identification of genetic modifiers in lrrk2 …
TRANSCRIPT
IDENTIFICATION OF GENETIC MODIFIERS IN LRRK2 PARKINSONISM
by
Joanne Trinh
BSc, The University of British Columbia, 2012
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
in
The Faculty of Graduate and Postdoctoral Studies
(Medical Genetics)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
December 2016
© Joanne Trinh, 2016
ii
Abstract
Genetic studies have been extremely informative to the pathophysiology of PD. The most
common pathogenic mutation discovered is LRRK2 p.G2019S which accounts for 30-40% of
Parkinson disease (PD) in North African Arab Berbers, 18-30% in Ashkenazi Jews and 1-3% in
Caucasians. Although LRRK2 p.G2019S parkinsonism is considered a monogenic form of
disease, disease penetrance of motor symptoms is variable. We hypothesize that genetic factors
can modulate the phenoconversion of LRRK2 p.G2019S which could lead to treatments that
prevent onset or delay disease progression.
Clinical characterization of LRRK2 p.G2019S carriers from Tunisia was performed by
analysis of motor and non-motor features. Genetic analysis of age of onset as a genetic trait was
performed in a cohort of Tunisian Arab Berbers with LRRK2 p.G2019S. Short-tandem repeat
genotyping (4cM resolution) and non-parametric and model-based genome-wide linkage was
evaluated in 41 multi-incident LRRK2 p.G2019S families. High-density locus-specific
genotyping and association analyses were also performed in 232 unrelated LRRK2 p.G2019S
carriers. Genome sequencing in a subset of 25 subjects informed imputation and haplotype
analyses. Validation analysis used Sanger sequencing and Taqman genotyping on additional
LRRK2 p.G2019S carriers originating from Algeria, France and Norway. Whole transcriptome,
candidate gene and protein expression was assessed in striatum from 60 human brains.
Significant linkage was identified on chromosome 1q23.3-24.3 (model-based LOD=4.99,
D1S2768). In the chromosome 1q23.3-24. interval higher-resolution SNP genotyping,
association and haplotype mapping nominated genetic variability within DNM3 as an age of
onset modifier of disease penetrance (rs2421947 nominal p<10-5
; haplotype p=1.67 x 10-7
). In
terms of age of onset the penetrance of parkinsonism in LRRK2 p.G2019S carriers varies as a
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function of DNM3 genotype; rs2421947 is a haplotype-tag for which median onset in GG
carriers is 13 years younger than CC carriers (HR 1.63 CI=1.05-2.63, p=0.03). DNM3 rs2421947
variability is also directly correlated with dynamin 3 mRNA and protein expression in human
brain striatum (p<0.05).
Dynamin 3, shown to complex with endophilin A, LRRK2 and vacuolar protein sorting
35, localizes to the endocytic machinery of dendritic spines to modulate receptor recycling and
excitatory synaptic transmission, now suggests novel targets for therapeutic development in
Parkinson’s disease.
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Preface
All of the work presented was conducted at the Centre for Applied Neurogenetics (CAN),
part of the Djavad Mowafaghian Centre for Brain Health (CBH) at the University of British
Columbia. CAN was established by Dr. Matthew Farrer (Principal Investigator). Study and
experimental approaches were approved by the University of British Columbia Ethics Board.
UBC Research Ethics (H10-02191) and ethics certificate (#5885 – 13) for Disease penetrance of
LRRK2 Gly2019Ser parkinsonism, LRRK2 G2019S disease penetrance modifiers and
Clinicogenetic studies of LRRK2 G2019S in Tunisia was obtained.
All manuscripts published or in preparation have been written under the guidance of Dr.
Matthew Farrer. I collected all genetic data and performed clinical and genetic analysis with
contributions from collaborators (Dr. Jan Aasly, Dr. Faycel Hentati, Dr. Suzanne Lesage, Dr.
Alexis Brice, Dr. Tatiana Foroud, Dr. Rick Myers), graduate student (Emil Gustavsson), and
committee members (Dr. Angie Brooks-Wilson, Dr. Denise Daley, Dr. Carolyn Brown).
Chapter 1: Parts of this chapter has been published as a review: Trinh et al (2013) Advances in
the genetics of Parkinson disease, Nature Neurology Reviews. All tables and figures have been
adapted and added on from Trinh et al 2013.
Chapter 2: Parts of chapter contains published data from Trinh et al (2014) Disease penetrance of
late-onset Parkinson disease, JAMA Neurology and Trinh et al (2014) and LRRK2
parkinsonism in Tunisia and Norway: A comparative analysis of disease penetrance (2014)
Neurology. Published work was done through collaborations with Dr. Jan Aasly from Trondheim
University and Dr. Faycel Hentati from Tunis Neurologie institute.
v
Chapter 3: Parts of chapter contains published data Trinh et al (2013) A comparative study on
LRRK2 parkinsonism. Neurobiology of Aging. The collection of data and questionnaires was
funded by the Michael J Fox Foundation. Conference abstract: A comparative study of LRRK2
G2019S parkinsonism and idiopathic Parkinson’s disease in Tunisia. 3rd World Parkinson
Congress. October 1-4, 2013. Montreal, Canada. Conference abstract: Identification of LRRK2
p.G2019S disease modifiers. 62nd Annual Meeting of The American Society of Human
Genetics, November 6-10, 2012 in San Francisco, California.
Chapter 4: Written as a manuscript: DNM3 modifies age of onset in LRRK2 parkinsonism.
Lancet Neurology. 2015 (accepted). Conference abstract: DNM3; a genetic modifier of LRRK2
parkinsonism. 64th Annual Meeting of The American Society of Human Genetics . October 18-
22 2014, San Diego, California, USA.
Chapter 5: Section 1 has been used in a DFG grant, project title “Reduced penetrance in
hereditary movement disorders: elucidating mechanisms of endogenous disease protection”.
For the use of article, figures and tables, all copyright permissions have been obtained through
journal publishers.
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Table of contents
Abstract ........................................................................................................................................... ii
Preface............................................................................................................................................ iv
Table of contents ............................................................................................................................ vi
List of tables ................................................................................................................................... ix
List of figures ................................................................................................................................. xi
List of abbreviations .................................................................................................................... xiii
Glossary of terms ........................................................................................................................ xvii
Acknowledgments........................................................................................................................ xix
1. Chapter 1: Introduction ....................................................................................................... 1
1.1. General features of Parkinson disease ........................................................................ 1
1.1.1. Motor features ..................................................................................................... 1
1.1.2. Non-motor features ............................................................................................. 2
1.1.3. Pathology ............................................................................................................ 2
1.2. Identification of genetic mutations in PD ................................................................... 3
1.2.1. Linkage analysis.................................................................................................. 3
1.2.2. Next generation sequencing ................................................................................ 4
1.2.3. Genome-wide case-control association............................................................... 5
1.3. Genes implicated in late-onset autosomal dominant PD ............................................ 6
1.3.1. SNCA .................................................................................................................. 6
1.3.2. LRRK2 ................................................................................................................ 7
1.3.3. MAPT ................................................................................................................. 8
1.3.4. EIF4G1 ................................................................................................................ 9
1.3.5. VPS35 and DNAJC13....................................................................................... 10
1.3.6. CHCHD2........................................................................................................... 11
1.3.7. Recessively inherited gene mutations ............................................................... 12
1.4. GWAS in PD............................................................................................................. 13
1.5. Neurobiological interactions: is there one pathway for PD? .................................... 20
1.6. Reduced penetrance .................................................................................................. 23
2. Chapter 2: Disease penetrance estimates of mutations in late-onset PD .......................... 25
2.1. Introduction: penetrance estimates ........................................................................... 25
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2.2. Methods..................................................................................................................... 28
2.3. Results ....................................................................................................................... 29
2.3.1. SNCA: description of duplications, triplication and point mutations ............... 29
2.3.2. LRRK2 penetrance findings between populations ........................................... 36
2.3.3. Other autosomal dominantly-inherited mutations in familial PD ..................... 37
2.4. Discussion ................................................................................................................. 40
3. Chapter 3: A clinical comparison between LRRK2 parkinsonism and idiopathic PD ..... 53
3.1. General clinical features of LRRK2 parkinsonism ................................................... 53
3.2. Methods..................................................................................................................... 54
3.2.1. Motor symptom assessment .............................................................................. 54
3.2.2. Non-motor symptom assessment ...................................................................... 55
3.2.3. Genetic assessment and statistical analysis....................................................... 56
3.2.4. Michael J Fox Foundation (MJFF) database storage ........................................ 56
3.3. Results ....................................................................................................................... 60
3.3.1. Motor features ................................................................................................... 60
3.3.2. Non-motor features ........................................................................................... 68
3.3.3. Disease progression .......................................................................................... 74
3.4. Discussion ................................................................................................................. 75
4. Chapter 4: Dynamin 3 modifies age at onset in LRRK2 parkinsonism ............................ 79
4.1. Introduction ............................................................................................................... 79
4.2. Methods..................................................................................................................... 79
4.2.1. Discovery cohort and replication series ............................................................ 79
4.2.2. Linkage analysis and STR genotyping ............................................................. 80
4.2.3. Genome-wide SNP genotyping and association ............................................... 81
4.2.4. Whole genome sequencing and imputation ...................................................... 82
4.2.5. Sequencing and genotyping .............................................................................. 83
4.2.6. Brains, RNA, ampliseq transcriptome, antibodies ............................................ 83
4.3. Results ....................................................................................................................... 85
4.3.1. Linkage and association of LRRK2 p.G2019S families ................................... 85
4.3.2. Higher resolution mapping ............................................................................... 86
4.3.3. DNM3 expression in brain ................................................................................ 87
