0094 1.0000

TRPM3 rs1106948 9 74017174 T C 0.603 0.383 0.555 2.44 (1.22-4.87) 0.0107 1.0000

rs1891301 9 74018496 T C 0.577 0.383 0.536 2.19 (1.10-4.36) 0.0241 1.0000

rs6560200 9 73980222 C T 0.603 0.379 0.534 2.48 (1.24-4.95) 0.0100 1.0000 rs11142822 9 74042243 G T 0.962 0.850 0.917 4.41 (1.14-17.09) 0.0212 1.0000

rs12350232 9 74032148 T G 0.603 0.400 0.552 2.27 (1.14-4.52) 0.0183 1.0000 TRPM8 rs6758653 2 234912799 G A 0.756 0.550 0.643 2.54 (1.23-5.25) 0.0108 1.0000

rs11563204 2 234917377 A G 0.355 0.117 0.206 4.17 (1.67-10.41) 0.0014 1.0000

rs17865678 2 234919314 A G 0.460 0.167 0.279 4.25 (1.89-9.57) 0.0003 1.0000 Study by Marshall-Gradisnik and colleagues (2016b) in an Australian cohort of 11 CFS cases and 11 controls.

CHRM2 rs1424569 7 136569416 A G 0.600 0.333 0.491 3.00 (0.88-10.27) 0.0300 1.0000

CHRM3 rs1134 1 239872172 C T 0.654 0.375 0.576 3.15 (0.92-10.78) 0.0200 1.0000 rs576386 1 239995289 C G 0.519 0.250 0.384 3.24 (0.90-11.62) 0.0400 1.0000

rs619214 1 239958622 T G 0.700 0.389 0.536 3.67 (1.05-12.82) 0.0300 1.0000

rs685550 1 239924408 C T 0.222 0.0417 0.243 6.57 (0.65-66.86) 0.0500 1.0000

rs1019882 1 239898856 A G 0.611 0.375 0.577 2.62 (0.78-8.84) 0.0500 1.0000

rs1155611 1 239897827 C T 0.611 0.375 0.576 2.62 (0.78-8.84) 0.0500 1.0000

rs1155612 1 239897705 A G 0.600 0.333 0.486 3.00 (0.88-10.27) 0.0300 1.0000 rs1416789 1 239901645 A G 0.611 0.375 0.549 2.62 (0.78-8.84) 0.0500 1.0000

rs1544170 1 239908236 G A 0.630 0.375 0.545 2.83 (0.83-9.62) 0.0400 1.0000

rs1867263 1 239807920 G A 0.696 0.458 0.611 2.71 (0.79-9.34) 0.0400 1.0000 rs1867264 1 239845277 T A 0.720 0.364 0.390 4.50 (1.26-16.08) 0.0000 1.0000

rs1867265 1 239840107 G A 0.679 0.417 0.609 2.96 (0.86-10.14) 0.0300 1.0000

rs1899616 1 239818568 G A 0.673 0.333 0.576 4.12 (1.17-14.47) 0.0100 1.0000 rs2083817 1 239833605 T A 0.685 0.417 0.408 3.05 (0.89-10.49) 0.0300 1.0000

rs2163546 1 240057960 G A 0.539 0.273 0.519 3.11 (0.88-10.95) 0.0400 1.0000

rs2165872 1 239826988 C T 0.685 0.417 0.593 3.05 (0.89-10.49) 0.0300 1.0000 rs3738436 1 239872493 C A 0.611 0.375 0.576 2.62 (0.78-8.84) 0.0500 1.0000

rs6429147 1 239794794 G C 0.704 0.458 0.597 2.81 (0.81-9.71) 0.0400 1.0000

rs6684622 1 239877537 G C 0.620 0.318 0.544 3.50 (1.01-12.12) 0.0200 1.0000 rs6688537 1 239850588 C A 0.593 0.333 0.494 2.91 (0.85-9.94) 0.0300 1.0000

rs6694220 1 239883616 A G 0.577 0.333 0.483 2.73 (0.80-9.29) 0.0500 1.0000

rs6700643 1 239798921 T C 0.704 0.458 0.583 2.81 (0.81-9.71) 0.0400 1.0000 rs7511970 1 239883255 G A 0.611 0.375 0.576 2.62 (0.78-8.84) 0.0500 1.0000

rs7513746 1 239862411 A G 0.611 0.375 0.576 2.62 (0.78-8.84) 0.0500 1.0000

rs7551001 1 239844600 A G 0.679 0.417 0.580 2.96 (0.86-10.14) 0.0300 1.0000






rs10754677 1 239833100 A G 0.615 0.375 0.498 2.67 (0.79-9.01) 0.0500 1.0000

rs10802795 1 239870775 T C 0.611 0.375 0.546 2.62 (0.78-8.84) 0.0500 1.0000

rs10802802 1 239909942 G A 0.625 0.333 0.577 3.33 (0.97-11.49) 0.0200 1.0000

rs10925941 1 239812538 G A 0.704 0.458 0.582 2.81 (0.81-9.71) 0.0400 1.0000

rs10925964 1 239902514 T A 0.611 0.375 0.574 2.62 (0.78-8.84) 0.0500 1.0000

rs11585281 1 239909651 C T 0.611 0.333 0.577 3.14 (0.92-10.79) 0.0200 1.0000 rs12029701 1 239910601 T C 0.611 0.333 0.576 3.14 (0.92-10.79) 0.0200 1.0000

rs12093821 1 239824248 G A 0.704 0.417 0.564 3.32 (0.96-11.57) 0.0200 1.0000 rs12743042 1 239888304 T C 0.630 0.364 0.577 2.98 (0.87-10.14) 0.0300 1.0000

rs16838637 1 239828350 A G 0.679 0.417 0.563 2.96 (0.86-10.14) 0.0300 1.0000

CHRM5 rs511422 15 34282982 C T 0.446 0.208 0.398 3.06 (0.81-11.57) 0.0400 1.0000 rs603152 15 34294637 A C 0.464 0.208 0.410 3.29 (0.87-12.42) 0.0300 1.0000

rs646950 15 34291660 T C 0.462 0.208 0.409 3.26 (0.86-12.28) 0.0300 1.0000

CHRNA2 rs2741341 8 27330286 C T 0.518 0.208 0.427 4.08 (1.08-15.38) 0.0100 1.0000 CHRNA4 rs11698563 20 61992285 C A 0.712 0.292 0.33 5.99 (1.63-22.02) 0.0000 1.0000

CHRNA9 rs4861065 4 40344395 C T 0.393 0.125 0.276 4.53 (0.98-20.84) 0.0200 1.0000

rs4861323 4 40355815 A G 0.821 0.542 0.777 3.89 (0.98-15.41) 0.0100 1.0000

rs7669882 4 40350651 A G 0.393 0.125 0.217 4.53 (0.98-20.84) 0.0200 1.0000

rs10009228 4 40356422 G A 0.821 0.500 0.775 4.60 (1.16-18.18) 0.0000 1.0000

rs10015231 4 40337566 C T 0.804 0.583 0.754 2.92 (0.76-11.28) 0.0400 1.0000 CHRNB1 rs2302767 17 7350544 T C 0.722 0.500 0.690 2.6 (0.74-9.10) 0.0500 1.0000

rs3829603 17 7347042 C A 0.741 0.500 0.694 2.86 (0.80-10.15) 0.0500 1.0000

rs4151134 17 7347123 T C 0.679 0.375 0.535 3.52 (1.02-12.20) 0.0100 1.0000 CHRNB4 rs12440298 15 78927589 T G 0.982 0.833 0.984 11.00 (0.39-313.06) 0.0100 0.9305

CHRND rs2767 2 233400074 T C 0.643 0.333 0.646 3.60 (1.04-12.49) 0.0100 1.0000

rs2853457 2 233397968 A G 0.500 0.250 0.423 3.00 (0.84-10.75) 0.0400 1.0000 rs3762529 2 233392449 T C 0.654 0.375 0.641 3.15 (0.92-10.78) 0.0200 1.0000

rs3791729 2 233395297 C T 0.643 0.375 0.665 3.00 (0.88-10.24) 0.0300 1.0000

rs3828246 2 233398215 C T 0.827 0.583 0.749 3.41 (0.85-13.73) 0.0200 1.0000 rs4973537 2 233391965 A G 0.643 0.375 0.642 3.00 (0.88-10.24) 0.0300 1.0000

rs11674608 2 233404294 C G 0.660 0.222 0.646 6.79 (1.78-25.88) 0.0000 1.0000

rs12463989 2 233395029 T C 0.643 0.333 0.645 3.60 (1.04-12.49) 0.0100 1.0000 rs12466358 2 233397525 T G 0.827 0.583 0.748 3.41 (0.85-13.73) 0.0200 1.0000

rs13026409 2 233402507 C T 0.821 0.583 0.748 3.29 (0.83-13.08) 0.0200 1.0000

rs67583510 2 233405650 G A 0.815 0.546 0.747 3.67 (0.94-14.34) 0.0200 1.0000 rs112001880 2 233403760 I D 0.643 0.333 0.646 3.60 (1.04-12.49) 0.0100 1.0000

CHRNE rs33970119 17 4804902 G A 0.964 0.833 0.938 5.40 (0.44-66.84) 0.0400 1.0000

CHRNG rs13018423 2 233408283 C T 0.821 0.583 0.736 3.29 (0.83-13.08) 0.0200 1.0000






TRPC6 rs11224816 11 101396286 T C 0.519 0.208 0.490 4.10 (1.09-15.46) 0.0100 1.0000

TRPM3 rs1317103 9 73195703 C T 0.352 0.083 0.233 5.97 (1.04-34.27) 0.0100 1.0000

rs3812532 9 73483594 C A 0.630 0.375 0.570 2.83 (0.83-9.62) 0.0400 1.0000

rs4620343 9 73736643 T C 0.411 0.167 0.480 3.48 (0.85-14.22) 0.0300 1.0000

rs10780950 9 73193428 T C 0.289 0.083 0.173 4.46 (0.76-26.21) 0.0500 1.0000

TRPM4 rs11083963 19 49665340 A G 0.717 0.458 0.555 3.00 (0.86-10.48) 0.0300 1.0000 TRPV2 rs3514 17 4801594 G C 0.926 0.750 0.162 4.17 (0.65-26.90) 0.0300 1.0000

rs2075763 17 4802685 C T 0.963 0.833 0.942 5.20 (0.44-62.13) 0.0500 0.9999 rs7222754 17 16329745 T C 0.442 0.208 0.385 3.01 (0.80-11.39) 0.0500 1.0000

rs12602006 17 16337288 A G 0.731 0.500 0.650 2.71 (0.77-9.56) 0.0500 1.0000

rs12942540 17 4804073 G C 0.926 0.750 0.852 4.17 (0.65-26.90) 0.0300 1.0000 rs35400274 17 4803711 G A 0.929 0.750 0.849 4.33 (0.66-28.62) 0.0300 1.0000

TRPV3 rs4790519 17 3456735 C T 0.556 0.250 0.440 3.75 (1.04-13.49) 0.0100 1.0000

Chr: Chromosome; RA: risk allele; OA: other allele; OR: odds ratio; CI: confidence interval. a Prevalence shown is for the 1000 Genomes European population. bPower to detect allelic difference at an alpha level of 0.05 in the fatigue cohort. cPrevalence shown in parentheses is for the 1000 Genomes Japanese population.


Table 7.2. Summary of SNPs from genome-wide association studies for chronic fatigue syndrome.

SNP Chr SNP position RA Freq OR (95% CI) p-value Genea

Study by Smith and colleagues (2011) in an American population of 40 cases and 40 controls.

rs2105239 1 14857643 A 0.410 3.40 (1.73-6.66) 3.00 × 10-4 -

rs10489599 1 16585818 C 0.117 4.69 (1.85-11.85) 6.00 × 10-4 FBXO42

rs6694861 1 114063592 G 0.526 3.34 (1.66-6.72) 8.00 × 10-4 MAGI3

rs1157819 1 209604734 A 0.181 3.65 (1.72-7.78) 6.00 × 10-4 MIR205

rs6721414 2 18494495 C 0.132 4.10 (1.77-9.46) 8.00 × 10-4 DA335567

rs6710681 2 64413221 C 0.295 3.41 (1.76-6.59) 2.00 × 10-4 BF513477

rs356653 2 101539790 C 0.628 4.02 (1.80-9.02) 7.00 × 10-4 NPAS2

rs10496982 2 146308114 A 0.825 15.91 (2.04-124.27) 3.00 × 10-4 - rs2715898 2 201556388 A 0.444 3.75 (1.87-7.53) 2.00 × 10-4 DA324672

rs10510985 3 69663871 G 0.321 3.79 (1.95-7.35) 2.00 × 10-4 - rs4894505 3 175920884 G 0.150 3.78 (1.77-8.07) 8.00 × 10-4 BG218013

rs1377828 3 176245042 C 0.163 3.78 (1.79-7.96) 6.00 × 10-4 DA709814

rs10516187 4 7778524 G 0.775 7.26 (2.04-25.79) 6.00 × 10-4 AFAP1

rs2389957 4 120695322 G 0.463 2.96 (1.53-5.73) 5.00 × 10-4 -

rs33013 5 80060016 G 0.563 3.37 (1.65-6.89) 6.00 × 10-4 MSH3

rs10514211 5 80482514 G 0.000 NC 6.00 × 10-4 RASGRF2

rs3797302 5 145889123 G 0.771 9.78 (2.15-44.41) 9.00 × 10-4 TCERG1

rs4714468 6 41452996 G 0.756 6.12 (1.98-18.95) 7.00 × 10-4 DQ141194

rs6915865 6 91082281 C 0.350 3.05 (1.58-5.89) 7.00 × 10-4 - rs10498968 6 91083880 G 0.128 4.25 (1.90-9.51) 5.00 × 10-4 -

rs9320409 6 97530846 T 0.363 2.97 (1.56-5.68) 8.00 × 10-4 KLHL32

rs2047179 6 98878812 T 0.554 3.78 (1.78-8.03) 3.00 × 10-4 - rs2247215 6 101966454 A 0.500 3.21 (1.63-6.31) 5.00 × 10-4 GRIK2

rs2247218 6 101966553 T 0.473 4.23 (2.04-8.78) 1.00 × 10-4 GRIK2

rs4245562 7 54403985 C 0.162 3.63 (1.69-7.77) 9.00 × 10-4 BX111274

rs10499740 7 54530581 C 0.474 3.32 (1.66-6.66) 9.00 × 10-4 -

rs723886 7 68159592 G 0.500 3.21 (1.63-6.31) 1.00 × 10-3 -

rs3801293 7 96324499 C 0.788 9.44 (2.10-42.51) 8.00 × 10-4 SHFM1

rs1499646 8 75068112 C 0.090 4.35 (1.75-10.82) 9.00 × 10-4 -

rs543736 8 104012949 A 0.141 3.71 (1.68-8.19) 7.00 × 10-4 LOC100131813

rs4236780 8 107936951 G 0.075 4.68 (1.78-12.29) 6.00 × 10-4 - rs871024 9 21803880 A 0.311 3.22 (1.64-6.3) 6.00 × 10-4 MTAP

rs4978076 9 26524684 C 0.351 3.30 (1.70-6.41) 6.00 × 10-4 -

rs10511961 9 71497485 C 0.289 3.38 (1.72-6.62) 7.00 × 10-4 PIP5K1B

rs10509412 10 89599354 G 0.237 3.40 (1.70-6.80) 3.00 × 10-4 CFLP1

rs1325904 10 90280938 T 0.118 3.79 (1.64-8.75) 8.00 × 10-4 C10orf59

rs726817 10 95459817 T 0.618 3.50 (1.62-7.54) 6.00 × 10-4 FRA10AC1

rs10509958 10 114054601 A 0.516 3.56 (1.67-7.59) 4.00 × 10-4 TECTB

rs734640 11 17613348 C 0.114 4.99 (2.09-11.94) 4.00 × 10-4 OTOG

rs10500964 11 23596570 T 0.013 17.97 (2.30-140.50) 1.00 × 10-4 - rs10500965 11 23596625 T 0.013 20.39 (2.63-157.97) 1.00 × 10-4 -

rs10501068 11 26769636 G 0.203 3.74 (1.83-7.66) 3.00 × 10-4 CN274762

rs10501376 11 58971766 C 0.859 NC 5.00 × 10-4 DTX4

rs10488767 11 110458835 A 0.229 3.38 (1.64-6.96) 9.00 × 10-4 ARHGAP20

rs1881470 11 127333840 G 0.152 3.39 (1.34-8.61) 5.00 × 10-4 -

rs10505778 12 14125564 A 0.410 3.79 (1.95-7.38) 2.00 × 10-4 GRIN2B

rs10506025 12 27726370 G 0.244 3.64 (1.83-7.22) 1.00 × 10-4 PPFIBP1

rs4931109 12 29005992 T 0.050 5.92 (1.91-18.31) 9.00 × 10-4 -

rs167337 12 52182052 T 0.763 5.90 (1.89-18.36) 9.00 × 10-4 SCN8A

rs1144418 12 65293514 C 0.632 3.97 (1.76-8.93) 4.00 × 10-4 FLJ41278

rs7994531 13 42977439 C 0.600 7.11 (2.71-18.64) 1.00 × 10-4 BG220650

rs10507556 13 47970075 A 0.013 14.44 (1.83-114.05) 1.00 × 10-3 - rs1931035 13 79274470 G 0.724 7.25 (2.36-22.33) 1.00 × 10-4 -

rs1359536 13 79275793 T 0.782 10.87 (2.42-48.86) 5.00 × 10-4 -

rs547571 13 97231270 G 0.775 7.45 (2.10-26.46) 2.00 × 10-4 HS6ST3

rs1555589 13 100480664 A 0.613 4.27 (1.79-10.17) 4.00 × 10-4 CLYBL

rs7325773 13 104182490 C 0.663 4.46 (1.88-10.60) 6.00 × 10-4 CA425896

rs3759688 14 60975579 T 0.00 NC 8.00 × 10-4 SIX6

rs2372200 14 83029545 A 0.649 4.88 (1.95-12.17) 4.00 × 10-4 -

rs6503623 17 39503659 G 0.013 18.33 (2.36-142.62) 4.00 × 10-4 KRTHA3A

rs400322 19 55172578 G 0.689 6.04 (2.15-16.99) 1.00 × 10-4 LILRB4

rs2059152 19 56318188 G 0.769 5.70 (1.83-17.74) 1.00 × 10-3 NLRP11

rs10500321 19 56319571 T 0.769 5.70 (1.83-17.74) 8.00 × 10-4 NLRP11

rs382958 19 56439438 T 0.486 3.67 (1.80-7.51) 2.00 × 10-4 NLRP13

rs1399592 21 39053862 T 0.351 3.21 (1.64-6.29) 4.00 × 10-4 KCNJ6

Study by Rajeevan and colleagues (2015) in an American cohort of 50 cases and 121 controls.

rs829370 1 21933193 C 0.880 4.00 (0.77-20.72) 1.80 × 10-3 RAP1GAP

rs2235937 1 29631909 A 0.160 1.73 (0.77-3.87) NS PTPRU

rs10800118 1 165599774 C 0.400 1.55 (0.80-3.01) NS MGST3

rs17591814 1 186846598 C 0.300 1.91 (0.97-3.77) 1.00 × 10-2 PLA2G4A

Footnotes for Table 7.2 on page 143.


Table 7.2. Continued Summary of SNPs from genome-wide association studies for chronic fatigue

syndrome.


rs1061147b 1 196654324 C 0.280 2.00 (1.01-3.98) 6.50 × 10-3 CFH

rs7529589 1 196658279 C 0.280 2.07 (1.04-4.10) 4.30 × 10-3 CFH

rs3020729 2 87012293 T 0.040 4.47 (1.36-14.64) 2.70 × 10-3 CD8A

rs13010656 2 203297068 T 0.440 1.93 (0.99-3.78) 5.70 × 10-3 BMPR2

rs1048829b 2 203430456 T 0.440 1.93 (0.99-3.78) 5.70 × 10-3 BMPR2

rs7616342 3 19433647 G 0.470 2.09 (1.05-4.12) 2.20 × 10-3 KCNH8

rs2228428 3 32995928 T 0.570 2.01 (0.98-4.12) 4.50 × 10-3 CCR4

rs3774268 3 186954324 A 0.771 2.48 (0.92-6.68) 3.90 × 10-3 MASP1

rs1801058 4 3039150 C 0.276 1.93 (0.97-3.84) 1.07 × 10-2 GRK4

rs8336b 4 95211610 A 0.560 1.40 (0.71-2.76) NS SMARCAD1

rs2016483 4 95229039 A 0.490 1.73 (0.88-3.39) 2.22 × 10-2 SMARCAD1

rs9200 5 41142606 G 0.340 2.08 (1.06-4.06) 2.90 × 10-3 C6

rs2014012 5 58612388 A 0.160 2.89 (1.36-6.15) 2.22 × 10-2 PDE4D

rs353254b 5 148748736 A 0.470 1.48 (0.76-2.87) NS IL17B

PCYOX1L

rs372402 5 148752020 T 0.470 1.60 (0.82-3.12) 4.82 × 10-2 IL17B

PCYOX1L

rs4151667 6 31914024 A 0.900 3.72 (0.64-21.52) 5.90 × 10-3 CFB

rs17500510 6 32712818 A 0.850 2.21 (0.68-7.12) 3.12 × 10-2 HLA-DQA2

rs2071800b 6 32714143 T 0.908 1.78 (0.45-7.00) NS HLA-DQA2

rs2582 6 32974551 A 0.830 1.62 (0.60-4.41) NS HLA-DOA

rs733590 6 36645203 C 0.500 1.75 (0.89-3.44) 1.94 × 10-2 CDKN1A

rs2395655b 6 36645696 G 0.450 1.86 (0.95-3.63) 9.40 × 10-3 CDKN1A

rs4242391 8 23000183 C 0.340 1.78 (0.91-3.49) 1.82 × 10-2 TNFRSF10D

rs11575584 9 34661994 A 0.900 1.80 (0.48-6.80) NS CCL27

rs11257804 10 12496055 A 0.560 2.43 (1.16-5.09) 3.00 × 10-4 CAMK1D

rs549908b 11 112020916 G 0.680 1.57 (0.73-3.35) NS IL18

TEX12

rs11214105 11 112037653 A 0.680 1.91 (0.86-4.21) 1.56 × 10-2 IL18

TEX12

rs3802814 11 126162607 A 0.810 2.04 (0.74-5.66) 2.94 × 10-2 TIRAP

rs8177374b 11 126162843 T 0.810 1.60 (0.62-4.11) NS TIRAP

rs4251545 12 44180295 G 0.040 3.03 (0.85-10.72) 3.60 × 10-2 IRAK4

rs9550987 13 24167505 A 0.670 2.27 (1.00-5.15) 2.10 × 10-3 TNFRSF19

rs3751488 14 23304094 G 0.146 2.48 (1.13-5.46) 4.20 × 10-3 MRPL52

rs10498445 14 52740441 C 0.160 2.31 (1.07-5.02) 5.40 × 10-3 PTGDR

rs6115b 14 95053890 G 0.500 2.14 (1.07-4.29) 1.60 × 10-3 SERPINA5

rs6112 14 95054176 T 0.540 2.10 (1.03-4.26) 2.40 × 10-3 SERPINA5

rs6108 14 95058631 A 0.510 1.94 (0.98-3.87) 5.70 × 10-3 SERPINA5

rs9113b 14 95059076 T 0.510 1.91 (0.96-3.79) 7.10 × 10-3 SERPINA5

rs12439525 15 75087405 C 0.020 3.92 (0.75-20.5) NS LMAN1L

rs3803568b 15 75108636 C 0.010 6.54 (0.78-54.95) 3.84 × 10-2 LMAN1L

rs1051007 17 4636813 C 0.800 3.54 (1.06-11.77) 2.00 × 10-4 MED11 CXCL16

rs11658971b 17 4637698 A 0.810 2.89 (0.92-9.10) 1.90 × 10-3 MED11

CXCL16

rs2277680 17 4638563 A 0.390 1.59 (0.82-3.09) NS CSCL16

rs1050998b 17 4638737 T 0.390 1.56 (0.81-3.04) NS CSCL16

rs280502 19 10491475 T 0.830 2.15 (0.72-6.41) 2.59 × 10-2 TYK2

rs2278831 19 52131119 G 0.890 3.22 (0.67-15.54) 9.10 × 10-3 SIGLEC5

rs4819388 21 45647421 C 0.200 1.83 (0.87-3.86) 3.28 × 10-2 ICOSLG

rs228941b 22 37523721 C 0.190 1.81 (0.85-3.86) 4.07 × 10-2 IL2RB

rs228945 22 37525880 A 0.170 2.19 (1.02-4.71) 8.00 × 10-3 IL2RB

rs4253760 22 46622384 G 0.720 2.27 (0.94-5.48) 3.70 × 10-3 PPARA

Study by Schlauch and colleagues (2016) in a cohort of 42 cases and 38 controls.

rs2981884 1 3002072 C 0.803 NC 5.62 × 10-6 PRDM16

rs349391 1 4422005 C 0.303 1.73 (0.90-3.32) 7.67 × 10-6 -

rs349390 1 4423896 C 0.303 1.73 (0.90-3.32) 7.67 × 10-6 - rs12034948 1 4910371 G 0.053 8.53 (2.82-25.77) 2.00 × 10-5 -

rs17426290 1 5683697 T 0.250 2.60 (1.33-5.10) 1.54 × 10-7 -

rs686190 1 12223839 G 0.053 11.65 (3.88-34.92) 1.11 × 10-9 TNFRSF1B

rs7529216 1 18737780 G 0.039 9.17 (2.63-32.03) 1.52 × 10-6 -

rs3920498 1 22492887 C 0.711 8.15 (2.66-24.97) 2.68 × 10-5 LOC105376850

rs16826918 1 22644857 G 0.079 10.11 (3.96-25.82) 1.13 × 10-9 - rs2025499 1 35090749 G 0.079 5.83 (2.26-15.07) 2.82 × 10-5 LOC105378641

rs3913434 1 37449595 T 0.013 46.15 (6.11-348.47) 1.26 × 10-11 GRIK3

rs547977 1 44105650 G 0.000 NC 1.32 × 10-5 - rs12408925 1 48473192 G 0.276 5.24 (2.66-10.31) 2.61 × 10-7 -

rs12407818 1 52904410 C 0.263 3.23 (1.66-6.29) 8.75 × 10-7 ZCCHC11




syndrome.


rs17130776 1 69095403 T 0.197 4.69 (2.31-9.54) 7.17 × 10-7 -

rs11163916 1 84691327 G 0.000 NC 5.44 × 10-6 PRKACB

rs233122 1 85790058 C 0.000 NC 3.27 × 10-7 DDAH1

rs6675622 1 94447733 T 0.395 9.20 (4.28-19.77) 5.94 × 10-9 -

rs3861911 1 106465037 C 0.132 4.27 (1.93-9.47) 2.31 × 10-5 LOC401957

rs7537461 1 113383662 C 0.289 3.79 (1.96-7.35) 8.53 × 10-8 LINC01356

rs7540424 1 116721566 A 0.553 2.43 (1.24-4.74) 2.37 × 10-5 -

rs7552454 1 144938879 A 0.197 3.88 (1.91-7.88) 2.11 × 10-7 PDE4DIP

rs2644567 1 146951082 G 0.724 10.31 (2.93-36.24) 1.41 × 10-5 LINC00624

rs11205084 1 152684306 G 0.303 2.79 (1.45-5.35) 7.25 × 10-6 -

rs12086522 1 153139169 T 0.092 6.07 (2.48-14.82) 2.61 × 10-7 LOC105371447

rs6679280 1 154017616 T 0.066 9.66 (3.53-26.41) 1.72 × 10-9 NUP210L

rs10737169 1 154653704 A 0.224 5.36 (2.68-10.75) 2.51 × 10-8 -

rs822027 1 155745810 A 0.013 35.53 (4.69-269.28) 2.52 × 10-9 GON4L

rs822020 1 155754117 C 0.026 20.56 (4.71-89.74) 5.15 × 10-7 GON4L

rs7549528 1 167485852 C 0.000 NC 1.54 × 10-8 CD247

rs275154 1 168061777 G 0.145 4.22 (1.95-9.14) 1.72 × 10-7 GPR161

rs2421987 1 172100831 A 0.132 1.80 (0.77-4.19) 2.47 × 10-5 DNM3

rs12120556 1 172115154 A 0.132 1.93 (0.83-4.46) 1.16 × 10-5 DNM3

rs17368935 1 172306391 G 0.066 9.66 (3.53-26.41) 1.72 × 10-9 DNM3

rs6662412 1 180237765 G 0.013 30.00 (3.94-228.2) 7.69 × 10-8 LHX4

rs589402 1 182542311 T 0.697 8.68 (2.84-26.52) 3.92 × 10-6 RNASEL

rs6656441 1 186182382 C 0.013 23.44 (3.06-179.51) 4.27 × 10-6 LOC105371654

rs689462 1 186651083 C 0.092 8.54 (3.52-20.76) 2.08 × 10-9 PTGS2

rs2816936 1 199982900 A 0.513 14.99 (5.46-41.14) 4.91 × 10-8 - rs7517843 1 212673179 G 0.000 NC 3.15 × 10-5 -

rs1926721 1 230864830 A 0.000 NC 3.15 × 10-5 LOC105373166

rs1458597 1 234154862 G 0.000 NC 3.15 × 10-5 SLC35F3

rs1367276 2 3662060 A 0.618 1.97 (1.00-3.91) 1.97 × 10-5 COLEC11

rs10207238 2 5615223 C 0.013 20.45 (2.66-157.42) 2.62 × 10-5 -

rs270838 2 7783504 C 0.105 7.73 (3.31-18.05) 3.61 × 10-11 - rs16861920 2 14759552 C 0.053 8.53 (2.82-25.77) 1.76 × 10-6 -

rs1366834 2 16389782 G 0.013 21.92 (2.86-168.29) 1.07 × 10-5 -

rs4099911 2 16454575 A 0.408 1.45 (0.78-2.72) 7.55 × 10-6 - rs654807 2 16457284 C 0.421 1.38 (0.74-2.57) 2.57 × 10-5 -

rs798368 2 16845834 T 0.605 1.84 (0.94-3.59) 1.78 × 10-5 FAM49A

rs16987589 2 20586506 A 0.013 23.44 (3.06-179.51) 4.27 × 10-6 - rs17043470 2 22397424 A 0.816 NC 1.52 × 10-5 -

rs2602803 2 30818644 G 0.303 2.20 (1.15-4.21) 9.83 × 10-6 LCLAT1

rs985257 2 38283228 A 0.237 4.30 (2.17-8.51) 2.15 × 10-5 RMDN2

rs1157185 2 38285735 T 0.237 4.30 (2.17-8.51) 5.84 × 10-6 RMDN2

rs1367696 2 38286914 T 0.237 4.30 (2.17-8.51) 5.84 × 10-6 RMDN2

rs13421497 2 45861591 G 0.053 9.49 (3.15-28.59) 1.57 × 10-5 - rs6757543 2 45977472 G 0.237 3.38 (1.71-6.67) 3.68 × 10-6 PRKCE

rs1007540 2 49209108 G 0.368 3.25 (1.70-6.21) 1.17 × 10-8 FSHR

rs6757577 2 65877598 A 0.066 11.18 (4.10-30.51) 2.77 × 10-10 - rs283825 2 79232491 G 0.342 2.83 (1.49-5.38) 4.41 × 10-7 -

rs13398697 2 103757890 A 0.066 7.49 (2.72-20.60) 1.05 × 10-6 -

rs1486178 2 108681163 T 0.000 NC 5.44 × 10-6 LINC01594

rs17041554 2 111564301 A 0.237 5.24 (2.63-10.42) 5.63 × 10-7 ACOXL

rs11895045 2 127492155 T 0.013 20.45 (2.66-157.42) 2.62 × 10-5 -

rs3732196 2 128744457 A 0.000 NC 8.56 × 10-7 SAP130

rs10928930 2 130498982 T 0.026 14.80 (3.36-65.16) 2.23 × 10-6 LOC105373643

rs16842140 2 140129426 G 0.263 2.94 (1.51-5.72) 9.07 × 10-7 LOC105373643

rs6744124 2 142815895 G 0.000 NC 3.15 × 10-5 LRP1B

rs13393078 2 146393980 C 0.079 7.55 (2.94-19.36) 1.24 × 10-7 -

rs2127978 2 157785902 T 0.092 4.93 (2.00-12.12) 1.97 × 10-5 -

rs6735919 2 166489066 T 0.250 2.73 (1.39-5.35) 3.82 × 10-6 CSRNP3

rs12165212 2 176079816 G 0.737 9.64 (2.73-34.01) 1.85 × 10-5 LOC105373751

rs11679695 2 206733638 G 0.224 2.73 (1.37-5.45) 2.80 × 10-6 -

rs16827966 2 232207158 T 0.013 41.67 (5.51-315.00) 5.32 × 10-11 ARMC9

rs622060 2 234948684 C 0.513 2.12 (1.11-4.03) 2.76 × 10-5 -

rs9683305 3 66866 C 0.447 4.87 (2.42-9.79) 3.28 × 10-5 -

rs2200706 3 3673608 T 0.263 5.91 (2.98-11.74) 5.48 × 10-10 - rs2193766 3 8829321 G 0.763 NC 7.80 × 10-8 OXTR

rs1597474 3 13542671 T 0.053 5.26 (1.70-16.27) 1.24 × 10-5 HDAC11

rs12629385 3 14407232 T 0.066 8.74 (3.19-23.95) 5.27 × 10-7 - rs11917596 3 19131933 C 0.026 13.13 (2.97-58.04) 6.34 × 10-6 -

rs197770 3 37515827 G 0.382 2.77 (1.46-5.26) 1.50 × 10-7 ITGA9




syndrome.