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4.3.4. Replication cohorts ........................................................................................... 88
4.4. Discussion ................................................................................................................. 88
5. Chapter 5: Elucidating mechanisms of reduced penetrance in Mendelian disease ........ 113
5.1. The importance of reduced penetrance ................................................................... 113
5.2. Factors that influence penetrance............................................................................ 114
5.3. Methods and approaches to identify genetic modifiers .......................................... 116
5.4. Dynamin 3 as potential therapeutic target of LRRK2 parkinsonism ...................... 118
5.5. Conclusion .............................................................................................................. 119
References ................................................................................................................................... 121
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List of tables
Table 1. Phenotypes associated with genes implicated in late-onset Lewy body PD ................... 15
Table 2. Selected genome-wide association studies in Parkinson disease.................................... 19
Table 3. Estimates of LRRK2 p.G2019S age-associated cumulative incidence ........................... 27
Table 4. Summary of patients included for each mutation into penetrance estimates .................. 31
Table 5. Demographics of unrelated patients and control subjects .............................................. 58
Table 6. Demographics of patients with a family history of parkinsonism within 1o .................. 59
Table 7. Clinical summary of patients .......................................................................................... 61
Table 8. Parkinsonism in LRRK2 p.G2019S carriers by gender ................................................. 62
Table 9. UPDRS Part IA Mentation, Behaviour and Mood ......................................................... 63
Table 10. UPDRS Part IB Mentation, Behaviour and Mood ........................................................ 64
Table 11. UPDRS Part II Activities of Daily Living .................................................................... 65
Table 12. UPDRS Part III ............................................................................................................. 66
Table 13. UPDRS Part IV Complications of Therapy .................................................................. 67
Table 14. Autonomic dysfunction (SCOPA-Aut) individual scores ............................................ 69
Table 15. Summary of autonomic assessments compared between LRRK2 parkinsonism and iPD
....................................................................................................................................................... 71
Table 16. Summary of cognitive assessment compared between iPD, LRRK2 parkinsonism and
control subjects ............................................................................................................................. 72
Table 17. Comparison of sleep scales among LRRK2 parkinsonism and iPD.............................. 73
Table 18. Rate of disease progression associated with age at onset in patients ............................ 74
Table 19. Demographics of discovery cohorts: Tunisian Arab-Berber LRRK2 p.G2019S carriers
....................................................................................................................................................... 96
x
Table 20 . Demographics of LRRK2 p.G2019S carriers: replication series ................................. 97
Table 21. Demographics of healthy control brains for expression analysis ................................. 98
Table 22. Primer pairs and custom TaqMan probe design for different DNM3 transcript isoforms
in human striatum ......................................................................................................................... 99
Table 23. PLINK association underneath linkage regions.......................................................... 100
Table 24. DNM3 haplotypes associated with AAO.................................................................... 101
Table 25. DNM3 transcript levels correlate with LRRK2, VPS35 and SYNJ1 expression in striatal
tissue transcriptome data from normal controls (n=17). ............................................................. 102
Table 26. Sensitivity analysis for different age cut-offs on chromosome 1q23.3-24.3 using non-
parametric linkage ....................................................................................................................... 103
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List of figures
Figure 1. Neurobiological Interactions between implicated genes for PD ................................... 22
Figure 2. Kaplan-Meier survival curves for SNCA mutations. .................................................... 33
Figure 3 Kaplan-Meier survival curves for SNCA ....................................................................... 35
Figure 4. Population-specific penetrance estimates of LRRK2 p.G2019S mutations. ................. 38
Figure 5. Kaplan-Meier survival curves for LRRK2 mutations. .................................................. 38
Figure 6. Kaplan-Meier survival curves for VPS35, EIF4G1 and DNAJC13 mutations. ............ 39
Figure 7. Comparison of SNCA and LRRK2 mutations. ............................................................. 45
Figure 8. Cumulative Incidence of SNCA triplication carriers. ................................................... 45
Figure 9. Cumulative Incidence of SNCA duplication carriers. ................................................... 46
Figure 10. Cumulative Incidence of LRRK2 p.N1437H carriers. ................................................ 46
Figure 11. Cumulative Incidence of LRRK2 p.R1441C carriers. ................................................. 47
Figure 12. Cumulative Incidence of LRRK2 p.R1441G carriers. ................................................ 47
Figure 13. Cumulative Incidence of LRRK2 p.Y1699C carriers. ............................................... 48
Figure 14. Cumulative Incidence of LRRK2 p.G2019S carriers. ................................................. 48
Figure 15. Cumulative Incidence of Ashkenazi Jewish LRRK2 p.G2019S carriers. ................... 49
Figure 16. Cumulative Incidence of Tunisian Arab-Berber LRRK2 p.G2019S carriers. ............. 49
Figure 17. Cumulative Incidence of Norwegian LRRK2 p.G2019S carriers. .............................. 50
Figure 18. Cumulative Incidence of EIF4G1 p.R1205H carriers. ................................................ 50
Figure 19. Cumulative Incidence of VPS35 p.D620N carriers. ................................................... 51
Figure 20. Cumulative Incidence of DNAJC13 p.N855S carriers................................................ 51
Figure 21. World map with LRRK2 mutations ............................................................................ 52
Figure 22. Chromosome 1 linkage peak ....................................................................................... 94
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Figure 23. Age-associated cumulative incidence of LRRK2 p.G2019S carriers. ........................ 95
Figure 24. Whole genome sequencing and imputation workflow .............................................. 104
Figure 25. A schematic of the thirteen dynamin isoforms. ......................................................... 105
Figure 26. Multipoint model-based and non-parametric linkage analysis of Tunisian Arab-Berber
LRRK2 p.G2019S families. ........................................................................................................ 107
Figure 27 Chromosome 1 Q-Q plot values ................................................................................. 108
Figure 28. DNM3 transcript levels normalized by geometric mean of housekeeping genes ...... 109
Figure 29. Dynamin 3 protein levels normalized by GAPDH .................................................... 110
Figure 30. Dynamin 3 staining in cortical neurons ..................................................................... 111
Figure 31. Flow diagram of discovery and replication cohorts .................................................. 112
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List of abbreviations
AAO Age at onset
ABCA7 ATP-Binding Cassette, Sub-Family A (ABC1), Member 7
AD Alzheimer’s disease
ALS Amyotrophic lateral sclerosis
AOO Age of onset
APOC3 Apolipoprotein C-III
APOE Apolipoprotein class E
APP Amyloid Beta (A4) Precursor
ATP13A2 ATPase Type 13A2
BACE1 Beta-site amyloid precursor protein cleaving enzyme 1
BLBD Brainstem Lewy body disease
C9orf72 Chromosome 9 open reading frame 72
CF Cystic fibrosis
CFTR Cystic fibrosis transmembrane conductance regulator
CHCHD2 Coiled-coil-helix-coiled-coil-helix domain containing 2
CI Confidence interval
CRF Clinical research forms
CNV Copy number variation
DaT Dopamine transporters
DJ1 Protein deglycase peptidase C56 family
DLB Dementia with lewy bodies
DLBD Diffuse Lewy body disease
DNAJC13 DnaJ (Hsp40) Homolog, Subfamily C, Member 13
DNA Deoxyribonucleic acid
DNM3 Dynamin 3
DYT1 Torsion dystonia-1
EIF4G1 Eukaryotic Translation Initiation Factor 4 Gamma, 1
ESP Exome sequencing project
ET Essential tremor
xiv
ExAC Exome aggregation consortium
F-DOPA Fluorodopa
FTD Frontotemporal dementia
GAK-DGKQ Cyclin G-associated kinase/ Diacylglycerol Kinase loci in GWAS
GAPDH Glyceraldehyde-3-phosphate dehydrogenase
GCH1 GTP cyclohydrolase 1
GDS Geriatric depression scale
GRS Genetic risk score
GSK GlaxoSmithKline
GTP Guanosine-5’-triphosphate
GWAS Genome-wide association studies
HD Huntington disease
hLOD heterogeneity LOD
HPRT Hypoxanthine phosphoribosyl-transferase
HTT Huntingtin
HWE Hardy-Weinberg equilibrium
IBD Identity by descent
IBS Identity by state
IPD Idiopathic PD
IPSC Induced human pluripotent stem cells
LD Linkage disequilibrium
LDL Low-density lipoprotein
L-dopa Levodopa
LOD Logarithm of odds
LRRK2 Leucine-rich repeat kinase 2
MAF Minor allele frequency
MAPT Microtubule-Associated Protein Tau
MDS-UPDRS Movement disorders society unified Parkinson disease rating scale
MJFF Michael J Fox Foundation
MMSE Mini-Mental State Examination
MOCA Montreal Cognitive Assessment
xv
MSA Multiple system atrophy
NBIA Neurodegeneration with brain iron accumulation
NPC1L1 Niemann-Pick C1-Like 1
NPL Nonparametric linkage
NUCKS1 Nuclear Casein Kinase And Cyclin-Dependent Kinase Substrate 1
OR Odds Ratio
PCSK9 Proprotein convertase subtilisin/kexin type 9
PD Parkinson disease
PINK1 PTEN-induced putative kinase 1
PLA2G6 Phospholipase A2, Group VI
PRIMA Preferred Reporting Items for Systematic Reviews and Meta-analyses
PRKN Parkin; official name PARK2
RAB7L1 Member of the RAS Oncogenefamily; also known as RAB29
REM Rapid eye movement
REP1 Dinucleotide repeat sequence in promoter of SNCA
RIN RNA integrity number
RNA Ribonucleic acid
RME-8 Receptor mediated endocytosis 8
SCOPA-AUT Scales for Outcomes in Parkinson’s disease – Autonomic
SGCE Sarcoglycan, Epsilon
SLC41A1 Solute carrier family 41 magnesium transporter, member 1
SLC45A3 Solute carrier family 45, member 3
SNCA alpha-synuclein
SNP Single nucleotide polymorphism
STR Short tandem repeats
SYNJ1 Synaptojanin 1
SYP Synaptophysin
TDP TAR DNA-binding protein
THAP1 THAP Domain Containing, Apoptosis Associated Protein 1
TLBD Transitional Lewy body disease
TMEM175 Transmembrane protein 175
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TOR1A Torsin family 1, member A (torsin A)
UPDRS Unified parkinson disease rating scale
VCF Variant calling file
VPS26 Vacuolar protein sorting 26
VPS29 Vacuolar protein sorting 29
VPS35 Vacuolar protein sorting 35
WES Whole exome sequencing
WGS Whole genome sequencing
xvii
Glossary of terms
1000 Genomes First project to sequence the genomes of a large number of people to
provide a comprehensive resource on human genetic variation
(www.1000genomes.org )
Apoptosis Process of programmed cell death
Bradykinesia Slowness of movement
ClinVar A freely accessible, public archive of reports of the relationships among
human variations and phenotype with supporting evidence
(www.ncbi.nlm.nih.gov/clinvar/)
dbSNP Database of single nucleotide polymorphisms
(www.ncbi.nlm.nih.gov/SNP)
DYT1 Dystonia caused by mutations in TOR1A
ENCODE The Encyclopedia of DNA Elements
(www.genome.ucsc.edu/ENCODE/)
Exome Part of the genome formed by exons, sequences which when
transcribed remain within the mature RNA after introns are removed by
RNA splicing
Familial With family history
Idiopathic Disease with unknown pathogenesis
Imprinting Epigenetic phenomenon by which certain genes are expressed in a
parent-of-origin-specific manner
Levodopa Drug treatment for symptoms of Parkinson disease
Low frequency variant Changes in the genome that deviates from the reference that is 1-5%
minor allele frequency in the general population
Modifiers Potential factors that influence the disease phenotype
Monogenic Inheritance of a phenotype controlled by one gene
Mutation Change in the genome that is rare <1%.
PLINK Free, open-source whole genome association analysis toolset, designed
to perform a range of basic, large-scale analyses in computationally
efficient manner
xviii
(http://pngu.mgh.harvard.edu/~purcell/plink.index.shtml)
Polymorphism Common change in the genome (minor allele frequency >5%
Postural instability Disabling sign of Parkinson disease influenced by balance
Rare variant Changes in the genome that deviates from the reference that is <1%
minor allele frequency in the general population
Rigidity Stiffness of the body
Sporadic Disease with unknown pathogenesis and no family history
Transcriptomics Study of the transcriptome: the complete set of RNA transcripts that are
produced by the genome under specific circumstances or in a specific
cell, using high throughput technologies such as microarray analysis
Tremor Involuntary quivering movement
Variant Changes in the genome that deviates from the reference
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Acknowledgments
I would like to thank the many patients and their families who volunteered, and the
longitudinal efforts of the clinical teams involved. Initial studies in Tunisia on familial
parkinsonism were in collaboration with Lefkos Middleton, Rachel Gibson and the
GlaxoSmithKline PD Programme Team (2002-2005). Subsequent clinical and molecular genetic
analysis was supported through Mayo Foundation, GlaxoSmithKline and National Institutes of
Health. The Michael J Fox Foundation generously supported clinical studies of LRRK2
p.G2019S in Tunisia and subsequent whole genome sequencing (2008-2011), Canada Excellence
Research Chairs program, CIHR/IRSC 275675 (2010-2017) and the Don Rix BC Leadership
Chair in Genetic Medicine. Replication series were made possible through the support of the
France-Parkinson Association, the Roger de Spoelberch Foundation (R12123DD), the French
program “Investissements d’avenir” (ANR-10-IAIHU-06), the Research Council of Norway,
Reberg’s legacy, the Norwegian Parkinson Foundation, Parkinson’s Study Group (PSG)
PROGENI Investigators.
I would like to thank members of my thesis committee, Dr. Angie Brooks-Wilson, Dr.
Denise Daley, Dr. Carolyn Brown, for their mentorship and a positive research environment. The
critical questions have allowed me to refocus and enhance this dissertation. Most importantly,
they have helped me mature into a more independent scientist.
A special thanks to Dr. Matthew Farrer for his knowledge, intellect and supervision.
Thank you for believing in this project and my abilities. The opportunities you have given me
have been helpful for my academic career. I would like to thank my closest friend, Dr. Carles
Vilarino-Guell for his ongoing intellectual and emotional support. I wouldn’t have been able to
do this without you. Matt and Carles, I will always look up to you and your scientific values. I
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would also like to thank all the lab members at the Centre for Applied Neurogenetics, especially
Emil Gustavsson and Jas Khinda for their love and humor.
I am deeply grateful for the graduate scholarships/financial support I received from the
Canadian institutes of health reesearch (CIHR), UBC Faculty of Medicine (FoM), UBC Four
year fellowship (4YF), Michael Smith Foreign Exchange Supplement (MSFSS), James Miller
committee, Genome BC (LEEF), and the Simons Foundation.
Lastly, I would like to thank my mom, Linda Ninh, my dad, David Trinh, my uncle Thien
(Tommy) Ha Trinh and my sister, Angel Trinh for their unconditional love and encouragement. I
dedicate my thesis dissertation to my sister, Angel Trinh, who has been through a difficult battle
with neurological complications. You have motivated me in every way throughout my graduate
career. I owe an immense debt to my family who has sacrificed so much to give me every
opportunity to pursue my passions and fulfill my ambitions.
1
1. Chapter 1: Introduction
1.1. General features of Parkinson disease
1.1.1. Motor features
Parkinson disease (PD) is the most common neurodegenerative movement disorder with
age-related prevalence (Bower, Maraganore, McDonnell, & Rocca, 1999). The mean age of
onset is 70 years although 4% of patients develop early-onset disease before the age of 50
(Schrag & Schott, 2006). Approximately 1% of the population is affected at 65 years, increasing
to 4–5% in 85-year-olds (de Lau & Breteler, 2006). The burden to patients, families, caregivers
and society is increasing steadily with population aging and the increased proportion of ‘baby
boomers’ aging.
Parkinsonism is characterized clinically by motor dysfunction; a triad of resting tremor,
bradykinesia, rigidity and postural instability (Fahn, 2003; L. W. Ferguson, Rajput, Muhajarine,
Shah, & Rajput, 2008). Initially, the symptoms are insidious and typically asymmetric, and most
patients suffer an inexorable decline. In diagnosed subjects ‘tremor-dominant’, ‘akinetic-rigid’,
or ‘mixed’ subtypes may dominate. However, an individual’s age-at-onset, disease course or
subsequent co-morbidities are difficult to predict. (Burn et al., 2006; L. W. Ferguson, et al.,
2008) A beneficial response to levodopa drug therapy (which treats the clinical motor features)
may remain late into the disease. However, with disease progression, optimizing the treatment to
patients is challenging and generally requires increased dosing. Side effects include troubling
‘on/off’ motor fluctuations and peak-dose dyskinesias (uncontrolled hyperkinetic movements).
(Fahn, 2000) The progressive loss of dopaminergic innervation to the striatum may be confirmed
using several imaging modalities, including DaTscan,18
F-DOPA positron emission tomography,
and via metabolic changes in brain glucose utilization and blood flow.
2
1.1.2. Non-motor features
Non-motor features of PD include autonomic (constipation, cardiac denervation,
impotence, orthostatic hypotension and seborrhea), cognitive (bradyphrenia, cognitive decline
and dementia), psychiatric (depression, apathy, hallucinations and delusions), sensory problems
(hyposmia, anosmia and pain) and sleep disorders (REM sleep behavior disorder and excessive
daytime somnolence). (Chaudhuri, Healy, Schapira, & National Institute for Clinical, 2006)
These symptoms may be problematic long before the onset of movement disorder and are
difficult to treat. For a neuropathologic diagnosis of PD there must be evidence of neuronal loss
in the substantia nigra pars compacta accompanied by Lewy body pathology (alpha-
synucleinopathy; brainstem (BLBD) or more transitional Lewy body disease (TLBD)) in
surviving neurons. (Braak et al., 2003; Goedert, Spillantini, Del Tredici, & Braak, 2013a, 2013b;
Spillantini et al., 1997) With revised clinical criteria, the majority of patients with probable PD
that come to autopsy are now confirmed pathologically (Goedert, et al., 2013a). Fatigue is
difficult to treat and an important problem by patients. Similar to sleep disturbance, fatigue is
also almost universal in patients with PD (Alves, Wentzel-Larsen, & Larsen, 2004; Brown,
Dittner, Findley, & Wessely, 2005).
1.1.3. Pathology
A clinical diagnosis of dementia with Lewy bodies (DLB) is associated with much more
extensive cortical and limbic Lewy pathology and pathologically defined as diffuse Lewy body
disease (DLBD), that is often but not invariably associated with parkinsonism (Goedert, et al.,
2013a). Similarly, multiple system atrophy (MSA) has parkinsonism and prominent
dysautonomia but is characterized pathologically by glial cytoplasmic alpha-synuclein inclusions
3
(Fellner, Wenning, & Stefanova, 2015; Koga et al., 2015). More sparse or ‘incidental’ Lewy
body pathology is often found in the healthy aged. Conversely, and more rarely, patients with
parkinsonism and clinically atypical ‘Parkinson-plus’ syndromes may have tauopathy (such as
post-encephalytic parkinsonism, cortico-basal degeneration, progressive supranuclear palsy
(Golbe, 1999; Papapetropoulos et al., 2005) and the parkinsonism-dementia complex of Guam
(lytico-bodig)), (Forman et al., 2002) ubiquitin or TDP-43 (TAR DNA-binding protein)
proteinopathy (such as Perry syndrome) (Tsuboi et al., 2008) or have non-specific findings such
as nigral neuronal cell loss with gliosis (including rapid-onset dystonia-parkinsonism, X-linked
dystonia-parkinsonism (Lubag) (Waters et al., 1993).
In PD, genetic mutations can now inform a diagnosis, disease-modeling and basic/pre-
clinical research. However, as the rare may inform the general, the text is also punctuated with
references to molecular findings from Parkinson-plus syndromes. Past discoveries, especially
within monogenic families, appear to coalesce about three interconnected processes: 1) synaptic
transmission (exo-, endocytosis) and endosomal receptor sorting and recycling; 2) lysosomal-
autophagy, and; 3) mitochondrial quality control and stress response. The emerging synthesis
may provide a unified molecular foundation for hypothesis-testing, pharmaceutical development
and future trials aimed at disease modification, not only symptomatic relief.
1.2. Identification of genetic mutations in PD
1.2.1. Linkage analysis
Linkage analysis methods were theoretically developed in the 1950-70s, but applied in
thel late 1990s and early 2000 to analyze rare traits influenced by a major variant or strong
genetic effects in families. Linkage analysis is a study of genetic markers and recombination in
4
families with disease. Traditionally, microsatellites were used as genetic markers for linkage.
These include polymorphic sequences of DNA that are characterized by repeated sequences.
They can be repeats of 2(dinucleotide), 3(trinucleotide), 4(tetranucleotide), which makes them
highly heterozygous. The probability of genetic markers segregating with disease within families
is calculated and represented as a logarithm of odds (LOD) score (Dawn Teare & Barrett, 2005).
Two genetic loci are linked if transmitted from parent to offspring. This concept is used to
identify variants that segregate with a disease phenotype in a family. The LOD score is the
function of the recombination fraction, the higher the LOD score the higher the evidence of
cosegregation (of disease marker and phenotype). When a significant linkage region is identified
(LOD > 3.0) fine-mapping and sequencing underneath the linkage peak is often pursued to
elucidate the causal variant (Dawn Teare & Barrett, 2005). There are now many linkage marker
sets publicly available (DeCode, Marshfield resources). There are multiple methods for linkage.
One is the ‘parametric’ model which requires estimation of disease penetrance, mode of
inheritance (i.e. is it dominant or recessive), disease marker allele frequencies. Often times, these
allele frequencies are taken from population studies and inheritance is estimated from the
pedigree information. Parametric linkage uses identity by descent within a family. However, a
combined score across families is possible. Another is the non-parametric or ‘model-free’
linkage which does not require an input of inheritance and penetrance estimates. In PD, linked
regions to disease were often given a “PARK” locus designation. For example, before the gene
was identified, the region containing LRRK2 was named “PARK8”.