rs9844641 3 43476335 A 0.750 27.67 (3.60-212.56) 6.65 × 10-6 ANO10

rs9311374 3 45672925 T 0.816 NC 5.62 × 10-6 LIMD1

rs7426702 3 46095074 T 0.132 4.27 (1.93-9.47) 2.31 × 10-5 -

rs4473594 3 46337356 A 0.092 8.54 (3.52-20.76) 1.81 × 10-8 -

rs6445832 3 56905923 G 0.079 8.75 (3.42-22.38) 4.36 × 10-10 ARHGEF3

rs17060061 3 59031063 G 0.763 NC 7.80 × 10-8 C3orf67

rs11711551 3 66697961 G 0.132 5.20 (2.35-11.48) 2.31 × 10-5 -

rs17047694 3 68475098 T 0.303 3.07 (1.60-5.90) 3.66 × 10-9 FAM19A1

rs4422316 3 69202605 T 0.000 NC 3.27 × 10-7 -

rs12629627 3 73629548 G 0.355 3.10 (1.63-5.92) 1.76 × 10-5 PDZRN3

rs6797416 3 80211780 G 0.000 NC 1.71 × 10-9 - rs17019070 3 81603483 C 0.184 3.66 (1.78-7.53) 6.92 × 10-6 GBE1

rs7613828 3 85851246 A 0.737 NC 7.03 × 10-7 CADM2

rs1523773 3 97019048 T 0.000 NC 4.73 × 10-11 EPHA6

rs41330648 3 100760807 G 0.118 8.19 (3.62-18.54) 1.28 × 10-7 -

rs340170 3 111826736 G 0.000 NC 5.44 × 10-6 C3orf52

rs2733416 3 114082645 G 0.000 NC 1.71 × 10-9 ZBTB20

rs11914436 3 121123223 A 0.368 2.65 (1.40-5.02) 1.93 × 10-7 STXBP5L

rs361236 3 136729811 A 0.276 3.33 (1.72-6.45) 1.07 × 10-8 IL20RB

rs890527 3 140774853 T 0.289 3.12 (1.62-6.01) 4.60 × 10-9 SPSB4

rs2196007 3 144120914 T 0.053 8.07 (2.66-24.44) 1.11 × 10-5 -

rs7610618 3 149157706 T 0.013 26.61 (3.49-203.06) 1.57 × 10-6 - rs4505649 3 156146480 C 0.000 NC 3.15 × 10-5 KCNAB1

rs17780243 3 158712150 T 0.368 2.52 (1.33-4.77) 1.95 × 10-6 -

rs41469844 3 176694604 C 0.342 5.10 (2.60-10.01) 1.86 × 10-5 - rs16844808 4 3717581 T 0.079 4.94 (1.90-12.86) 2.42 × 10-6 -

rs17675581 4 5080187 G 0.276 2.75 (1.42-5.32) 3.05 × 10-5 STK32B

rs10009657 4 9833734 G 0.750 6.67 (2.15-20.65) 5.76 × 10-6 SLC2A9

rs16877795 4 11087682 G 0.171 4.85 (2.33-10.10) 1.49 × 10-7 -

rs1873717 4 11767981 T 0.184 4.87 (2.37-10.02) 1.23 × 10-6 LOC105374484

rs41423649 4 21898055 C 0.711 11.00 (3.14-38.57) 5.73 × 10-6 KCNIP4

rs7672066 4 26728719 G 0.000 NC 8.56 × 10-7 TBC1D19

rs1433429 4 35899687 C 0.197 3.88 (1.91-7.88) 2.71 × 10-5 -

rs10517378 4 36532369 C 0.250 3.15 (1.61-6.17) 1.13 × 10-5 LOC105374400

rs2303409 4 37846939 C 0.132 4.71 (2.13-10.43) 3.19 × 10-5 PGM2

rs13148734 4 63330858 A 0.500 3.67 (1.84-7.30) 2.73 × 10-5 LOC100131441

rs4510466 4 113901071 C 0.000 NC 1.22 × 10-7 ANK2

rs17865437 4 118341034 A 0.671 3.25 (1.47-7.20) 7.39 × 10-7 RPSAP35

rs17861907 4 118355398 G 0.671 3.25 (1.47-7.20) 7.39 × 10-7 LINC01378

rs11934366 4 145541864 A 0.000 NC 1.32 × 10-5 - rs1961484 4 156307533 A 0.066 10.65 (3.90-29.08) 1.21 × 10-7 -

rs2882361 4 161379616 G 0.513 10.44 (4.26-25.54) 3.02 × 10-8 -

rs12331711 4 162419624 G 0.066 8.74 (3.19-23.95) 1.52 × 10-6 FSTL5

rs3792615 4 164532801 T 0.724 15.65 (3.53-69.47) 1.66 × 10-5 MARCH1

rs4692612 4 171537901 T 0.092 5.77 (2.36-14.11) 1.17 × 10-5 -

rs6854376 4 174202313 T 0.053 10.53 (3.50-31.63) 1.71 × 10-8 GALNT7

rs2685850 4 190778060 A 0.316 5.75 (2.91-11.36) 2.70 × 10-5 -

rs16886994 5 20464699 G 0.026 13.95 (3.16-61.54) 1.32 × 10-5 CDH18

rs1428323 5 29912034 A 0.237 2.79 (1.41-5.52) 3.28 × 10-5 - rs6892871 5 36669569 G 0.026 7.40 (1.62-33.74) 1.81 × 10-5 SLC1A3

rs7726463 5 37955073 G 0.355 3.83 (1.99-7.38) 5.64 × 10-7 -

rs6871885 5 53296188 A 0.092 6.38 (2.61-15.57) 1.42 × 10-6 ARL15

rs6450296 5 54464679 A 0.118 4.58 (2.01-10.44) 5.16 × 10-6 CDC20B

rs16888306 5 57920048 C 0.079 5.83 (2.26-15.07) 1.31 × 10-5 RAB3C

rs10056584 5 60401442 A 0.026 13.13 (2.97-58.04) 1.46 × 10-5 NDUFAF2

rs6449669 5 62929018 T 0.289 3.61 (1.87-6.98) 6.54 × 10-6 -

rs6863118 5 71240748 G 0.105 7.37 (3.15-17.22) 6.22 × 10-9 -

rs609539 5 106904997 C 0.0132 26.61 (3.49-203.06) 6.14 × 10-7 EFNA5

rs254577 5 134422204 C 0.264 11.04 (5.28-23.07) 2.35 × 10-11 C5orf66

rs6877860 5 135271527 T 0.145 3.28 (1.51-7.16) 2.78 × 10-5 FBXL21

rs11954603 5 136488381 C 0.237 2.19 (1.10-4.35) 1.30 × 10-5 SPOCK1

rs889083 5 145941549 G 0.224 2.87 (1.44-5.71) 3.14 × 10-6 CTB-99A3.1

rs17722227 5 148407945 A 0.026 8.04 (1.77-36.46) 7.70 × 10-6 SH3TC2

rs7701654 5 149956501 G 0.013 30.00 (3.94-228.20) 7.69 × 10-8 - rs10074876 5 152867311 C 0.026 16.59 (3.78-72.77) 1.07 × 10-7 GRIA1

rs6892217 5 171474120 T 0.224 8.19 (4.01-16.73) 6.61 × 10-10 STK10

rs7768988 6 7830384 T 0.237 6.11 (3.05-12.24) 7.15 × 10-7 BMP6

rs6940702 6 11702281 A 0.000 NC 2.18 × 10-6 LOC340184




syndrome.


rs41378447 6 22141745 T 0.092 10.84 (4.46-26.34) 1.06 × 10-11 CASC15

NBAT1

rs4714199 6 38891068 C 0.158 5.59 (2.64-11.85) 3.92 × 10-6 DNAH8

rs2436739 6 40496341 G 0.000 NC 3.15 × 10-5 LRFN2

rs2274515 6 42933526 T 0.316 3.52 (1.83-6.77) 2.19 × 10-5 PEX6

rs2748997 6 52601316 C 0.079 9.18 (3.59-23.48) 2.76 × 10-8 -

rs9283919 6 54114066 G 0.053 11.08 (3.69-33.24) 7.87 × 10-7 MLIP

rs9446695 6 73232658 T 0.026 17.53 (4.00-76.78) 3.46 × 10-8 - rs6927507 6 77792085 G 0.184 3.32 (1.61-6.85) 1.99 × 10-6 LOC105377862

rs16890805 6 80195421 T 0.105 4.48 (1.90-10.59) 3.34 × 10-6 LCA5

rs12055682 6 81189123 G 0.263 4.79 (2.43-9.41) 2.99 × 10-9 - rs7739542 6 84716154 C 0.776 NC 2.38 × 10-7 -

rs9362453 6 88516498 G 0.039 9.17 (2.63-32.03) 6.40 × 10-6 LOC101928911

rs3798405 6 116738674 C 0.000 NC 3.15 × 10-5 DSE

rs606324 6 124855854 A 0.079 6.48 (2.52-16.69) 4.39 × 10-8 NKAIN2

rs7747443 6 136713749 C 0.053 10.00 (3.32-30.08) 2.13 × 10-5 MAP7

rs6923953 6 136726688 C 0.053 10.00 (3.32-30.08) 2.13 × 10-5 MAP7

rs997139 6 136751118 G 0.053 10.00 (3.32-30.08) 2.13 × 10-5 MAP7

rs6926583 6 136752092 C 0.053 10.00 (3.32-30.08) 2.13 × 10-5 MAP7

rs11154872 6 136797757 C 0.053 9.49 (3.15-28.59) 2.64 × 10-5 MAP7

rs3778315 6 136850687 G 0.039 12.17 (3.52-42.07) 1.65 × 10-5 MAP7

rs7742257 6 144987202 T 0.053 8.07 (2.66-24.44) 4.89 × 10-6 UTRN

rs9485028 6 146209670 A 0.066 5.04 (1.80-14.1) 2.53 × 10-5 SHPRH

rs17085519 6 154870688 G 0.026 13.13 (2.97-58.04) 6.34 × 10-6 LOC100129996

rs1859512 7 8535486 T 0.066 6.73 (2.44-18.58) 8.54 × 10-6 NXPH1

rs6973776 7 38319378 T 0.000 NC 3.27 × 10-7 TARP

rs2237406 7 39423305 T 0.026 13.13 (2.97-58.04) 6.34 × 10-6 POU6F2

rs7789233 7 51947068 A 0.000 NC 1.32 × 10-5 - rs11506050 7 52160653 G 0.013 20.45 (2.66-157.42) 2.62 × 10-5 -

rs1195242 7 68686390 C 0.000 NC 1.32 × 10-5 -

rs41456945 7 71149459 C 0.026 18.50 (4.23-80.94) 1.07 × 10-8 WBSCR17

rs1859790 7 75916073 T 0.092 6.70 (2.75-16.34) 1.56 × 10-7 SRRM3

rs17156195 7 81981170 T 0.118 5.32 (2.34-12.07) 9.34 × 10-6 CACNA2D1

rs4623336 7 98088932 T 0.158 4.85 (2.29-10.27) 2.68 × 10-8 - rs41385645 7 98974038 T 0.132 6.29 (2.85-13.88) 1.99 × 10-7 ARPC1B

rs17475512 7 102512488 G 0.382 3.62 (1.88-6.96) 2.56 × 10-6 FBXL13

FAM185A

rs6957524 7 106186766 G 0.013 26.61 (3.49-203.06) 6.14 × 10-7 -

rs213981 7 117254527 G 0.329 4.31 (2.22-8.35) 2.94 × 10-7 CFTR

rs7783582 7 123169504 T 0.039 9.17 (2.63-32.03) 1.62 × 10-5 IQUB

rs1526415 7 125079875 T 0.132 4.06 (1.83-9.02) 3.05 × 10-5 LOC100506664

rs1222400 7 133817981 T 0.039 12.83 (3.72-44.3) 2.89 × 10-8 LRGUK

rs2960770 7 142012291 C 0.539 5.67 (2.60-12.33) 3.01 × 10-5 TRB

rs11972875 7 154647387 G 0.053 7.20 (2.37-21.90) 3.27 × 10-5 DPP6

rs6950641 7 157029359 T 0.092 5.48 (2.23-13.42) 6.49 × 10-6 UBE3C

rs41363145 8 6064108 C 0.079 6.82 (2.65-17.54) 1.32 × 10-6 - rs2916699 8 6214922 A 0.026 13.13 (2.97-58.04) 1.46 × 10-5 -

rs11984468 8 16367555 C 0.184 4.87 (2.37-10.02) 2.04 × 10-5 LOC101929028

rs17643851 8 18437729 G 0.079 8.75 (3.42-22.38) 9.93 × 10-8 PSD3

rs17733133 8 22385061 G 0.171 4.85 (2.33-10.10) 2.60 × 10-5 PPP3CC

rs17052315 8 24517047 A 0.053 10.00 (3.32-30.08) 5.97 × 10-8 -

rs4236924 8 41975979 C 0.079 5.83 (2.26-15.07) 1.31 × 10-5 - rs4738955 8 63260182 A 0.382 1.70 (0.91-3.19) 8.10 × 10-6 NKAIN3

rs1350060 8 63848337 A 0.211 3.10 (1.54-6.23) 2.01 × 10-6 NKAIN3

rs16937494 8 72101749 G 0.039 9.17 (2.63-32.03) 1.62 × 10-5 LOC105375894

rs2033069 8 81079607 A 0.539 5.67 (2.60-12.33) 1.03 × 10-5 TPD52

rs7010471 8 97350955 G 0.053 12.24 (4.09-36.66) 2.49 × 10-10 -

rs16883408 8 113331553 C 0.237 5.51 (2.76-10.98) 1.06 × 10-8 CSMD3

rs7830366 8 114880641 T 0.316 3.52 (1.83-6.77) 4.48 × 10-6 -

rs6470455 8 127678907 G 0.342 4.81 (2.46-9.39) 7.73 × 10-6 -

rs7834482 8 127785745 G 0.461 2.93 (1.52-5.63) 2.83 × 10-5 LOC105375753

rs2648883 8 129076594 G 0.250 4.20 (2.14-8.26) 2.27 × 10-5 PVT1

rs16902672 8 129165859 C 0.211 5.25 (2.60-10.59) 1.77 × 10-8 -

rs7011650 8 134390976 T 0.263 3.08 (1.58-6.00) 5.89 × 10-6 LOC105375771

rs12001751 9 2295279 T 0.000 NC 5.44 × 10-6 LOC105375955

rs12551218 9 9058949 T 0.079 5.23 (2.02-13.57) 3.31 × 10-6 PTPRD

rs2891242 9 25062644 C 0.171 4.85 (2.33-10.10) 9.33 × 10-6 - rs10114442 9 27335837 A 0.000 NC 2.18 × 10-6 MOB3B

rs7847862 9 35933210 G 0.329 3.49 (1.82-6.70) 3.47 × 10-7 -




syndrome.


rs7863401 9 35942406 A 0.645 5.23 (2.20-12.46) 2.44 × 10-5 -

rs12684292 9 35944224 T 0.645 5.23 (2.20-12.46) 2.44 × 10-5 - rs7019283 9 35945975 C 0.632 5.54 (2.33-13.16) 8.05 × 10-6 -

rs13285078 9 35946275 C 0.632 5.54 (2.33-13.16) 8.05 × 10-6 -

rs12235235 9 36091133 T 0.079 10.61 (4.15-27.08) 5.76 × 10-16 RECK

rs2988013 9 37062647 C 0.053 8.07 (2.66-24.44) 2.38 × 10-5 -

rs7019328 9 74835176 T 0.645 14.88 (4.29-51.64) 5.66 × 10-6 GDA

rs10121299 9 79397301 C 0.289 4.42 (2.27-8.61) 6.62 × 10-9 PRUNE2

PCA3

rs17085969 9 85640254 T 0.750 27.67 (3.60-212.56) 8.99 × 10-7 RASEF

rs10978470 9 109136192 G 0.158 5.59 (2.64-11.85) 4.32 × 10-9 - rs1610024 9 111614766 A 0.250 4.64 (2.35-9.14) 2.89 × 10-7 -

rs10980229 9 112925977 G 0.289 2.45 (1.28-4.72) 1.41 × 10-5 PALM2-AKAP2

rs10817082 9 113474166 C 0.632 6.42 (2.6-15.83) 2.53 × 10-5 MUSK

rs7849492 9 122619031 C 0.145 5.91 (2.74-12.75) 9.95 × 10-10 -

rs7020077 9 127010874 A 0.184 4.22 (2.05-8.68) 1.87 × 10-5 -

rs7859623 9 127314768 C 0.000 NC 3.15 × 10-5 NR6A1

rs7853174 9 129419990 G 0.513 1.26 (0.68-2.36) 2.17 × 10-5 LMX1B

rs10988052 9 131353253 G 0.079 6.15 (2.39-15.86) 4.71 × 10-6 SPTAN1

rs418216 10 4668586 T 0.000 NC 1.32 × 10-5 - rs4242794 10 5536590 A 0.000 NC 3.27 × 10-7 LOC105376379

rs7095919 10 11267360 G 0.079 5.83 (2.26-15.07) 1.31 × 10-5 CELF2

rs584569 10 30496246 A 0.066 8.74 (3.19-23.95) 2.84 × 10-8 -

rs2490495 10 32726529 G 0.342 2.02 (1.07-3.82) 2.97 × 10-5 LOC101929431

rs12761944 10 32803484 A 0.342 2.02 (1.07-3.82) 2.97 × 10-5 CCDC7

rs1763788 10 32917853 A 0.342 2.02 (1.07-3.82) 2.97 × 10-5 CCDC7

rs1577372 10 32938382 A 0.342 2.02 (1.07-3.82) 2.97 × 10-5 CCDC7

rs1762529 10 32968080 A 0.342 2.02 (1.07-3.82) 2.97 × 10-5 CCDC7

rs2784574 10 32976689 G 0.342 2.02 (1.07-3.82) 2.97 × 10-5 CCDC7

rs11009106 10 33123413 C 0.329 2.24 (1.18-4.26) 1.42 × 10-5 CCDC7

rs2995467 10 33135952 G 0.342 2.02 (1.07-3.82) 2.97 × 10-5 CCDC7

rs11010290 10 36060309 T 0.395 6.04 (2.99-12.21) 1.61 × 10-6 -

rs7895391 10 47006369 T 0.145 5.37 (2.49-11.59) 7.78 × 10-7 -

rs12572431 10 47592472 G 0.250 3.46 (1.77-6.79) 9.90 × 10-6 ANTXRLP1

rs6479969 10 52900003 G 0.382 5.54 (2.78-11.05) 1.28 × 10-7 PRKG1

rs1915603 10 66455644 G 0.039 9.73 (2.79-33.90) 5.15 × 10-8 -

rs16926249 10 71100726 G 0.474 3.13 (1.61-6.08) 1.31 × 10-5 HK1

rs2288374 10 79761041 T 0.132 4.06 (1.83-9.02) 9.09 × 10-6 POLR3A

rs17112444 10 101749974 A 0.026 21.64 (4.96-94.39) 8.02 × 10-10 DNMBP

rs1932556 10 120251528 T 0.605 54.13 (7.15-409.98) 1.63 × 10-9 - rs10788258 10 123936896 T 0.092 7.39 (3.04-17.99) 1.04 × 10-7 TACC2

rs2421122 10 124473379 C 0.000 NC 3.27 × 10-7 -

rs2803453 10 131075207 C 0.000 NC 2.18 × 10-6 - rs1041296 10 132003031 G 0.211 4.76 (2.37-9.59) 6.89 × 10-9 -

rs9419277 10 133834704 G 0.026 14.8 (3.36-65.16) 8.93 × 10-7 -

rs11021876 11 11509885 T 0.013 28.28 (3.71-215.42) 2.21 × 10-7 GALNT18

rs11027583 11 23897934 T 0.092 7.39 (3.04-17.99) 7.03 × 10-9 -

rs11038285 11 45071321 G 0.013 30.00 (3.94-228.20) 7.69 × 10-8 -

rs7121660 11 45358483 A 0.447 4.53 (2.27-9.03) 4.10 × 10-6 - rs1977985 11 59052769 G 0.750 9.00 (2.54-31.85) 2.96 × 10-5 SLC25A47P1

rs3017495 11 70666253 T 0.026 13.95 (3.16-61.54) 2.42 × 10-6 SHANK2

rs7107438 11 75709833 C 0.026 12.33 (2.78-54.66) 1.60 × 10-5 UVRAG

rs17133553 11 99316881 A 0.276 5.53 (2.80-10.91) 4.74 × 10-8 CNTN5

rs10789931 11 112842773 T 0.303 3.74 (1.94-7.23) 3.08 × 10-5 NCAM1

rs12417706 11 116059440 T 0.105 7.02 (3.00-16.42) 1.90 × 10-6 - rs3867246 11 124271743 T 0.132 5.20 (2.35-11.48) 1.88 × 10-9 -

rs4144897 11 128181191 T 0.118 5.06 (2.23-11.51) 5.12 × 10-6 -

rs7119924 11 130004026 C 0.184 3.66 (1.78-7.53) 1.58 × 10-7 APLP2

rs12305678 12 2763539 G 0.224 4.41 (2.21-8.79) 7.87 × 10-9 CACNA1C

rs11062852 12 3936632 C 0.197 4.07 (2.00-8.26) 4.84 × 10-9 PARP11

rs14541 12 8800566 G 0.224 4.20 (2.11-8.37) 1.70 × 10-6 MFAP5

rs11056347 12 15243991 A 0.053 9.49 (3.15-28.59) 5.27 × 10-7 -

rs16927111 12 24207823 G 0.211 3.25 (1.62-6.54) 1.69 × 10-5 SOX5

rs11168709 12 38984042 T 0.013 35.53 (4.69-269.28) 2.52 × 10-9 - rs2062758 12 39045452 T 0.105 7.02 (3.00-16.42) 1.18 × 10-5 -

rs12300888 12 52531500 C 0.079 6.82 (2.65-17.54) 2.65 × 10-6 -

rs17123453 12 60553534 C 0.066 6.37 (2.30-17.62) 2.25 × 10-5 - rs41441747 12 61745898 C 0.197 4.27 (2.10-8.66) 6.72 × 10-7 -

rs7307225 12 71898358 G 0.316 3.19 (1.66-6.11) 2.53 × 10-5 LGR5




syndrome.


rs17019561 12 92118436 C 0.013 23.44 (3.06-179.51) 1.02 × 10-5 -

rs12312259 12 92148729 C 0.158 6.46 (3.05-13.69) 3.60 × 10-10 - rs17024760 12 96220100 T 0.211 7.11 (3.49-14.49) 3.22 × 10-7 LOC105369921

rs7301442 12 104026861 T 0.013 30.00 (3.94-228.20) 7.69 × 10-8 STAB2

rs17035358 12 104709745 A 0.066 9.66 (3.53-26.41) 1.72 × 10-9 TXNRD1

rs9668748 12 114387000 C 0.079 8.75 (3.42-22.38) 4.02 × 10-6 RBM19

rs7960674 12 123089209 C 0.000 NC 5.44 × 10-6 KNTC1

rs7306948 12 123345347 G 0.145 4.43 (2.05-9.59) 1.01 × 10-5 HIP1R

rs866781 12 128357089 A 0.000 NC 8.56 × 10-7 -

rs12317807 12 130395910 T 0.132 4.71 (2.13-10.43) 1.47 × 10-9 -

rs1696407 12 130459987 G 0.197 3.70 (1.82-7.51) 1.46 × 10-5 LOC105370076

rs2801659 13 19383274 C 0.237 5.24 (2.63-10.42) 1.32 × 10-5 -

rs9285128 13 21088186 A 0.250 4.00 (2.04-7.86) 2.15 × 10-9 CRYL1

rs7987491 13 21672725 G 0.158 4.20 (1.98-8.91) 1.19 × 10-5 -

rs17079111 13 24218816 G 0.053 9.00 (2.98-27.15) 1.53 × 10-6 TNFRSF19

rs9581771 13 27452351 T 0.092 7.76 (3.19-18.87) 7.96 × 10-9 -

rs17647077 13 46617212 G 0.000 NC 1.32 × 10-5 ZC3H13

rs41464146 13 47764748 C 0.026 18.50 (4.23-80.94) 3.22 × 10-8 -

rs9585049 13 100047159 T 0.039 15.75 (4.58-54.13) 5.25 × 10-10 -

rs10047684 13 105365339 A 0.237 4.51 (2.28-8.94) 7.31 × 10-8 - rs2017563 13 110262110 A 0.013 41.67 (5.51-315.00) 1.55 × 10-7 LOC105370359

rs9301483 13 111530373 A 0.250 5.40 (2.72-10.71) 8.08 × 10-8 - rs7321094 13 111684162 T 0.237 4.30 (2.17-8.51) 1.45 × 10-6 -

rs2204978 14 22518491 A 0.211 3.58 (1.78-7.19) 1.26 × 10-7 TRA

rs17255510 14 22662856 C 0.171 10.23 (4.82-21.71) 6.61 × 10-10 TRA

rs11157573 14 22889777 G 0.158 5.09 (2.40-10.77) 2.97 × 10-10 TRA

rs10144138 14 22933962 T 0.026 27.75 (6.38-120.63) 6.99 × 10-14 TRA

TRD

rs4982735 14 23626745 C 0.000 NC 3.27 × 10-7 SLC7A8

rs17256392 14 23987437 A 0.026 7.40 (1.62-33.74) 1.81 × 10-5 THTPA

rs17781246 14 41939907 G 0.289 2.57 (1.34-4.95) 1.48 × 10-6 - rs2816751 14 49812483 C 0.184 5.11 (2.48-10.51) 5.43 × 10-10 -

rs10146102 14 51850519 T 0.079 7.18 (2.80-18.43) 1.09 × 10-6 -

rs17127809 14 55188357 T 0.053 8.53 (2.82-25.77) 1.76 × 10-6 SAMD4A

rs7154569 14 61945965 T 0.000 NC 2.18 × 10-6 PRKCH

rs17098846 14 62032938 A 0.026 13.95 (3.16-61.54) 2.42 × 10-6 LOC101927780

rs10483750 14 63135397 T 0.079 5.83 (2.26-15.07) 1.31 × 10-5 - rs7153874 14 66308460 G 1.000 NC 1.32 × 10-5 -

rs7143222 14 73071319 T 0.000 NC 1.54 × 10-8 DPF3

rs2079989 14 73244469 C 0.421 1.51 (0.81-2.83) 6.68 × 10-6 DPF3

rs10133617 14 81134332 T 0.039 10.31 (2.97-35.84) 2.36 × 10-6 CEP128

rs17120254 14 85209862 A 0.605 NC 5.20 × 10-13 -

rs10129777 14 85516453 G 0.079 7.93 (3.10-20.32) 6.97 × 10-6 - rs8016502 14 85657757 G 0.013 20.45 (2.66-157.42) 2.62 × 10-5 LOC105370604

rs10137248 14 90320818 G 0.039 7.11 (2.01-25.14) 6.59 × 10-6 EFCAB11

rs2249954 14 92383999 G 0.079 10.11 (3.96-25.82) 5.47 × 10-11 FBLN5

rs10144861 14 92465346 G 0.145 4.65 (2.15-10.05) 2.74 × 10-5 TRIP11

rs7159091 14 95345271 C 0.039 6.64 (1.87-23.56) 1.58 × 10-5 -

rs17092382 14 95920937 A 0.000 NC 1.54 × 10-8 SYNE3

rs12443497 15 52373388 T 0.013 21.92 (2.86-168.29) 1.07 × 10-5 -

rs2869820 15 79217808 T 0.000 NC 4.39 × 10-8 CTSH

rs9920285 15 79488084 A 0.039 10.31 (2.97-35.84) 2.36 × 10-6 ANKRD34C-AS1

rs8029503 15 92488592 T 0.105 8.10 (3.47-18.93) 5.66 × 10-11 SLCO3A1

rs9744291 15 98552768 G 0.276 5.84 (2.95-11.57) 1.08 × 10-5 LOC105371008

rs8050875 16 11223537 G 0.724 15.65 (3.53-69.47) 6.91 × 10-6 CLEC16A

rs16970887 16 21115345 C 0.066 6.37 (2.30-17.62) 2.25 × 10-5 DNAH3

rs16973831 16 24572946 T 0.000 NC 1.22 × 10-7 RBBP6

rs13339179 16 25117049 T 0.039 12.83 (3.72-44.3) 2.89 × 10-8 LCMT1-AS1

rs16975878 16 26454601 G 0.789 22.13 (2.86-171.49) 2.06 × 10-5 -

rs6497951 16 26528582 T 0.000 NC 5.44 × 10-6 -

rs3095598 16 52566862 C 0.039 18.25 (5.32-62.62) 1.02 × 10-10 TOX3

rs41368852 16 65930903 G 0.039 12.83 (3.72-44.30) 3.28 × 10-6 -

rs4843884 16 86030100 G 0.066 8.31 (3.03-22.79) 1.83 × 10-6 -

rs8046503 16 86115955 A 0.316 3.19 (1.66-6.11) 8.41 × 10-6 - rs8057267 16 87541080 G 0.171 4.41 (2.11-9.19) 1.06 × 10-7 LOC101928737

rs7404102 16 88282637 A 0.000 NC 5.44 × 10-6 ZNF469

rs7220341 17 4214705 G 0.276 4.97 (2.53-9.75) 5.41 × 10-10 UBE2G1

rs6502875 17 5612641 G 0.316 3.90 (2.02-7.53) 5.97 × 10-8 -

rs16956158 17 6594844 G 0.447 3.95 (2.01-7.77) 6.93 × 10-7 SLC13A5




syndrome.


rs16944757 17 11349689 A 0.053 7.63 (2.51-23.15) 2.80 × 10-5 SHISA6

rs4792493 17 14404572 G 0.000 NC 3.27 × 10-7 - rs3095168 17 16260198 A 0.237 3.22 (1.63-6.36) 1.91 × 10-6 -

rs271662 17 48018370 C 0.132 5.72 (2.59-12.62) 2.60 × 10-6 -

rs2192217 17 54500732 A 0.026 12.33 (2.78-54.66) 1.60 × 10-5 ANKFN1

rs9913705 17 60004928 G 0.013 25.00 (3.27-191.10) 1.65 × 10-6 INTS2

rs8073194 17 63601658 T 0.013 20.45 (2.66-157.42) 2.62 × 10-5 -

rs6504560 17 65959300 T 0.026 17.53 (4.00-76.78) 2.69 × 10-7 BPTF

rs690607 17 72888412 A 0.158 4.85 (2.29-10.27) 1.56 × 10-7 FADS6

rs29110 18 9951994 C 0.013 20.45 (2.66-157.42) 2.62 × 10-5 VAPA

rs496731 18 26491368 T 0.474 3.56 (1.81-6.98) 5.52 × 10-6 - rs1362859 18 32918639 G 0.329 2.86 (1.50-5.45) 8.99 × 10-8 ZNF24

rs948440 18 34820988 C 0.132 6.92 (3.14-15.26) 3.92 × 10-10 CELF4

rs12607783 18 49038486 A 0.132 4.71 (2.13-10.43) 4.31 × 10-8 LINC01630

rs12965947 18 49880082 A 0.053 12.24 (4.09-36.66) 5.82 × 10-7 DCC

rs9964872 18 62176077 A 0.026 17.53 (4.00-76.78) 9.92 × 10-8 -

rs9946817 18 70367007 C 0.158 2.96 (1.38-6.34) 5.35 × 10-7 - rs4892034 18 70399988 A 0.118 3.53 (1.53-8.11) 1.58 × 10-5 -

rs11873202 18 72716230 G 0.000 NC 8.56 × 10-7 ZNF407

rs682564 18 76177100 A 0.026 13.95 (3.16-61.54) 2.42 × 10-6 - rs243391 19 4449808 G 0.276 3.33 (1.72-6.45) 2.96 × 10-6 UBXN6

rs16994314 19 7176974 T 0.118 6.45 (2.85-14.61) 2.42 × 10-7 INSR

rs10402951 19 7555092 C 0.342 2.97 (1.56-5.67) 1.13 × 10-5 PEX11G

rs479448 19 7831061 C 0.026 13.13 (2.97-58.04) 6.34 × 10-6 CLEC4M

rs4808297 19 21935700 T 0.132 4.06 (1.83-9.02) 1.16 × 10-5 ZNF100

rs16970196 19 35794984 A 0.750 6.67 (2.15-20.65) 5.76 × 10-6 MAG

rs6508891 19 40128737 T 0.118 5.58 (2.46-12.67) 1.75 × 10-7 LOC100129935

rs7253295 19 50106308 A 0.829 NC 1.52 × 10-5 PRR12

rs6055456 20 7985376 C 0.105 4.97 (2.11-11.70) 1.83 × 10-5 TMX4

rs6074914 20 15519613 A 0.658 8.22 (2.96-22.80) 3.20 × 10-5 MACROD2

rs7347140 20 40738421 T 0.026 14.80 (3.36-65.16) 2.23 × 10-6 PTPRT

rs6093591 20 40831399 T 0.184 4.03 (1.96-8.28) 6.67 × 10-7 PTPRT

rs7272593 20 44432523 G 0.053 8.07 (2.66-24.44) 4.89 × 10-6 DNTTIP1

rs41493945 20 50957627 A 0.013 48.53 (6.43-366.21) 6.25 × 10-13 LOC105372666

rs2294584 20 51309773 T 0.000 NC 1.32 × 10-5 LOC105372666

rs927651 20 52772896 G 0.461 2.93 (1.52-5.63) 3.21 × 10-5 CYP24A1

rs6098723 20 54259204 G 0.013 20.45 (2.66-157.42) 2.62 × 10-5 LOC105372676

rs4812100 20 58145558 G 0.289 4.20 (2.16-8.16) 2.21 × 10-6 -

rs9977796 21 16868783 G 0.000 NC 8.56 × 10-7 -

rs13052044 21 19655756 T 0.158 5.33 (2.52-11.30) 2.66 × 10-5 TMPRSS15

rs7279994 21 31753700 C 0.211 3.75 (1.87-7.54) 2.22 × 10-5 -

rs9984519 21 34196975 T 0.316 1.97 (1.03-3.76) 8.22 × 10-6 -

rs8130198 21 43630324 C 0.382 2.27 (1.20-4.28) 6.63 × 10-6 ABCG1

rs3788079 21 45348179 C 0.000 NC 3.42 × 10-12 AGPAT3

rs7290437 22 22456739 G 0.250 2.73 (1.39-5.35) 3.52 × 10-8 IGL

rs16985794 22 22561277 C 0.066 6.02 (2.17-16.69) 1.58 × 10-6 IGL

rs16980810 22 26219764 A 0.158 4.62 (2.18-9.80) 6.05 × 10-6 MYO18B

rs12170932 22 27338123 T 0.013 21.92 (2.86-168.29) 1.07 × 10-5 -

rs2015035 22 30771554 T 0.039 12.17 (3.52-42.07) 3.10 × 10-5 CCDC157

rs6008155 22 47749241 G 0.039 9.73 (2.79-33.90) 6.32 × 10-6 LOC339685

rs11090847 22 48899419 T 0.171 3.63 (1.74-7.60) 2.92 × 10-5 FAM19A5

rs5770525 22 49930353 G 0.500 4.25 (2.10-8.61) 1.11 × 10-5 C22orf34

rs9628158 22 50255459 T 0.000 NC 2.18 × 10-6 ZBED4

ALG12 aNearest physical genes were reported. Chr: Chromosome; RA: risk allele; Freq: frequency of risk allele in controls; OR: odds ratio; CI: confidence interval; NS: not significant. NC: Not calculable due to an allele frequency

of 0 in either the cases or controls.