1.2.2. Next generation sequencing
Whole exome or genome sequencing is another approach to identify causal variants
segregating with disease. This involves massive parallel sequencing of ‘short-sequences’ that are
5
aligned to a reference genome through computational methods. Variants that deviate from the
reference genome are identified and called. However, this leads to many variations which may or
may not have an impact on disease. Some more challenges involve the inability to detect
differences in repetitive elements, other large repeat expansions. In the case of exome
sequencing, only coding variations are captured and the initial hypothesis includes only protein-
coding variation (non-synonymous, deletions, frameshifts and loss of function mutations). The
advantages of looking at exome sequencing include the ability to determine pathogenic impact
through annotation and available protein crystal structures. Large publicly-available datasets can
be used as references. The ‘ExAC’ database (exome aggregation consortium) is one such
database to determine potential pathogenicity of a variant of interest. Over 60,000 individuals
have been exome sequenced in this consortium, which allows a reliable estimation of frequency
in the general population. Many prediction tools have allowed us to estimate how amino-acid
changes influence the folding. On the other hand, non-coding regulatory regions may also
contribute to disease pathogenesis. These regions are not covered in exome sequencing.
Although whole-genome sequencing databases are soon to be available, the annotation in these
regions is not as informative as coding regions.
1.2.3. Genome-wide case-control association
Genome-wide association studies test common variants’ association with common
disease. Linkage detects segments of inheritance within pedigrees, and association detects alleles
whose presence is correlated with a trait. Association makes use of linkage disequilibrium (the
likelihood of two markers traveling together in a population), thus association takes into account
the identity-by-state status rather than the identity-by-descent. Linkage analysis can localize 5-
10cM but association extends less than 1cM. There are two main types of genetic association:
6
one is the population-based and the other family-based. Population-based association compares
genetic polymorphisms across case and controls. Genetic polymorphisms are variations in the
genome which can be common (frequency>5%), low (frequency<5% and >1%) or rare
(frequency<1%). Most recently, testing for association in GWAS is at an upwards of 500,000-5
million markers. Family-based association investigates transmission disequilibrium of alleles
through pedigrees. This method is not as commonly used, since it is easier to obtain independent
cases rather than families. Also case-control design was demonstrated to have greater power than
family based designs, provided the disease allele is common (Risch & Merikangas, 1996) . This
analysis has been a seminal driver for case-control designs (Risch & Merikangas, 1996) . Allelic
and genotypic frequencies between cases and controls are compared and the expected
contribution of these genetic polymorphisms are low (effect sizes or odds ratios = 1-1.5).
1.3. Genes implicated in late-onset autosomal dominant PD
A summary of pathogenic mutations and genes implicated in late-onset autosomal
dominant PD is described in Table 1.
1.3.1. SNCA
SNCA encodes for protein a-synuclein. Single nucleotide polymorphisms (SNPs) in
SNCA are found to be associated with PD across multiple ethnic populations: Caucasian,
Japanese, Tunisian Arab Berbers. A mega-meta GWAS of PD has replicated and shown SNCA
to have the most robust effect (Nalls, Pankratz, Lill, Do, Hernandez, Saad, DeStefano, Kara,
Bras, Sharma, Schulte, Keller, Arepalli, Letson, Edsall, Stefansson, Liu, Pliner, Lee, Cheng,
International Parkinson's Disease Genomics, et al., 2014). There is association of the promoter
(REP1) in the 5’ end of SNCA. REP1 associated SNPs also influence transcription of SNCA.
7
However, the functional consequence of other associations within intron 4 and the 3’ end of
SNCA has yet to be discovered.
Many pathogenic mutations within SNCA have been found, traditionally through linkage
analysis and fine mapping. The first mutation identified was SNCA p.A53T (Polymeropoulos et
al., 1997). Since then, the field has identified more pathogenic point mutations such as p.A30P,
p.E46K, p.H50Q, p.G51D and copy number variations such as duplications and triplications
(Lesage, Anheim, Letournel, Bousset, Honore, Rozas, Pieri, Madiona, Durr, Melki, Verny,
Brice, et al., 2013; Trinh & Farrer, 2013). Alpha-synuclein is a key component of Lewy body
inclusion. Patients with these missense variations have predominantly DLBD pathology. In
addition to DLBD, duplication and triplication carriers have prominent nigral and hippocampal
neuronal loss. SNCA copy numbers lead to earlier onset and more fulminant LBD and dementia
is a prominent clinical feature. SNCA mutations are overall rare and p.A53T seems to be the
most frequent one.
1.3.2. LRRK2
LRRK2 (Leucine-rich repeat kinase 2) mutations confer the highest genotypic risk for
PD. Thus far, there are six pathogenic mutations identified in LRRK2: p.N1437H,
p.R1441C/G/H, p.Y1699C, p.G2019S, p.I2020T. LRRK2 p.G2019S is especially frequent in PD
patients of Ashkenazi Jewish or North African Arab-Berber origin, accounting for 13% and 30%
of cases in these populations respectively. Common risk factors include p.R1628P and G2385R.
Genome-wide association studies have highlighted and replicated LRRK2 as an associated gene
for PD. The effect of LRRK2 in GWAS studies has been smaller than that of SNCA. Large-scale
genotyping and gene sequencing of LRRK2 have identified risk factors associated with PD. One
8
example is LRRK2 p.G2385R as a risk factor in Asian populations. There’s also evidence of a
haplotype in LRRK2 that is inversely associated with PD and other Parkinson-plus syndromes,
suggesting that LRRK2 variants in cis or in trans can have different influences on PD risk
(Heckman, Elbaz, et al., 2014; Heckman, Schottlaender, et al., 2014; Heckman et al., 2013; Trinh
& Farrer, 2013; Trinh, Farrer, Ross, & Guella, 1993)
LRRK2 parkinsonism has pleomorphic pathology. At autopsy, patients with LRRK2
parkinsonism typically have Lewy body or neurofibrillary tangle pathology, with nigral neuronal
loss and gliosis in some cases, TDP-43 proteinopathies have also been observed. Pleomorphic
pathology can be evident even within families with the same mutation. Intracellular Lewy bodies
and Lewy neurities, by definition the pathological hallmark of PD, are largely comprised of
aggregated a-synuclein. Clinically, patients with mutations in LRRK2 closely resemble patients
with idiopathic PD.
1.3.3. MAPT
There are two major haplotypes for MAPT (Tau): H1 and H2. The ancestral haplotype
for MAPT involves a paracentric inversion spanning 1.5 Mb (Zody et al., 2008). The H1 allele is
overrepresented in patients(Skipper et al., 2004; Spillantini & Goedert, 2001). Importantly, the
most significant associations of MAPT H1 and an H1-subtype (H1c; defined by the major allele
of rs242557) in neurodegeneration are with progressive supranuclear palsy, corticobasal
degeneration and Parkinson–dementia complex of Guam. These disorders are rare forms of
parkinsonism defined by their primary neurofibrillary tangle pathologies consisting of
hyperphosphorylated 4R tau—a tau protein isoform with four microtubule-binding domains that
results from alternative gene splicing and inclusion of MAPT exon 10. Similar to SNCA and
9
LRRK2, MAPT has been consistently associated with PD in GWAS studies. (Simon-Sanchez &
Gasser, 2015) Postmortem studies of Lewy body disease in which patients had a longitudinal
clinical diagnosis of PD have also observed a MAPT H1 association. The H1 association in
Alzheimer’s disease is not as compelling but there are mutations in MAPT segregating in
frontal-temporal dementia (FTD)(Rademakers, Cruts, & van Broeckhoven, 2004; Rademakers et
al., 2003; Roks et al., 1999).
1.3.4. EIF4G1
EIF4G1 encodes for eukaryotic translation initiation factor 4 gamma 1. A dominantly
inherited p.R1205H is linked to late-onset PD. Although seen in multiple families in the initial
paper, there is incomplete penetrance of the mutation. Support for EIF4G1 in PD remains
equivocal. Some studies have shown that the p.R1205H mutation is present in more controls than
patients in Iceland (N. Nichols, Bras, Hernandez, Jansen, Lesage, Lubbe, Singleton, et al., 2015;
Siitonen et al., 2013). The study assessed the relevance of EIF4G1 in a large cohort by imputing
the p.R1205H mutation. They found 76 icelandic subjects older than 65 years of age that carried
the mutation. Another study with the NeuroX chip assayed over 12,000 patients and controls and
found 5 control subjects carrying the p.R1205H mutation. The control subjects range between
68-75 years of age. This led the studies to conclude that p.R1205H is a benign variant. However,
imputation accuracy needs to be taken into account and the evidence for p.R1205H is still
arguable. Reduced penetrance of the mutation could be an explanation for the observed
asymptomatic carriers.
Exome sequencing was performed for the original French families affected with
Parkinson disease. Importantly, EIF4G1 p.R1205H remains the only mutation identified in
10
chromosome 3q26 linked to parkinsonism . It has not been identified by the Exome Aggregation
Consortium (ExAC; Jan 13th 2015 release) of 60,706 subjects, that includes contributions for the
1000 Genomes and NHLBI-GO Exome Sequencing Project (ESP). Of course this does not rule
out non-coding variation that is in close proximity to p.R1205H and could be the causal
mutation.
1.3.5. VPS35 and DNAJC13
VPS35 p.D620N causes autosomal dominantly inherited parkinsonism. The mutation
segregated in a Mendelian fashion. (Vilarino-Guell et al., 2011; Zimprich et al., 2011) The
mutation was found in large multi-incident families through exome sequencing. The families do
not share haplotypes and the mutation seems to have arisen de novo. Thus far, VPS35 p.D620N
is extremely rare and other pathogenic mutations in VPS35 have yet to be found. VPS26 and
VPS29 bind to VPS35 to form a functional retromer. However, variants identified thus far in
these genes do not seem to segregate with disease (Gustavsson, Guella, et al., 2015).
Nonetheless, VPS35 p.D620N has been independently replicated in large Dutch, French,
Japanese families and other sporadic patients has made a convincing case as a gene for PD
(Ando et al., 2012; Kumar et al., 2012; Lesage et al., 2012; Sharma et al., 2012) . VPS35 is also
implicated in other neurodegenerative diseases. Haploinsufficiency in VPS35 increases the
neuropathology with AD and the protein conforms to the spatiotemporal model of AD(Small et
al., 2005; Wen et al., 2011) . VPS35 could regulate Abeta peptide levels(Small, et al., 2005). The
retromer sorts cargo from endosomes in all cell types. VPS35 is expressed in axons and dendrites
of neurons, involved in retrograde tracking of APP, BACE1 and is important in plasma
membrane trafficking (Bhalla et al., 2012; Steinberg et al., 2013) . VPS35 may have neuron-
11
specific functions: there is evidence that overexpressing VPS35 is neuroprotective to
dopaminergic neurons (Bi, Li, Huang, & Zhou, 2013).