Table 7.3. Summary of SNPs from genome-wide association study for self-reported tiredness.a

SNP Chromosome SNP position Gene p-value

1:64178756_C_Tb 1 64178756 - 1.36 × 10-11

rs142592148 1 75842193 SLC44A5 5.88 × 10-8

rs7219015 17 2555592 PAFAH1B1 6.86 × 10-8 aRisk alleles and effect sizes were not reported. bAffymetrix ID is reported because

this SNP does not have an rs ID. The study by Deary and colleagues (2017) was

conducted in a UK population. Tiredness was assessed by the question: “Over the past two weeks, how often have you felt tired or had little energy?”. 6,948

individuals responded “nearly every day”, 6,404 individuals responded “more than

half the days”, 44,208 individuals responded “several days”, and 51,416 individuals responded “not at all”.

Table 7.4. Summary of genes from gene-based association analysis of self-reported tiredness.

Gene Chromosome Start Stop p-value

DRD2 11 113280317 113346001 2.94 × 10-7 PRRC2C 1 171454666 171562650 1.43 × 10-6

C3orf84 3 49215069 49229291 1.45 × 10-6

ANO10 3 43407818 43663560 1.52 × 10-6 ASXL3 18 31158541 31327399 2.67 × 10-6

RHOA 3 49396578 49449526 4.07 × 10-6

CTNND1 11 57529234 57586652 4.09 × 10-6 THEM4 1 151843342 151882361 5.44 × 10-6

FBXO21 12 117581585 117628300 5.66 × 10-6 ADARB1 21 46494493 46646478 6.01 × 10-6

NAPA 19 47990891 48018515 6.06 × 10-6

KANSL1L 2 210885435 211036051 6.24 × 10-6 RHCG 15 90014638 90039799 6.90 × 10-6

PLAC8 4 84011201 84035911 6.95 × 10-6

KLF7 2 207945529 208030614 7.40 × 10-6 RPE 2 210867352 210886291 1.00 × 10-5

TMX2 11 57479995 57508445 1.36 × 10-5

SNF8 17 47007458 47022154 1.38 × 10-5

CCDC36 3 49235861 49295537 1.38 × 10-5

SSBP4 19 18530146 18545372 1.87 × 10-5

ISYNA1 19 18545198 18549111 1.93 × 10-5 RELT 11 73087405 73108519 2.37 × 10-5

CSMD3 8 113235157 114449242 2.49 × 10-5

ZDHHC5 11 57435474 57468659 2.66 × 10-5 METTL16 17 2319343 2415200 2.67 × 10-5

SRRM4 12 119419300 119600856 3.03 × 10-5

BSN 3 49591922 49708982 3.20 × 10-5 NRXN1 2 50145643 51259674 3.25 × 10-5

ZNF780A 19 40575059 40596845 3.30 × 10-5

SMC1B 22 45739944 45809500 3.33 × 10-5 TCTA 3 49449639 49453909 3.36 × 10-5

GIP 17 47035918 47045955 3.45 × 10-5

CKMT1A 15 43985084 43991420 4.09 × 10-5 NICN1 3 49459766 49466757 4.18 × 10-5

UBE2Z 17 46985731 47006422 5.11 × 10-5

DAG1 3 49506146 49573048 5.26 × 10-5

ATP11B 3 182511291 182639423 5.28 × 10-5

PSMC4 19 40477073 40487353 5.44 × 10-5

FAM168A 11 73117028 73309228 5.86 × 10-5 CCNT2 2 135676392 135716915 6.25 × 10-5

OPA1 3 193310933 193415600 6.42 × 10-5

CATSPER2 15 43922772 43941039 6.52 × 10-5 ZBTB37 1 173837493 173855774 6.67 × 10-5

ELL 19 18553473 18632937 6.91 × 10-5

SERPING1 11 57365027 57382326 7.49 × 10-5 PLGRKT 9 5357966 5437937 7.89 × 10-5

PRR12 19 50094912 50129696 8.37 × 10-5

UBA7 3 49842638 49851391 8.48 × 10-5 CAMK1D 10 12391583 12871735 9.36 × 10-5

The study by Deary and colleagues (2017) was conducted in a UK

population. Tiredness was assessed by the question: “Over the past two weeks, how often have you felt tired or had little energy?”. 6,948

individuals responded “nearly every day”, 6,404 individuals responded

“more than half the days”, 44,208 individuals responded “several days”,

and 51,416 individuals responded “not at all”.


Table 7.5. Summary of SNPs from genome-wide association studies of depression phenotypes, in

Europeans.

SNP Chr SNP position RA Freq OR (95% CI) p-value Gene

Study by Hyde and colleagues (2016) in a cohort of 130,620 self-reported and clinically evaluated cases

and 347,620 controls.a

rs10514299 5 87663610 C 0.2406 1.05 (1.04-1.07) 9.99 × 10-16 -

rs1518395 2 58208074 A 0.6130 1.03 (1.02-1.05) 4.32 × 10-12 -

rs2179744 22 41621714 G 0.2817 1.04 (1.02-1.05) 6.03 × 10-11 L3MBTL2 rs11209948 1 72811904 G 0.6389 1.04 (1.02-1.05) 8.38 × 10-11 -

rs454214 5 88003403 T 0.4319 1.03 (1.02-1.05) 1.09 × 10-9 -

rs301806 1 8482078 T 0.4481 1.03 (1.02-1.04) 1.90 × 10-9 RERE rs1475120 6 105389953 G 0.5475 1.03 (1.02-1.04) 4.17 × 10-9 -

rs10786831 10 106614571 G 0.3993 1.03 (1.02-1.04) 8.11 × 10-9 SORCS3

rs12552 13 53625781 G 0.4450 1.05 (1.03-1.06) 8.16 × 10-9 OLFM4 rs6476606 9 rs6476606 G 0.3628 1.03 (1.02-1.04) 1.20 × 10-8 PAX5

rs8025231 15 37648402 A 0.4275 1.04 (1.02-1.05) 1.23 × 10-8 -

rs12065553 1 80793118 A 0.2797 1.03 (1.02-1.05) 1.32 × 10-8 -

rs1656369 3 158280085 A 0.6703 1.04 (1.02-1.05) 1.34 × 10-8 -

rs4543289 5 164484948 T 0.5199 1.03 (1.02-1.04) 1.36 × 10-8 -

rs2125716 12 84941429 G 0.2345 1.04 (1.02-1.05) 3.05 × 10-8 - rs2422321 1 rs2422321 A 0.4410 1.03 (1.02-1.04) 3.18 × 10-8 -

rs7044150 9 2982931 T 0.6155 1.03 (1.02-1.05) 4.31 × 10-8 -

Study by Power and colleagues (2017) in a cohort of 22,158 MDD and recurrent MDD cases (stratified

into age of onset) and 133,749 controls.

rs7647854 3 184876783 G 0.1600 1.16 (1.11-1.21) 5.20 × 10-11 - Study by Okbay and colleagues (2016) in a cohort of 180,866 individuals with depressive symptoms.

rs7973260 12 118375486 A 0.1900 1.03 (1.02-1.04) 1.80 × 10-9 KSR2

rs62100776 18 50754633 T 0.5600 1.03 (1.02-1.03) 8.50 × 10-9 DCC Study by Direk and colleagues (2016) in a cohort of 98,345 individuals with MDD and recurrent MDD or

depressive symptoms.

rs9825823 3 61082153 T 0.4600 NC 8.20 × 10-9 FHIT

Chr: Chromosome; RA: risk allele; Freq: frequency of risk allele in controls; OR: odds ratio; CI: confidence interval; NS: not significant. NC: Not calculable. ap-values reported are from the meta-analyses of the discovery

and replication analyses, while the OR and CI reported are with respect to the discovery cohort as the effect was

not reported for the meta-analysis

7.3.2 Study Cohorts, Genotyping Data and Quality-control

CFS Cohort

The present study was conducted using data from two community cohorts. The CFS

cohort was established at the Menzies Health Institute Queensland and consisted of

47 cases (9 males and 38 females) and 55 controls (17 males and 38 females). All

cases met the 1994 Centres for Disease Control criteria for CFS (Fukuda et al.,

1994). Participants were assessed at the National Centre for Neuroimmunology and

Emerging Diseases and provided signed consent to participate in the study, which

was approved by the Griffith University Human Research Ethics Committee.

Genotyping of the CFS cohort was conducted using the Illumina Human Omni

Express Exome-8v1_B Array. The inclusion threshold for common SNP markers

was a minimum call rate of 95%, MAF greater than 0.01, HWE p-value greater than

1 × 10-6, and a genotyping call (GC) threshold greater than 0.7. While the inclusion

threshold for exome SNP markers was a minimum call rate of 95%, a minor allele

count of three in cases and controls separately, a HWE p-value greater than 1 × 10-6,

and a GC threshold greater than 0.7. Finally, only autosomal chromosome SNP


markers were included in the analysis. After quality control the CFS genotype dataset

contained 582,261 common SNP markers and 26,957 exome SNP markers (totalling

609,218 SNP markers), of which 276,392 mapped to 16,486 genes. Given the CFS

cohort was densely genotyped for common and exome variants imputation was not

conducted as sufficient coverage was provided for replication of previous CGA and

GWA results.

Fatigue Cohort

The fatigue cohort was obtained as part of the over 50’s (aged) study conducted at

QIMR Berghofer (QIMRB). Within the study 2,281 twin pairs from the Australian

twin registry were invited to complete a mailed Health and Lifestyle Questionnaire

(Bucholz et al., 1998; Mosing et al., 2012). Informed written consent was obtained

from each participant, and the study was approved by the Human Research Ethics

Committee (HREC) of QIMRB. The fatigue classification utilised within this study

was assessed by the Schedule of Fatigue and Anergia (SOFA) (Hickie et al., 1996),

which was originally designed to identify CFS cases. Consequently, the fatigued

state identified by the SOFA should be similar to the fatigue experienced by CFS

patients. Ten questions are contained in the SOFA, however, a shorter eight item

version was included in the Health and Lifestyle Questionnaire due to two questions

being replicated within the General Health Questionnaire (GHQ) (Goldberg &

Blackwell, 1970), that was also administered to participants. Responses to the eight

SOFA and two GHQ items were used to assess fatigue within the cohort, as

previously detailed (Corfield et al., 2016a). Individuals were classified as fatigued if

they reported three or more of the ten fatigue symptoms (muscle pain at rest, post-

exertional muscle pain, post-exertional muscle fatigue, post-exertional fatigue,

hypersomnia, insomnia, poor concentration, speech problems, poor memory, and

headaches), over the past few weeks. The over 50’s study comprised 3,061

individuals with a fatigue classification.

Genotyping data was available for a subset of the over 50’s study, from a larger

genotyping project—which was conducted in numerous waves. In depth explanation

of the genotyping and quality control methods utilised have previously been detailed

(Mbarek et al., 2016; Medland et al., 2009). Briefly, standard quality-control

measures were utilised across the project (minimum call rate of 95%, MAF ≥ 0.01,

HWE p-value < 1 × 10-6, and GC threshold of 0.7) which was conducted within each


batch. Genotyping data passing QC was merged into a single dataset, which was

imputed up to the haplotype reference consortium (HRC) version r1.1 using the

Michigan Imputation server (Das et al., 2016). The best guess genotypes (reaching

the 95% threshold) for all autosomal chromosome SNP markers were used in the

study. Additionally, ancestry outliers were excluded from the study, whereby,

individuals of non-European decent as determined by principal-components analysis

were removed from the dataset if they were more than six standard deviations from

the mean European population of principle components (i.e., principle component

one and two). Resulting in a final fatigue genotype dataset of 307 cases (85 males

and 222 females) and 744 controls (181 males and 563 females), with 3,197,479 SNP

markers passing quality-control, of which 1,255,622 mapped to 14,129 genes.


GWA analysis within the CFS cohort was conducted in Plink (Purcell et al., 2007).

The analysis was conducted utilising a chi-squared allelic test with one degree of

freedom assuming a log-additive model. Meanwhile, GWA analysis within the

fatigue cohort was conducted in GEMMA (Zhou & Stephens, 2012). The analysis

was conducted utilising univariate linear mixed modelling, including a genetic

relationship matrix, to account for the twin structure of the cohort. Linear mixed

modelling is a powerful technique, which prevents false positive associations by

accounting for underlying structure (namely population stratification or genetic

relatedness) within the data (Yang et al., 2014; Zhou & Stephens, 2012). In 2014,

Eu- Ahsunthornwattana and colleagues (2014) concluded linear-mixed models

effectively controlled for false positives in family-based case-control GWA analyses

after comparison with alternative approaches for analysing binary disease traits. In

particular, GEMMA was shown to robustly control genomic inflation for both binary

and quantitative traits.

Results from the GWA analyses were used to assess the association of

previously implicated SNPs within the study cohorts. Additionally, the level of

linkage disequilibrium within genomic regions of interest from the complete GWA

SNP datasets were further investigated utilising locus zoom plots (Pruim et al.,

2010). Furthermore, the results from the GWA analyses were utilised to conduct a

gene-based analysis, using MAGMA (de Leeuw et al., 2015), with a window size of

0kb (this package and settings were utilised to enable direct comparison with results


from the gene-based analysis in the UKbiobank tiredness dataset (Deary et al.,

2017)). Results from the gene-based analysis were used to assess the association of

previously implicated genes within the study cohorts.

Power calculations were utilised to determine the level of power we have

within the fatigue cohort to replicate the results of previously implicated CGA SNPs

(Purcell et al., 2003). Power calculations (Table 7.1) were conducted assuming a

multiplicative model (which is equivalent to a log-additive model—which is utilised

in Plink), with disease prevalence for fatigue of 30.7% (Corfield et al., In press), a

significance threshold of 0.05, and the 1000 Genomes Project allele frequencies

within a European population for each CGA SNP was utilised (The 1000 Genomes

Project Consortium, 2015).

7.4 RESULTS

7.4.1 Previously Implicated SNPs and Genes

CFS CGA Studies

Of the 151 previously implicated CFS CGA SNPs, 47 passed QC in the CFS dataset

and 72 were in the fatigue dataset. Two SNPs reached the Bonferroni adjusted p-

value of 0.0011 (0.05/47) in the CFS cohort (rs655207, p = 0.0006 and rs4738202, p

= 0.0009) with four additional SNPs reaching nominal significance (rs6650469, p =

0.0016; rs6429157, p = 0.0114; rs12914385, p = 0.0224; and rs951266 p = 0.0235)—

all six SNPs had effects in the same direction as previously reported (Supplementary

Table 7.2). However, all of these SNPs were previously reported within the studies

by Marshall-Gradisnik and colleagues (2015a; 2016a; 2015b) which included

samples from the same CFS cohort analysed in our study. Power calculations

indicated we have over 90% power, at a p-value threshold of 0.05, to identify all 72

of the previously implicated CFS CGA SNPs within the fatigue dataset. However, no

evidence for an association was observed, even at a p-value threshold of 0.05, for any

of the 72 SNPs within the fatigue cohort (Supplementary Table 7.2).

Meanwhile of the 39 previously implicated CFS CGA genes, 37 were included

in the CFS cohort and 35 were included in the fatigue dataset. Four genes (CHRNA5,

p = 0.0358; TRPC4, p = 0.0360; TRPC6, p = 0.0372; and TRPA1, p = 0.0466)

reached nominal significance within the CFS cohort while only one reached nominal

significance (TRPA1, p = 0.0376) in the fatigue cohort. Notably, TRPA1 reached


nominal significance in both cohorts (Supplementary Table 7.3) although the

Bonferroni adjusted p-value of 0.0013 in the CFS cohort and 0.0014 in the fatigue

cohort was not reached.

CFS GWA Studies

Of the 524 previously implicated CFS GWA SNPs, 81 passed QC in the CFS dataset

and 110 were in the fatigue dataset. Three of the SNPs within the CFS cohort

reached nominal significance (rs400322, p = 0.0326; rs197770, p = 0.0359; and

rs9200, p = 0.0369) with rs400322 and rs9200 having effects in the same direction

(Supplementary Table 7.4). However, this does not reach the Bonferroni adjusted p-

value of 0.0006 (0.05/81). Similarly, four SNPs within the fatigue dataset had p-

values less than 0.05 (rs7306948, p = 0.0024; rs6721414, p = 0.0034; rs10121299, p

= 0.0072; and rs1157185, p = 0.0405), although rs1157185 was the only SNP with

the same effect direction (Supplementary Table 7.4). However, this does not reach

the Bonferroni adjusted p-value of 0.0005 (0.05/110).

Meanwhile, of the 319 previously implicated CFS GWA genes, 252 were

included in the CFS dataset and 237 were included in the fatigue dataset. Nineteen of

the genes within our CFS cohort reached nominal significance (PLA2G4A, p =

0.0001; MOB3B, p = 0.0003; AFAP1, p = 0.0075; RASEF, p = 0.0108; CELF4, p =

0.0112; ITGA9, p = 0.0119; ARPC1B, p = 0.0132; TXNRD1, p = 0.0183; NR6A1, p =

0.0228; PTGS2, p = 0.0235; FSHR, p = 0.0244; CDH18, p = 0.0264; SLC13A5, p =

0.0267; PDE4D, p = 0.0285; DNMBP, p = 0.0287; FBXL13, p = 0.0383; SLC35F3, p

= 0.0396; FAM185A, p = 0.0452; CLEC16A, p = 0.0477) (Supplementary Table 7.5),

with PLA2G4A reaching the Bonferroni adjusted p-value of 0.0002 (0.05/252).

Similarly, seven genes within our fatigue cohort reached nominal significance

(PRUNE2; p = 0.0003; RNASEL, p = 0.0045; COLEC11, p = 0.0080; TNFRSF10D, p

= 0.0275; HS6ST3, p = 0.0281; ANK2, p = 0.0458; and ARL15, p = 0.0476)

(Supplementary Table 7.5), however, none of the genes reached the Bonferroni

adjusted p-value of 0.0002 (0.05/237).

Tiredness GWA and Gene-Based Analyses

None of the tiredness GWA SNPs were in the CFS or fatigue cohorts. Although

looking at the regions immediately surrounding the SNPs (50kb upstream and

downstream), no peak in association signal was observed in either cohort

(Supplementary Figure 7.1). However, of the 51 previously implicated tiredness


genes, 50 were included in the CFS cohort and 41 were included in the fatigue

cohort. Comparing the gene-based analysis results from the UKbiobank tiredness

association analysis with results from our cohorts revealed two of the genes within

our CFS cohort reached nominal significance (DAG1, p = 0.0269 and ZBTB37, p =

0.0412) (Supplementary Table 7.6), however, this does not reach the Bonferroni

adjusted p-value of 0.0010 (0.05/50). Similarly, four of the genes within our fatigue

cohort reached nominal significance (PLGRKT, p = 0.0027; KANSL1L, p = 0.0033;

RPE, p = 0.0054; and ZBTB37, p = 0.0457) (Supplementary Table 7.6), however, this

does not reach the Bonferroni adjusted p-value of 0.0012 (0.05/41). Notably,

ZBTB37 reached nominal significance within both cohorts, however, no evidence for

association was observed for the five genes which reached genome-wide significance

in the gene-based analysis of tiredness.

MDD GWA Studies

Of the 21 SNPs associated with MDD 11 passed QC in the CFS cohort and 12 were

in the fatigue dataset. One SNP reached nominal significance and had the same

direction of effect (rs10514299, p = 0.0264) in the CFS cohort (Supplementary Table

7.7), however this did not reach the Bonferroni adjusted significance threshold of

0.0045. Meanwhile, no evidence for association was observed within the fatigue

cohort (Supplementary Table 7.7). Additionally, all eight genes investigated were

included in the CFS and fatigue dataset. However, no evidence for association was

observed in either cohort (Supplementary Table 7.8).

7.4.2 Genome-wide association results

CFS

The GWA analysis conducted for CFS in 47 cases and 55 controls had a genomic

inflation (λ) of 0.99 (Supplementary Figure 7.2). Three SNPs were suggestively

associated (p < 1 × 10-5) with CFS (Table 7.6) in the overall GWA analysis (Figure

7.1) rs12473577 on chromosome 2 (p = 2.65 × 10-6), rs652252 on chromosome 1 (p

= 4.53 × 10-6), and rs1888140 on chromosome 13 (p = 9.66 × 10-6). Regional

association plots for these loci are shown in Supplementary Figures 7.3.

Additionally, three genes were suggestively associated (p < 1 × 10-4) with CFS

(Table 7.7) in the gene-based analysis CAPRIN1 (p = 3.17 × 10-5), EMCN (p = 4.13 ×

10-5), and CDCP2 (p = 5.34 × 10-5).


Fatigue

The GWA analysis conducted for fatigue in 307 cases and 744 controls had a

genomic inflation (λ) of 1.02 (Supplementary Figure 7.4). Fifty-seven SNPs were

suggestively associated with fatigue in the overall GWA analysis (Figure 7.2).

Although high levels of LD are observed between the SNPs (Supplementary Figures

7.5), as shown in the regional association plots for the 6 genomic locations. The top

SNP from each of the six genomic locations is rs874681 on chromosome 11 (p =

8.09 × 10-7), rs16849948 on chromosome 3 (p = 1.81 × 10-6), rs1701470 on

chromosome 10 (p = 5.20 × 10-6), rs352582 on chromosome 5 (p = 5.25 × 10-6),

rs359477 on chromosome 5 (p = 7.34 × 10-6), and rs4237354 on chromosome 10 (p =

8.89 × 10-6) (Table 7.6). Additionally, two genes were suggestively associated (p < 1

× 10-4) with fatigue (Table 7.7) in the gene-based analysis PLXDC2 (p = 5.80 × 10-6)

and TBCA (p = 6.74 × 10-6).


Figure 7.1. Manhattan plot of the chronic fatigue syndrome (CFS) cohort genome-wide association raw p-values. The horizontal dashed line corresponds to the genome-wide

significance threshold (p < 5 × 10-8). The three genes suggestively associated (p < 1 × 10-4) with CFS in gene-based analyses are indicated in green (CDCP2), pink (EMCN),

and blue (CAPRIN1).


Figure 7.2. Manhattan plot of the fatigue cohort genome-wide association raw p-values. The horizontal dashed line corresponds to the genome-wide significance threshold (p

< 5 × 10-8). The two genes suggestively associated (p < 1 × 10-4) with fatigue in gene-based analyses are indicated in pink (TBCA) and blue (PLXDC2).


Table 7.6. Summary of SNPs reaching suggestive significance thresholds for chronic fatigue syndrome and fatigue.

SNP Chr SNP position RA OA Frequency of RA OR (95% CI) p-value Variant type Gene symbol


rs12473577 2 53516993 A G 0.373 3.97 (2.21-7.14) 2.65 × 10-6 Intergenic -

rs652252 1 238749403 C T 0.582 4.91 (2.40-10.03) 4.53 × 10-6 Intergenic - rs1888140 13 44817707 A G 0.164 4.13 (2.16-7.90) 9.66 × 10-6 Intergenic Between SMIM2 and SERP2

Fatigue

rs874681 11 20625343 T C 0.242 1.12 (1.07-1.17) 8.09 × 10-7 Intron SLC6A5 rs16849948 3 94498339 A G 0.031 1.33 (1.18-1.49) 1.81 × 10-6 Intergenic -

rs1701470 10 12154833 C T 0.706 1.11 (1.06-1.16) 5.20 × 10-6 Intron DHTKD1

rs352582 5 77033413 G A 0.631 1.10 (1.06-1.15) 5.25 × 10-6 Intron TBCA rs359477 5 173305432 C T 0.463 1.10 (1.05-1.14) 7.34 × 10-6 Intergenic -

rs4237354 10 20459237 G A 0.554 1.10 (1.05-1.14) 8.89 × 10-6 Intron PLXDC2

Chr: Chromosome; RA: risk allele; OA: other allele; OR: odds ratio; CI: confidence interval.

Table 7.7. Summary of genes reaching suggestive significance thresholds from gene-based association analysis for chronic fatigue syndrome and fatigue.

Gene Chromosome Start Stop Number of SNPsa P-value


CAPRIN1 11 34073230 34124157 6 3.17 × 10-5

EMCN 4 101316498 101439250 24 4.13 × 10-5 CDCP2 1 54604668 54618679 13 5.34 × 10-5

Fatigue

PLXDC2 10 20105118 20575199 549 5.80 × 10-6 TBCA 5 76986995 77072185 114 6.74 × 10-6 aNumber of SNPs found within the start and stop sites.


7.5 DISCUSSION

To date, CGA and GWA studies investigating CFS have been conducted in very

small cohorts. The CFS cohort within the present study is similar in size to the

previously conducted GWA studies. However, the fatigue cohort was larger than any

of the previously published CFS association studies. Considering the self-report

questionnaire utilised within the fatigue cohort was originally designed to identify

CFS cases, the fatigue symptoms assessed are characteristic of those experienced by

individuals with CFS. Therefore, the fatigue cohort utilised was ideal to investigate if

less severe fatigue phenotypes are associated with similar genetic contributions as

CFS.

The inability to replicate the majority of previous association studies results

within our CFS or fatigue cohort indicates they are likely false positives. Although,

the CGA analysis of DISC1 was conducted in a Japanese cohort. Results from the

present study indicate DISC1 is not associated with fatigue in Europeans. However,

the genetic contribution of fatigue may be population specific, therefore, to

determine if the DISC1 association is a true finding the result needs to be replicated

in a Japanese cohort.

Schlauch and colleagues (2016) stated that 28 of the SNPs suggestively

associated with CFS by Smith and colleagues (2011) were contained in the

genotyping data of their cohort. Of these 28 SNPs, Schlauch et al (2016) indicated

rs10509412 is associated with CFS in their cohort. However, this SNP is not listed in

their Supplementary material (Supplementary Table 7.1) which included all

autosomal SNPs associated with CFS at a threshold of p < 3.3 × 10-5. No evidence of

association was observed with rs10509412 in our CFS cohort (p = 0.0870).

Of the remaining previously implicated SNPs and genes, the only evidence for

replication was observed for rs655207 located in an intron within TRPC4 (p =

0.0006), rs4738202 located in an intron within TRPA1 (p = 0.0009), and PLA2G4A

(p = 0.0001) in the CFS cohort. All three genes are associated with the

immunological system, providing support for the immune system playing a key role

in the pathophysiology of CFS. However, the majority of candidate gene studies

have focused on genes associated with the immune system; therefore it is not

possible to determine if genes related to other systems in the human body are


associated with CFS from this analysis. Additionally, both SNPs which were

replicated in the CFS cohort were identified by Marshall-Gradisnik and colleagues

(2015a; 2016b; 2015b) and it is unknown if these samples are independent from our

CFS cohort. Therefore, further investigation in larger cohorts are necessary to

determine if these genes contribute to the development of a CFS and other fatigue

phenotypes. Particularly considering the results from the UKbiobank tiredness GWA

and gene-based analysis (which were vastly more powerful) were not replicated in

either cohort. Although the fatigue severity investigated in the current study was

higher—indicating different genes may be associated with varying severities to the

fatigue continuum further follow-up of the novel tiredness associations is warranted.

Results from the GWA and gene-based analyses in our CFS cohort identified

three suggestively associated SNPs (rs12473577, rs652252, and rs1888140) and

three suggestively associated genes (CAPRIN1, EMCN, and CDCP2). However,

considering the small sample size of this cohort and that evidence for an association

was not observed in the fatigue cohort these results require replication. Similarly,

results from the GWA and gene-based analysis in our fatigue cohort identified six

genomic regions of interest (with the top SNPs (rs874681, rs16849948, rs1701470,

rs352582, rs359477, and rs4237354) and two suggestively associated genes

(PLXDC2 and TBCA). Considering the larger cohort size, the SNPs and genes

suggestively associated with fatigue are less likely to be false positives. Furthermore,

considering the clusters of SNPs suggestively associated with fatigue on

chromosome 3, chromosome 5, chromosome 10, and chromosome 11 indicates these

regions warrant further investigation in larger fatigue cohorts.

The genes which are implicated from these genomic locations are SLC6A5,

DHTKD1, TBCA, and PLXDC2. SLC6A5 (solute carrier family 6 member 5), which

encodes a neurotransmitter transporter. Functional characterisation of SLC6A5

indicates the gene plays a crucial role in extracellular glycine clearance during

glycine-mediated neurotransmission. Considering changes in brain morphology and

decreased basal ganglia activation have been implicated in the pathophysiology of

CFS (Barnden et al., 2015; Miller et al., 2014; Tang et al., 2015), this region warrants

further investigation to determine the functional implications and importance of

SLC6A5 involvement in fatigue. DHTKD1 (dehydrogenase E1 and transketolase

domain containing 1), which encodes a mitochondrial protein. Functional


characterisation of DHTKD1 indicates the gene plays a crucial role in mitochondrial

biogenesis and reactive oxygen species degradation (Xu et al., 2013). Considering

mitochondrial dysfunction has previously been implicated in the pathophysiology of

CFS (Booth et al., 2012; Gorman et al., 2015; Meeus et al., 2013; Myhill et al.,

2013), this region warrants further investigation to determine the functional

implications and importance of DHTKD1 genetic variants in fatigue. TBCA (tublin

folding cofactor A), encodes a protein involved in the correct folding of beta-tublin

and is essential for cell viability (Nolasco et al., 2005; Tian et al., 1996). The

metabolism of proteins is the main pathway related to TBCA. Therefore, there are

numerous roles TBCA could play in the development or maintenance of a fatigued

state. Finally, PLXDC2 (plexin domain containing 2) which is a transmembrane

protein that acts as a cell-surface receptor for the pigment epithelium-derived factor

protein (Cheng et al., 2014). However, little else is known about the functionality of

PLXDC2. Therefore, we are unable to speculate about the functional role of PLXD2

in fatigue.

To our knowledge, this is the first study investigating the molecular genetics of

fatigue. Although, Deary and colleagues (2017) investigated the molecular genetics

of self-reported tiredness in the UKbiobank, our fatigue phenotype is more severe

and the physical symptoms reported are those required for diagnosis with CFS. The

increased phenotypic similarity between our fatigue cohort and individuals with CFS

indicates the underlying genetics are likely comparable. Therefore, enabling us to

replicate our CFS analysis in a larger sample and address the main limitation of this

study, which was the small CFS cohort sample size. Although, the CFS cohort

sample size was comparable to previously published CFS GWA studies and our main

aim was to replicate previous findings. The use of twin data, in the fatigue cohort,

could be considered a limitation. However, Minică and colleagues (2014) have

shown that the type I error rate is not affected by the inclusion of MZ twin pairs

within GWA studies. Finally, our analyses within the fatigue cohort were restricted

to individuals of European origin because the generalizability of the study was

underpowered.

In summary, results presented in the present study indicate that the majority of

previously reported nominally and genome-wide significant SNPs and genes from

CGA and GWA studies of CFS are likely false positives. Similar studies


investigating the genetic association findings of other common, complex, traits have

comparable results (de Vries et al., 2015; Hirschhorn et al., 2002). However, our

study has identified three SNPs and three genes which warrant further investigation

in larger CFS and fatigue samples. Additionally, six genomic locations of interest

which are potentially associated with fatigue were identified. Future investigations

into the genetic contribution of fatigue and CFS should concentrate on utilising

larger, well characterised cohorts with stringent quality control, to advance our

knowledge of the underlying biology of these traits, while minimising the publication

of false positive association results.