DNAJC13 p.N855S has been found in one large Mennonite kindred by exome
sequencing. However, there is a phenocopy and unaffected carriers in the family. The mutation
needs to be independently replicated in PD. In the meantime, DNAJC13 p.N855S mutations have
also been found to be implicated in Essential Tremor (ET). Other variants in DNAJC13 besides
p.N855S are extremely rare and there is lack of segregation analysis done in families thus far
(Gustavsson, Trinh, et al., 2015; Rajput et al., 2015; Vilarino-Guell et al., 2014)
Both DNAJC13 and VPS35 are the first genes found to be implicated in PD through next
generation sequencing and open new methods for the application of exome and whole genome
sequencing in mapping genes for disease. Since the discovery of VPS35, genes in early-onset
parkinsonism have also been identified by exome sequencing. For example, SYNJ1 mutations
seem to segregate with early-onset disease in some families (Olgiati et al., 2014; Quadri et al.,
2013) . SYNJ1 was first discovered through homozygosity mapping and exome sequencing in an
Italian consanguineous family with parkinsonism and dystonia (Quadri, et al., 2013). However,
this thesis will focus on autosomal dominant Parkinson disease rather than early-onset Parkinson
disease/ Parkinson-plus syndrome.
1.3.6. CHCHD2
CHCHD2 (full name is coiled-coil-helix-coiled-coil-helix domain containing 2) has been
implicated in late-onset autosomal PD. CHCHD2 p.T61I was described in two large Japanese
families with autosomal dominant PD (Funayama et al., 2015). Furthermore, CHCHD2
p.R145Q, and 300+5G>A were also identified in other smaller Japanese families with autosomal
12
dominant PD. There is no evidence of common variants in CHCHD2 being associated with PD.
Further sequencing revealed rare exonic mutations with unknown significance in LBD patients:
the majority of these rare variants were located within the gene’s mitochondrial targeting
sequence (Ogaki et al., 2015).
CHCHD2 contains cysteine-x9-cysteine motifs that are important for regulating enzymes
in the mitochondrial respiratory chain. Mutations in Parkin and PINK1 (PTEN-induced
putative kinase 1) in juvenile/early-onset parkinsonism are important in the mitochondria
respiratory chain. PINK1 knock-outs show reduced mitochondrial ATP synthesis (Grunewald et
al., 2009; Pilsl & Winklhofer, 2012; Rakovic et al., 2011; Vos, Verstreken, & Klein, 2015).
Thus, CHCHD2 is functionally compelling and replication of these three mutations in other
families is warranted.
1.3.7. Recessively inherited gene mutations
Recessively inherited mutations (homozygous or compound heterozygous loss of
function) have also been identified by linkage analysis in parkin (PARK2;PRKN) (Cookson et
al., 2003; Mata et al., 2005; Tan et al., 2003; West, Lockhart, O'Farell, & Farrer, 2003), PTEN-
induced putative kinase 1 (PINK1) (Ishihara-Paul et al., 2008; Lee et al., 2009; Toft et al., 2007)
and DJ-1 (PARK7) (Lockhart, Bounds, et al., 2004; Lockhart, Lincoln, et al., 2004; Maraganore
et al., 2004) , albeit clinical syndromes with juvenile (≤20 years at diagnosis) or early-onset
disease (≤45 years at diagnosis). While the majority of cases that have come to autopsy suffer
neuronal loss without Lewy body pathology there are noteworthy exceptions in compound
heterozygotes (Farrer et al., 2001; Samaranch et al., 2010) . PRKN loss-of-function may explain
13
~15% of early-onset cases and the majority (~50%) in which there is a family history of
parkinsonism and/or parental consanguinity, albeit without Lewy pathology.
Early-onset parkinsonism accounts for <4% of PD in the community although it is more
frequently encountered in movement disorders neurology clinics. Recessively inherited
mutations have been also implicated in rare, rather atypical Parkinson-plus disorders including
Kufor-Rakeb syndrome due to mutations in ATP13A2, neuroaxonal dystrophy due to loss of
PLA2G6, (Morgan et al., 2006; Paisan-Ruiz, Washecka, Nath, Singleton, & Corder, 2009) and
neurodegeneration with brain iron accumulation (NBIA) due to mutations PANK2, C2orf37,
C19orf12, FA2H and WDR45 (Gregory & Hayflick, 2011; Haack, Hogarth, Gregory, Prokisch,
& Hayflick, 2013; Haack et al., 2012)
1.4. GWAS in PD
Genetic association study (GWAS) looks to find alleles that are observed more often than
expected by chance in individuals with a trait of interest than those without. There are many
strengths in this approach and GWAS have made important contributions in the scientific field.
Most notably, in neurodegeneration, the APOE association was identified for Alzheimer’s
disease (AD) (Harold et al., 2009; Lambert et al., 2013) . The APOE allele had a large and robust
effect on AD. The SNCA signal in PD is the most robust across populations in GWAS.
Furthermore, many genes that have been linked (identified through families) have also been
nominated in GWAS. A basic summary of associated genes found from GWAS is present in
Table 2. SNCA, LRRK2, GCH1 are a few genes that harbor genetic risk factors and pathogenic
mutations segregating in families (Nalls, Pankratz, Lill, Do, Hernandez, Saad, DeStefano, Kara,
Bras, Sharma, Schulte, Keller, Arepalli, Letson, Edsall, Stefansson, Liu, Pliner, Lee, Cheng,
14
International Parkinson's Disease Genomics, et al., 2014; Simon-Sanchez et al., 2011). GWAS
findings can be followed up with additional sequencing to identify genetic variants of
pathogenicity. For example, loss-of function (LOF) mutations in ABCA7 in patients with AD
have been identified. In fact, an ABCA7 LOF mutation segregated in late-onset autosomal
dominant AD families (Cuyvers et al., 2015; Hollingworth et al., 2011) .
Like many studies, there are also caveats to GWAS. One main problem is difficulty in
pin-pointing the real associated genes and/or functional variants.The PARK16 locus contains
five genes (SLC45A3, NUCKS1, RAB7L1, SLC41A1) (Trinh, Vilarino-Guell, & Ross, 2015;
Vilarino-Guell et al., 2010). RAB7L1 seems to be the most studied and most compelling
candidate. RAB7L1 has been shown to co-immunoprecipitate with VPS35 and LRRK2 (D. A.
MacLeod et al., 2013). RAB7L1 seems to interact with common variants in LRRK2 to modify
risk. However, the effect of RAB7L1 is different across populations. Thus, elucidating the real
functional variant for RAB7L1 is challenging. Another example is the GAK-DGKQ locus has
also been nominated by GWAS and the genomic region consists of three genes. GAK is the most
interesting, due to its involvement in clathrin-mediated endocytosis. Even if the locus points to
one gene of interest, there may be multiple risk variants to consider. There are multiple variants
associated in SNCA and the effect between two variants very close together is difficult to
distinguish because they co-segregate during inheritance. However, each variant can alter
expression levels of SNCA differently. A novel strategy to identify such functional variants is
with human pluripotent stem cells (IPSC) and genome editing techniques. Through
CRISPR/Cas9, a common PD-associated risk variant in a non-coding distal enhancer element
(located in intron 4) was found to regulate the expression of α-synuclein (Soldner et al., 2016).
15
Table 1. Phenotypes associated with genes implicated in late-onset Lewy body PD
Disease
OMIM
identifier
Gene Mutations Age at
onset
(range)
Synopsis of
clinical
features
Predominant
pathology
References
Dominantly inherited late-onset PD
168601 SNCA Missense:
Ala30Pro,
Glu46Lys,
His50Gln,
Gly51Asp,
Ala53Thr
60 years
(30–80)
Levodopa-
responsive
parkinsonis
m
Diffuse
LBD
(Kruger et
al., 1998;
Lesage,
Anheim,
Letournel,
Bousset,
Honore,
Rozas, Pieri,
Madiona,
Durr, Melki,
Verny, &
Brice, 2013;
Polymeropo
ulos, et al.,
1997;
Proukakis et
al., 2013;
Zarranz et
al., 2004)
605543 SNCA Locus
duplication
(and
triplication)
31–71 years
(24–48)
Levodopa-
responsive
parkinsonis
m, cognitive
decline,
autonomic
dysfunction
and
dementia;
progression
more rapid
in SNCA
triplication
cases
Diffuse
LBD, with
prominent
nigral and
hippocampal
(CA2–3)
neuronal
loss
(Chartier-
Harlin et al.,
2004; J.
Fuchs et al.,
2008;
Ibanez et al.,
2004;
Nishioka,
Wider, et
al., 2010;
Singleton et
al., 2003)
607060 LRRK2 Missense:
Asn1437His,
Arg1441Cys/
Gly/His,
Tyr1699Cys,
Gly2019Ser,
Ile2020Thr
60 years
(32–79)
Levodopa-
responsive
parkinsonis
m consistent
with
sporadic
PD;
Brainstem
LBD,
neurofibrilla
ry tangle or
TDP-43
pathology
and/or nigral
(Paisan-
Ruiz, Lang,
et al., 2005;
Ross et al.,
2011;
Zimprich et
al., 2004)
16
Disease OMIM
identifier
Gene Mutations Age at onset
(range)
Synopsis of clinical
features
Predominant pathology
References
Common
polymorphis
ms:
Ala419Val,
Arg1628Pro,
Gly2385Arg
(Asia)
Protective
haplotype:
Asn551Lys–
Arg1398His
–
Lys1423Lys
occasionally
dystonia,
amyotrophy,
gaze palsy
and
dementia
neuronal
loss
614203 VPS35 Missense:
Asp620Asn
53 years
(40–68)
Tremor-
dominant
levodopa-
responsive
parkinsonis
m,
dyskinesia
and
dystonia,
occasionally
dementia
Inconclusive
, possibly
without
LBD
(Ando, et
al., 2012;
Kumar, et
al., 2012;
Nuytemans
et al., 2013;
Sheerin et
al., 2012;
Vilarino-
Guell, et al.,
2011;
Zimprich, et
al., 2011)
616361 DNAJC13 Missense:
Arg855Ser
67 years
(57.5-76.5)
Slowly
progressive,
late-onset
asymmetric
parkinsonis
m, good
response to
L-dopa.
Lewy body
inclusions in
carriers and
also
DNAJC13
staining
within these
inclusions
(Vilarino-
Guell, et al.,
2014)
616244 CHCHD2 Missense:
Thr61Ile
55.5 years
(48-61)
Typical
parkinsonso
nian features
(bradykinesi
a, rigidity,
gait). L-
dopa
responsive
NA: Post-
mortem yet
to be tested
for Lewy
body
inclusions
(Funayama,
et al., 2015)
17
Disease OMIM
identifier
Gene Mutations Age at onset
(range)
Synopsis of clinical
features
Predominant pathology
References
Juvenile and early-onset recessively inherited parkinsonism
600116 PARK2 Numerous
missense,
exon
deletion and
duplication
mutations
<45 years
(12–58)
Levodopa-
responsive
parkinsonis
m, often
juvenile and
typically
slowly
progressive
Predominant
ly nigral
neuronal
loss,
occasionally
with
synuclein or
tau
pathology
(Kitada et
al., 1998)
605909 PINK1 Missense:
Gln129X,
Gln129fsX1
57,
Pro196Leu,
Gly309Asp
Trp437X,
Gly440Glu,
Gln456X
Rare: locus
and exon
deletion
Typically
<45 years
(18–56)
Levodopa-
responsive
parkinsonis
m, often
akinetic
with
postural
instability/g
ait
disturbance
with slow
progression;
sleep benefit
One case
with LBD
(Ishihara-
Paul, et al.,
2008;
Samaranch,
et al., 2010;
Valente,
Abou-
Sleiman, et
al., 2004;
Valente,
Salvi, et al.,
2004)
606324 DJ-1 Missense:
Glu163Lys,
Leu166Pro
Exon 1–5
deletion,
g.168–
185dup
<40 years
(24–39)
Levodopa-
responsive
parkinsonis
m,
psychologic
al and
behavioural
disturbances
,
amyotrophy
and
cognitive
impairment
Unknown (Annesi et
al., 2005;
Bonifati et
al., 2003)
18
Disease OMIM
identifier
Gene Mutations Age at onset
(range)
Synopsis of clinical
features
Predominant pathology
References
606693 ATP13A2 Missense:
Phe182Leu,
Gly504Arg,
Gly877Arg,
1019GfsX10
21
Exon 13
1306+5G>A
Exon 16
22-bp
deletion
<20 years
(10–33)
Levodopa-
responsive
atypical
parkinsonis
m associated
with
supranuclear
gaze palsy,
spasticity
and
dementia
Neuroradiol
ogical
atrophy with
iron
accumulatio
n in basal
ganglia
(Di Fonzo et
al., 2007;
Ramirez et
al., 2006)
Abbreviations: fs, frameshift; LBD, Lewy body disease; OMIM, Online Mendelian Inheritance in Man;
PD, Parkinson disease; X, stop codon.
19
Table 2. Selected genome-wide association studies in Parkinson disease
Gene Chromosome Population
SNCA 4q21 USA, UK, France, Japan
MAPT 17q21.1 USA, UK, France
LRRK2 12q12 USA, Japan
HLA-DRA 6q21.3 USA, UK
GAK–DGKQ 4p16 USA, UK
PARK16 1q32 USA, UK, Japan
BST1 4p15 France, USA
20
1.5. Neurobiological interactions: is there one pathway for PD?
Many genes implicated in PD are expressed in the endosomes, synaptic vesicle sorting
and recycling and membrane curvature (Figure 1). Alpha-synuclein is important at the
presynaptic terminals and promotes exocytosis. Alpha-synuclein has roles in membrane
curvature and is expressed in endosomes, multi-vesicular bodies and lysosomes. There is
evidence that a-synuclein is also involved in endocytosis with dynamins during clathrin-
mediated endocytosis (Vargas et al., 2014). At the post-synapse (medium spiny neurons),
LRRK2 is involved with endocytosis by phosphorylating endophilin A at S75 (Matta et al.,
2012). Activation of LRRK2 and PINK1 (recessive mutations cause early-onset parkinsonism)
phosphorylate Rab family GTPases(Lai et al., 2015; Steger et al., 2016). An unbiased phospho-
proteomics approach identified Rabs with a pThr73 autophosphorylation site (Rab3 , Rab8 and
Rab 10) as LRRK2 substrates in vitro. Furthermore, Rab8A, Rab8B and Rab13 are indirectly
phosphorylated by PINK1 (Lai, et al., 2015) . Loss of Rab39B causes early-onset
parkinsonism(Wilson et al., 2014). Rabs are important for vesicular trafficking and cellular
compartmentalization(Clague & Rochin, 2016).
There is also evidence that suggests LRRK2 regulates chaperone-mediated autophagy,
microtubule stabilization, mitochondria and Golgi pathways. LRRK2 co-immunoprecipitates
with VPS35 and RME-8 (DNAJC13), and is involved in actin polymerization(Munsie et al.,
2015). VPS35 is part of the retromer, formed with VPS26 and VPS29. The retromer complex
mediates cargo recognition of early endosomes and membrane recruitment. VPS35 mediates
recycling from endosomes to the Golgi apparatus. LRRK2, VPS35 and RME-8 directly mediate
endosomal protein sorting and recycling, including the delivery of synaptic neurotransmitter
receptors and lysosomal proteins to either degradation or endosome to membrane recycling.
21
VPS35 has been shown to interact with eIF4G1 in yeast to modulate alpha-synuclein
toxicity(Dhungel et al., 2015) .
Perhaps impairment in synaptic vesicle trafficking and recycling is central to the
pathophysiology of PD. When this process is perturbed, cargo retention in the endosome lead to
the formation of multivesicular bodies that are destined to fuse with lysosomes for exosomal
release. Cell-to-cell transmission of alpha-synuclein proteinopathy has been a highlight in recent
research (Luk, Kehm, Carroll, et al., 2012; Luk, Kehm, Zhang, et al., 2012; Wang et al., 2012)
and this may be a consequence of cargo retention and synaptic vesicle trafficking/recycling.
22
Figure 1. Neurobiological Interactions between implicated genes for PD
Key molecular processes in neurons for important genes implicated in PD. Dopaminergic neuron
is in green, glutamatergic cortical neuron in blue and medium spiny neuron is in yellow.
23
1.6. Reduced penetrance
It has been almost 20 years since the discovery of the first SNCA mutation in familial
PD. Before the discovery of genes implicated in PD, many scientists had thought that PD was
caused by environmental factors. Genetics have been extremely informative in the biology of
PD, which could lead to new therapies that could help all those with the disease. However,
treatment to prevent symptom onset or delay progression has yet to be developed.
Interestingly, large numbers of putative pathogenic mutation carriers are free of disease:
there is evidence of reduced penetrance in patients carrying known pathogenic mutations.
Penetrance is formally known as conditional probability of being affected with a disease given a
genotype. Penetrance can be age-dependent and may even border ‘variable expressivity’ in very
subtle disease manifestations (expressivity describes the extent a certain phenotype manifests).
Within genomic research, reduced penetrance has been neglected and is now emerging as a new
field of research. The identification of genetic, environmental, lifestyle and biological factors
influencing the phenomena of reduced penetrance is of great interest in neurodegeneration. The
idea behind discoveries of ‘protective’ genetic factors can help developing relevant therapeutic
targets to halt the development of disease. In large-scale 1000 Genomes and ExAC projects,
many pathogenic mutation carriers have been identified. For example, there are 47 LRRK2
p.G2019S mutation carriers in the ExAC database that are potentially asymptomatic, although
these individuals are not well phenotyped (exac.broadinstitute.org). In fact, an average genome
has 150 sites of protein truncating variants, 10-12,000 sites with protein-altering variants and
even up to 30 mutations implicated in rare disease (Auton et al., 2015) . Thus, penetrance
estimates may be more reduced than what is estimated in literature. Recognizing the potential of
disease modifiers or protective factors has already sparked research initiatives such as the
24
‘resilience project’ (resilienceproject.me) at Mount Sinai, ‘wellderly’ (scripps.org) at Scripps
amongst others. These projects focus on healthy, elderly individuals. Studying modifier genes in
Parkinson disease and other neurodegenerative disorders are more difficult as this requires much
more stringent phenotyping from neurologists with clinical expertise.
LRRK2 p.G2019S is the most common gene mutation in familial PD and accounts for the
highest attributable risk in PD. The high frequency of LRRK2 p.G2019S in North African Arab
Berbers and Ashkenazi Jewish populations give a larger sample size to discover genetic
modifiers that can influence penetrance. Although LRRK 2 p.G2019S parkinsonism is
considered a monogenic form of disease, the mutation is not fully penetrant. We hypothesize that
genetic factors can modulate phenoconversion of LRRK2 p.G2019S.We postulate that the novel
modifier genes and DNA variants that are identified will advance our understanding of the
biological mechanisms of LRRK2. Second, these genetic factors may prove to be useful
therapeutic targets that could be used to delay the onset of PD among those with LRRK2
mutations. Third, screening of these genetic variants could be included as part of LRRK2 genetic
testing and results provided as part of genetic counseling to yield better estimates of the likely
onset of PD for a particular at-risk individual.
There are three main studies in this thesis 1) comparative analysis of disease penetrance
of mutations implicated in late-onset autosomal dominant PD 2) detailed clinical analysis of
LRRK2 p.G2019S carriers compared to idiopathic PD 3) Identification of a potential age-at-
onset modifier in LRRK2 parkinsonism.
25
2. Chapter 2: Disease penetrance estimates of mutations in late-onset PD
2.1. Introduction: penetrance estimates
There have been many pathogenic mutations identified for PD. However, these mutations are
not 100% penetrant. Penetrance is defined as the probability of individuals with a given genotype
who exhibit a certain phenotype. There are many methods to assess penetrance in age-associated
diseases. In late-onset autosomal dominant PD, the disease onset, progression, pathology and
clinical features of mutation carriers can be vastly distinct. For example, SNCA has been
associated with LOPD in every population tested (Nalls, Pankratz, Lill, Do, Hernandez, Saad,
DeStefano, Kara, Bras, Sharma, Schulte, Keller, Arepalli, Letson, Edsall, Stefansson, Liu, Pliner,
Lee, Cheng, Ikram, et al., 2014; Simon-Sanchez & Gasser, 2015). However, a penetrance
comparison of point mutations and copy number variations in SNCA has not been assessed in
detail. On the other hand, the penetrance of LRRK2 p.G2019S has been explored through
various case sampling and statistical analyses (Table 1) (Healy et al., 2008; Marder et al., 2015;
Trinh, Amouri, et al., 2014; Trinh, Guella, & Farrer, 2014) . The two main methods that have
been used to assess the penetrance of LRRK2 p.G2019S are cumulative incidence plots and kin-
cohort. Some studies have inferred genotypes within families (Marder, et al., 2015). The
penetrance varies between 10-50% at age 60 (Table 3). The statistical analyses used are also
quite variable. The first study on LRRK2 p.G2019S published an age-dependent penetrance
within families and derived an estimation by a simple equation (proportion of affected/total
carriers) (Kachergus et al., 2005) . Kachergus et al report at age 50 the LRRK2 p.G2019S
mutation is 17% penetrant and at age 70 it is 85%. A world-wide consortium of LRRK2 carriers
in Europe have reported a similar estimation age 59-79 (28-74%). This was further replicated in
26
Tunisian Arab Berbers (Hulihan et al., 2008) . Interestingly, the Ashkenazi Jewish LRRK2
carriers and Italian LRRK2 carriers had a more reduced estimation ranging from 15-32%.