Chapter 8: General Discussion 159

Chapter 8: General Discussion

The findings detailed within this dissertation, evaluated the comorbidity and genetics

of fatigue and depression. Within this chapter, a summary of results will be

presented, followed by an explanation of the strengths and limitations of the analyses

conducted within this thesis, before the future directions and conclusions are

outlined.

8.1 SUMMARY OF FINDINGS

Initially, a phenotypic analysis was conducted, which involved a symptomatic

analysis of fatigue and depression. In Chapter 3 the prevalence and risk of co-

occurring fatigue and depression were reported, in addition to the results of the

symptomatic analysis. Within the study cohort, 6.7% of individuals were fatigued

and depressed. The key finding identified within Chapter 3 was that the overlapping

symptoms of fatigue and depression do not facilitate the association between the

traits. Specifically, a significantly increased risk of depression in fatigued

individuals, compared to non-fatigued individuals and the total population, was

observed independently of the overlapping symptoms. Furthermore, a significantly

increased risk of fatigue in depressed individuals, compared to non-depressed

individuals and the total population was observed independently of the overlapping

symptoms. To our knowledge, this is the first time the risk of co-occurring fatigue

and depression has been assessed independently of their overlapping symptoms.

In Chapter 4 and 5 the familiality and heritability of fatigue and depression

were investigated. In Chapter 4, a familial contribution to fatigue was identified with

a significant additive genetic contribution of 40%. Importantly, sex-specific effects

were not identified. Comparison of these findings with previous studies (Schur et al.,

2007; Sullivan et al., 2005), led to the conclusion that the etiology and heritability of

fatigue may vary across the lifespan. Similarly, within Chapter 5 the heritability of a

broad depression phenotype (major or minor depression) was higher in older adults

(aged 50-92) at 48% compared 40% in younger adults (aged 23-38)—indicating the

etiology and heritability of depression may also vary across the lifespan. This finding

is substantiated by Power and colleagues (2017) recent finding that rs7647854 is

160 Chapter 8: General Discussion

associated with MDD onset in adults aged 27 or older. Further investigation within

Chapter 5 revealed minor and major depression lie on a genetic continuum, with

minor depression having an additive genetic contribution of 37%, in older adults. To

our knowledge, this is the first investigation into the heritability of minor depression.

Although, a genetic continuum between MDD and depressive symptoms or

subthreshold depression phenotypes (such as MiDD or dysthymia) has been

suggested (Ayuso-Mateos et al., 2010; Lobo & Agius, 2012). This is the first study

that substantiates this hypothesis, with Direk and colleagues (2016), identification of

a risk locus associated with a broad depression phenotype (of MDD and depression

symptoms) providing further evidence supporting the clinical relevance of

investigating a broad depression phenotype.

The results of Chapters 4 and 5 were used as the foundations for the genetic

relationship analysis conducted in Chapter 6. A significant additive genetic

correlation of 0.71 was identified between depression and fatigue. The significance

of additive genetic factors common to both depression and fatigue remained

significant independently of the traits overlapping symptoms. Previous studies

provided some evidence that additive genetic factors were common to both

depression and fatigue (Fowler et al., 2006; Hur et al., 2012). However, the role of

the traits overlapping symptoms had never been investigated. Our results provide

further support to the findings of Chapter 3, which indicated the association between

fatigue and depression is independent of the traits overlapping symptoms. Rather, the

association is likely attributable to the non-causal genetic model of inheritance.

Finally, within Chapter 7 the molecular genetics of fatigue and CFS were

investigated. Four genomic regions of interest, potentially associated with fatigue

were identified, which are located downstream of C5orf38 on chromosome 5, within

DHTKD1 on chromosome 10, within SLC6A5 on chromosome 11, and between

TSHZ2 and ZNF217 on chromosome 20. Meanwhile, the inability to replicate

previous CFS CGA and in particular GWA results highlights the need for increased

cohort sizes, which are deeply phenotyped, and collaboration between researchers to

facilitate the identification of robustly associated risk loci for CFS and prevent the

publication of questionable and inaccurate results.


8.2 LIMITATIONS

The use of twin data throughout this dissertation can be viewed as both an advantage

and disadvantage. Potential confounding from health-care seeking behaviour was

removed through the use of a community-based cohort. Although the use of self-

report rather than interview-based fatigue and in particular depression introduces the

potential for misclassification, thereby, reducing power throughout the studies.

However, the criteria for prolonged fatigue and CF classification only requires self-

reported fatigue. Therefore, clinical fatigue data experienced over a comparable

timeframe is not available. Meanwhile, the classification of minor and major

depression was conducted based on the DSM major depressive episode criteria which

are used to establish a clinical diagnosis of MDD. Furthermore, the prevalence of

minor and major depression within the study cohorts utilised throughout this

dissertation were consistent with previously published cohorts with similar age

ranges (Centers for Disease Control and Prevention, 2010).

The analyses conducted within Chapter 4, 5, and 6 would not have been possible

without the utilisation of twin data. The main limitation of twin modelling is that the

data may not be representative of the overall population. However, utilisation of twin

data has numerous advantages including the ability to estimate the relative

contribution of genetic and environmental factors to the variation of a trait. The

calculation of heritability estimates is based on the assumption that MZ twin pairs

share 100% of their genes while DZ twin pairs only share 50%. Although SNP-based

heritability and genetic correlation estimates can be calculated, using LD score

regression (Bulik-Sullivan et al., 2015a; Bulik-Sullivan et al., 2015b), the small

number of individuals with genotyping data (307 cases and 744 controls) was not

powerful enough for these analyses to be conducted within this dissertation. The

limitation of the small fatigue and CFS cohorts was observed within Chapter 7 by the

inability to identify genetic risk loci significantly associated with either phenotype.

Although identification of genomic regions of interest was possible within the fatigue

cohort that warrant further investigation, the small sample size prevented

investigation of gene-gene and gene-environment interactions. Furthermore, the

contribution of genetic pleiotropy (where a single gene confers an increased risk to

multiple traits) was unable to be investigated for fatigue and depression within this

dissertation due to the small sample sizes. However, adequately sized cohorts which

162 Chapter 8: General Discussion

enable the identification of robustly associated gene-gene and gene-environment

interactions are just starting to be published for complex traits. Therefore, the

identification of gene-gene and gene-environment interactions and genetic pleiotropy

should be viewed as possible avenues for future investigation for fatigue and

depression, rather than limitations.

8.3 FUTURE DIRECTIONS

This dissertation increases our understanding of the comorbidity and genetics of

fatigue and depression. However, the results detailed and technological advances

lead to numerous possible avenues for future investigations. Possible analyses to

investigate the molecular genetic overlap between fatigue and depression include the

utilisation of summary statistics from GWA analyses conducted in the psychiatric

genetic consortium, 23&Me, and UK Biobank datasets to determine if polygenic risk

scores for MDD, self-reported major depression, and self-reported tiredness,

respectively, can predict fatigue.

Building on recent findings by Power et al (2017) and Pearson et al (2016),

additional avenues for future investigation within large population cohorts include

the utilisation of additional phenotypic data within genetic analyses to reduce

heterogeneity. Based on the results of Chapter 4 and previous studies we

hypothesised the etiology of fatigue may differ with age. One possible avenue for

future investigation is to conduct a GWA analysis, based on the method utilised by

Power and colleagues (2017), to investigate the genetics of fatigue across the

lifespan. This avenue of investigation could facilitate the elucidation of the molecular

mechanisms of fatigue that differ with age. Furthermore, the investigation should

consider the possibility of genetic effects that are specific to males or females, within

different age groups. Results from these analyses could then be utilised to investigate

changes in SNP-based heritability estimates and SNP effects with age (and sex) for

fatigue. Similar methods could be utilised to investigate potential changes in SNP-

based heritability estimates and SNP effects with age and sex for depression.

The investigation into the SNP-based heritability of depression symptoms

clusters conducted by Pearson et al (2016), can be expanded to identify genetic risk

loci associated with specific depression symptom clusters in larger GWA cohorts.

Similarly, the analysis could be replicated within large fatigue cohorts to determine if


the SNP-based heritability varies within specific fatigue symptom clusters and

identify genetic risk loci associated with the varying symptom domains.

Additionally, the analysis could be expanded to investigate the comorbidity of

fatigue and depression by utilising key distinguishing symptoms to select more

homogeneous subgroups, as suggested within Chapter 3. The molecular mechanisms

underlying the comorbidity could be further investigated through the use of fatigue

symptom clustering within a GWA and SNP-based heritability analysis of

depression. Considering we determined within Chapter 3 and 6 that overlapping

symptoms are not driving the comorbidity between fatigue and depression replicating

this analysis by investigating the depression symptom clustering within GWA and

SNP-based heritability analysis of fatigue may provide insight into the functional

biology associated with varying phenotype presentations. Importantly, minor

depression cases should be utilised within these analyses given the results detailed in

Chapter 5 revealed minor and major depression exist on a single genetic continuum.

Finally, within Chapter 7 we emphasised the importance of utilising large,

well-characterised cohorts with stringent quality control measures. Recent advances

in statistical methods have produced the development of a genome-wide association

study by proxy (GWAX) method, whereby first-degree relatives of cases are used (as

proxy cases) in randomly ascertained cohorts (Liu et al., 2017). Considering the low

prevalence of CFS, the GWAX approach may provide an effective strategy to gain

power by increasing study cohort size, thereby enabling identification and replication

of genetic risk loci robustly associated with the trait.

8.4 CONCLUSIONS

In conclusion, the high levels of comorbidity observed between fatigue and

depression are independent of the traits overlapping symptoms and is likely

attributable to the non-causal genetic relationship which exists between the traits.

Whereby, a significant proportion of the comorbidity between fatigue and depression

is explained by shared genetic factors.

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Appendices 191

Appendices

Appendix A

Chapter 3: Supplementary Data

Supplementary Table 3.1. Fatigue and depression symptom counts in the phenotypic subgroups.

Symptom

Non-depressed,

non-fatigued

(N = 1,750)

Non-depressed,

fatigued

(N = 590)

MiDD,

non-fatigued

(N = 83)

MDD,

non-fatigued

(N = 16)

MiDD,

fatigued

(N = 142)

MDD,

fatigued

(N = 34)

Complete cohort

(N = 2,615)

Fatigue symptoms Muscle pain at rest 40 (2.3) 194 (32.9) 5 (6.0) 0 (0.0) 48 (33.8) 12 (35.3) 299 (11.4)

Post-exertional muscle pain 213 (12.2) 392 (66.4) 8 (9.6) 1 (6.3) 95 (66.9) 19 (55.9) 728 (27.8)

Post-exertional muscle fatigue 211 (12.1) 486 (82.4) 11 (13.3) 1 (6.3) 102 (71.8) 27 (79.4) 838 (32.0) Post-exertional fatigue 75 (4.3) 338 (57.3) 9 (10.8) 1 (6.3) 85 (59.9) 22 (64.7) 530 (20.3)

Hypersomnia 116 (6.6) 242 (41.0) 4 (4.8) 0 (0.0) 59 (41.5) 18 (52.9) 439 (16.8)

Insomnia 53 (3.0) 77 (13.1) 22 (26.5) 11 (68.8) 53 (37.3) 26 (76.5) 242 (9.3) Poor concentration 37 (2.1) 85 (14.4) 12 (14.5) 7 (43.8) 68 (47.9) 23 (67.6) 232 (8.9)

Speech problems 112 (6.4) 195 (33.1) 2 (2.4) 0 (0.0) 48 (33.8) 14 (41.2) 371 (14.2)

Poor memory 115 (6.6) 224 (38.0) 6 (7.2) 0 (0.0) 58 (40.8) 15 (44.1) 418 (16.0) Headaches 191 (10.9) 234 (39.7) 13 (15.7) 1 (6.3) 75 (52.8) 16 (47.1) 530 (20.3)

Depression symptoms Depressed mood 38 (2.2) 17 (2.9) 59 (71.1) 16 (100.0) 104 (73.2) 32 (94.1) 266 (10.2)

Anhedonia 51 (2.9) 44 (7.5) 61 (73.5) 16 (100.0) 95 (66.9) 33 (97.1) 300 (11.2)

Insomnia 8 (0.5) 17 (2.9) 11 (13.3) 14 (87.5) 35 (24.6) 30 (88.2) 115 (4.4)

Psychomotor agitation 2 (0.1) 6 (1.0) 3 (3.6) 6 (37.5) 18 (12.7) 18 (52.9) 53 (2.0)

Loss of energy 3 (0.2) 2 (0.3) 5 (6.0) 7 (43.8) 8 (5.6) 17 (50.0) 42 (1.6)

Feeling worthless 55 (3.1) 50 (8.5) 51 (61.4) 16 (100.0) 94 (66.2) 33 (97.1) 299 (11.4) Inability to concentrate 19 (1.1) 22 (3.7) 22 (26.5) 9 (56.3) 37 (26.1) 23 (67.6) 132 (5.0)

Suicidal thoughts 1 (0.1) 1 (0.2) 4 (4.8) 7 (43.8) 11 (7.7) 17 (50.0) 41 (1.6)

For each of the seven phenotypic subgroups, the number of individuals and proportion of individuals (in brackets) reporting a specific symptom.

192 Appendices

Appendix B


Supplementary Table 6.1. Cross-tabulationa of depression and fatigue status within twin pairs.

Non-depressed MiDD MDD Total

Complete twin pairs

Non-fatigued 784 61 14.5 859.5

Fatigued 301.5 48.5 10.5 360.5

Total 1085.5 109.5 25 1220

MZ

Total

Non-fatigued 411 29.5 5.5 446

Fatigued 157.5 31.5 8 197 Total 568.5 61 13.5 643

MZ

female

Non-fatigued 307.5 19 5 331.5

Fatigued 124 28.5 7 159.5

Total 431.5 47.5 12 491

MZ

male

Non-fatigued 103.5 10.5 0.5 114.5

Fatigued 33.5 3 1 37.5 Total 137 13.5 1.5 152

DZss

Total

Non-fatigued 212 16.5 6 234.5

Fatigued 90.5 10.5 0.5 101.5 Total 302.5 27 6.5 336

DZss

female

Non-fatigued 164.5 15 4 183.5

Fatigued 70 9 0.5 79.5 Total 234.5 24 4.5 263

DZss

male

Non-fatigued 47.5 1.5 2 51

Fatigued 20.5 1.5 0 22 Total 68 3 2 73

DZos

female-male

Non-fatigued 163 11 5 179

Fatigued 54 7 1 62 Total 217 18 6 241

DZos

male-female


Fatigued 53 6 3 62

Total 212 25 4 241 aTables were made symmetrical in same-sex twin pairs by averaging over using either twin 1 or twin 2 as proband. For

example, within the complete twin pairs there was 298 twin pairs where twin 1 was fatigued and twin 2 was non-depressed and 305 twin pairs where twin 2 was fatigued and twin 1 was non-depressed. Therefore, the cross-tabulation averaging over

twin 1 or twin 2 as proband is (298+305)/2=301.5.

Appendices 193

Supplementary Table 6.2. Relative riska of depression and fatigue in males and females.

Proband–co-twin Depressed–non-fatigued Depressed–fatigued Fatigued–non-depressed Fatigued–depressed

Complete cohort (N = 1220) 0.78 (0.67-0.91) 1.58 (1.28-1.96) 0.92 (0.87-0.96) 1.86 (1.36-2.56)

MZ total (N = 643) 0.65 (0.51-0.83) 1.91 (1.49-2.46) 0.87 (0.80-0.94) 2.56 (1.67-3.90)

MZ female (N = 491) 0.57 (0.41-0.78) 2.08 (1.61-2.68) 0.84 (0.77-0.92) 3.07 (1.90-4.98) MZ male (N = 152) 0.97 (0.71-1.34) 1.09 (0.45-2.65) 0.99 (0.87-1.12) 1.11 (0.38-3.28)

DZss total (N = 336) 0.96 (0.75-1.23) 1.10 (0.66-1.84) 0.99 (0.91-1.07) 1.13 (0.57-2.23)

DZss female (N = 262) 0.95 (0.72-1.25) 1.12 (0.64-1.95) 0.98 (0.89-1.08) 1.15 (0.55-2.40) DZss male (N = 73) 1.00 (0.55-1.82) 1.00 (0.25-3.98) 1.00 (0.87-1.15) 0.99 (0.16-6.30)

DZos female-male (N = 241) 0.88 (0.67-1.16) 1.37 (0.77-2.46) 0.96 (0.86-1.06) 0.96 (0.86-1.08)

DZos male-female (N = 241) 0.92 (0.71-1.19) 1.34 (0.73-2.47) 1.44 (0.65-3.21) 1.30 (0.62-2.70) aRelative risks were calculated with respect to non-depressed or non-fatigued status in twin 1.

Supplementary Table 6.3. Relative riska of depression and fatigue within monozygotic (MZ), same-sex dizygotic (DZss), and opposite-sex dizygotic (DZos) twin pairs.

Proband–co-twin MZ (N = 643) DZss (N = 336) DZos (N = 241)

(Female-Male)

DZos (N = 241)

(Male-Female)

MiDD–non-fatigued 0.67 (0.52-0.87) 0.87 (0.64-1.18) 1.01 (0.80-1.28) 0.81 (0.56-1.19)

MiDD–fatigued 1.86 (1.41-2.45) 1.31 (0.79-2.16) 0.96 (0.46-2.00) 1.56 (0.84-2.92) MDD–non-fatigued 0.56 (0.30-1.07) 1.22 (0.89-1.66) 0.33 (0.06-1.82) 1.11 (0.77-1.60)

MDD–fatigued 2.14 (1.35-3.39) 0.48 (0.08-2.98) 3.00 (1.63-5.53) 0.67 (0.11-4.07)

Fatigued–MiDD 2.41 (1.51-3.86) 1.48 (0.71-3.09) 1.84 (0.75-4.53) 0.91 (0.38-2.18) Fatigued–MDD 3.29 (1.12-9.61) 0.39 (0.05-3.17) 0.58 (0.07-4.85) 8.66 (0.92-81.74) aRelative risks were calculated with respect to non-depressed or non-fatigued status in twin 1.

194 Appendices

Supplementary Table 6.4. Polychoric correlations with their 95% confidence intervals for fatigue

and depression in twin pairs according to zygosity.

Twin 1 Twin 2

Minor depression (non-depressed, MiDD)

MiDD Fatigue MiDD Fatigue Monozygotic twin pairs (N = 643 pairs)

Twin 1 MiDD 1.00

Fatigue 0.49 (0.35-0.64)a 1.00 Twin 2 MiDD 0.37 (0.17-0.56)b 0.32 (0.16-0.47)c 1.00

Fatigue 0.32 (0.16-0.49)c 0.43 (0.31-0.54)b 0.42 (0.28-0.57)a 1.00

Dizygotic twin pairs (N = 577 pairs) Twin 1 MiDD 1.00

Fatigue 0.49 (0.34-0.64)a 1.00

Twin 2 MiDD 0.21 (-0.04-0.45)b 0.18 (-0.01-0.37)c 1.00 Fatigue 0.03 (-0.17-0.22)c 0.14 (0.001-0.28)b 0.54 (0.40-0.69)a 1.00

Major depression (non-depressed, MDD)

MDD Fatigue MDD Fatigue

Monozygotic twin pairs (N = 643 pairs)

Twin 1 MDD 1.00

Fatigue 0.36 (0.12-0.59)a 1.00 Twin 2 MDD 0.46 (-0.01-0.93)b 0.43 (0.15-0.70)c 1.00

Fatigue 0.28 (0.03-0.52)c 0.43 (0.31-0.54)b -a 1.00

Dizygotic twin pairs (N = 577 pairs) Twin 1 MDD 1.00

Fatigue 0.47 (0.20-0.74)a 1.00 Twin 2 MDD -b -0.32 (-0.67-0.02)c 1.00

Fatigue 0.14 (-0.18-0.46)c 0.14 (0.001-0.28)b 0.56 (0.33-0.79)a 1.00

Three-category depression (non-depressed, MiDD, MDD)

Depression Fatigue Depression Fatigue


Twin 1 Depression 1.00 Fatigue 0.46 (0.33-0.59)a 1.00

Twin 2 Depression 0.48 (0.33-0.62)b 0.36 (0.22-0.50)c 1.00

Fatigue 0.32 (0.18-0.47)c 0.43 (0.31-0.54)b 0.51 (0.39-0.64)a 1.00 Dizygotic twin pairs (N = 577 pairs)

Twin 1 Depression 1.00

Fatigue 0.50 (0.36-0.64)a 1.00 Twin 2 Depression 0.24 (0.04-0.43)b 0.05 (-0.13-0.23)c 1.00

Fatigue 0.06 (-0.12-0.24)c 0.14 (0.001-0.28)b 0.57 (0.45-0.70)a 1.00 aPhenotypic correlation between depression and fatigue. bTwin correlation. cCross-twin cross-trait correlation

Supplementary Table 6.5. Bivariate heritability model fits.

Model Minus two log-

likelihood χ2 Δ df p-value AIC

MiDD

ACE 4228.32 -5401.68

AE 4228.71 0.39 3 0.94 -5407.29

CE 4239.54 11.21 3 0.01 -5396.46

E 4295.28 66.96 6 1.71 × 10-12 -5346.72

ADE 4227.72 -0.61 0 1.00 -5402.28 MDD

ACE 3330.32 -5961.68

AE 3330.62 0.30 3 0.96 -5967.38

CE 3342.63 12.32 3 0.01 -5955.37

E 3381.46 51.14 6 2.78 × 10-9 -5922.50

ADE 3327.68 -2.64 0 1.00 -5964.33 Three-category depression

ACE 4647.74 -5080.26

AE 4649.28 1.54 3 0.67 -5084.72

CE 4661.68 13.94 3 3.00 × 10-3 -5072.32

E 4724.88 77.14 6 1.39 × 10-14 -5015.12

ADE 4647.42 -0.32 0 1.00 -5080.58

Note: Fit statistics are compared to ACE model and best-fitting models are indicated in bold. χ2: likelihood-ratio chi-squared

test; Δ df: difference in degrees of freedom.

Appendices 195

Supplementary Table 6.6. Co-twin control of minor depression and fatigue.

Sample

Risk factor: MiDD

Outcome: fatigue

Risk factor: fatigue

Outcome: MiDD

General Population 7.39 (4.33-12.61) [1,247] 7.39 (4.33-12.61) [1,247]

Discordant DZ 5.75 (2.89-11.48) [78] 5.47 (2.58-11.59) [201] Discordant MZ 1.77 (0.96-3.25) [85] 1.83 (0.97-3.46) [192]

MiDD: Minor depressive disorder. aWithin the General Population sample N is the number of individuals, while within the

Discordant MZ and DZ samples N is the number of discordant twin pairs.

Supplementary Table 6.7. Cross-tabulationa of depression and fatigue status within twin pairs

independent of overlapping symptoms.

Non-depressed MiDD MDD Total

MZ Total

Non-fatigued 264.5 4.5 0 269

Fatigued 34 2 0 36

Total 298.5 6.5 0 305

DZss Total

Non-fatigued 126.5 2.5 0 129

Fatigued 24 1 0 25

Total 150.5 3.5 0 154

DZos female-male


Fatigued 10 1 0 11

Total 100 3 0 103

DZos male-female


Fatigued 15 0 0 15

Total 99 4 0 103 aTables were made symmetrical in same-sex twin pairs by averaging over using either twin 1 or twin 2 as proband.

Supplementary Table 6.8. Relative riska of depression and fatigue estimated independently of

overlapping symptoms within monozygotic (MZ), same-sex dizygotic (DZss), and opposite-sex

dizygotic (DZos) twin pairs.

Proband - co-twin MZ (N = 319) DZss (N = 160) DZos (N = 119)

(Female-Male)

DZos (N = 119)

(Male-Female)

Depressed -non-fatigued 0.78 (0.47-1.31) 0.85 (0.44-1.65) 1.18 (1.08-1.28) 0.74 (0.33-1.65) Depressed - fatigued 2.70 (0.82-8.93) 1.79 (0.33-9.77) 0 3.33 (0.61-18.34)

Fatigued - non-depressed 0.96 (0.89-1.04) 0.98 (0.90-1.06) 0.93 (0.41-42.45) 1.05 (1.00-1.10)

Fatigued - depressed 3.32 (0.65-16.93) 2.06 (0.21-20.16) 4.18 (0.41-42.45) 0 aRelative risks were calculated with respect to non-depressed or non-fatigued status in twin 1.

Supplementary Table 6.9. Polychoric correlations with their 95% confidence intervals for fatigue

and depression independent of overlapping symptoms in twin pairs according to zygosity.

Twin 1 Twin 2

MiDD Fatigue MiDD Fatigue


Twin 1 MiDD 1.00 Fatigue 0.38 (0.01-0.74)a 1.00

Twin 2 MiDD -b 0.37 (-0.05-0.80)c 1.00

Fatigue 0.23 (-0.18-0.65)c 0.20 (-0.07-0.48)b -0.77 (-1.00-1.00)a 1.00 Dizygotic twin pairs (N = 577 pairs)

Twin 1 MiDD 1.00

Fatigue 0.50 (0.19-0.80)a 1.00 Twin 2 MiDD -b 0.16 (-0.38-0.71)c 1.00

Fatigue 0.08 (-0.33-0.49)c 0.10 (-0.18-0.38)b 0.40 (-0.04-0.85)a 1.00 aPhenotypic correlation between depression and fatigue. bTwin correlation. cCross-twin cross-trait correlation.

196 Appendices

Supplementary Figure 6.1. Path diagram of the bivariate Cholesky model variance estimates (with

their 95% confidence intervals) for minor depressive disorder (MiDD) and fatigue. The observed traits

are shown in the rectangles. Similarly, the latent variables (additive genetic factors: A, and unique

environmental factors: E) are depicted by circles. The arrows depict the relationship between the

variables. The genetic and environmental correlations between MiDD and fatigue were 0.76 (0.52-

1.00) and 0.29 (0.11-0.46), respectively.


their 95% confidence intervals) for major depressive disorder (MDD) and fatigue. The observed traits

are shown in the rectangles. Similarly, the latent variables (additive genetic factors: A, and unique

environmental factors: E) are depicted by circles. The arrows depict the relationship between the

variables. The genetic and environmental correlations between MDD and fatigue were 0.57 (0.21-

1.00) and 0.46 (0.09-0.52), respectively.

Appendices 197


their 95% confidence intervals) for three-category depression (non-depressed, MiDD, MDD) and

fatigue. The observed traits are shown in the rectangles. Similarly, the latent variables (additive

genetic factors: A, and unique environmental factors: E) are depicted by circles. The arrows depict the

relationship between the variables. The genetic and environmental correlations between MiDD and

fatigue were 0.71 (0.51-0.93) and 0.35 (0.18-0.51), respectively.


their 95% confidence intervals) for depression and fatigue independent of their overlapping

symptomology. The observed traits are shown in the rectangles. Similarly, the latent variables

(additive genetic factors: A, and unique environmental factors: E) are depicted by circles. The arrows

depict the relationship between the variables. The genetic and environmental correlations between

MiDD and fatigue were 1.00 (0.43-1.00) and 0.11 (0.00-0.43), respectively.

198 Appendices

Appendix C


Supplementary Table 7.1. Calculation of Hardy-Weinberg equilibrium (HWE) p-value and allele frequencies for 442 SNPs reported by Schlauch and colleagues (2016) as

being associated with chronic fatigue syndrome. SNP ID’s highlighted in red do not meet the reported MAF (< 0.05) or HWE (p < 8 E-4) thresholds in cases, controls, or the

total cohort separately.

SNP Reported GWA p-value RA Freq RA

OA Freq OA HWE p-value

Case Control Total Case Control Total Case Control Total

rs12235235 5.76 × 10-16 T 0.476 0.079 0.288 C 0.524 0.921 0.713 1.30 × 10-7 8.40 × 10-5 0.0486

rs10144138 6.99 × 10-14 T 0.429 0.026 0.238 C 0.571 0.974 0.763 1.20 × 10-6 0.8677 0.0053

rs17120254 5.20 × 10-13 A 1.000 0.605 0.813 T 0.000 0.395 0.188 NC 0.0473 0.8906

rs41493945 6.25 × 10-13 A 0.393 0.013 0.213 G 0.607 0.987 0.788 2.70 × 10-5 0.9345 0.0158

rs3788079 3.42 × 10-12 C 0.345 0.000 0.181 A 0.655 1.000 0.819 0.0006 NC 0.0477

rs41378447 1.06 × 10-11 T 0.524 0.092 0.319 C 0.476 0.908 0.681 0.0006 0.1887 0.5613

rs3913434 1.26 × 10-11 T 0.381 0.013 0.206 C 0.619 0.987 0.794 0.0009 0.9345 0.1008

rs5967529 1.69 × 10-11 A 0.833 0.211 0.538 G 0.167 0.789 0.463 0.0016 0.7579 9.80 × 10-7

rs254577 2.35 × 10-11 C 0.798 0.263 0.544 T 0.202 0.737 0.456 0.1001 0.7579 0.0501

rs270838 3.61 × 10-11 C 0.476 0.105 0.300 A 0.524 0.895 0.700 1.30 × 10-7 0.4683 0.0010

rs1523773 4.73 × 10-11 T 0.321 0.000 0.169 A 0.679 1.000 0.831 0.0021 NC 0.0694

rs16827966 5.32 × 10-11 T 0.357 0.013 0.194 C 0.643 0.987 0.806 0.0003 0.9345 0.0316

rs2249954 5.47 × 10-11 G 0.464 0.079 0.281 A 0.536 0.921 0.719 1.20 × 10-5 0.5972 0.0167

rs8029503 5.66 × 10-11 T 0.488 0.105 0.306 C 0.512 0.895 0.694 1.50 × 10-5 0.3187 0.0653

rs3095598 1.02 × 10-10 C 0.429 0.039 0.244 T 0.571 0.961 0.756 0.0030 0.8000 0.2876

rs7010471 2.49 × 10-10 G 0.405 0.053 0.238 A 0.595 0.947 0.763 1.00 × 10-5 0.7320 0.0053

rs6757577 2.77 × 10-10 A 0.440 0.066 0.263 G 0.560 0.934 0.738 0.0001 0.6642 0.0425

rs11157573 2.97 × 10-10 G 0.488 0.158 0.331 A 0.512 0.842 0.669 1.50 × 10-5 0.0123 0.1609

rs16987633 3.46 × 10-10 A 0.774 0.355 0.575 G 0.226 0.645 0.425 1.90 × 10-5 0.0477 0.0111

rs12312259 3.60 × 10-10 C 0.548 0.158 0.363 T 0.452 0.842 0.638 4.00 × 10-5 0.2478 0.0290

rs948440 3.92 × 10-10 C 0.512 0.132 0.331 T 0.488 0.868 0.669 1.50 × 10-5 0.3503 0.0159

Footnotes for Supplementary Table 7.1 are on page 214.