27
Table 3. Estimates of LRRK2 p.G2019S age-associated cumulative incidence
Ethnicity Sample Statistical Analysis Age range
(penetrance)
Reference
Norwegian,
American (United
States), Irish and
Polish
13 LRRK2
families
22 familial
affected
carriers
Proportion of
affected/total carriers
50-70 (17-85%) (Kachergus,
et al., 2005)
French and North
African families
2 LRRK2
families
6 familial
affected
carriers
Not reported 55-76 (33-100%)
(Lesage et
al., 2005)
Ashkenazi Jews 2975 familial
relatives of
459 probands
Kin-cohort (Wacholder
et al., 1998)
Relatives were not
genotyped for mutation:
probability of carrying
mutation was estimated
60-80 (12-24%)
(Clark et
al., 2006)
Ashkenazi Jews 22 affected
carriers
Penetrance calculated
from odds ratio
Lifetime risk = 35% (Ozelius et
al., 2006)
Italian (UK
Parkinson’s
Disease Brain
Bank)
36 familial
affected
carriers
Kaplan Meier 60-80 (15-32%)
(Goldwurm
et al., 2011;
Goldwurm
et al., 2007)
World-wide
(mostly
European)
133 LRRK2
families
327 affected
members
Kaplan Meier 59-79 (28-74%)
(Healy, et
al., 2008)
28
2.2. Methods
In this study, we have created a large meta-analysis of published literature on disease onset
and clinical phenotypes of late-onset autosomal dominant Parkinson disease. Published literature
was included if there was information on ethnicity, mutation, age and age-at-onset/first motor
symptom and confirmation of mutation (not inferred). If age-at-onset or age at last contact were
not available in the published literature, we requested information from corresponding authors.
We also excluded autosomal recessive genes. Preferred Reporting Items for Systematic Reviews
and Meta-analyses (PRIMA) guidelines were followed (Vrabel, 2015) . Literature search
involved keywords: SNCA point mutation, SNCA duplication, VPS35, EIF4G1, LRRK2,
DNAJC13, Parkinson disease, autosomal dominant, late-onset parkinsonism. Published studies
were included if they have the following information: 1) ethnicity of patient or unaffected
Ethnicity Sample Statistical Analysis Age range
(penetrance)
Reference
Arab-Berber 72 affected
carriers
Kaplan Meier 60-80 (50-100%)
(Hulihan, et
al., 2008)
European
countries, mainly
Italy
154 first
degree
relatives and
190 second
degree
relatives of
10 probands
with
p.G2019S
Kin-cohort (Wacholder,
et al., 1998)
*No relatives were
genotyped for mutation:
probability of carrying
mutation was estimated
1st degree
60-80 (12-33%)
2nd
degree
60-80 (10-30%)
(Goldwurm,
et al., 2011)
Northern Spain
(Cantabria)
32 carriers Kaplan Meier 60-80 (12-47%)
(Sierra et
al., 2011)
29
individual; 2) confirmation of mutation (not inferred); 3) age of patient or unaffected individual;
4) age of onset of patient; 5) gender of the patient or unaffected individual; 6) first motor
symptom of patient, and;7) non-motor symptom of patient .
When age of onset, age, gender and age-at-last contact data was not available in the
published article, information was requested through the corresponding authors. If this
information was not obtainable, the study subjects were excluded. We also excluded articles:1)
about autosomal recessive parkinsonism; 2) that reported duplicate data; 3) that were not written
in English, and; 4) genes for which significant genetic linkage was not reported.
The age-associated cumulative incidence (disease penetrance) was estimated using a Kaplan-
Meier method with age-at-onset as the time variable; asymptomatic carriers were right censored
at the age-at-last contact or age-at-death (JMP software, SAS Institute Inc., Cary, NC). Statistical
comparisons between survival curves were done with log-rank tests unless otherwise stated.
2.3. Results
2.3.1. SNCA: description of duplications, triplication and point mutations
SNCA harbors both copy number variation and point mutations in patients with PD.
Clinically, SNCA triplication carriers have an earlier onset, faster progression and more
fulminant disease compared with duplication carriers. These findings from duplication carriers
are more closely comparable to typical late-onset PD. SNCA triplication and duplication carriers
seldom have dementia. However, the frequency of SNCA mutation carriers are extremely rare
and thus, clinical comparisons are difficult to interpret.
We assessed the cumulative incidence of SNCA copy number and point mutation carriers.
Penetrance of triplications was higher than duplications and point mutations (log rank p<0.01)
30
Penetrance of triplications had a lower quartile of 31 years, median of 39 years and upper
quartile of 46 years (Figure 2). Point mutations had a lower quartile of 42 years, median of 49
years and upper quartile of 60 (n=59). Point mutations were comparable to duplications (log rank
p=0.97) which had a lower quartile of 40 years, median of 48 years and upper quartile of 61
years (n=41). We observed penetrance differences in point mutations. However, the sample
sizes were too small for a meaningful interpretation. SNCA p.A53T (n=35) had a mean age-at-
onset of 45.9 years; p.A30P (n=5) had a mean age-at-onset 59.8 years; p.E46K had a mean age at
onset of 62.3 years; p.H50Q (n=3) had a mean age-at-onset 64.7 years and p.G51D (n=3) had a
mean age-at-onset of 32.7 years (Figures 3a-f). A summary of patients included for each
mutation is described in Table 4.
Cumulatively, SNCA mutations (triplications, duplications, point mutations) had an earlier
onset age (mean age at onset 38.5-49.5 years) compared to LRRK2 mutations (mean age at onset
46.8-68.8 years). Unlike SNCA, two copies of the mutant allele (i.e. homozygous G2019S
mutations) do not confer significantly higher risk or higher penetrance .
31
Table 4. Summary of patients included for each mutation into penetrance estimates
Mutation Carriers
included (n)
Affected
carriers (n)
Unaffected
carriers (n)
Ethnic
backgrounds
References
SNCA point
mutation
47 43 4 Greek,
British,
Korean,
Polish,
Swedish,
English
(Golbe,
1999)
SNCA
duplications
41 39 2 French,
Korean,
Italian
(J. Fuchs et
al., 2007; J.
Fuchs, et al.,
2008;
Nishioka et
al., 2009)
SNCA
triplications
15 15 0 Swedish-
American,
Japanese
(Farrer et al.,
2004;
Nishioka, et
al., 2009)
LRRK2
N1437H
10 9 1 Norwegian (Johansen,
White,
Farrer, &
Aasly, 2011)
LRRK2
R1441C
27 17 10 Norwegian (Haugarvoll
et al., 2008)
LRRK2
R1441G
104 62 42 Basque (Haugarvoll
& Wszolek,
2009; Marti-
Masso et al.,
2009;
Pchelina,
Ivanova,
Emel'ianov,
&
Iakimovskii,
2011; Ruiz-
Martinez et
al., 2010)
LRRK2
Y1699C
7 7 0 Norwegian (Khan et al.,
2005;
Zimprich, et
al., 2004)
LRRK2
G2019S
330 291 39 Norwegian,
Tunisian,
Ashkenazi Jewish
(Healy, et al.,
2008; Trinh,
Amouri, et al., 2014)
32
Mutation Carriers
included (n)
Affected
carriers (n)
Unaffected
carriers (n)
Ethnic
backgrounds
References
LRRK2 I2020T 29 23 6 Japanese (Tomiyama
et al., 2006)
VPS35 D620N 61 54 7 German,
Tunisian,
Yemen Jews,
Japanese,
French
(Ando, et al.,
2012;
Lesage, et al.,
2012;
Sharma, et
al., 2012;
Sheerin, et
al., 2012;
Vilarino-
Guell, et al.,
2011;
Zimprich, et
al., 2011)
EIF4G1
R1205H
20 20 0 French,
Tunisian,
Yemen Jews
(Chartier-
Harlin et al.,
2011; N.
Nichols,
Bras,
Hernandez,
Jansen,
Lesage,
Lubbe, &
Singleton,
2015;
Nuytemans,
et al., 2013;
Siitonen, et
al., 2013)
DNAJC13
N855S
18 12 6 Mennonites-
Canadian
(Vilarino-
Guell, et al.,
2014)
Total
Combined
709 592 117
33
Figure 2. Kaplan-Meier survival curves for SNCA mutations.
The probability of being affected at median age 56 is 0.90 for SNCA triplication carriers, 0.70
for SNCA duplication and missense carriers.
34
A
B
C
35
Figure 3 Kaplan-Meier survival curves for SNCA
a) cumulative incidence for all SNCA point mutation carriers, b) p.A30P, c) p.A53T, d) p.E46K,
e) p.G51D, f) p.H50Q. The dotted lines represent confidence intervals.
D
E
F
36
2.3.2. LRRK2 penetrance findings between populations
The LRRK2 p.G2019S mutations account for up to 15% in Ashkenazi Jewish populations,
30% in Arab Berber populations and 1% in Caucasian populations. We assessed the penetrance
of LRRK2 in the Arab Berber population with an expanding a Tunisian cohort since 2005
(Figure 4) (Hulihan, et al., 2008). The Arab Berber population cumulative incidence and mean
age at onset estimates were consistent with previous studies: mean age at onset 57.1 years;
95%CI, 45.5-68.7 years n=220 (Healy, et al., 2008; Hulihan, et al., 2008). However, the
Norwegian penetrance estimates (mean age at onset 63 years; 95% CI 51.4-74.6 years) were
reduced compared to Tunisia (p<0.0001). Lastly, the Ashkenazi Jewish population from Israel
were comparable to Tunisia (mean age at onset 57.9 years, 95% CI, 54-63 years) (Figure 4).