Appendices 199

Supplementary Table 7.1. Continued Calculation of Hardy-Weinberg equilibrium (HWE) p-value and allele frequencies for 442 SNPs reported by Schlauch and colleagues

(2016) as being associated with chronic fatigue syndrome. SNP ID’s highlighted in red do not meet the reported MAF (< 0.05) or HWE (p < 8 E-4) thresholds in cases,

controls, or the total cohort separately.




rs6445832 4.36 × 10-10 G 0.429 0.079 0.263 A 0.571 0.921 0.738 1.20 × 10-6 0.5972 0.0015

rs9585049 5.25 × 10-10 T 0.393 0.039 0.225 A 0.607 0.961 0.775 0.0004 0.8000 0.0505

rs7220341 5.41 × 10-10 G 0.655 0.276 0.475 A 0.345 0.724 0.525 0.0006 0.0186 0.0236

rs2816751 5.43 × 10-10 C 0.536 0.184 0.369 T 0.464 0.816 0.631 5.80 × 10-7 0.7547 0.0048

rs2200706 5.48 × 10-10 T 0.679 0.263 0.481 C 0.321 0.737 0.519 0.0021 0.1723 0.2575

rs17255510 6.61 × 10-10 C 0.679 0.171 0.438 T 0.321 0.829 0.563 0.3434 0.0308 0.0098

rs6892217 6.61 × 10-10 T 0.702 0.224 0.475 C 0.298 0.776 0.525 0.0447 0.3047 0.1860

rs17112444 8.02 × 10-10 A 0.369 0.026 0.206 G 0.631 0.974 0.794 0.0018 0.8677 0.1008

rs7849492 9.95 × 10-10 C 0.500 0.145 0.331 T 0.500 0.855 0.669 3.70 × 10-6 0.7893 0.0159

rs686190 1.11 × 10-9 G 0.393 0.053 0.231 A 0.607 0.947 0.769 2.70 × 10-5 0.7320 0.0071

rs16826918 1.13 × 10-9 G 0.464 0.079 0.281 A 0.536 0.921 0.719 0.0017 0.5972 0.1979

rs12317807 1.47 × 10-9 T 0.417 0.132 0.281 C 0.583 0.868 0.719 3.70 × 10-6 0.0567 0.0167

rs5974598 1.55 × 10-9 C 0.762 0.303 0.544 T 0.238 0.697 0.456 0.0428 0.6895 0.2902

rs1932556 1.63 × 10-9 T 0.988 0.605 0.806 G 0.012 0.395 0.194 0.9378 0.9573 0.0320

rs6797416 1.71 × 10-9 G 0.286 0.000 0.150 A 0.714 1.000 0.850 0.0095 NC 0.1145

rs2733416 1.71 × 10-9 G 0.286 0.000 0.150 A 0.714 1.000 0.850 0.0095 NC 0.1145

rs17035358 1.72 × 10-9 A 0.405 0.066 0.244 G 0.595 0.934 0.756 1.00 × 10-5 0.6642 0.0039

rs17368935 1.72 × 10-9 G 0.405 0.066 0.244 A 0.595 0.934 0.756 1.00 × 10-5 0.6642 0.0039

rs6679280 1.72 × 10-9 T 0.405 0.066 0.244 C 0.595 0.934 0.756 1.00 × 10-5 0.6642 0.0039

rs3867246 1.88 × 10-9 T 0.440 0.132 0.294 G 0.560 0.868 0.706 3.40 × 10-7 0.6272 0.0015

rs689462 2.08 × 10-9 C 0.464 0.092 0.288 A 0.536 0.908 0.713 0.0017 0.1887 0.3788

rs9285128 2.15 × 10-9 A 0.571 0.250 0.419 C 0.429 0.750 0.581 1.20 × 10-6 0.7456 0.0056

rs822027 2.52 × 10-9 A 0.321 0.013 0.175 G 0.679 0.987 0.825 0.0021 0.9345 0.0578

rs11168709 2.52 × 10-9 T 0.321 0.013 0.175 C 0.679 0.987 0.825 0.0021 0.9345 0.0578

rs12055682 2.99 × 10-9 G 0.631 0.263 0.456 A 0.369 0.737 0.544 0.0137 0.0003 0.1315

rs17047694 3.66 × 10-9 T 0.571 0.303 0.444 A 0.429 0.697 0.556 1.20 × 10-6 0.2427 0.0313

rs10978470 4.32 × 10-9 G 0.512 0.158 0.344 A 0.488 0.842 0.656 1.50 × 10-5 0.9488 0.0273


200 Appendices







rs890527 4.60 × 10-9 T 0.560 0.289 0.431 A 0.440 0.711 0.569 3.40 × 10-7 0.5199 0.0074

rs11062852 4.84 × 10-9 C 0.500 0.197 0.356 A 0.500 0.803 0.644 6.00 × 10-5 0.0099 0.2939

rs16992281 5.22 × 10-9 A 0.357 0.000 0.188 C 0.643 1.000 0.813 0.2695 NC 0.0021

rs6675622 5.94 × 10-9 T 0.857 0.395 0.638 C 0.143 0.605 0.363 0.2801 0.9573 0.0916

rs6863118 6.22 × 10-9 G 0.464 0.105 0.294 A 0.536 0.895 0.706 0.0002 0.4683 0.0354

rs10121299 6.62 × 10-9 C 0.643 0.289 0.475 T 0.357 0.711 0.525 0.0003 0.5199 0.3581

rs12014391 6.66 × 10-9 A 0.714 0.211 0.475 G 0.286 0.789 0.525 0.2348 2.10 × 10-7 8.40 × 10-8

rs12391243 6.68 × 10-9 C 0.821 0.355 0.600 G 0.179 0.645 0.400 0.1589 0.0233 0.0039

rs1041296 6.89 × 10-9 G 0.560 0.211 0.394 A 0.440 0.789 0.606 0.0001 0.1991 0.2604

rs11027583 7.03 × 10-9 T 0.429 0.092 0.269 G 0.571 0.908 0.731 0.0003 0.1887 0.1140

rs12305678 7.87 × 10-9 G 0.560 0.224 0.400 A 0.440 0.776 0.600 7.60 × 10-6 0.9265 0.0253

rs9581771 7.96 × 10-9 T 0.440 0.092 0.275 C 0.560 0.908 0.725 0.0001 0.5317 0.0231

rs4022211 9.09 × 10-9 G 0.774 0.316 0.556 T 0.226 0.684 0.444 0.0120 0.1791 0.0175

rs16883408 1.06 × 10-8 C 0.631 0.237 0.444 G 0.369 0.763 0.556 0.0018 0.9060 0.4272

rs41456945 1.07 × 10-8 C 0.333 0.026 0.188 T 0.667 0.974 0.813 0.0012 0.8677 0.0390

rs361236 1.07 × 10-8 A 0.560 0.276 0.425 G 0.440 0.724 0.575 7.60 × 10-6 0.0887 0.1145

rs1007540 1.17 × 10-8 G 0.655 0.368 0.519 A 0.345 0.632 0.481 0.0006 0.0074 0.8326

rs7143222 1.54 × 10-8 T 0.262 0.000 0.138 C 0.738 1.000 0.863 0.0215 NC 0.1539

rs17092382 1.54 × 10-8 A 0.262 0.000 0.138 G 0.738 1.000 0.863 0.0215 NC 0.1539

rs7549528 1.54 × 10-8 C 0.262 0.000 0.138 T 0.738 1.000 0.863 0.0215 NC 0.1539

rs6854376 1.71 × 10-8 T 0.369 0.053 0.219 C 0.631 0.947 0.781 0.0002 0.7320 0.0123

rs16902672 1.77 × 10-8 C 0.583 0.211 0.406 G 0.417 0.789 0.594 0.0366 0.0012 0.1948

rs4473594 1.81 × 10-8 A 0.464 0.092 0.288 G 0.536 0.908 0.713 0.0119 0.5317 0.3788

rs10737169 2.51 × 10-8 A 0.607 0.224 0.425 G 0.393 0.776 0.575 0.0037 0.3047 0.8369

rs7883119 2.57 × 10-8 G 0.798 0.355 0.588 T 0.202 0.645 0.413 0.4912 1.10 × 10-5 1.50 × 10-5

rs4623336 2.68 × 10-8 T 0.476 0.158 0.325 C 0.524 0.842 0.675 3.30 × 10-6 0.9488 0.0055

rs2748997 2.76 × 10-8 C 0.440 0.079 0.269 T 0.560 0.921 0.731 0.0487 0.0887 0.8996


Appendices 201







rs584569 2.84 × 10-8 A 0.381 0.066 0.231 G 0.619 0.934 0.769 6.70 × 10-5 0.6642 0.0071

rs13339179 2.89 × 10-8 T 0.345 0.039 0.200 C 0.655 0.961 0.800 0.0006 0.8000 0.0253

rs1222400 2.89 × 10-8 T 0.345 0.039 0.200 C 0.655 0.961 0.800 0.0006 0.8000 0.0253

rs2882361 3.02 × 10-8 G 0.917 0.513 0.725 C 0.083 0.487 0.275 0.5558 0.5134 0.2742

rs41464146 3.22 × 10-8 C 0.333 0.026 0.188 T 0.667 0.974 0.813 0.0109 0.8677 0.1835

rs9446695 3.46 × 10-8 T 0.321 0.026 0.181 A 0.679 0.974 0.819 0.0021 0.8677 0.0477

rs7290437 3.52 × 10-8 G 0.476 0.250 0.369 A 0.524 0.750 0.631 3.30 × 10-6 0.0231 0.0625

rs12607783 4.31 × 10-8 A 0.417 0.132 0.281 T 0.583 0.868 0.719 3.70 × 10-6 0.6272 0.0032

rs606324 4.39 × 10-8 A 0.357 0.079 0.225 G 0.643 0.921 0.775 0.0003 0.0887 0.0505

rs2869820 4.39 × 10-8 T 0.250 0.000 0.131 A 0.750 1.000 0.869 0.0308 NC 0.1766

rs6643261 4.55 × 10-8 A 0.452 0.026 0.250 G 0.548 0.974 0.750 0.0008 7.10 × 10-10 2.50 × 10-9

rs17133553 4.74 × 10-8 A 0.679 0.276 0.488 T 0.321 0.724 0.513 0.0182 0.3728 0.6586

rs2816936 4.91 × 10-8 A 0.940 0.513 0.738 G 0.060 0.487 0.263 0.6817 0.1955 0.0015

rs1915603 5.15 × 10-8 G 0.286 0.039 0.169 A 0.714 0.961 0.831 0.0095 5.70 × 10-5 0.3083

rs17052315 5.97 × 10-8 A 0.357 0.053 0.213 G 0.643 0.947 0.788 0.0003 0.7320 0.0158

rs6502875 5.97 × 10-8 G 0.643 0.316 0.488 A 0.357 0.684 0.513 0.0003 0.8744 0.1776

rs10047684 7.31 × 10-8 A 0.583 0.237 0.419 T 0.417 0.763 0.581 0.0008 0.0557 0.0209

rs11038285 7.69 × 10-8 G 0.286 0.013 0.156 T 0.714 0.987 0.844 0.0095 0.9345 0.0977

rs7701654 7.69 × 10-8 G 0.286 0.013 0.156 A 0.714 0.987 0.844 0.0095 0.9345 0.0977

rs6662412 7.69 × 10-8 G 0.286 0.013 0.156 A 0.714 0.987 0.844 0.0095 0.9345 0.0977

rs7301442 7.69 × 10-8 T 0.286 0.013 0.156 A 0.714 0.987 0.844 0.0095 0.9345 0.0977

rs2193766 7.80 × 10-8 G 1.000 0.763 0.888 A 0.000 0.237 0.113 NC 0.0557 0.2569

rs17060061 7.80 × 10-8 G 1.000 0.763 0.888 A 0.000 0.237 0.113 NC 0.0557 0.2569

rs9301483 8.08 × 10-8 A 0.643 0.250 0.456 G 0.357 0.750 0.544 0.0241 0.1597 0.5439

rs7537461 8.53 × 10-8 C 0.607 0.289 0.456 A 0.393 0.711 0.544 0.0004 0.1521 0.4563

rs1362859 8.99 × 10-8 G 0.583 0.329 0.463 C 0.417 0.671 0.538 6.50 × 10-5 0.0338 0.3421

rs9964872 9.92 × 10-8 A 0.321 0.026 0.181 G 0.679 0.974 0.819 0.0182 0.8677 0.2200


202 Appendices







rs17643851 9.93 × 10-8 G 0.429 0.079 0.263 A 0.571 0.921 0.738 0.0872 0.0887 0.7783

rs10788258 1.04 × 10-7 T 0.429 0.092 0.269 C 0.571 0.908 0.731 0.0030 0.5317 0.1140

rs8057267 1.06 × 10-7 G 0.476 0.171 0.331 A 0.524 0.829 0.669 5.40 × 10-5 0.3096 0.0565

rs10074876 1.07 × 10-7 C 0.310 0.026 0.175 T 0.690 0.974 0.825 0.0037 0.8677 0.0578

rs1961484 1.21 × 10-7 A 0.429 0.066 0.256 G 0.571 0.934 0.744 0.2801 0.6642 0.6613

rs16973831 1.22 × 10-7 T 0.238 0.000 0.125 A 0.762 1.000 0.875 0.0428 NC 0.2013

rs4510466 1.22 × 10-7 C 0.238 0.000 0.125 A 0.762 1.000 0.875 0.0428 NC 0.2013

rs13393078 1.24 × 10-7 C 0.393 0.079 0.244 T 0.607 0.921 0.756 0.0004 0.5972 0.0228

rs2204978 1.26 × 10-7 A 0.488 0.211 0.356 G 0.512 0.789 0.644 7.60 × 10-7 0.7579 0.0027

rs41330648 1.28 × 10-7 G 0.524 0.118 0.331 A 0.476 0.882 0.669 0.7459 0.4076 0.2621

rs6479969 1.28 × 10-7 G 0.774 0.382 0.588 C 0.226 0.618 0.413 0.0582 0.7141 0.8581

rs16877795 1.49 × 10-7 G 0.500 0.171 0.344 A 0.500 0.829 0.656 0.0007 0.3096 0.2241

rs197770 1.50 × 10-7 G 0.631 0.382 0.513 A 0.369 0.618 0.488 0.0002 0.0899 0.3678

rs17426290 1.54 × 10-7 T 0.464 0.250 0.363 C 0.536 0.750 0.638 5.80 × 10-7 0.1597 0.0077

rs2017563 1.55 × 10-7 A 0.357 0.013 0.194 G 0.643 0.987 0.806 0.2695 0.9345 0.0042

rs690607 1.56 × 10-7 A 0.476 0.158 0.325 G 0.524 0.842 0.675 0.0006 0.1991 0.2118

rs1859790 1.56 × 10-7 T 0.405 0.092 0.256 C 0.595 0.908 0.744 0.0002 0.5317 0.0126

rs7119924 1.58 × 10-7 C 0.452 0.184 0.325 T 0.548 0.816 0.675 2.20 × 10-6 0.4431 0.0055

rs275154 1.72 × 10-7 G 0.417 0.145 0.288 A 0.583 0.855 0.713 6.50 × 10-5 0.1146 0.0486

rs6508891 1.75 × 10-7 T 0.429 0.118 0.281 A 0.571 0.882 0.719 2.30 × 10-5 0.4076 0.0032

rs11914436 1.93 × 10-7 A 0.607 0.368 0.494 G 0.393 0.632 0.506 0.0004 0.0074 0.8219

rs41385645 1.99 × 10-7 T 0.488 0.132 0.319 A 0.512 0.868 0.681 0.0134 0.6272 0.5613

rs7552454 2.11 × 10-7 A 0.488 0.197 0.350 G 0.512 0.803 0.650 7.60 × 10-7 0.6229 0.0008

rs11021876 2.21 × 10-7 T 0.274 0.013 0.150 C 0.726 0.987 0.850 0.0145 0.9345 0.1145

rs7739542 2.38 × 10-7 C 1.000 0.776 0.894 C 0.000 0.224 0.106 NC 0.0757 0.2876

rs16994314 2.42 × 10-7 T 0.464 0.118 0.300 A 0.536 0.882 0.700 0.0119 0.4076 0.2415

rs12086522 2.61 × 10-7 T 0.381 0.092 0.244 G 0.619 0.908 0.756 0.0009 0.1887 0.0950


Appendices 203







rs12408925 2.61 × 10-7 G 0.667 0.276 0.481 A 0.333 0.724 0.519 0.6434 3.50 × 10-5 0.0008

rs2980018 2.65 × 10-7 T 0.738 0.250 0.506 C 0.262 0.750 0.494 0.3717 0.0231 0.0001

rs6504560 2.69 × 10-7 T 0.321 0.026 0.181 C 0.679 0.974 0.819 0.0979 0.8677 0.6360

rs5942699 2.70 × 10-7 G 0.464 0.053 0.269 A 0.536 0.947 0.731 0.0676 0.0036 4.00 × 10-5

rs12559754 2.86 × 10-7 G 0.857 0.447 0.663 A 0.143 0.553 0.338 0.2801 0.0039 0.0006

rs1610024 2.89 × 10-7 A 0.607 0.250 0.438 G 0.393 0.750 0.563 0.1083 0.0017 0.0939

rs213981 2.94 × 10-7 G 0.679 0.329 0.513 T 0.321 0.671 0.488 0.0182 0.0338 0.3738

rs17024760 3.22 × 10-7 T 0.655 0.211 0.444 C 0.345 0.789 0.556 0.1735 0.5043 0.0175

rs4242794 3.27 × 10-7 A 0.226 0.000 0.119 G 0.774 1.000 0.881 0.0582 NC 0.2281

rs4792493 3.27 × 10-7 G 0.226 0.000 0.119 A 0.774 1.000 0.881 0.0582 NC 0.2281

rs4982735 3.27 × 10-7 C 0.226 0.000 0.119 A 0.774 1.000 0.881 0.0582 NC 0.2281

rs6973776 3.27 × 10-7 T 0.226 0.000 0.119 C 0.774 1.000 0.881 0.0582 NC 0.2281

rs4422316 3.27 × 10-7 T 0.226 0.000 0.119 C 0.774 1.000 0.881 0.0582 NC 0.2281

rs233122 3.27 × 10-7 C 0.250 0.000 0.131 T 0.750 1.000 0.869 0.6070 NC 0.5420

rs2421122 3.27 × 10-7 C 0.238 0.000 0.125 T 0.762 1.000 0.875 0.2401 NC 0.7983

rs7847862 3.47 × 10-7 G 0.631 0.329 0.488 A 0.369 0.671 0.513 0.0002 0.4139 0.0249

rs283825 4.41 × 10-7 G 0.595 0.342 0.475 A 0.405 0.658 0.525 0.0002 0.0658 0.3581

rs822020 5.15 × 10-7 C 0.357 0.026 0.200 T 0.643 0.974 0.800 0.6657 0.8677 0.0504

rs12629385 5.27 × 10-7 T 0.381 0.066 0.231 A 0.619 0.934 0.769 0.1704 0.0274 0.6498

rs11056347 5.27 × 10-7 A 0.345 0.053 0.206 G 0.655 0.947 0.794 0.0062 0.7320 0.1008

rs9946817 5.35 × 10-7 C 0.357 0.158 0.263 T 0.643 0.842 0.738 0.0034 0.0002 0.7672

rs579751 5.59 × 10-7 C 0.810 0.382 0.606 A 0.190 0.618 0.394 0.6002 0.0021 0.0004

rs17041554 5.63 × 10-7 A 0.619 0.237 0.438 G 0.381 0.763 0.563 0.0428 0.9060 0.8871

rs7726463 5.64 × 10-7 G 0.679 0.355 0.525 A 0.321 0.645 0.475 0.0021 0.5729 0.1715

rs12965947 5.82 × 10-7 A 0.405 0.053 0.238 G 0.595 0.947 0.763 0.9392 0.7320 0.1246

rs609539 6.14 × 10-7 C 0.262 0.013 0.144 T 0.738 0.987 0.856 0.0215 0.9345 0.1332

rs6957524 6.14 × 10-7 G 0.262 0.013 0.144 A 0.738 0.987 0.856 0.0215 0.9345 0.1332


204 Appendices







rs6093591 6.67 × 10-7 T 0.476 0.184 0.338 C 0.524 0.816 0.663 3.30 × 10-6 0.1639 0.0004

rs41441747 6.72 × 10-7 C 0.512 0.197 0.363 G 0.488 0.803 0.638 0.0002 0.1296 0.0077

rs16956158 6.93 × 10-7 G 0.762 0.447 0.613 A 0.238 0.553 0.388 0.0428 0.0039 0.0603

rs7613828 7.03 × 10-7 A 1.000 0.737 0.875 G 0.000 0.263 0.125 NC 0.2523 0.0049

rs7768988 7.15 × 10-7 T 0.655 0.237 0.456 C 0.345 0.763 0.544 0.4923 0.0936 0.0501

rs17130776 7.17 × 10-7 T 0.536 0.197 0.375 C 0.464 0.803 0.625 0.0017 0.6229 0.1211

rs2223341 7.27 × 10-7 G 0.464 0.053 0.269 A 0.536 0.947 0.731 0.0144 0.0036 2.90 × 10-6

rs17865437 7.39 × 10-7 A 0.869 0.671 0.775 C 0.131 0.329 0.225 0.0020 0.0025 0.5008

rs17861907 7.39 × 10-7 G 0.869 0.671 0.775 A 0.131 0.329 0.225 0.0020 0.0025 0.5008

rs7895391 7.78 × 10-7 T 0.476 0.145 0.319 C 0.524 0.855 0.681 0.0051 0.7893 0.2732

rs9283919 7.87 × 10-7 G 0.381 0.053 0.225 A 0.619 0.947 0.775 0.9503 0.0036 0.0586

rs9977796 8.56 × 10-7 G 0.310 0.000 0.163 A 0.690 1.000 0.838 0.0041 NC 1.30 × 10-6

rs7672066 8.56 × 10-7 G 0.214 0.000 0.113 A 0.786 1.000 0.888 0.0771 NC 0.2569

rs11873202 8.56 × 10-7 A 0.214 0.000 0.113 G 0.786 1.000 0.888 0.0771 NC 0.2569

rs866781 8.56 × 10-7 A 0.214 0.000 0.113 G 0.786 1.000 0.888 0.0771 NC 0.2569

rs3732196 8.56 × 10-7 T 0.214 0.000 0.113 C 0.786 1.000 0.888 0.0771 NC 0.2569

rs16987453 8.56 × 10-7 G 0.226 0.000 0.119 T 0.774 1.000 0.881 0.3112 NC 0.8911

rs12407818 8.75 × 10-7 C 0.536 0.263 0.406 T 0.464 0.737 0.594 1.20 × 10-5 0.7579 0.0159

rs4289946 8.81 × 10-7 C 0.571 0.237 0.413 G 0.429 0.763 0.588 4.40 × 10-6 0.3098 0.0001

rs5909213 8.81 × 10-7 C 0.571 0.237 0.413 T 0.429 0.763 0.588 4.40 × 10-6 0.3098 0.0001

rs5909214 8.81 × 10-7 G 0.571 0.237 0.413 A 0.429 0.763 0.588 4.40 × 10-6 0.3098 0.0001

rs5909220 8.81 × 10-7 T 0.571 0.237 0.413 C 0.429 0.763 0.588 4.40 × 10-6 0.3098 0.0001

rs9419277 8.93 × 10-7 G 0.286 0.026 0.163 A 0.714 0.974 0.838 0.0095 0.8677 0.0827

rs17085969 8.99 × 10-7 T 0.988 0.750 0.875 C 0.012 0.250 0.125 0.9378 0.0399 0.2013

rs16842140 9.07 × 10-7 G 0.512 0.263 0.394 A 0.488 0.737 0.606 0.0020 0.0048 0.8502

rs13398697 1.05 × 10-6 A 0.345 0.066 0.213 T 0.655 0.934 0.788 0.0006 0.6642 0.0158

rs10146102 1.09 × 10-6 T 0.381 0.079 0.238 C 0.619 0.921 0.763 0.0074 0.5972 0.1209


Appendices 205







rs1873717 1.23 × 10-6 T 0.524 0.184 0.363 C 0.476 0.816 0.638 0.0293 0.1639 0.2242

rs41363145 1.32 × 10-6 C 0.369 0.079 0.231 G 0.631 0.921 0.769 0.0018 0.5972 0.0392

rs6871885 1.42 × 10-6 A 0.393 0.092 0.250 T 0.607 0.908 0.750 0.0037 0.5317 0.0736

rs7321094 1.45 × 10-6 T 0.571 0.237 0.413 C 0.429 0.763 0.588 0.0030 0.9060 0.2281

rs17781246 1.48 × 10-6 G 0.512 0.289 0.406 A 0.488 0.711 0.594 1.50 × 10-5 0.1521 0.0514

rs7529216 1.52 × 10-6 G 0.274 0.039 0.163 T 0.726 0.961 0.838 0.0954 5.70 × 10-5 0.9264

rs12331711 1.52 × 10-6 G 0.381 0.066 0.231 A 0.619 0.934 0.769 0.1704 0.6642 0.8611

rs17079111 1.53 × 10-6 G 0.333 0.053 0.200 A 0.667 0.947 0.800 0.0109 0.7320 0.1242

rs7610618 1.57 × 10-6 T 0.262 0.013 0.144 C 0.738 0.987 0.856 0.1333 0.9345 0.5530

rs16985794 1.58 × 10-6 C 0.298 0.066 0.188 G 0.702 0.934 0.813 0.0060 0.0274 0.1835

rs11010290 1.61 × 10-6 T 0.798 0.395 0.606 C 0.202 0.605 0.394 0.4912 0.0056 0.0020

rs9913705 1.65 × 10-6 G 0.250 0.013 0.138 A 0.750 0.987 0.863 0.0308 0.9345 0.1539

rs14541 1.70 × 10-6 G 0.548 0.224 0.394 A 0.452 0.776 0.606 0.0251 0.0499 0.7798

rs16861920 1.76 × 10-6 C 0.321 0.053 0.194 T 0.679 0.947 0.806 0.0021 0.7320 0.0316

rs17127809 1.76 × 10-6 T 0.321 0.053 0.194 C 0.679 0.947 0.806 0.0021 0.7320 0.0316

rs2207301 1.83 × 10-6 G 0.571 0.132 0.363 A 0.429 0.868 0.638 0.0384 0.0009 4.40 × 10-6

rs4843884 1.83 × 10-6 G 0.369 0.066 0.225 A 0.631 0.934 0.775 0.0714 0.6642 0.5008

rs12417706 1.90 × 10-6 T 0.452 0.105 0.288 C 0.548 0.895 0.713 0.3204 0.3187 0.4489

rs3095168 1.91 × 10-6 A 0.500 0.237 0.375 G 0.500 0.763 0.625 3.70 × 10-6 0.9060 0.0029

rs17780243 1.95 × 10-6 T 0.595 0.368 0.488 A 0.405 0.632 0.513 1.00 × 10-5 0.9123 0.0071

rs6927507 1.99 × 10-6 G 0.429 0.184 0.313 A 0.571 0.816 0.688 1.20 × 10-6 0.7547 0.0004

rs1350060 2.01 × 10-6 A 0.452 0.211 0.338 G 0.548 0.789 0.663 2.20 × 10-6 0.7579 0.0022

rs6940702 2.18 × 10-6 A 0.202 0.000 0.106 G 0.798 1.000 0.894 0.1001 NC 0.2876

rs10114442 2.18 × 10-6 T 0.202 0.000 0.106 C 0.798 1.000 0.894 0.1001 NC 0.2876

rs9628158 2.18 × 10-6 T 0.202 0.000 0.106 C 0.798 1.000 0.894 0.1001 NC 0.2876

rs7154569 2.18 × 10-6 C 0.202 0.000 0.106 T 0.798 1.000 0.894 0.1001 NC 0.2876

rs2803453 2.18 × 10-6 A 0.226 0.000 0.119 G 0.774 1.000 0.881 0.8956 NC 0.3516


206 Appendices







rs2516025 2.19 × 10-6 C 0.357 0.013 0.194 T 0.643 0.987 0.806 0.0018 0.9345 5.50 × 10-7

rs4812100 2.21 × 10-6 G 0.631 0.289 0.469 A 0.369 0.711 0.531 0.0714 0.0264 0.2769

rs7347140 2.23 × 10-6 T 0.286 0.026 0.163 C 0.714 0.974 0.838 0.0663 0.8677 0.3607

rs10928930 2.23 × 10-6 T 0.286 0.026 0.163 C 0.714 0.974 0.838 0.0663 0.8677 0.3607

rs9920285 2.36 × 10-6 A 0.298 0.039 0.175 G 0.702 0.961 0.825 0.0060 0.8000 0.0578

rs10133617 2.36 × 10-6 T 0.298 0.039 0.175 A 0.702 0.961 0.825 0.0060 0.8000 0.0578

rs16844808 2.42 × 10-6 T 0.298 0.079 0.194 C 0.702 0.921 0.806 0.0447 8.40 × 10-5 0.9982

rs17098846 2.42 × 10-6 A 0.274 0.026 0.156 G 0.726 0.974 0.844 0.0145 0.8677 0.0977

rs682564 2.42 × 10-6 A 0.274 0.026 0.156 T 0.726 0.974 0.844 0.0145 0.8677 0.0977

rs3017495 2.42 × 10-6 T 0.274 0.026 0.156 C 0.726 0.974 0.844 0.0145 0.8677 0.0977

rs5909082 2.49 × 10-6 T 0.548 0.224 0.394 C 0.452 0.776 0.606 4.00 × 10-6 0.3998 5.70 × 10-5

rs17475512 2.56 × 10-6 G 0.690 0.382 0.544 A 0.310 0.618 0.456 0.0290 0.0172 0.2902

rs271662 2.60 × 10-6 C 0.464 0.132 0.306 T 0.536 0.868 0.694 0.2027 0.0567 0.4309

rs12300888 2.65 × 10-6 C 0.369 0.079 0.231 A 0.631 0.921 0.769 0.0714 0.0887 0.8611

rs11679695 2.80 × 10-6 G 0.440 0.224 0.338 A 0.560 0.776 0.663 7.60 × 10-6 0.3047 0.0106

rs243391 2.96 × 10-6 G 0.560 0.276 0.425 A 0.440 0.724 0.575 0.0001 0.9362 0.0418

rs889083 3.14 × 10-6 G 0.452 0.224 0.344 A 0.548 0.776 0.656 0.0042 0.0038 0.8223

rs41368852 3.28 × 10-6 G 0.345 0.039 0.200 A 0.655 0.961 0.800 0.9968 0.8000 0.2085

rs12551218 3.31 × 10-6 T 0.310 0.079 0.200 A 0.690 0.921 0.800 0.0037 0.0887 0.1242

rs16890805 3.34 × 10-6 T 0.345 0.105 0.231 C 0.655 0.895 0.769 0.0006 0.3187 0.0392

rs6757543 3.68 × 10-6 G 0.512 0.237 0.381 T 0.488 0.763 0.619 0.0020 0.0936 0.4403

rs6735919 3.82 × 10-6 T 0.476 0.250 0.369 C 0.524 0.750 0.631 3.30 × 10-6 0.5887 0.0048

rs4714199 3.92 × 10-6 C 0.512 0.158 0.344 A 0.488 0.842 0.656 0.9971 0.2478 0.4433

rs589402 3.92 × 10-6 T 0.952 0.697 0.831 A 0.048 0.303 0.169 0.7459 0.0075 0.0694

rs9668748 4.02 × 10-6 C 0.429 0.079 0.263 T 0.571 0.921 0.738 0.4179 0.5972 0.0440

rs7121660 4.10 × 10-6 A 0.786 0.447 0.625 G 0.214 0.553 0.375 0.0771 0.7956 0.7205

rs16987589 4.27 × 10-6 A 0.238 0.013 0.131 G 0.762 0.987 0.869 0.0428 0.9345 0.1766


Appendices 207







rs6656441 4.27 × 10-6 C 0.238 0.013 0.131 T 0.762 0.987 0.869 0.0428 0.9345 0.1766

rs9698174 4.45 × 10-6 A 0.452 0.237 0.350 G 0.548 0.763 0.650 4.70 × 10-9 0.9060 5.40 × 10-7

rs7830366 4.48 × 10-6 T 0.619 0.316 0.475 A 0.381 0.684 0.525 0.0009 0.1791 0.0236

rs10988052 4.71 × 10-6 G 0.345 0.079 0.219 A 0.655 0.921 0.781 0.0006 0.5972 0.0123

rs7272593 4.89 × 10-6 G 0.310 0.053 0.188 A 0.690 0.947 0.813 0.0037 0.7320 0.0390

rs7742257 4.89 × 10-6 T 0.310 0.053 0.188 C 0.690 0.947 0.813 0.0037 0.7320 0.0390

rs4144897 5.12 × 10-6 T 0.405 0.118 0.269 C 0.595 0.882 0.731 0.5726 0.0001 0.0668

rs6450296 5.16 × 10-6 A 0.381 0.118 0.256 G 0.619 0.882 0.744 0.0009 0.4679 0.0563

rs12001751 5.44 × 10-6 T 0.190 0.000 0.100 C 0.810 1.000 0.900 0.1273 NC 0.3203

rs6497951 5.44 × 10-6 T 0.190 0.000 0.100 C 0.810 1.000 0.900 0.1273 NC 0.3203

rs11163916 5.44 × 10-6 G 0.190 0.000 0.100 A 0.810 1.000 0.900 0.1273 NC 0.3203

rs7404102 5.44 × 10-6 A 0.190 0.000 0.100 G 0.810 1.000 0.900 0.1273 NC 0.3203

rs1486178 5.44 × 10-6 T 0.190 0.000 0.100 C 0.810 1.000 0.900 0.1273 NC 0.3203

rs340170 5.44 × 10-6 G 0.190 0.000 0.100 A 0.810 1.000 0.900 0.1273 NC 0.3203

rs7960674 5.44 × 10-6 C 0.190 0.000 0.100 A 0.810 1.000 0.900 0.1273 NC 0.3203

rs496731 5.52 × 10-6 T 0.762 0.474 0.625 G 0.238 0.526 0.375 0.0428 0.0238 0.1896

rs2981884 5.62 × 10-6 T 1.000 0.816 0.913 T 0.000 0.184 0.088 NC 0.1639 0.3911

rs9311374 5.62 × 10-6 C 1.000 0.803 0.906 T 0.000 0.197 0.094 NC 0.6229 0.6960

rs7019328 5.66 × 10-6 T 0.964 0.645 0.813 C 0.036 0.355 0.188 0.8103 0.1185 0.0021

rs41423649 5.73 × 10-6 C 0.964 0.711 0.844 T 0.036 0.289 0.156 0.8103 0.0849 0.4189

rs16970196 5.76 × 10-6 A 0.952 0.750 0.856 G 0.048 0.250 0.144 0.0021 0.0399 0.5530

rs10009657 5.76 × 10-6 G 0.952 0.750 0.856 A 0.048 0.250 0.144 0.0021 0.0399 0.5530

rs1157185 5.84 × 10-6 T 0.571 0.237 0.413 C 0.429 0.763 0.588 0.0872 0.0936 0.5221

rs1367696 5.84 × 10-6 T 0.571 0.237 0.413 C 0.429 0.763 0.588 0.0872 0.0936 0.5221

rs7011650 5.89 × 10-6 T 0.524 0.263 0.400 C 0.476 0.737 0.600 5.40 × 10-5 0.7579 0.0253

rs16980810 6.05 × 10-6 A 0.464 0.158 0.319 G 0.536 0.842 0.681 0.0119 0.2478 0.1073

rs6008155 6.32 × 10-6 G 0.286 0.039 0.169 C 0.714 0.961 0.831 0.0095 0.8000 0.0694


208 Appendices







rs479448 6.34 × 10-6 C 0.262 0.026 0.150 T 0.738 0.974 0.850 0.0215 0.8677 0.1145

rs11917596 6.34 × 10-6 C 0.262 0.026 0.150 T 0.738 0.974 0.850 0.0215 0.8677 0.1145

rs17085519 6.34 × 10-6 G 0.262 0.026 0.150 A 0.738 0.974 0.850 0.0215 0.8677 0.1145

rs2237406 6.34 × 10-6 T 0.262 0.026 0.150 C 0.738 0.974 0.850 0.0215 0.8677 0.1145

rs9362453 6.40 × 10-6 G 0.274 0.039 0.163 A 0.726 0.961 0.838 0.3727 5.70 × 10-5 0.4659

rs6654507 6.44 × 10-6 G 0.548 0.118 0.344 A 0.452 0.882 0.656 0.0061 0.0226 2.20 × 10-6

rs6950641 6.49 × 10-6 T 0.357 0.092 0.231 C 0.643 0.908 0.769 0.0003 0.5317 0.0071

rs6449669 6.54 × 10-6 T 0.595 0.289 0.450 A 0.405 0.711 0.550 0.0129 0.1521 0.9280

rs10137248 6.59 × 10-6 G 0.226 0.039 0.138 A 0.774 0.961 0.863 0.0582 5.70 × 10-5 0.6290

rs8130198 6.63 × 10-6 C 0.583 0.382 0.488 T 0.417 0.618 0.513 0.0064 0.0021 0.6586

rs9844641 6.65 × 10-6 A 0.988 0.750 0.875 G 0.012 0.250 0.125 0.9378 0.7456 0.4433

rs2079989 6.68 × 10-6 C 0.524 0.421 0.475 T 0.476 0.579 0.525 3.70 × 10-6 0.0686 0.0265

rs8050875 6.91 × 10-6 G 0.976 0.724 0.856 A 0.024 0.276 0.144 0.8744 0.4647 0.7527

rs17019070 6.92 × 10-6 C 0.452 0.184 0.325 T 0.548 0.816 0.675 0.0005 0.4431 0.0787

rs10129777 6.97 × 10-6 G 0.405 0.079 0.250 A 0.595 0.921 0.750 0.9392 0.5972 0.2330

rs5930683 7.06 × 10-6 A 0.226 0.224 0.225 G 0.774 0.776 0.775 1.50 × 10-9 0.3998 0.0001

rs11205084 7.25 × 10-6 G 0.548 0.303 0.431 A 0.452 0.697 0.569 4.00 × 10-5 0.6895 0.0262

rs4099911 7.55 × 10-6 A 0.500 0.408 0.456 C 0.500 0.592 0.544 0.0007 0.0017 0.7685

rs349391 7.67 × 10-6 C 0.429 0.303 0.369 T 0.571 0.697 0.631 0.0009 0.0075 0.3081

rs349390 7.67 × 10-6 C 0.429 0.303 0.369 T 0.571 0.697 0.631 0.0009 0.0075 0.3081

rs17722227 7.70 × 10-6 A 0.179 0.026 0.106 G 0.821 0.974 0.894 0.1589 7.10 × 10-10 0.9092

rs6470455 7.73 × 10-6 G 0.714 0.342 0.538 A 0.286 0.658 0.463 0.2801 0.2631 0.1941

rs7019283 8.05 × 10-6 C 0.905 0.632 0.775 T 0.095 0.368 0.225 0.2676 0.0277 0.5008

rs13285078 8.05 × 10-6 C 0.905 0.632 0.775 G 0.095 0.368 0.225 0.2676 0.0277 0.5008

rs4738955 8.10 × 10-6 A 0.512 0.382 0.450 G 0.488 0.618 0.550 7.60 × 10-7 0.3132 0.0051

rs9984519 8.22 × 10-6 T 0.476 0.316 0.400 C 0.524 0.684 0.600 0.0056 0.0044 0.5762

rs8046503 8.41 × 10-6 A 0.595 0.316 0.463 G 0.405 0.684 0.538 0.0002 0.1791 0.0060


Appendices 209







rs1859512 8.54 × 10-6 T 0.321 0.066 0.200 C 0.679 0.934 0.800 0.0021 0.6642 0.0253

rs5942870 8.88 × 10-6 A 0.810 0.474 0.650 C 0.190 0.526 0.350 4.30 × 10-8 0.1075 5.40 × 10-7

rs2288374 9.09 × 10-6 T 0.381 0.132 0.263 C 0.619 0.868 0.738 0.0074 0.0567 0.3824

rs2891242 9.33 × 10-6 C 0.500 0.171 0.344 T 0.500 0.829 0.656 0.1228 0.3096 0.7864

rs17156195 9.34 × 10-6 T 0.417 0.118 0.275 A 0.583 0.882 0.725 0.0366 0.4076 0.2503

rs873276 9.37 × 10-6 G 0.476 0.237 0.363 T 0.524 0.763 0.638 0.0007 0.0557 0.0299

rs2602803 9.83 × 10-6 G 0.488 0.303 0.400 T 0.512 0.697 0.600 7.60 × 10-7 0.6895 0.0015

rs12572431 9.90 × 10-6 G 0.536 0.250 0.400 A 0.464 0.750 0.600 0.0002 0.2342 0.0069

rs7306948 1.01 × 10-5 G 0.429 0.145 0.294 A 0.571 0.855 0.706 0.0030 0.7893 0.1177

rs17019561 1.02 × 10-5 C 0.238 0.013 0.131 T 0.762 0.987 0.869 0.2401 0.9345 0.7108

rs2033069 1.03 × 10-5 A 0.869 0.539 0.713 G 0.131 0.461 0.288 0.3288 0.9692 0.4489

rs5930684 1.06 × 10-5 G 0.798 0.776 0.788 A 0.202 0.224 0.213 1.90 × 10-9 0.3998 0.0003

rs1366834 1.07 × 10-5 G 0.226 0.013 0.125 A 0.774 0.987 0.875 0.0582 0.9345 0.2013

rs12170932 1.07 × 10-5 T 0.226 0.013 0.125 C 0.774 0.987 0.875 0.0582 0.9345 0.2013

rs12443497 1.07 × 10-5 T 0.226 0.013 0.125 A 0.774 0.987 0.875 0.0582 0.9345 0.2013

rs9744291 1.08 × 10-5 G 0.690 0.276 0.494 A 0.310 0.724 0.506 0.4809 0.0887 0.0037

rs5770525 1.11 × 10-5 G 0.810 0.500 0.663 A 0.190 0.500 0.338 0.1273 0.0231 0.0519

rs2196007 1.11 × 10-5 T 0.310 0.053 0.188 G 0.690 0.947 0.813 0.0290 0.7320 0.1835

rs10402951 1.13 × 10-5 C 0.607 0.342 0.481 T 0.393 0.658 0.519 0.0243 0.0104 0.5098

rs10517378 1.13 × 10-5 C 0.512 0.250 0.388 A 0.488 0.750 0.613 0.0134 0.0231 0.9953

rs4808297 1.16 × 10-5 T 0.381 0.132 0.263 C 0.619 0.868 0.738 0.9503 2.10 × 10-6 0.0096

rs12120556 1.16 × 10-5 A 0.226 0.132 0.181 G 0.774 0.868 0.819 0.0582 2.10 × 10-6 0.3013

rs4692612 1.17 × 10-5 T 0.369 0.092 0.238 G 0.631 0.908 0.763 0.0137 0.5317 0.1209

rs2062758 1.18 × 10-5 T 0.452 0.105 0.288 A 0.548 0.895 0.713 0.8010 0.3187 0.0645

rs7987491 1.19 × 10-5 G 0.440 0.158 0.306 T 0.560 0.842 0.694 0.0013 0.2478 0.0178

rs1597474 1.24 × 10-5 T 0.226 0.053 0.144 C 0.774 0.947 0.856 0.8956 7.10 × 10-10 0.0330

rs17341595 1.27 × 10-5 C 0.810 0.803 0.806 T 0.190 0.197 0.194 4.30 × 10-8 0.1296 0.0042


210 Appendices







rs11954603 1.30 × 10-5 C 0.405 0.237 0.325 T 0.595 0.763 0.675 1.00 × 10-5 0.4357 0.0055

rs16926249 1.31 × 10-5 G 0.738 0.474 0.613 A 0.262 0.526 0.388 0.1333 0.0004 0.0188

rs4236924 1.31 × 10-5 C 0.333 0.079 0.213 T 0.667 0.921 0.788 0.0012 0.5972 0.0158

rs16888306 1.31 × 10-5 C 0.333 0.079 0.213 T 0.667 0.921 0.788 0.0012 0.5972 0.0158

rs7095919 1.31 × 10-5 G 0.333 0.079 0.213 A 0.667 0.921 0.788 0.0012 0.5972 0.0158

rs10483750 1.31 × 10-5 T 0.333 0.079 0.213 C 0.667 0.921 0.788 0.0012 0.5972 0.0158

rs2801659 1.32 × 10-5 C 0.619 0.237 0.438 T 0.381 0.763 0.563 0.9503 0.0936 0.0332

rs16886994 1.32 × 10-5 G 0.274 0.026 0.156 C 0.726 0.974 0.844 0.3727 0.8677 0.9683

rs418216 1.32 × 10-5 T 0.214 0.000 0.113 C 0.786 1.000 0.888 0.3261 NC 0.0260

rs547977 1.32 × 10-5 T 0.179 0.000 0.094 G 0.821 1.000 0.906 0.1589 NC 0.3548

rs17647077 1.32 × 10-5 G 0.179 0.000 0.094 C 0.821 1.000 0.906 0.1589 NC 0.3548

rs2294584 1.32 × 10-5 A 0.179 0.000 0.094 G 0.821 1.000 0.906 0.1589 NC 0.3548

rs7789233 1.32 × 10-5 G 0.821 1.000 0.906 G 0.179 0.000 0.094 0.1589 NC 0.3548

rs7153874 1.32 × 10-5 G 0.190 0.000 0.100 A 0.810 1.000 0.900 0.6002 NC 0.8038

rs1195242 1.32 × 10-5 C 0.190 0.000 0.100 A 0.810 1.000 0.900 0.6002 NC 0.8038

rs11934366 1.32 × 10-5 A 0.190 0.000 0.100 G 0.810 1.000 0.900 0.6002 NC 0.8038

rs10980229 1.41 × 10-5 G 0.500 0.289 0.400 A 0.500 0.711 0.600 6.00 × 10-5 0.0849 0.0154

rs2644567 1.41 × 10-5 G 0.964 0.724 0.850 A 0.036 0.276 0.150 2.20 × 10-5 0.9362 0.0537

rs11009106 1.42 × 10-5 C 0.524 0.329 0.431 G 0.476 0.671 0.569 0.0007 0.0222 0.1547

rs2916699 1.46 × 10-5 A 0.262 0.026 0.150 T 0.738 0.974 0.850 0.1333 0.8677 0.4830

rs10056584 1.46 × 10-5 A 0.262 0.026 0.150 G 0.738 0.974 0.850 0.1333 0.8677 0.4830

rs1696407 1.46 × 10-5 G 0.476 0.197 0.344 A 0.524 0.803 0.656 0.0006 0.1296 0.0069

rs17043470 1.52 × 10-5 A 1.000 0.829 0.919 G 0.000 0.171 0.081 NC 0.2034 0.4289

rs7253295 1.52 × 10-5 A 1.000 0.816 0.913 G 0.000 0.184 0.088 NC 0.7547 0.5874

rs13421497 1.57 × 10-5 G 0.345 0.053 0.206 C 0.655 0.947 0.794 0.4974 0.0036 0.0140

rs4892034 1.58 × 10-5 A 0.321 0.118 0.225 T 0.679 0.882 0.775 0.0979 0.0001 0.5425

rs7159091 1.58 × 10-5 C 0.214 0.039 0.131 T 0.786 0.961 0.869 0.0771 5.70 × 10-5 0.7108


Appendices 211







rs2192217 1.60 × 10-5 A 0.250 0.026 0.144 G 0.750 0.974 0.856 0.0308 0.8677 0.1332

rs17002379 1.60 × 10-5 T 0.512 0.145 0.338 C 0.488 0.855 0.663 0.5393 0.7893 0.0519

rs7107438 1.60 × 10-5 C 0.250 0.026 0.144 T 0.750 0.974 0.856 0.2578 7.10 × 10-10 0.0024

rs16937494 1.62 × 10-5 G 0.274 0.039 0.163 A 0.726 0.961 0.838 0.0145 0.8000 0.0827

rs7783582 1.62 × 10-5 T 0.274 0.039 0.163 C 0.726 0.961 0.838 0.0145 0.8000 0.0827

rs3778315 1.65 × 10-5 G 0.333 0.039 0.194 A 0.667 0.961 0.806 0.3545 0.8000 0.0320

rs3792615 1.66 × 10-5 T 0.976 0.724 0.856 C 0.024 0.276 0.144 0.8744 0.9362 0.2212

rs16927111 1.69 × 10-5 G 0.464 0.211 0.344 C 0.536 0.789 0.656 1.20 × 10-5 0.5043 0.0014

rs12629627 1.76 × 10-5 G 0.631 0.355 0.500 A 0.369 0.645 0.500 0.0018 0.8852 0.1797

rs798368 1.78 × 10-5 T 0.738 0.605 0.675 C 0.262 0.395 0.325 0.0215 0.0006 0.1937

rs17256392 1.81 × 10-5 A 0.167 0.026 0.100 G 0.833 0.974 0.900 0.1949 7.10 × 10-10 0.8038

rs6892871 1.81 × 10-5 G 0.167 0.026 0.100 A 0.833 0.974 0.900 0.1949 7.10 × 10-10 0.8038

rs6055456 1.83 × 10-5 C 0.369 0.105 0.244 T 0.631 0.895 0.756 0.0018 0.4683 0.0228

rs12165212 1.85 × 10-5 G 0.964 0.737 0.856 A 0.036 0.263 0.144 0.8103 0.0277 0.1332

rs41469844 1.86 × 10-5 C 0.726 0.342 0.544 A 0.274 0.658 0.456 0.9081 0.0658 0.0160

rs7020077 1.87 × 10-5 A 0.488 0.184 0.344 G 0.512 0.816 0.656 0.0134 0.7547 0.2241

rs2127978 1.97 × 10-5 T 0.333 0.092 0.219 C 0.667 0.908 0.781 0.0109 0.1887 0.2317

rs1367276 1.97 × 10-5 A 0.762 0.618 0.694 G 0.238 0.382 0.306 0.0428 0.0002 0.0657

rs12034948 2.00 × 10-5 G 0.321 0.053 0.194 A 0.679 0.947 0.806 0.3434 0.7320 0.9982

rs11984468 2.04 × 10-5 C 0.524 0.184 0.363 G 0.476 0.816 0.638 0.3459 0.4431 0.4717

rs16975878 2.06 × 10-5 G 0.988 0.789 0.894 A 0.012 0.211 0.106 0.9378 0.1002 0.2876

rs7747443 2.13 × 10-5 C 0.357 0.053 0.213 G 0.643 0.947 0.788 0.2695 0.7320 0.0236

rs6923953 2.13 × 10-5 C 0.357 0.053 0.213 T 0.643 0.947 0.788 0.2695 0.7320 0.0236

rs6926583 2.13 × 10-5 C 0.357 0.053 0.213 T 0.643 0.947 0.788 0.2695 0.7320 0.0236

rs997139 2.13 × 10-5 G 0.357 0.053 0.213 A 0.643 0.947 0.788 0.2695 0.7320 0.0236

rs5956823 2.13 × 10-5 A 0.917 0.658 0.794 C 0.083 0.342 0.206 0.5558 0.0130 0.1008

rs985257 2.15 × 10-5 A 0.571 0.237 0.413 T 0.429 0.763 0.588 0.2801 0.0936 0.2707


212 Appendices







rs7853174 2.17 × 10-5 G 0.571 0.513 0.544 A 0.429 0.487 0.456 0.0193 0.0001 0.2902

rs2274515 2.19 × 10-5 T 0.619 0.316 0.475 C 0.381 0.684 0.525 0.0074 0.1791 0.0694

rs7279994 2.22 × 10-5 C 0.500 0.211 0.363 A 0.500 0.789 0.638 0.0055 0.7579 0.2242

rs16970887 2.25 × 10-5 C 0.310 0.066 0.194 G 0.690 0.934 0.806 0.0037 0.6642 0.0316

rs17123453 2.25 × 10-5 C 0.310 0.066 0.194 G 0.690 0.934 0.806 0.0037 0.6642 0.0316

rs2648883 2.27 × 10-5 G 0.583 0.250 0.425 A 0.417 0.750 0.575 0.4122 0.0231 0.1043

rs11711551 2.31 × 10-5 G 0.440 0.132 0.294 A 0.560 0.868 0.706 0.1785 0.6272 0.9584

rs3861911 2.31 × 10-5 C 0.393 0.132 0.269 T 0.607 0.868 0.731 0.0004 0.3503 0.0066

rs7426702 2.31 × 10-5 T 0.393 0.132 0.269 C 0.607 0.868 0.731 0.0004 0.3503 0.0066

rs7540424 2.37 × 10-5 A 0.750 0.553 0.656 G 0.250 0.447 0.344 9.70 × 10-6 0.2922 0.0242

rs2988013 2.38 × 10-5 C 0.310 0.053 0.188 T 0.690 0.947 0.813 0.1440 0.7320 0.5510

rs4393091 2.42 × 10-5 G 0.500 0.171 0.344 C 0.500 0.829 0.656 3.70 × 10-6 0.8982 2.20 × 10-6

rs7863401 2.44 × 10-5 A 0.905 0.645 0.781 G 0.095 0.355 0.219 0.2676 0.0477 0.5880

rs12684292 2.44 × 10-5 T 0.905 0.645 0.781 C 0.095 0.355 0.219 0.2676 0.0477 0.5880

rs2421987 2.47 × 10-5 A 0.214 0.132 0.175 G 0.786 0.868 0.825 0.0771 2.10 × 10-6 0.2300

rs7307225 2.53 × 10-5 G 0.595 0.316 0.463 A 0.405 0.684 0.538 0.0650 0.0159 0.3959

rs10817082 2.53 × 10-5 C 0.917 0.632 0.781 G 0.083 0.368 0.219 0.5558 0.4195 0.9105

rs9485028 2.53 × 10-5 A 0.262 0.066 0.169 G 0.738 0.934 0.831 0.0215 0.0274 0.3083

rs654807 2.57 × 10-5 C 0.500 0.421 0.463 T 0.500 0.579 0.538 0.0007 0.0046 0.6168

rs5948927 2.60 × 10-5 T 0.524 0.197 0.369 C 0.476 0.803 0.631 6.20 × 10-5 0.6229 9.60 × 10-5

rs17733133 2.60 × 10-5 G 0.500 0.171 0.344 A 0.500 0.829 0.656 0.3545 0.3096 0.4433

rs10207238 2.62 × 10-5 C 0.214 0.013 0.119 T 0.786 0.987 0.881 0.0771 0.9345 0.2281

rs29110 2.62 × 10-5 C 0.214 0.013 0.119 A 0.786 0.987 0.881 0.0771 0.9345 0.2281

rs11506050 2.62 × 10-5 G 0.214 0.013 0.119 C 0.786 0.987 0.881 0.0771 0.9345 0.2281

rs6098723 2.62 × 10-5 G 0.214 0.013 0.119 A 0.786 0.987 0.881 0.0771 0.9345 0.2281

rs8016502 2.62 × 10-5 G 0.214 0.013 0.119 A 0.786 0.987 0.881 0.0771 0.9345 0.2281

rs8073194 2.62 × 10-5 T 0.214 0.013 0.119 C 0.786 0.987 0.881 0.0771 0.9345 0.2281


Appendices 213







rs11895045 2.62 × 10-5 T 0.214 0.013 0.119 C 0.786 0.987 0.881 0.0771 0.9345 0.2281

rs11154872 2.64 × 10-5 C 0.345 0.053 0.206 T 0.655 0.947 0.794 0.4974 0.7320 0.0761

rs13052044 2.66 × 10-5 T 0.500 0.158 0.338 C 0.500 0.842 0.663 0.7576 0.9488 0.3453

rs3920498 2.68 × 10-5 C 0.952 0.711 0.838 G 0.048 0.289 0.163 0.7459 0.0849 0.3607

rs2685850 2.70 × 10-5 A 0.726 0.316 0.531 G 0.274 0.684 0.469 0.1508 0.0970 0.0009

rs1433429 2.71 × 10-5 C 0.488 0.197 0.350 T 0.512 0.803 0.650 0.0134 0.5946 0.3764

rs13148734 2.73 × 10-5 A 0.786 0.500 0.650 G 0.214 0.500 0.350 0.0771 0.1048 0.2796

rs5912816 2.74 × 10-5 C 0.821 0.487 0.663 T 0.179 0.513 0.338 0.0051 0.9966 0.0145

rs10144861 2.74 × 10-5 G 0.440 0.145 0.300 A 0.560 0.855 0.700 0.0487 0.7893 0.5229

rs622060 2.76 × 10-5 C 0.690 0.513 0.606 T 0.310 0.487 0.394 1.60 × 10-5 0.1926 0.0313

rs6877860 2.78 × 10-5 T 0.357 0.145 0.256 C 0.643 0.855 0.744 0.3617 2.70 × 10-5 0.1071

rs16944757 2.80 × 10-5 A 0.298 0.053 0.181 G 0.702 0.947 0.819 0.0447 0.7320 0.2200

rs2025499 2.82 × 10-5 G 0.333 0.079 0.213 A 0.667 0.921 0.788 0.0109 0.5972 0.0809

rs7834482 2.83 × 10-5 G 0.714 0.461 0.594 A 0.286 0.539 0.406 0.2801 0.0001 0.0072

rs11090847 2.92 × 10-5 T 0.429 0.171 0.306 C 0.571 0.829 0.694 0.0003 0.8982 0.0178

rs1977985 2.96 × 10-5 G 0.964 0.750 0.863 A 0.036 0.250 0.138 2.20 × 10-5 0.7456 0.1608

rs12761944 2.97 × 10-5 A 0.512 0.342 0.431 C 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs1763788 2.97 × 10-5 A 0.512 0.342 0.431 G 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs1577372 2.97 × 10-5 A 0.512 0.342 0.431 G 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs1762529 2.97 × 10-5 A 0.512 0.342 0.431 G 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs2490495 2.97 × 10-5 G 0.512 0.342 0.431 C 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs2784574 2.97 × 10-5 G 0.512 0.342 0.431 A 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs2995467 2.97 × 10-5 G 0.512 0.342 0.431 A 0.488 0.658 0.569 0.0020 0.0130 0.3334

rs2960770 3.01 × 10-5 C 0.869 0.539 0.713 T 0.131 0.461 0.288 0.0020 0.5390 0.0033

rs17675581 3.05 × 10-5 G 0.512 0.276 0.400 A 0.488 0.724 0.600 0.0002 0.1230 0.0154

rs1526415 3.05 × 10-5 T 0.381 0.132 0.263 A 0.619 0.868 0.738 6.70 × 10-5 0.3503 0.0015

rs10789931 3.08 × 10-5 T 0.619 0.303 0.469 C 0.381 0.697 0.531 0.0428 0.6895 0.7952


214 Appendices







rs2015035 3.10 × 10-5 T 0.333 0.039 0.194 G 0.667 0.961 0.806 0.1052 0.8000 0.0042

rs2436739 3.15 × 10-5 G 0.190 0.000 0.100 A 0.810 1.000 0.900 0.6337 NC 0.1360

rs3798405 3.15 × 10-5 G 0.167 0.000 0.088 A 0.833 1.000 0.913 0.1949 NC 0.3911

rs7859623 3.15 × 10-5 C 0.167 0.000 0.088 T 0.833 1.000 0.913 0.1949 NC 0.3911

rs6744124 3.15 × 10-5 C 0.167 0.000 0.088 T 0.833 1.000 0.913 0.1949 NC 0.3911

rs4505649 3.15 × 10-5 G 0.167 0.000 0.088 A 0.833 1.000 0.913 0.1949 NC 0.3911

rs7517843 3.15 × 10-5 C 0.167 0.000 0.088 A 0.833 1.000 0.913 0.1949 NC 0.3911

rs1926721 3.15 × 10-5 G 0.167 0.000 0.088 A 0.833 1.000 0.913 0.1949 NC 0.3911

rs1458597 3.15 × 10-5 A 0.167 0.000 0.088 G 0.833 1.000 0.913 0.1949 NC 0.3911

rs2303409 3.19 × 10-5 C 0.417 0.132 0.281 A 0.583 0.868 0.719 0.8531 0.0009 0.0423

rs4130583 3.20 × 10-5 G 0.476 0.211 0.350 C 0.524 0.789 0.650 3.70 × 10-6 0.5043 5.60 × 10-5

rs6074914 3.20 × 10-5 A 0.940 0.658 0.806 G 0.060 0.342 0.194 0.0190 0.6904 0.0320

rs927651 3.21 × 10-5 G 0.714 0.461 0.594 A 0.286 0.539 0.406 0.0095 0.5390 0.5771

rs11972875 3.27 × 10-5 G 0.286 0.053 0.175 A 0.714 0.947 0.825 0.0095 0.7320 0.0578

rs1428323 3.28 × 10-5 A 0.464 0.237 0.356 G 0.536 0.763 0.644 1.20 × 10-5 0.9060 0.0027

rs9683305 3.28 × 10-5 C 0.798 0.447 0.631 T 0.202 0.553 0.369 0.4912 0.3601 0.1338

RA: risk allele; OA: other allele, Freq: frequency; HWE: Hardy-Weinberg equilibrium.

Appendices 215

Supplementary Table 7.2. Summary of association results for SNPs from previous chronic fatigue

syndrome (CFS) candidate gene association analyses within our CFS and fatigue cohorts.

SNP Chr SNP position RA OA Freq OR (95% CI) p-value Effect

directiona

CFS cohort (Bonferroni adjusted p-value = 0.0011)

rs655207 13 38368012 G T 0.346 2.67 (1.51-4.72) 0.0006 +

rs4738202 8 72940861 A G 0.218 2.77 (1.51-5.10) 0.0009 +

rs6650469 13 38367949 T C 0.364 2.47 (1.40-4.34) 0.0016 + rs6429157 1 239981643 G A 0.346 2.06 (1.17-3.63) 0.0114 +

rs12914385 15 78898723 T C 0.264 1.98 (1.10-3.57) 0.0224 +

rs951266 15 78878541 T C 0.218 2.03 (1.10-3.77) 0.0235 + rs2383844 8 72961252 G A 0.409 1.71 (0.98-2.99) 0.0570 +

rs12602006 17 16337288 A G 0.582 1.69 (0.95-3.03) 0.0748 +

rs2918419 5 142722353 G A 0.155 1.77 (0.88-3.57) 0.1060 - rs1317103 9 73195703 T C 0.727 1.70 (0.87-3.33) 0.1203 -

rs7520974 1 240067260 A G 0.500 1.54 (0.88-2.69) 0.1280 +

rs6578398 11 3638061 G A 0.700 1.49 (0.79-2.81) 0.2164 -

rs3829603 17 7347042 A C 0.273 1.44 (0.79-2.62) 0.2274 -

rs4620530 1 240063821 G T 0.473 1.38 (0.79-2.40) 0.2518 -

rs3743074 15 78909480 T C 0.600 1.35 (0.76-2.41) 0.2999 + rs589962 1 239989964 T C 0.664 1.33 (0.73-2.42) 0.3570 +

rs2741343 8 27326127 C T 0.436 1.29 (0.74-2.25) 0.3637 +

rs891398 8 27324822 C T 0.436 1.29 (0.74-2.25) 0.3637 + rs3763619 9 73225802 C A 0.600 1.29 (0.73-2.29) 0.3804 -

rs1424569 7 136569416 A G 0.418 1.28 (0.73-2.22) 0.3859 + rs7865858 9 73204431 G A 0.591 1.28 (0.72-2.26) 0.3953 -

rs2075748 11 62688269 A G 0.218 1.30 (0.68-2.47) 0.4259 +

rs1867263 1 239807920 A G 0.282 1.25 (0.69-2.28) 0.4578 - rs11563204 2 234917377 A G 0.182 1.30 (0.65-2.57) 0.4600 +

rs10780950 9 73193428 C T 0.791 1.29 (0.64-2.62) 0.4815 -

rs6188 5 142680344 T G 0.315 1.23 (0.69-2.21) 0.4818 - rs7543259 1 239979186 A G 0.227 1.23 (0.65-2.33) 0.5220 +

rs11142508 9 73231662 T C 0.627 1.21 (0.68-2.15) 0.5224 -

rs11823728 11 62676802 C T 0.964 1.74 (0.31-9.70) 0.5250 + rs11224816 11 101396286 T C 0.491 1.18 (0.68-2.05) 0.5592 +

rs763780 6 52101739 C T 0.045 1.43 (0.42-4.85) 0.5624 -

rs2767 2 233400074 T C 0.600 1.18 (0.67-2.08) 0.5747 + rs852977 5 142687494 G A 0.327 1.17 (0.65-2.08) 0.6056 -

rs10925941 1 239812538 A G 0.318 1.16 (0.65-2.08) 0.6195 -

rs10754677 1 239833100 A G 0.564 1.14 (0.65-1.99) 0.6434 + rs2071167 17 42287519 G A 0.727 1.16 (0.62-2.17) 0.6489 -

rs17865678 2 234919314 A G 0.264 1.13 (0.61-2.08) 0.7066 +

rs1328153 9 73416062 T C 0.800 1.13 (0.56-2.29) 0.7288 - rs7669882 4 40350651 G A 0.682 1.10 (0.61-2.00) 0.7543 -

rs4861065 4 40344395 T C 0.673 1.09 (0.60-1.97) 0.7744 -

rs6313 13 47469940 C T 0.591 1.07 (0.61-1.87) 0.8222 - rs1891301 9 74018496 C T 0.446 1.05 (0.60-1.83) 0.8638 -

rs685550 1 239924408 C T 0.236 1.05 (0.55-1.99) 0.8898 +

rs1860661 19 1650134 G A 0.355 1.03 (0.58-1.83) 0.9154 + rs603152 15 34294637 A C 0.355 1.03 (0.58-1.83) 0.9154 +

rs6311 13 47471478 C T 0.591 1.02 (0.58-1.79) 0.9441 -

rs10009228 4 40356422 A G 0.136 1.02 (0.46-2.26) 0.9681 - Fatigue cohort (Bonferroni adjusted p-value = 0.0007)

rs10115622 9 73306551 C A 0.684 1.04 (1.00-1.09) 0.0774 +

rs726169 1 239794277 T C 0.671 1.03 (0.99-1.08) 0.1396 + rs10009228 4 40356422 A G 0.184 1.04 (0.99-1.09) 0.1558 -

rs1867264 1 239845277 T A 0.358 1.03 (0.99-1.08) 0.1584 +

rs4861323 4 40355815 G A 0.183 1.04 (0.98-1.09) 0.1708 - rs3762529 2 233392449 A G 0.615 1.03 (0.99-1.08) 0.1898 +

rs4973537 2 233391965 A G 0.614 1.03 (0.98-1.07) 0.2113 +

rs2083817 1 239833605 T A 0.375 1.03 (0.98-1.07) 0.2355 + rs1867265 1 239840107 T C 0.357 1.03 (0.98-1.07) 0.2452 -

rs7865858 9 73204431 A G 0.372 1.02 (0.98-1.07) 0.2784 +

rs12463989 2 233395029 T C 0.628 1.02 (0.98-1.07) 0.2853 + rs7520974 1 240067260 G A 0.456 1.02 (0.98-1.06) 0.2949 -

rs7551001 1 239844600 G A 0.385 1.02 (0.98-1.07) 0.2951 -

rs2165872 1 239826988 A G 0.375 1.02 (0.98-1.07) 0.2964 - rs6669810 1 240068629 G C 0.454 1.02 (0.98-1.06) 0.3034 -

rs7180002 15 78873993 A T 0.663 1.02 (0.98-1.07) 0.3137 -

rs951266 15 78878541 G A 0.663 1.02 (0.98-1.07) 0.3186 - rs2767 2 233400074 A G 0.628 1.02 (0.98-1.07) 0.3279 +

rs12093821 1 239824248 A G 0.401 1.02 (0.98-1.06) 0.3925 -


216 Appendices

Supplementary Table 7.2. Continued Summary of association results for SNPs from previous

chronic fatigue syndrome (CFS) candidate gene association analyses within our CFS and fatigue

cohorts.


directiona

rs16838637 1 239828350 G A 0.402 1.02 (0.98-1.06) 0.4339 -

rs10754677 1 239833100 G A 0.444 1.02 (0.97-1.06) 0.4580 -

rs12914385 15 78898723 C T 0.611 1.02 (0.97-1.06) 0.4605 - rs6188 5 142680344 A C 0.322 1.01 (0.97-1.06) 0.5016 -

rs852977 5 142687494 G A 0.324 1.01 (0.97-1.06) 0.5052 -

rs1106948 9 74017174 T C 0.547 1.01 (0.97-1.06) 0.5289 + rs1899616 1 239818568 T C 0.389 1.01 (0.97-1.06) 0.5381 -

rs1134 1 239872172 T C 0.405 1.01 (0.97-1.06) 0.5389 -

rs3738436 1 239872493 T G 0.405 1.01 (0.97-1.06) 0.5389 - rs763780 6 52101739 T C 0.957 1.03 (0.93-1.14) 0.5420 +

rs6578398 11 3638061 A G 0.265 1.01 (0.97-1.06) 0.5480 +

rs1891301 9 74018496 T C 0.538 1.01 (0.97-1.06) 0.5485 + rs6429157 1 239981643 G A 0.426 1.01 (0.97-1.05) 0.5551 +

rs12682832 9 73220691 G A 0.626 1.01 (0.97-1.06) 0.5581 -

rs7511970 1 239883255 A G 0.407 1.01 (0.97-1.06) 0.5638 - rs7513746 1 239862411 G A 0.404 1.01 (0.97-1.05) 0.5942 -

rs619214 1 239958622 T G 0.527 1.01 (0.97-1.05) 0.6080 +

rs4243084 15 78911672 G C 0.655 1.01 (0.97-1.05) 0.6210 + rs1373998 5 55255565 G A 0.877 1.02 (0.95-1.08) 0.6227 -

rs6694220 1 239883616 G A 0.483 1.01 (0.97-1.05) 0.6461 -

rs6684622 1 239877537 C G 0.43 1.01 (0.97-1.05) 0.6791 - rs7108612 11 3650086 T G 0.103 1.01 (0.95-1.08) 0.6930 +

rs655207 13 38368012 G T 0.424 1.01 (0.97-1.05) 0.6959 +

rs10802795 1 239870775 C T 0.431 1.01 (0.97-1.05) 0.7193 - rs2985167 13 38230542 A G 0.627 1.01 (0.97-1.05) 0.7414 +

rs6429147 1 239794794 C G 0.374 1.01 (0.97-1.05) 0.7614 -

rs2869546 15 78907345 T C 0.626 1.01 (0.96-1.05) 0.7680 + rs6560200 9 73980222 C T 0.531 1.01 (0.97-1.05) 0.7816 +

rs1160742 9 73314011 G A 0.601 1.01 (0.96-1.05) 0.7972 -

rs6650469 13 38367949 T C 0.43 1.00 (0.97-1.05) 0.8285 + rs511422 15 34282982 A G 0.66 1.00 (0.95-1.04) 0.8330 -

rs4861065 4 40344395 T C 0.684 1.00 (0.96-1.05) 0.8350 - rs2741343 8 27326127 A G 0.505 1.00 (0.96-1.05) 0.8442 -

rs3743075 15 78909452 T C 0.367 1.00 (0.95-1.04) 0.8600 -

rs603152 15 34294637 G T 0.645 1.00 (0.95-1.04) 0.8675 - rs1867263 1 239807920 G A 0.637 1.00 (0.96-1.04) 0.8681 +

rs3743074 15 78909480 G A 0.368 1.00 (0.95-1.04) 0.8707 -

rs10925941 1 239812538 G A 0.612 1.00 (0.96-1.04) 0.8954 + rs2302767 17 7350544 A G 0.713 1.00 (0.96-1.05) 0.9029 +

rs860458 5 142696036 G A 0.832 1.00 (0.95-1.06) 0.9059 +

rs2918419 5 142722353 T C 0.832 1.00 (0.95-1.06) 0.9076 + rs17865678 2 234919314 G A 0.736 1.00 (0.96-1.05) 0.9247 -

rs6758653 2 234912799 G A 0.667 1.00 (0.96-1.04) 0.9468 +

rs891398 8 27324822 T C 0.508 1.00 (0.96-1.04) 0.9755 - rs1799724 6 31542482 C T 0.936 1.00 (0.92-1.09) 0.9775 -

rs3752411 14 21968876 G A 0.863 1.00 (0.94-1.06) 0.9829 -

rs7669882 4 40350651 G A 0.69 1.00 (0.96-1.05) 0.9840 -

rs6700643 1 239798921 T C 0.612 1.00 (0.96-1.04) 0.9851 +

rs3829603 17 7347042 C A 0.715 1.00 (0.96-1.05) 0.9856 +

rs11142508 9 73231662 T C 0.623 1.00 (0.96-1.04) 0.9878 - rs646950 15 34291660 G A 0.646 1.00 (0.96-1.04) 0.9879 -

rs11563204 2 234917377 G A 0.79 1.00 (0.95-1.05) 0.9915 -

rs10015231 4 40337566 C T 0.787 1.00 (0.95-1.05) 0.9952 +

Chr: chromosome; RA: risk allele; OA: other allele; OR: odds ratio; CI: confidence interval. aSNPs with effects in the same direction are indicated by + while SNPs with effects in opposite directions are

indicated by -.

Appendices 217

Supplementary Table 7.3. Summary of association results for genes from previous chronic fatigue

syndrome (CFS) candidate gene association analyses within our CFS and fatigue cohorts.

Gene Chromosome Start Stop Number of SNPs p-value


CHRNA5 15 78857862 78887611 7 0.0358 TRPC4 13 38210773 38443939 72 0.0360

TRPC6 11 101322295 101454687 33 0.0372

TRPA1 8 72933486 72987819 28 0.0466 TRPV3 17 3413796 3461289 17 0.0546

EIF3A 10 120794541 120840959 5 0.0773

TRPM4 19 49661016 49715098 9 0.0790 CHRNE 17 4801064 4806369 3 0.0878

TRPV2 17 16318856 16340317 7 0.1176

CHRNA3 15 78885394 78913637 13 0.1314 BMP2K 4 79697532 79837519 5 0.1640

CHRNB4 15 78916636 78933587 5 0.1852

UBTF 17 42282401 42298994 3 0.1894

CHRM5 15 34260446 34357295 18 0.2313

SORL1 11 121322912 121504471 53 0.2806

CHRM3 1 239549876 240078750 119 0.2989 NR3C1 5 142657496 143113322 139 0.3168

CHRNA4 20 61974662 62009487 6 0.3189

TRPM8 2 234826043 234928166 57 0.3408 CHRNB1 17 7348406 7360932 4 0.3567

CHRNA10 11 3686817 3692614 1 0.4366 CHRM1 11 62676151 62689012 4 0.4504

IFNG 12 68548550 68553521 1 0.4723

CHRNA2 8 27317278 27336813 17 0.4845 TCF3 19 1609289 1652328 4 0.5453

CHRNG 2 233404424 233411038 1 0.5908

METTL3 14 21966282 21979517 7 0.6675 DISC1 1 231762561 232177018 136 0.6966

PEX16 11 45931220 45939674 2 0.7277

CHRND 2 233390870 233401375 2 0.7531 IL6ST 5 55230923 55290821 6 0.8501

CHRM2 7 136553399 136703720 41 0.8671

TRPM3 9 73143979 74061782 258 0.8729 HTR2A 13 47405677 47471211 41 0.8955

FAM126B 2 201838441 201936392 2 0.9344

TNF 6 31543344 31546113 3 0.9663 CHRNA9 4 40337346 40357234 10 0.9795

Fatigue cohort (Bonferroni adjusted p-value = 0.0014)

TRPA1 8 72933486 72987819 126 0.0376 TRPV2 17 16318856 16340317 9 0.0632

TRPC4 13 38210773 38443939 296 0.1494

HTR2A 13 47405677 47471211 76 0.1561 BMP2K 4 79697532 79837519 1 0.1734

TRPM3 9 73143979 74061782 1122 0.2399

CHRNA9 4 40337346 40357234 72 0.2451 CHRND 2 233390870 233401375 9 0.3086

CHRM3 1 239549876 240078750 384 0.4147

SORL1 11 121322912 121504471 122 0.4236 IFNG 12 68548550 68553521 3 0.5242

CHRNG 2 233404424 233411038 6 0.5350

PEX16 11 45931220 45939674 1 0.5439 TRPM4 19 49661016 49715098 4 0.5469

TRPV3 17 3413796 3461289 15 0.5518

IL6ST 5 55230923 55290821 28 0.5932 CHRM2 7 136553399 136703720 188 0.6297

UBTF 17 42282401 42298994 8 0.6546

CHRNA5 15 78857862 78887611 32 0.7246 CHRM5 15 34260446 34357295 95 0.7309

CHRNA2 8 27317278 27336813 31 0.7533

CHRM1 11 62676151 62689012 2 0.7681 CHRNA3 15 78885394 78913637 35 0.8059

CHRNB4 15 78916636 78933587 2 0.8265

METTL3 14 21966282 21979517 16 0.8288 NR3C1 5 142657496 143113322 371 0.8289

DISC1 1 231762561 232177018 235 0.8552

CHRNA4 20 61974662 62009487 3 0.8631 CHRNB1 17 7348406 7360932 4 0.8869

TRPM8 2 234826043 234928166 140 0.8915

CHRNE 17 4801064 4806369 3 0.8991

218 Appendices

Supplementary Table 7.3. Continued Summary of association results for genes from previous

chronic fatigue syndrome (CFS) candidate gene association analyses within our CFS and fatigue

cohorts.


FAM126B 2 201838441 201936392 39 0.9141 TCF3 19 1609289 1652328 1 0.9720

TRPC6 11 101322295 101454687 399 0.9811

TNF 6 31543344 31546113 2 0.9898

Appendices 219

Supplementary Table 7.4. Summary of association results for SNPs from previous chronic fatigue

syndrome (CFS) genome-wide association analyses within our CFS and fatigue cohorts.


directiona


rs400322 19 55172578 G A 0.627 1.94 (1.05-3.59) 0.0326 +

rs197770 3 37515827 T C 0.827 2.59 (1.04-6.48) 0.0359 -

rs9200 5 41142606 G A 0.482 1.81 (1.03-3.18) 0.0369 + rs10500964 11 23596570 T C 0.082 2.13 (0.89-5.12) 0.0858 +

rs10509412 10 89599354 C A 0.300 1.66 (0.93-2.95) 0.0870 +

rs3751488 14 23304094 G A 0.782 1.91 (0.90-4.06) 0.0909 + rs10506025 12 27726370 A G 0.327 1.59 (0.90-2.81) 0.1097 -

rs8177374 11 126162843 C T 0.855 2.12 (0.83-5.39) 0.1101 -

rs4692612 4 171537901 G T 0.955 4.43 (0.51-38.63) 0.1424 - rs2389957 4 120695322 G A 0.582 1.53 (0.86-2.73) 0.1448 +

rs1051007 17 4636813 C T 0.127 1.74 (0.82-3.69) 0.1478 +

rs11658971 17 4637698 A G 0.127 1.74 (0.82-3.69) 0.1478 +

rs1931035 13 79274470 G A 0.809 1.78 (0.81-3.92) 0.1480 +

rs1061147 1 196654324 C A 0.618 1.53 (0.85-2.76) 0.1546 +

rs1325904 10 90280938 C T 0.736 1.62 (0.83-3.18) 0.1584 - rs16956158 17 6594844 A G 0.691 1.56 (0.83-2.93) 0.1692 -

rs11575584 9 34661994 A G 0.055 2.06 (0.72-5.91) 0.1698 +

rs9946817 18 70367007 G A 0.173 1.55 (0.78-3.07) 0.2052 + rs4894505 3 175920884 T G 0.618 1.46 (0.81-2.62) 0.2081 -

rs7307225 12 71898358 T C 0.900 1.98 (0.66-5.91) 0.2151 - rs1359536 13 79275793 T C 0.873 1.81 (0.70-4.70) 0.2160 +

rs3802814 11 126162607 G A 0.873 1.81 (0.70-4.70) 0.2160 -

rs4242391 8 23000183 T C 0.373 1.42 (0.81-2.49) 0.2204 - rs6710681 2 64413221 T C 0.380 1.38 (0.79-2.42) 0.2630 -

rs3020729 2 87012293 T C 0.791 1.51 (0.73-3.14) 0.2664 +

rs7121660 11 45358483 G A 0.709 1.43 (0.75-2.69) 0.2731 - rs10737169 1 154653704 T C 0.082 1.64 (0.66-4.09) 0.2828 +

rs4251545 12 44180295 A G 0.082 1.64 (0.66-4.09) 0.2828 -

rs2648883 8 129076594 G A 0.146 1.49 (0.72-3.09) 0.2845 + rs12317807 12 130395910 T C 0.018 2.40 (0.43-13.41) 0.3044 +

rs10501068 11 26769636 G T 0.409 1.33 (0.76-2.31) 0.3181 +

rs228941 22 37523721 G C 0.682 1.36 (0.74-2.51) 0.3236 - rs33013 5 80060016 G A 0.682 1.36 (0.74-2.51) 0.3236 +

rs4151667 6 31914024 A T 0.027 2.00 (0.47-8.62) 0.3418 +

rs2274515 6 42933526 G A 0.955 2.19 (0.41-11.56) 0.3443 - rs7849492 9 122619031 T C 0.955 2.19 (0.41-11.56) 0.3443 -

rs7994531 13 42977439 T C 0.182 1.38 (0.70-2.72) 0.3578 -

rs10402951 19 7555092 G A 0.282 1.32 (0.73-2.39) 0.3665 + rs4714468 6 41452996 A G 0.155 1.39 (0.67-2.85) 0.3742 -

rs7616342 3 19433647 A G 0.536 1.27 (0.73-2.22) 0.3940 -

rs3774268 3 186954324 A G 0.100 1.44 (0.61-3.40) 0.3974 + rs4253760 22 46622384 T G 0.787 1.32 (0.65-2.68) 0.4426 -

rs547571 13 97231270 G A 0.873 1.38 (0.57-3.34) 0.4779 +

rs549908 11 112020916 G T 0.255 1.24 (0.67-2.30) 0.4894 + rs8057267 16 87541080 T C 0.936 1.53 (0.43-5.39) 0.5063 -

rs2200706 3 3673608 G A 0.864 1.33 (0.57-3.11) 0.5151 -

rs7853174 9 129419990 A G 0.455 1.20 (0.69-2.08) 0.5170 - rs3095168 17 16260198 G A 0.964 1.74 (0.31-9.70) 0.5250 -

rs17500510 6 32712818 A G 0.091 1.33 (0.54-3.27) 0.5407 +

rs2249954 14 92383999 C T 0.027 1.59 (0.35-7.27) 0.5501 + rs6757543 2 45977472 A C 0.891 1.32 (0.51-3.37) 0.5658 -

rs3803568 15 75108636 T C 0.046 1.41 (0.41-4.76) 0.5838 -

rs2421987 1 172100831 A G 0.164 1.21 (0.59-2.49) 0.6029 + rs14541 12 8800566 T C 0.870 1.25 (0.53-2.97) 0.6105 -

rs1144418 12 65293514 A C 0.246 1.18 (0.63-2.20) 0.6132 -

rs997139 6 136751118 T C 0.809 1.15 (0.56-2.36) 0.7022 - rs1859512 7 8535486 T C 0.091 1.19 (0.47-3.00) 0.7110 +

rs11257804 10 12496055 G A 0.700 1.12 (0.61-2.06) 0.7132 -

rs10505778 12 14125564 G A 0.327 1.11 (0.62-1.99) 0.7203 - rs10509958 10 114054601 G A 0.318 1.11 (0.62-1.99) 0.7360 -

rs6108 14 95058631 T A 0.573 1.10 (0.63-1.92) 0.7396 -

rs1050998 17 4638737 C T 0.382 1.10 (0.63-1.93) 0.7435 - rs10980229 9 112925977 C T 0.236 1.11 (0.58-2.10) 0.7537 +

rs2278831 19 52131119 G A 0.064 1.18 (0.40-3.51) 0.7603 +

rs1555589 13 100480664 A G 0.618 1.09 (0.62-1.93) 0.7671 + rs11711551 3 66697961 G A 0.055 1.18 (0.37-3.80) 0.7788 +

rs7540424 1 116721566 A G 0.973 1.29 (0.21-7.89) 0.7825 +


220 Appendices


chronic fatigue syndrome (CFS) genome-wide association analyses within our CFS and fatigue

cohorts.


directiona

rs2395655 6 36645696 A G 0.646 1.06 (0.60-1.90) 0.8329 -

rs17643851 8 18437729 G A 0.027 1.18 (0.23-5.97) 0.8449 +

rs12120556 1 172115154 T C 0.182 1.07 (0.53-2.16) 0.8596 + rs829370 1 21933193 C T 0.100 1.07 (0.43-2.65) 0.8811 +

rs726817 10 95459817 T C 0.679 1.05 (0.57-1.91) 0.8850 +

rs11214105 11 112037653 A G 0.236 1.05 (0.55-1.99) 0.8898 + rs2071800 6 32714143 T C 0.091 1.06 (0.41-2.73) 0.9057 +

rs543736 8 104012949 A G 0.355 1.03 (0.58-1.83) 0.9154 +

rs10496982 2 146308114 A C 0.900 1.05 (0.42-2.65) 0.9189 + rs1048829 2 203430456 T G 0.473 1.02 (0.59-1.78) 0.9319 +

rs1433429 4 35899687 G A 0.082 1.04 (0.39-2.82) 0.9325 +

rs8336 4 95211610 A G 0.400 1.02 (0.58-1.78) 0.9507 + rs2247218 6 101966553 A G 0.700 1.01 (0.55-1.84) 0.9736 +

rs10510985 3 69663871 G A 0.564 1.00 (0.57-1.74) 0.9978 +

Fatigue cohort (Bonferroni adjusted p-value = 0.0005)

rs7306948 12 123345347 A G 0.957 1.17 (1.06-1.29) 0.0024 -

rs6721414 2 18494495 G C 0.589 1.06 (1.02-1.11) 0.0034 -

rs10121299 9 79397301 A G 0.935 1.12 (1.03-1.22) 0.0072 - rs1157185 2 38285735 T C 0.380 1.05 (1.00-1.09) 0.0405 +

rs1367696 2 38286914 A G 0.380 1.04 (1.00-1.09) 0.0598 +

rs4242391 8 23000183 T C 0.385 1.04 (1.00-1.08) 0.0659 - rs10789931 11 112842773 C T 0.882 1.06 (1.00-1.13) 0.0680 -

rs4245562 7 54403985 T C 0.699 1.04 (1.00-1.09) 0.0739 -

rs985257 2 38283228 A T 0.377 1.04 (0.99-1.08) 0.0862 + rs2016483 4 95229039 T A 0.565 1.04 (0.99-1.08) 0.0887 -

rs9320409 6 97530846 C T 0.463 1.03 (0.99-1.07) 0.1036 -

rs8336 4 95211610 C T 0.584 1.03 (0.99-1.08) 0.1163 - rs10501376 11 58971766 C G 0.900 1.05 (0.99-1.12) 0.1294 +

rs7537461 1 113383662 A C 0.886 1.05 (0.98-1.12) 0.1524 -

rs1325904 10 90280938 T C 0.270 1.03 (0.99-1.08) 0.1796 + rs4253760 22 46622384 G T 0.174 1.04 (0.98-1.10) 0.1877 +

rs1610024 9 111614766 A G 0.203 1.03 (0.98-1.09) 0.2025 + rs9200 5 41142606 C T 0.504 1.03 (0.98-1.07) 0.2231 +

rs400322 19 55172578 G A 0.716 1.03 (0.98-1.08) 0.2423 +

rs2602803 2 30818644 G T 0.100 1.04 (0.97-1.11) 0.2582 + rs10511961 9 71497485 G C 0.565 1.02 (0.98-1.07) 0.2617 -

rs17255510 14 22662856 C T 0.212 1.03 (0.98-1.08) 0.2632 +

rs4251545 12 44180295 A G 0.094 1.04 (0.97-1.11) 0.2778 - rs7834482 8 127785745 A G 0.787 1.03 (0.98-1.08) 0.2821 -

rs1881470 11 127333840 G C 0.740 1.02 (0.98-1.07) 0.3033 +

rs2247215 6 101966454 A G 0.701 1.02 (0.98-1.07) 0.3136 + rs10498445 14 52740441 G C 0.282 1.02 (0.98-1.07) 0.3185 -

rs11214105 11 112037653 A G 0.280 1.02 (0.98-1.07) 0.3270 +

rs10506025 12 27726370 G A 0.629 1.02 (0.98-1.06) 0.3341 + rs12055682 6 81189123 G A 0.138 1.03 (0.97-1.09) 0.3380 +

rs7529589 1 196658279 T C 0.398 1.02 (0.98-1.06) 0.3561 -

rs734640 11 17613348 A G 0.644 1.02 (0.98-1.06) 0.3580 -

rs2062758 12 39045452 T A 0.885 1.03 (0.97-1.10) 0.3759 +

rs7994531 13 42977439 T C 0.246 1.02 (0.97-1.07) 0.3762 -

rs1061147 1 196654324 A C 0.397 1.02 (0.98-1.06) 0.3897 - rs2228428 3 32995928 C T 0.701 1.02 (0.98-1.07) 0.4025 -

rs2247218 6 101966553 T C 0.702 1.02 (0.97-1.07) 0.4145 +

rs1926721 1 230864830 A G 0.968 1.04 (0.93-1.17) 0.4514 + rs3095168 17 16260198 C T 0.979 1.05 (0.92-1.21) 0.4518 -

rs4894505 3 175920884 T G 0.667 1.02 (0.97-1.06) 0.4552 -

rs2196007 3 144120914 C T 0.275 1.02 (0.97-1.07) 0.4563 - rs12408925 1 48473192 A G 0.757 1.02 (0.97-1.07) 0.4818 -

rs7307225 12 71898358 T C 0.888 1.02 (0.96-1.09) 0.4852 -

rs723886 7 68159592 G T 0.651 1.02 (0.97-1.06) 0.4880 + rs4151667 6 31914024 A T 0.039 1.04 (0.93-1.15) 0.4948 +

rs167337 12 52182052 G A 0.865 1.02 (0.96-1.08) 0.5065 -

rs11090847 22 48899419 C T 0.900 1.02 (0.96-1.10) 0.5080 - rs6926583 6 136752092 C G 0.841 1.02 (0.96-1.08) 0.5099 +

rs33013 5 80060016 A G 0.332 1.01 (0.97-1.06) 0.5233 -

rs997139 6 136751118 A G 0.841 1.02 (0.96-1.07) 0.5341 - rs10507556 13 47970075 G A 0.875 1.02 (0.96-1.08) 0.5392 -


Appendices 221



cohorts.


directiona

rs7616342 3 19433647 A G 0.587 1.01 (0.97-1.06) 0.5411 -

rs12305678 12 2763539 A G 0.937 1.03 (0.94-1.11) 0.5504 -

rs7726463 5 37955073 T C 0.907 1.02 (0.95-1.09) 0.5508 - rs2490495 10 32726529 C G 0.487 1.01 (0.97-1.06) 0.5589 -

rs1763788 10 32917853 T C 0.488 1.01 (0.97-1.06) 0.5832 +

rs2014012 5 58612388 A T 0.655 1.01 (0.97-1.06) 0.5836 + rs10517378 4 36532369 C A 0.078 1.02 (0.95-1.10) 0.5878 +

rs382958 19 56439438 A G 0.545 1.01 (0.97-1.05) 0.5976 +

rs12417706 11 116059440 T C 0.050 1.03 (0.93-1.13) 0.6091 + rs11154872 6 136797757 A G 0.846 1.01 (0.96-1.07) 0.6114 -

rs11009106 10 33123413 C G 0.487 1.01 (0.97-1.05) 0.6209 +

rs2995467 10 33135952 G A 0.487 1.01 (0.97-1.05) 0.6209 + rs12407818 1 52904410 C T 0.056 1.02 (0.93-1.12) 0.6330 +

rs1762529 10 32968080 T C 0.488 1.01 (0.97-1.05) 0.6355 +

rs2784574 10 32976689 A G 0.488 1.01 (0.97-1.05) 0.6355 - rs1577372 10 32938382 C T 0.488 1.01 (0.97-1.05) 0.6362 -

rs11711551 3 66697961 A G 0.951 1.02 (0.93-1.13) 0.6368 -

rs10800118 1 165599774 G C 0.543 1.01 (0.97-1.05) 0.6444 - rs10501068 11 26769636 T G 0.529 1.01 (0.97-1.05) 0.6708 -

rs6923953 6 136726688 T C 0.844 1.01 (0.96-1.07) 0.6748 -

rs2389957 4 120695322 C T 0.634 1.01 (0.97-1.05) 0.6755 + rs1377828 3 176245042 T G 0.772 1.01 (0.96-1.06) 0.6759 -

rs3797302 5 145889123 G C 0.906 1.02 (0.95-1.09) 0.6774 +

rs2816936 1 199982900 G A 0.100 1.01 (0.95-1.09) 0.6902 - rs1157819 1 209604734 T A 0.355 1.01 (0.97-1.05) 0.6955 -

rs2882361 4 161379616 C G 0.353 1.01 (0.97-1.05) 0.7117 -

rs7747443 6 136713749 A G 0.844 1.01 (0.96-1.07) 0.7240 - rs13010656 2 203297068 G T 0.480 1.01 (0.97-1.05) 0.7241 -

rs372402 5 148752020 T C 0.511 1.01 (0.97-1.05) 0.7249 +

rs12761944 10 32803484 T G 0.487 1.01 (0.97-1.05) 0.7306 + rs353254 5 148748736 A G 0.512 1.01 (0.97-1.05) 0.7415 +

rs6449669 5 62929018 T A 0.157 1.01 (0.95-1.07) 0.7521 + rs2715898 2 201556388 C T 0.488 1.01 (0.97-1.05) 0.7645 -

rs9946817 18 70367007 G A 0.198 1.01 (0.95-1.06) 0.7786 +

rs197770 3 37515827 G A 0.142 1.01 (0.95-1.07) 0.7834 + rs726817 10 95459817 T C 0.728 1.01 (0.96-1.05) 0.7932 +

rs6074914 20 15519613 G A 0.035 1.01 (0.91-1.13) 0.8017 -

rs3778315 6 136850687 A G 0.847 1.01 (0.95-1.06) 0.8176 - rs1801058 4 3039150 T C 0.417 1.00 (0.96-1.05) 0.8196 -

rs10817082 9 113474166 C G 0.613 1.00 (0.96-1.05) 0.8207 +

rs4892034 18 70399988 A T 0.790 1.00 (0.95-1.06) 0.8647 + rs543736 8 104012949 G A 0.635 1.00 (0.95-1.04) 0.8878 -

rs10489599 1 16585818 A G 0.608 1.00 (0.96-1.05) 0.8973 -

rs1048829 2 203430456 G T 0.521 1.00 (0.96-1.04) 0.9080 - rs1050998 17 4638737 A G 0.585 1.00 (0.96-1.05) 0.9101 +

rs549908 11 112020916 T G 0.689 1.00 (0.95-1.04) 0.9141 -

rs496731 18 26491368 C A 0.749 1.00 (0.95-1.05) 0.9152 -

rs228945 22 37525880 T C 0.704 1.00 (0.96-1.05) 0.9156 +

rs11984468 8 16367555 G C 0.955 1.01 (0.91-1.11) 0.9170 -

rs228941 22 37523721 C G 0.711 1.00 (0.95-1.04) 0.9227 + rs2277680 17 4638563 G A 0.585 1.00 (0.96-1.05) 0.9282 -

rs9283919 6 54114066 A G 0.952 1.00 (0.91-1.10) 0.9301 -

rs654807 2 16457284 A G 0.555 1.00 (0.96-1.04) 0.9496 - rs4978076 9 26524684 T C 0.500 1.00 (0.96-1.04) 0.9559 -

rs2193766 3 8829321 C T 0.977 1.00 (0.88-1.15) 0.9596 +

rs10402951 19 7555092 G A 0.293 1.00 (0.96-1.05) 0.9845 + rs8050875 16 11223537 C T 0.676 1.00 (0.96-1.04) 0.9950 +

rs283825 2 79232491 A G 0.861 1.00 (0.94-1.06) 0.9964 -

rs2200706 3 3673608 C T 0.880 1.00 (0.94-1.07) 0.9983 -

Chr: chromosome; RA: risk allele; OA: other allele; OR: odds ratio; CI: confidence interval. aSNPs with

effects in the same direction are indicated by + while SNPs with effects in opposite directions are

indicated by -.

222 Appendices

Supplementary Table 7.5. Summary of association results for genes from previous chronic fatigue

syndrome (CFS) genome-wide association analyses within our CFS and fatigue cohorts.


CFS cohort (Bonferroni-adjusted p-value = 0.0002)

PLA2G4A 1 186798032 186958113 37 0.0001

MOB3B 9 27325207 27530223 78 0.0003

AFAP1 4 7760440 7941653 66 0.0075

RASEF 9 85594500 85678043 13 0.0108

CELF4 18 34823003 35146000 107 0.0112

ITGA9 3 37493813 37861281 98 0.0119

ARPC1B 7 98972298 99004226 5 0.0132

TXNRD1 12 104609537 104744085 23 0.0183

NR6A1 9 127279554 127533589 8 0.0228

PTGS2 1 186640944 186649559 3 0.0235

FSHR 2 49189296 49381666 68 0.0244

CDH18 5 19473140 20575972 144 0.0264

SLC13A5 17 6588038 6616886 18 0.0268

PDE4D 5 58264865 59783925 294 0.0285

DNMBP 10 101635334 101769676 21 0.0287

FBXL13 7 102453308 102715288 36 0.0383

SLC35F3 1 234040679 234460262 134 0.0396

FAM185A 7 102389399 102449672 3 0.0452

CLEC16A 16 11038345 11276046 63 0.0477

PARP11 12 3907410 3982608 16 0.0552

POLR3A 10 79734907 79789298 7 0.0556

KCNAB1 3 155838337 156256927 75 0.0657

DPF3 14 73085561 73360824 91 0.0809

LRFN2 6 40359373 40555126 64 0.0810

PRDM16 1 2985565 3355185 113 0.0879

CTSH 15 79214092 79237436 5 0.0926

NLRP13 19 56403058 56443702 21 0.0957

HLA-DOA 6 32971959 32977389 28 0.1005

MYO18B 22 26138117 26453345 138 0.1030

BPTF 17 65821644 65980494 15 0.1065

TRIP11 14 92434243 92506484 13 0.1140

ANO10 3 43407818 43663560 31 0.1231

ZCCHC11 1 52888947 53018764 12 0.1238

TBC1D19 4 26585546 26758232 5 0.1283

SH3TC2 5 148361713 148442737 20 0.1300

ZNF24 18 32912178 32924428 5 0.1347

NCAM1 11 112831969 113149158 91 0.1413

TRB 7 141998851 142510972 83 0.1424

GDA 9 74729511 74867140 35 0.1464

MED11 17 4634723 4636902 1 0.1478

C3orf52 3 111805175 111837073 7 0.1481

CDKN1A 6 36644237 36655116 3 0.1606

FBXO42 1 16573339 16678965 14 0.1607

TCERG1 5 145826873 145891071 8 0.1675

CCL27 9 34661893 34662689 1 0.1698

KLHL32 6 97372496 97588630 49 0.1711

IL20RB 3 136676707 136729927 5 0.1712

TNFRSF10D 8 22993101 23021543 10 0.1736

SHANK2 11 70313961 70935808 131 0.1825

FBLN5 14 92335755 92414046 35 0.1835

ZNF407 18 72265106 72777628 78 0.1929

CFB 6 31913721 31919861 20 0.2048

C22orf34 22 50013290 50051190 5 0.2111

RECK 9 36036910 36124452 17 0.2162

ABCG1 21 43619799 43724497 79 0.2205

CCR4 3 32993066 32996403 1 0.2412

Appendices 223



cohorts.


MASP1 3 186933873 187009810 37 0.2416

MSH3 5 79950467 80172634 43 0.2461

MRPL52 14 23299088 23304246 8 0.2562

VAPA 18 9913955 9960018 18 0.2587

SPSB4 3 140770743 140867453 17 0.2673

INTS2 17 59942728 60005377 3 0.2716

UTRN 6 144606434 145174170 106 0.2794

SOX5 12 23682438 24715383 353 0.2836

NDUFAF2 5 60240956 60448864 17 0.2845

APLP2 11 129939716 130014706 14 0.2875

CFH 1 196621008 196716634 37 0.2954

UVRAG 11 75526212 75855282 29 0.2982

CYP24A1 20 52769985 52790516 23 0.3045

PDZRN3 3 73431582 73674072 79 0.3059

RMDN2 2 38152462 38294285 41 0.3141

GALNT18 11 11292421 11643561 244 0.3208

NUP210L 1 153965166 154127592 14 0.3257

NXPH1 7 8473585 8792593 103 0.3259

POU6F2 7 39017609 39504390 102 0.3426

FADS6 17 72873451 72889737 4 0.3498

DNM3 1 171810618 172387606 112 0.3519

GRIK3 1 37261128 37499844 42 0.3543

BMPR2 2 203241033 203432474 17 0.3584

IRAK4 12 44152747 44183346 11 0.3641

C5orf66 5 134368970 134680370 88 0.3665

CACNA1C 12 2079952 2807115 202 0.3666

ANK2 4 113739239 114304896 157 0.3751

AGPAT3 21 45285116 45407475 26 0.3817

MARCH1 4 164445450 165305093 169 0.3867

TACC2 10 123748689 124014057 124 0.3921

TMX4 20 7961713 8000393 16 0.3955

RAP1GAP 1 21922708 21996010 23 0.4007

KCNIP4 4 20730234 21950424 303 0.4019

SIGLEC5 19 52114756 52133727 13 0.4023

PRKCH 14 61788515 62017698 93 0.4047

SAP130 2 128698791 128785667 11 0.4078

PPARA 22 46546458 46639653 48 0.4085

IL18 11 112013974 112034840 5 0.4099

PRR12 19 50094912 50129696 2 0.4239

OXTR 3 8792094 8811300 18 0.4248

PDE4DIP 1 144851424 145076186 2 0.4298

DSE 6 116601231 116762422 24 0.4320

HK1 10 71029740 71161638 52 0.4381

EPHA6 3 96533425 97467786 89 0.4399

PRKCE 2 45878454 46415129 237 0.4429

C6 5 41142248 41261588 28 0.4432

HLA-DQA2 6 32709156 32714664 14 0.4438

CLYBL 13 100258919 100549388 102 0.4484

CNTN5 11 98891706 100229616 486 0.4511

FAM19A5 22 48885288 49147744 127 0.4520

KCNJ6 21 38996778 39288741 101 0.4543

SERPINA5 14 95047706 95059457 19 0.4671

PRUNE2 9 79226292 79521136 125 0.4678

SHISA6 17 11144740 11467380 120 0.4742

MTAP 9 21802635 21941040 32 0.4765

224 Appendices



cohorts.


GRIA1 5 152870084 153193429 83 0.4795

DNAH3 16 20944476 21170762 46 0.4799

HDAC11 3 13521671 13547924 10 0.4812

STK10 5 171469074 171615346 47 0.4818

C3orf67 3 58720199 59035804 21 0.4819

PTPRD 9 8314246 10612723 1009 0.4833

IL17B 5 148753830 148760848 3 0.4864

ARL15 5 53180578 53606403 125 0.4892

CSMD3 8 113235157 114449242 135 0.4895

PIP5K1B 9 71320188 71624092 76 0.4898

TARP 7 38299243 38357285 18 0.4965

TRD 14 22891537 22935569 16 0.5023

DTX4 11 58939812 58976060 9 0.5027

PSD3 8 18384813 18871196 262 0.5043

CSRNP3 2 166326157 166545917 51 0.5046

PPFIBP1 12 27677045 27848497 63 0.5058

ARMC9 2 232063294 232238606 33 0.5064

PEX11G 19 7541756 7555884 5 0.5097

RNASEL 1 182542769 182558420 8 0.5111

CACNA2D1 7 81575760 82073031 131 0.5115

TEX12 11 112004926 112043279 12 0.5210

CCDC7 10 32735057 33171805 35 0.5267

ZBTB20 3 114033348 114866132 139 0.5327

PTPRU 1 29563028 29653325 16 0.5337

NLRP11 19 56296763 56348128 35 0.5374

HIP1R 12 123319045 123347508 5 0.5403

RBM19 12 114254543 114404176 54 0.5430

TRA 14 22090057 23021075 363 0.5465

INSR 19 7112266 7294011 60 0.5500

GRIN2B 12 13713684 14133022 157 0.5526

LRGUK 7 133812105 133948933 18 0.5634

STXBP5L 3 120627050 121143608 33 0.5637

LHX4 1 180199433 180244188 27 0.5661

CD247 1 167399877 167487847 36 0.5669

LRP1B 2 140988996 142889270 511 0.5678

GON4L 1 155719450 155829185 7 0.5743

SYNE3 14 95883831 95983000 74 0.5844

SHPRH 6 146205943 146285421 8 0.5854

DNAH8 6 38683117 38998574 102 0.5859

SLCO3A1 15 92396938 92715665 177 0.5862

SPTAN1 9 131314837 131395944 6 0.5892

MFAP5 12 8798540 8815433 6 0.5960

FRA10AC1 10 95427640 95462329 15 0.5990

KCNH8 3 19189946 19577135 56 0.6014

FSTL5 4 162305044 163085186 158 0.6085

ZC3H13 13 46528600 46626896 10 0.6119

GBE1 3 81538850 81810950 34 0.6150

KNTC1 12 123011796 123110947 15 0.6158

TIRAP 11 126152800 126164828 11 0.6212

TECTB 10 114043213 114064793 9 0.6303

HS6ST3 13 96743093 97491816 132 0.6338

COLEC11 2 3642422 3692234 17 0.6346

LMX1B 9 129376722 129463311 23 0.6357

ARHGAP20 11 110447759 110583912 20 0.6366

CAMK1D 10 12391583 12871735 218 0.6378

Appendices 225



cohorts.


LIMD1 3 45636323 45722755 19 0.6454

IL2RB 22 37521878 37552414 15 0.6516

IQUB 7 123091993 123174718 12 0.6527

UBE3C 7 156931655 157062066 9 0.6565

CADM2 3 85008133 86123579 123 0.6578

CLEC4M 19 7828035 7834491 5 0.6586

ANKFN1 17 54230836 54560007 67 0.6605

FBXL21 5 135266006 135277367 10 0.6610

NPAS2 2 101436613 101613289 75 0.6631

GRK4 4 2965232 3042474 14 0.6639

MAP7 6 136663419 136871957 16 0.6663

PCYOX1L 5 148737570 148749221 3 0.6704

TMPRSS15 21 19641433 19775970 70 0.6716

TPD52 8 80947103 81083836 28 0.6762

MAGI3 1 113933087 114228545 30 0.6796

BMP6 6 7727011 7881972 45 0.6810

ACOXL 2 111490150 111875799 107 0.6869

LGR5 12 71832931 71980090 35 0.6949

PTGDR 14 52734310 52743808 11 0.6972

WBSCR17 7 70597523 71178586 153 0.6975

SLC1A3 5 36606457 36688436 44 0.6980

THTPA 14 23980969 24028790 17 0.7003

STK32B 4 5053527 5502728 131 0.7015

MUSK 9 113430935 113566386 44 0.7035

ZNF100 19 21906417 21950430 4 0.7046

NKAIN2 6 124124991 125146786 279 0.7075

UBE2G1 17 4172512 4269969 11 0.7200

MGST3 1 165600110 165625373 31 0.7241

CD8A 2 87011728 87035519 6 0.7249

CCDC157 22 30752627 30772818 6 0.7271

CXCL16 17 4636828 4643223 7 0.7284

NKAIN3 8 63161501 63912211 152 0.7286

SHFM1 7 96318079 96339203 2 0.7335

PRKG1 10 52750911 54058110 391 0.7477

PEX6 6 42931611 42946981 5 0.7486

LMAN1L 15 75105194 75118099 6 0.7498

MLIP 6 53883714 54131078 67 0.7555

SIX6 14 60975938 60978525 3 0.7588

LCA5 6 80194708 80247147 11 0.7598

ARHGEF3 3 56761446 57113336 107 0.7612

SCN8A 12 51984050 52206648 35 0.7691

PALM2-AKAP2 9 112542577 112934792 155 0.7718

UBXN6 19 4445003 4457791 5 0.7777

TNFRSF1B 1 12227044 12269279 20 0.7875

EFCAB11 14 90261499 90421148 36 0.7884

PPP3CC 8 22298483 22398657 15 0.7887

ZNF469 16 88493879 88507165 8 0.7918

CFTR 7 117120017 117308719 27 0.7934

FAM19A1 3 68040734 68594772 148 0.7973

ZBED4 22 50247497 50283726 7 0.8006

MAG 19 35782989 35804710 7 0.8086

PGM2 4 37828282 37864559 16 0.8134

SLC2A9 4 9827848 10041872 75 0.8169

CELF2 10 10838851 11378674 216 0.8179

GPR161 1 168048780 168106905 12 0.8230

226 Appendices



cohorts.


EFNA5 5 106712590 107006596 99 0.8252

SRRM3 7 75831211 75916609 6 0.8454

GRIK2 6 101841584 102517958 151 0.8477

PRKACB 1 84543745 84704181 24 0.8485

SAMD4A 14 55033815 55260033 107 0.8490

IGL 22 22380474 23265085 91 0.8583

LCLAT1 2 30670102 30867091 36 0.8618

RAB3C 5 57877950 58155221 83 0.8631

DDAH1 1 85784168 86044046 78 0.8638

LILRB4 19 55174271 55181810 7 0.8690

ICOSLG 21 45642874 45660881 4 0.8692

ALG12 22 50296852 50312106 3 0.8712

DCC 18 49866542 51062273 265 0.8790

CDC20B 5 54408799 54469005 19 0.8974

TNFRSF19 13 24144509 24250244 33 0.8976

FAM49A 2 16730727 16847134 42 0.8978

TOX3 16 52471682 52581714 18 0.8991

SPOCK1 5 136310987 136835018 140 0.9030

MACROD2 20 13976146 16033842 666 0.9076

CRYL1 13 20977806 21100012 39 0.9139

OTOG 11 17568920 17667491 31 0.9183

STAB2 12 103981069 104160502 79 0.9185

PTPRT 20 40701392 41818557 379 0.9193

RBBP6 16 24550866 24584184 7 0.9447

RASGRF2 5 80256491 80525981 120 0.9456

GALNT7 4 174089904 174245118 18 0.9481

TYK2 19 10461204 10491248 12 0.9501

CEP128 14 80962821 81408105 73 0.9511

DNTTIP1 20 44420576 44440066 2 0.9589

SLC7A8 14 23594504 23652869 32 0.9682

SMARCAD1 4 95128759 95212443 15 0.9777

Fatigue cohort (Bonferroni-adjusted p-value = 0.0002)

PRUNE2 9 79226292 79521136 383 0.0003

RNASEL 1 182542769 182558420 6 0.0045

COLEC11 2 3642422 3692234 55 0.0080

TNFRSF10D 8 22993101 23021543 4 0.0275

HS6ST3 13 96743093 97491816 182 0.0281

ANK2 4 113739239 114304896 551 0.0458

ARL15 5 53180578 53606403 539 0.0476

MGST3 1 165600110 165625373 54 0.0503

LRGUK 7 133812105 133948933 239 0.0637

HIP1R 12 123319045 123347508 37 0.0711

ZNF407 18 72265106 72777628 198 0.0770

POU6F2 7 39017609 39504390 386 0.0799

CD247 1 167399877 167487847 47 0.0889

DTX4 11 58939812 58976060 23 0.0908

BMPR2 2 203241033 203432474 70 0.0995

PTPRU 1 29563028 29653325 61 0.1068

HLA-DOA 6 32971959 32977389 16 0.1118

PPARA 22 46546458 46639653 55 0.1227

LHX4 1 180199433 180244188 19 0.1503

ZC3H13 13 46528600 46626896 43 0.1540

CSRNP3 2 166326157 166545917 145 0.1640

SYNE3 14 95883831 95983000 78 0.1736

CFH 1 196621008 196716634 149 0.1807

Appendices 227



cohorts.


HDAC11 3 13521671 13547924 1 0.1815

FSHR 2 49189296 49381666 375 0.1829

NKAIN2 6 124124991 125146786 664 0.1858

KNTC1 12 123011796 123110947 45 0.1886

GPR161 1 168048780 168106905 65 0.2007

NDUFAF2 5 60240956 60448864 270 0.2024

MAGI3 1 113933087 114228545 140 0.2058

ICOSLG 21 45642874 45660881 11 0.2066

SH3TC2 5 148361713 148442737 124 0.2220

PRR12 19 50094912 50129696 22 0.2230

PRDM16 1 2985565 3355185 147 0.2232

ALG12 22 50296852 50312106 16 0.2324

SLC1A3 5 36606457 36688436 86 0.2386

SOX5 12 23682438 24715383 827 0.2395

MRPL52 14 23299088 23304246 5 0.2396

C3orf67 3 58720199 59035804 38 0.2401

STAB2 12 103981069 104160502 171 0.2465

RMDN2 2 38152462 38294285 235 0.2474

NR6A1 9 127279554 127533589 50 0.2690

SHANK2 11 70313961 70935808 366 0.2708

LCA5 6 80194708 80247147 36 0.2729

TARP 7 38299243 38357285 10 0.2742

WBSCR17 7 70597523 71178586 703 0.2794

MFAP5 12 8798540 8815433 1 0.2858

ACOXL 2 111490150 111875799 380 0.3006

CEP128 14 80962821 81408105 672 0.3039

LCLAT1 2 30670102 30867091 172 0.3072

LMAN1L 15 75105194 75118099 9 0.3190

TOX3 16 52471682 52581714 58 0.3194

RBM19 12 114254543 114404176 176 0.3202

CFTR 7 117120017 117308719 104 0.3369

PPFIBP1 12 27677045 27848497 251 0.3409

SMARCAD1 4 95128759 95212443 89 0.3425

PTGS2 1 186640944 186649559 2 0.3426

CELF2 10 10838851 11378674 532 0.3472

CACNA1C 12 2079952 2807115 375 0.3474

GRIK2 6 101841584 102517958 511 0.3505

SCN8A 12 51984050 52206648 44 0.3524

PTPRT 20 40701392 41818557 916 0.3536

ZNF24 18 32912178 32924428 9 0.3541

CNTN5 11 98891706 100229616 2003 0.3609

DNTTIP1 20 44420576 44440066 13 0.3613

GON4L 1 155719450 155829185 30 0.3674

PIP5K1B 9 71320188 71624092 161 0.3679

GRIA1 5 152870084 153193429 451 0.3709

CLYBL 13 100258919 100549388 247 0.3744

TMPRSS15 21 19641433 19775970 132 0.3768

IRAK4 12 44152747 44183346 38 0.3922

CCR4 3 32993066 32996403 1 0.4025

NLRP13 19 56403058 56443702 39 0.4095

FBLN5 14 92335755 92414046 69 0.4197

OXTR 3 8792094 8811300 7 0.4240

RECK 9 36036910 36124452 60 0.4309

TXNRD1 12 104609537 104744085 189 0.4312

MARCH1 4 164445450 165305093 688 0.4318

228 Appendices



cohorts.


GDA 9 74729511 74867140 125 0.4370

RAB3C 5 57877950 58155221 382 0.4396

NLRP11 19 56296763 56348128 10 0.4460

TYK2 19 10461204 10491248 9 0.4462

FAM185A 7 102389399 102449672 11 0.4474

AGPAT3 21 45285116 45407475 88 0.4505

NCAM1 11 112831969 113149158 519 0.4520

CDH18 5 19473140 20575972 781 0.4542

TACC2 10 123748689 124014057 269 0.4564

CFB 6 31913721 31919861 10 0.4592

EFNA5 5 106712590 107006596 289 0.4633

EFCAB11 14 90261499 90421148 189 0.4644

SIX6 14 60975938 60978525 2 0.4668

CYP24A1 20 52769985 52790516 12 0.4709

PSD3 8 18384813 18871196 845 0.4788

CSMD3 8 113235157 114449242 1093 0.4812

STK10 5 171469074 171615346 135 0.4838

HLA-DQA2 6 32709156 32714664 39 0.4874

CELF4 18 34823003 35146000 282 0.4880

CDC20B 5 54408799 54469005 3 0.4880

IGL 22 22380474 23265085 93 0.4884

VAPA 18 9913955 9960018 52 0.4906

ZBED4 22 50247497 50283726 94 0.4917

SRRM3 7 75831211 75916609 77 0.4988

DNMBP 10 101635334 101769676 228 0.5000

SHPRH 6 146205943 146285421 71 0.5072

UTRN 6 144606434 145174170 227 0.5098

RASGRF2 5 80256491 80525981 307 0.5148

FBXL21 5 135266006 135277367 8 0.5174

CCDC7 10 32735057 33171805 600 0.5177

C5orf66 5 134368970 134680370 163 0.5195

TMX4 20 7961713 8000393 9 0.5217

PALM2-AKAP2 9 112542577 112934792 338 0.5228

SERPINA5 14 95047706 95059457 20 0.5249

FAM19A5 22 48885288 49147744 367 0.5252

TRA 14 22090057 23021075 1109 0.5254

ARHGEF3 3 56761446 57113336 284 0.5388

CCDC157 22 30752627 30772818 11 0.5413

DNM3 1 171810618 172387606 544 0.5421

KLHL32 6 97372496 97588630 235 0.5487

PRKCE 2 45878454 46415129 433 0.5492

C6 5 41142248 41261588 123 0.5518

TEX12 11 112004926 112043279 27 0.5523

MAP7 6 136663419 136871957 97 0.5547

SLC35F3 1 234040679 234460262 249 0.5570

PDE4D 5 58264865 59783925 956 0.5702

KCNJ6 21 38996778 39288741 266 0.5721

FBXL13 7 102453308 102715288 101 0.5757

MOB3B 9 27325207 27530223 196 0.5852

CTSH 15 79214092 79237436 28 0.5854

DNAH8 6 38683117 38998574 312 0.5856

THTPA 14 23980969 24028790 1 0.5936

MTAP 9 21802635 21941040 105 0.5994

TNFRSF1B 1 12227044 12269279 32 0.6015

TECTB 10 114043213 114064793 1 0.6025

Appendices 229



cohorts.


ANKFN1 17 54230836 54560007 146 0.6044

ANO10 3 43407818 43663560 12 0.6049

STK32B 4 5053527 5502728 246 0.6109

PCYOX1L 5 148737570 148749221 9 0.6142

NXPH1 7 8473585 8792593 324 0.6161

PRKCH 14 61788515 62017698 230 0.6207

CRYL1 13 20977806 21100012 164 0.6231

DPF3 14 73085561 73360824 276 0.6293

LGR5 12 71832931 71980090 238 0.6325

SPSB4 3 140770743 140867453 21 0.6364

IL18 11 112013974 112034840 18 0.6391

ZCCHC11 1 52888947 53018764 50 0.6402

ABCG1 21 43619799 43724497 65 0.6471

SPOCK1 5 136310987 136835018 603 0.6486

PTGDR 14 52734310 52743808 27 0.6501

PEX6 6 42931611 42946981 21 0.6546

GALNT7 4 174089904 174245118 32 0.6546

EPHA6 3 96533425 97467786 254 0.6547

FRA10AC1 10 95427640 95462329 56 0.6590

LRP1B 2 140988996 142889270 3044 0.6599

IQUB 7 123091993 123174718 67 0.6635

MYO18B 22 26138117 26453345 235 0.6704

FBXO42 1 16573339 16678965 128 0.6770

IL17B 5 148753830 148760848 9 0.6776

PRKG1 10 52750911 54058110 1607 0.6811

HK1 10 71029740 71161638 40 0.6892

PARP11 12 3907410 3982608 67 0.6915

SAMD4A 14 55033815 55260033 54 0.6926

BMP6 6 7727011 7881972 108 0.6970

RBBP6 16 24550866 24584184 40 0.6995

MAG 19 35782989 35804710 3 0.6995

UVRAG 11 75526212 75855282 71 0.7069

GBE1 3 81538850 81810950 153 0.7109

LIMD1 3 45636323 45722755 151 0.7258

KCNAB1 3 155838337 156256927 383 0.7336

KCNIP4 4 20730234 21950424 1557 0.7373

TRIP11 14 92434243 92506484 83 0.7443

CADM2 3 85008133 86123579 1366 0.7522

INTS2 17 59942728 60005377 2 0.7577

ARMC9 2 232063294 232238606 106 0.7592

CACNA2D1 7 81575760 82073031 530 0.7623

LMX1B 9 129376722 129463311 68 0.7623

AFAP1 4 7760440 7941653 352 0.7624

FAM49A 2 16730727 16847134 39 0.7721

PTPRD 9 8314246 10612723 3071 0.7734

UBE3C 7 156931655 157062066 169 0.7742

C22orf34 22 50013290 50051190 23 0.7761

KCNH8 3 19189946 19577135 121 0.7774

ITGA9 3 37493813 37861281 296 0.7823

TRB 7 141998851 142510972 255 0.7827

TCERG1 5 145826873 145891071 94 0.7885

SLCO3A1 15 92396938 92715665 259 0.7906

STXBP5L 3 120627050 121143608 73 0.7906

PLA2G4A 1 186798032 186958113 125 0.7972

MSH3 5 79950467 80172634 380 0.7979

230 Appendices



cohorts.


DCC 18 49866542 51062273 1918 0.8018

RASEF 9 85594500 85678043 55 0.8062

TRD 14 22891537 22935569 25 0.8071

GRK4 4 2965232 3042474 105 0.8166

GRIK3 1 37261128 37499844 29 0.8195

LRFN2 6 40359373 40555126 125 0.8290

CXCL16 17 4636828 4643223 12 0.8295

MUSK 9 113430935 113566386 133 0.8303

FSTL5 4 162305044 163085186 1194 0.8357

RAP1GAP 1 21922708 21996010 53 0.8407

CAMK1D 10 12391583 12871735 439 0.8452

POLR3A 10 79734907 79789298 34 0.8499

IL2RB 22 37521878 37552414 34 0.8516

INSR 19 7112266 7294011 153 0.8516

GALNT18 11 11292421 11643561 462 0.8585

CLEC4M 19 7828035 7834491 3 0.8595

TNFRSF19 13 24144509 24250244 140 0.8630

UBE2G1 17 4172512 4269969 38 0.8635

APLP2 11 129939716 130014706 40 0.8637

OTOG 11 17568920 17667491 27 0.8650

SIGLEC5 19 52114756 52133727 1 0.8675

SHISA6 17 11144740 11467380 268 0.8701

NPAS2 2 101436613 101613289 177 0.8707

CDKN1A 6 36644237 36655116 4 0.8758

PRKACB 1 84543745 84704181 85 0.8802

CD8A 2 87011728 87035519 1 0.8856

C3orf52 3 111805175 111837073 9 0.8866

CLEC16A 16 11038345 11276046 363 0.8878

TPD52 8 80947103 81083836 191 0.8895

PGM2 4 37828282 37864559 115 0.8948

SLC2A9 4 9827848 10041872 8 0.8950

MLIP 6 53883714 54131078 287 0.8968

TBC1D19 4 26585546 26758232 16 0.8973

NUP210L 1 153965166 154127592 7 0.9030

DDAH1 1 85784168 86044046 271 0.9140

PEX11G 19 7541756 7555884 13 0.9160

SLC7A8 14 23594504 23652869 37 0.9169

NKAIN3 8 63161501 63912211 817 0.9238

SAP130 2 128698791 128785667 22 0.9253

MASP1 3 186933873 187009810 79 0.9265

ZBTB20 3 114033348 114866132 384 0.9268

TIRAP 11 126152800 126164828 16 0.9279

PDZRN3 3 73431582 73674072 161 0.9305

SHFM1 7 96318079 96339203 14 0.9310

MACROD2 20 13976146 16033842 1999 0.9343

DNAH3 16 20944476 21170762 282 0.9522

FAM19A1 3 68040734 68594772 640 0.9560

DSE 6 116601231 116762422 114 0.9573

GRIN2B 12 13713684 14133022 589 0.9681

Appendices 231

Supplementary Table 7.6. Summary of association results for genes previously associated with self-

reported tiredness within our CFS and fatigue cohorts.



DAG1 3 49506136 49573051 3 0.0269

ZBTB37 1 173837220 173856596 1 0.0412

NAPA 19 47990891 48018515 4 0.0539

ANO10 3 43407818 43663560 31 0.1231

RHCG 15 90014638 90039799 7 0.1243

THEM4 1 151843342 151882361 8 0.1276

NICN1 3 49459766 49466777 1 0.1425

RHOA 3 49396569 49449526 3 0.1504

PRRC2C 1 171454652 171562650 27 0.1906

METTL16 17 2319343 2415200 17 0.2055

BSN 3 49591922 49708982 11 0.2058

TCTA 3 49449639 49453909 1 0.2423

SMC1B 22 45739944 45809500 15 0.2451

SSBP4 19 18529679 18545372 2 0.2494

ISYNA1 19 18545198 18549111 1 0.2546

ZDHHC5 11 57435223 57468659 4 0.2745

ELL 19 18553473 18632937 7 0.2985

CTNND1 11 57520756 57586652 6 0.3080

FBXO21 12 117581278 117628305 7 0.3420

SNF8 17 47007458 47022484 3 0.3483

OPA1 3 193310933 193415600 15 0.3969

CATSPER2 15 43922772 43941039 3 0.4218

PRR12 19 50094912 50129696 2 0.4239

SLC44A5 1 75667816 76081698 67 0.4335

ATP11B 3 182511291 182639423 10 0.4371

NRXN1 2 50145643 51259674 340 0.4537

C3orf84 3 49215069 49229291 3 0.4574

TMX2 11 57479995 57508445 4 0.4730

RPE 2 210867289 210889784 3 0.4761

CSMD3 8 113235157 114449242 135 0.4895

KANSL1L 2 210885435 211036068 5 0.4995

PAFAH1B1 17 2496923 2588909 15 0.5192

CCDC36 3 49235861 49295636 9 0.5327

UBA7 3 49842638 49851391 2 0.5365

GIP 17 47035918 47045955 4 0.5693

RELT 11 73087405 73108519 2 0.5835

CCNT2 2 135676363 135716915 8 0.5871

ZNF780A 19 40575059 40596845 3 0.5888

SERPING1 11 57365027 57382326 6 0.5940

CAMK1D 10 12391583 12871735 218 0.6378

UBE2Z 17 46985731 47006422 8 0.6420

PLGRKT 9 5357966 5438381 24 0.6994

ADARB1 21 46494493 46646478 29 0.7425

ASXL3 18 31158541 31331159 43 0.8406

DRD2 11 113280317 113346413 18 0.8721

KLF7 2 207938861 208031970 32 0.8908

PLAC8 4 84011201 84035911 5 0.9139

FAM168A 11 73111837 73309234 13 0.9197

PSMC4 19 40476912 40487671 1 0.9199

SRRM4 12 119419300 119600856 62 0.9491


PLGRKT 9 5357966 5438381 144 0.0027

KANSL1L 2 210885435 211036068 49 0.0033

RPE 2 210867289 210889784 14 0.0054

ZBTB37 1 173837220 173856596 25 0.0457

ASXL3 18 31158541 31331159 156 0.0959

232 Appendices

Supplementary Table 7.6. Continued Summary of association results for genes previously

associated with self-reported tiredness within our CFS and fatigue cohorts.


PRR12 19 50094912 50129696 22 0.2230

SNF8 17 47007458 47022484 28 0.2347

C3orf84 3 49215069 49229291 9 0.2385

KLF7 2 207938861 208031970 103 0.2677

UBE2Z 17 46985731 47006422 42 0.2966

SRRM4 12 119419300 119600856 198 0.3356

UBA7 3 49842638 49851391 7 0.3428

GIP 17 47035918 47045955 22 0.3995

THEM4 1 151843342 151882361 17 0.4092

CCDC36 3 49235861 49295636 29 0.4103

FBXO21 12 117581278 117628305 90 0.4361

FAM168A 11 73111837 73309234 84 0.4584

CSMD3 8 113235157 114449242 1093 0.4812

OPA1 3 193310933 193415600 169 0.5291

PRRC2C 1 171454652 171562650 31 0.5343

ELL 19 18553473 18632937 92 0.5729

ANO10 3 43407818 43663560 12 0.6049

SERPING1 11 57365027 57382326 30 0.6280

METTL16 17 2319343 2415200 51 0.6553

BSN 3 49591922 49708982 106 0.6689

TCTA 3 49449639 49453909 5 0.6888

RELT 11 73087405 73108519 6 0.7115

SLC44A5 1 75667816 76081698 459 0.7196

ZNF780A 19 40575059 40596845 7 0.7258

RHOA 3 49396569 49449526 65 0.7358

NICN1 3 49459766 49466777 5 0.7361

ZDHHC5 11 57435223 57468659 21 0.7462

SMC1B 22 45739944 45809500 86 0.7479

TMX2 11 57479995 57508445 17 0.7536

DAG1 3 49506136 49573051 69 0.7706

CTNND1 11 57520756 57586652 32 0.7715

NRXN1 2 50145643 51259674 1765 0.7827

PAFAH1B1 17 2496923 2588909 15 0.7985

CAMK1D 10 12391583 12871735 439 0.8452

ADARB1 21 46494493 46646478 128 0.8869

DRD2 11 113280317 113346413 86 0.9021

Appendices 233

Supplementary Table 7.7. Summary of association results for SNPs previously associated with

major depressive disorder within our CFS and fatigue cohorts.


directiona


rs10514299 5 87663610 C T 0.709 2.16 (1.09-4.30) 0.0264 +

rs7044150 9 2982931 T C 0.346 1.53 (0.87-2.69) 0.1394 +

rs7973260 12 118375486 G A 0.827 1.58 (0.71-3.51) 0.2628 -

rs12065553 1 80793118 A G 0.700 1.40 (0.75-2.63) 0.2900 +

rs10786831 10 106614571 G A 0.546 1.23 (0.70-2.14) 0.4697 +

rs301806 1 8482078 T C 0.519 1.20 (0.69-2.09) 0.5193 +

rs2125716 12 84941429 C T 0.791 1.20 (0.60-2.41) 0.6126 +

rs9825823 3 61082153 T C 0.436 1.14 (0.65-1.98) 0.6499 +

rs12552 13 53625781 T C 0.473 1.07 (0.62-1.86) 0.8126 -

rs6476606 9 37005561 G A 0.627 1.05 (0.59-1.86) 0.8707 +

rs7647854 3 184876783 G A 0.173 1.06 (0.51-2.18) 0.8794 +


rs8025231 15 37648402 A C 0.558 1.03 (0.99-1.07) 0.1403 +

rs9825823 3 61082153 C T 0.564 1.03 (0.98-1.07) 0.2388 -

rs7647854 3 184876783 A G 0.846 1.03 (0.97-1.09) 0.2848 -

rs1656369 3 158280085 T A 0.637 1.02 (0.98-1.07) 0.3868 -

rs1475120 6 105389953 G A 0.448 1.02 (0.97-1.06) 0.4738 +

rs4543289 5 164484948 G T 0.533 1.02 (0.97-1.06) 0.4831 -

rs10786831 10 106614571 G A 0.593 1.01 (0.97-1.06) 0.5416 +

rs2422321 1 73293393 A G 0.586 1.00 (0.95-1.04) 0.8411 +

rs1518395 2 58208074 G A 0.61 1.00 (0.95-1.04) 0.8506 -

rs12065553 1 80793118 A G 0.713 1.00 (0.96-1.05) 0.8948 +

rs12552 13 53625781 A G 0.433 1.00 (0.96-1.05) 0.9016 -

rs6476606 9 37005561 A G 0.378 1.00 (0.96-1.04) 0.9427 -

Chr: chromosome; RA: risk allele; OA: other allele; Freq: frequency; OR: odds ratio; CI: confidence interval. aSNPs with effects in the same direction are indicated by + while SNPs with effects in opposite

directions are indicated by -.

Supplementary Table 7.8. Summary of association results for genes previously associated with

major depressive disorder within our CFS and fatigue cohorts.

Gene Chromosome Start Stop Number of

SNPs p-value


KSR2 12 117890817 118406399 230 0.1991

L3MBTL2 22 41601312 41627276 6 0.2429

RERE 1 8412464 8877699 47 0.3513

SORCS3 10 106400859 107024993 122 0.3691

DCC 18 49866542 51062273 265 0.8790

PAX5 9 36833272 37034476 82 0.9057

FHIT 3 59735036 61237133 650 0.9307

OLFM4 13 53602876 53626196 23 0.9458


FHIT 3 59735036 61237133 1839 0.2139

OLFM4 13 53602876 53626196 16 0.3112

L3MBTL2 22 41601312 41627276 61 0.5274

RERE 1 8412464 8877699 89 0.5350

SORCS3 10 106400859 107024993 1280 0.5695

PAX5 9 36833272 37034476 247 0.5696

DCC 18 49866542 51062273 1918 0.8018

KSR2 12 117890817 118406399 463 0.8058

234 Appendices

Supplementary Figure 7.1. Regional association plots of the corresponding to the region 50kb upstream and downstream of the risk loci associated with self-reported

tiredness in the UK Biobank dataset (Deary et al., 2017). For each plot the −log10 P values (y-axis) of the SNPs are shown according to their chromosomal positions (x-axis).

The estimated recombination rates from the 1000 Genomes Project March 2012 release are shown as blue lines, and the genomic locations of genes within the regions of

interest in the NCBI Build 37 human assembly are shown as arrows. SNP colour represents LD with the most highly associated SNP at each locus. The figures were created

with Locus Zoom (Pruim et al., 2010). Figures A and B show the region surrounding 1:64178756_C_T in the CFS and fatigue cohorts, respectively. Figures C and D show

the region surrounding rs142592148, on chromosome 1, in the CFS and fatigue cohorts, respectively. Figures E and F show the region surrounding rs7219015, on

chromosome 17, in the CFS and fatigue cohorts, respectively.

A B C

D E F

Appendices 235

Supplementary Figure 7.2. Quantile-Quantile plot of observed vs. expected p-values for the genome-

wide association analysis of chronic fatigue syndrome (λ = 0.99).

236 Appendices

Supplementary Figure 7.3. Regional association plots corresponding to the 400kb region surrounding A. rs12473577, B. rs652252, and C. rs1888140 in the CFS cohort. For

each plot the −log10 P values (y-axis) of the SNPs are shown according to their chromosomal positions (x-axis). The estimated recombination rates from the 1000 Genomes

Project March 2012 release are shown as blue lines, and the genomic locations of genes within the regions of interest in the NCBI Build 37 human assembly are shown as

arrows. SNP colour represents LD with the most highly associated SNP at each locus. The figures were created with Locus Zoom (Pruim et al., 2010).

A B C

Appendices 237

Supplementary Figure 7.4. Quantile-Quantile plot of observed vs. expected p-values for the genome-

wide association analysis of fatigue (λ = 1.02).

238 Appendices

Supplementary Figure 7.5. Regional association plots corresponding to the 400kb region surrounding A. rs874681, B. rs16849948, C. rs1701470, D. rs352582, E. rs359477,

and F. rs4237354 in the fatigue cohort. For each plot the −log10 P values (y-axis) of the SNPs are shown according to their chromosomal positions (x-axis). The estimated

recombination rates from the 1000 Genomes Project March 2012 release are shown as blue lines, and the genomic locations of genes within the regions of interest in the

NCBI Build 37 human assembly are shown as arrows. SNP colour represents LD with the most highly associated SNP at each locus. The figures were created with Locus

Zoom (Pruim et al., 2010).

A B C

D E F

characterising the relationship between fatigue and depression · 2017-09-04 · depression and...

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