Furthermore, there are six pathogenic mutations in LRRK2 (p.N1437H, p.R1441C/G/H,
Y1699C, p.G2019S, p.I2020T). Higher penetrance for p.N1437H and p.Y1699C mutations,
may reflect the small sample size (n=10). The cumulative incidence of LRRK2 mutations are
significantly different from each other. Penetrance within the kinase domain (p.G2019S and
p.I2020T) are similar and significantly higher than the Roc domain mutations (p.R1441C/G/H)
(Figure 5). The p.N1437H is also in the Roc domain but hampered by small sample size. The
cumulative incidence of LRRK2 p.I2020T had a lower quartile of 51 years or younger, a median
of 55 years of age, and an upper quartile of 60 years or older (n = 29). The estimation was similar
to LRRK2 p.G2019S, which had a lower quartile of 49 years or younger, a median of 57 years,
and an upper quartile of 67 years or older (n = 330). Lastly, the cumulative incidence of LRRK2
p.R1441C and p.R1441G were the least penetrant. LRRK2 p.R1441C had a lower quartile of 65
years or younger, a median of 71 years, and an upper quartile of 77 years or older (n = 27).
p.R1441G had a lower quartile of 60 years or younger, a median of 65 years of age, and an upper
37
quartile of 72 years or older (n = 104).The LRRK2 p.I2020T mutation is largely Japanese,
R1441G is largely Basque, R1441C is mostly Belgian. These estimates could also reflect
diagnostic or referral differences across regions. Perhaps there is better clinical care for
neurodegenerative diseases in different parts of the wold which could reflect higher reporting of
disease and earlier age-at-onset estimates.
2.3.3. Other autosomal dominantly-inherited mutations in familial PD
VPS35 p.D620N (lower quartile ≤ 45years, median 49 years and upper quartile ≥ 59 years;
n= 61) was significantly more penetrant than EIF4G1 p.R1205H (lower quartile ≤ 56 years,
median 62 years and upper quartile ≥ 69.5 years; n=20) and DNAJC13 p.N855S (lower quartile
≤ 61 years, median 68 years and upper quartile ≥ 76 years; n=18) (Figure 6).
The age-dependent cumulative incidence was significantly different across mutations
(p<0.0001) . Overall SNCA triplication carriers (n=15) had the highest cumulative incidence
(penetrance) and LRRK2 p.G2019S carriers in Norway (n=84) had the lowest (Figure 7).
38
Figure 4. Population-specific penetrance estimates of LRRK2 p.G2019S mutations.
Figure 5. Kaplan-Meier survival curves for LRRK2 mutations.
39
Figure 6. Kaplan-Meier survival curves for VPS35, EIF4G1 and DNAJC13 mutations.
40
2.4. Discussion
This study summarizes and systematically compares the age-dependent cumulative incidence
of all known mutations leading to late-onset parkinsonism. Fifteen rare pathogenic variations in
five genes (SNCA, LRRK2, VPS35, EIF4G1, and DNAJC13) were assessed. All mutation
carriers were combined, whether from the literature or contributed by corresponding authors,
providing the most accurate penetrance estimates to date. Nevertheless, the study has many
limitations. These include cultural and environmental differences between populations, access to
health care and ascertainment bias. Various diagnostic criteria have to be considered, and the
movement disorders neurology expertise at different centres . Moreover, age at onset is
retrospective and subjective, dependent upon a variety of symptoms and signs, although well
correlated with age at diagnosis (Reider et al., 2003) .
All comparisons utilized the same statistical measure to estimate cumulative incidence which
simplifies comparisons between mutations. The Kaplan-Meier method is a reverse survival curve
analysis, ideally suited for sporadic patients and unrelated probands that censors for
asymptomatic carriers. In contrast, the kin-cohort method excludes probands, employing just
relatives with inferred genotypes to specifically avoid inflating penetrance estimates. However, a
disadvantage is that the phenotypic and genotypic information of the relatives may be inaccurate.
While analyses have been adapted to compensate for a variety of study designs, Kaplan-Meier
and kin-cohort are the major methods employed in penetrance estimates of PD. Bias from the
inclusion of probands and family members has been assessed using a variety of statistical
methods and sensitivity analyses for LRRK2 p.R1441G and p.G2019S show comparable results
(Trinh, Amouri, et al., 2014; Trinh, Guella, et al., 2014) . The sensitivity analysis compared
penetrance estimates derived from different methods and sample groups (kin-cohort methods
41
versus Kaplan meier as well as families versus unrelated patients). Herein we are limited by
published data, by the relatedness of subjects and the total number of carriers/families with each
gene. With these caveats acknowledged, confidence intervals are provided for genetic counseling
(Figure 7-20).
Penetrance estimates for monogenic parkinsonism vary by gene, by mutation and by
ethnicity. SNCA triplications are more penetrant than duplications for which genomic dosage has
been directly correlated to mRNA and protein expression (Farrer, et al., 2004; Nishioka, et al.,
2009) . Clinically, SNCA triplication carriers have an earlier onset, faster progression and more
fulminant disease compared to duplication carriers which more closely resembles late-onset
idiopathic PD (Muenter et al., 1998; Nishioka, Kefi, et al., 2010; Nishioka, et al., 2009) . Seldom
do SNCA triplications or duplication carriers have dementia as a first symptom; typically
cognitive decline is noted several years after the onset of parkinsonism. Nevertheless, many have
a clinical diagnosis of dementia with Lewy bodies (DLB), with diffuse Lewy body disease on
autopsy (DLBD). Overall SNCA point mutations and SNCA duplications are quite similar in
penetrance. While the majority of missense carriers of SNCA p.A53T have been described with
young onset parkinsonism, with an aggressive course (Golbe, 1999) , and most duplication
carriers are described as DLB, they are most comparable. The frequency of SNCA
multiplications and point mutations is extremely rare (less than 1% in different populations), thus
meaningful comparisons of clinical features is problematic, although a global study of SNCA
multiplication and missense carriers has recently been initiated (The Parkinson Progression
Markers Initiative by The Michael J Fox Foundation for Parkinson’s Research).
LRRK2 mutations confer the highest population-attributable risk to PD but the function
of the encoded protein still remains unclear. The majority of pathogenic variants are within three
42
contiguous domains: kinase, ROC and COR (Mills, Mulhern, Liu, Culvenor, & Cheng, 2014) .
Penetrance of mutations within the kinase domain (LRRK2 p.G2019S and p.I2020T) are similar,
and significantly higher than ROC domain mutation (p.R1441C and p.R1441G). LRRK2
p.R1441C and p.R1441G mutations have similar penetrance estimates (p=0.31). The sample size
was too small to compare p.R1441H. However, we observe higher penetrance of LRRK2
p.N1437H, which could be hampered by the rarity of this variant (n=10). COR domain mutations
are highly penetrant, but could also be due to a smaller sample size (n=7).
SNCA mutations (triplications, duplications and point mutations) had a larger effect, with
an earlier onset (AAO means were from 38.5-49.5 years) compared to LRRK2 mutations (AAO
means were from 46.8 to 68.8 years) . SNCA mutation carriers have a more aggressive
phenotype whereas LRRK2 carriers have a more benign clinical course compared to idiopathic
PD. In LRRK2 parkinsonism, there is less REM sleep behavior disorder and gastrointestinal
dysfunction (Trinh, Amouri, et al., 2014) which are two main clinical features affected by Braak
staging. SNCA mutation carriers primarily have Lewy-body-like inclusions of α-synuclein
aggregates (Conway et al 2000 oligomerization SNCA, Wood et al alpha-synuclein 1999). In
contrast, LRRK2 carriers (albeit p.N1437H, p.R1441C/G/H, p.G2019S, or p.I2020T) have
pleiomorphic pathologies including α-synuclein, 4-repeat-tau, or tar-dna binding-43
proteinopathies on a background of neuronal loss and gliosis. The clinical course probably
reflects the burden and type of end-stage pathology.
Age at onset is only one measure of variability across these pathogenic mutations. The
pathology in LRRK2 mutation carriers are extremely pleiomorphic. Furthermore, clinical
features such as cognitive decline/dementia, autonomic dysfunction can vary between mutation
carriers. The LRRK2 p.Y1699C mutation is most unusual with amotrophy, dementia and not just
43
parkinsonism, compared to LRRK2 p.I2020T which has typical parkinsonism which is
comparable to idiopathic PD and tauopathies (Hasegawa et al., 2009; Ujiie et al., 2012).
This study highlights the role of ethnicity or environmental factors as a major contributor
of penetrance. Stratification of LRRK2 p.G2019S parkinsonism by ethnicity was possible
because of the large sample size. Israeli Ashkenazi Jews have a significantly higher penetrance
compared to Norwegian LRRK2 p.G2019S mutation carriers, and are comparable in penetrance
to Tunisian Arab-Berbers. In New York, the disease in Ashkenazi Jewish carriers is less
penetrant (24% penetrance at age 80) (Figure 21); these differences may reflect a sample of 7
carriers, the exclusion of family members (Clark, et al., 2006) and environmental factors such as
orthodox or unorthodox practices. The study by Clark et al has now been further expanded to 90
LRRK2 carriers and include a much larger cohort of Ashkenazi LRRK2 G2019S in New York.
The reduced penetrance estimate still stands (26% at age 80 years). However, the statistical
method used was different and the sampling included more unaffected LRRK2 p.G2019S
carriers with family history (Marder, et al., 2015) . In contrast, similarities in age of onset
between Israeli Jews and Tunisian Arab-Berbers carriers may reflect similar genetic and
environmental backgrounds (Nebel et al., 2000) . The environment may also play a role in the
differences. Furthermore, ascertainment bias in the patients sample sets may influence the data.
These cohorts are predominantly tertiary referral clinic-based samples from specialist hospitals,
which means age-at-onset can be inflated as patients from the same family may be more aware of
symptoms. Nevertheless, ethnic differences are an important consideration in genetic counseling.
Mutations in SNCA, LRRK2, VPS35, EIF4G1 and DNAJC13 have been directly
implicated in familial parkinsonism (Trinh & Farrer, 2013) . These proteins are centrally
involved in synaptic transmission, early endosomal sorting and recycling, and lysosomal
44
autophagy. Indeed, LRRK2, VPS35, and DNAJC13 directly immunoprecipitate with members of
the WASH complex (Helfer et al., 2013) , which regulates actin remodelling and membrane
trafficking in these processes. Whether this network is similarly perturbed in idiopathic PD has
yet to be established. Differences in the penetrance estimates may reflect the type of substitution,
its location and functional consequence. Mutations may affect interactions with binding partners
and downstream signaling pathways, thus influencing expression (transcript or protein), and
ultimately compensatory mechanisms (genetic and non-genetic).
Age is considered the greatest risk factor for PD and genetic susceptibility is only one
influence. The penetrance of mutations in late-onset parkinsonism is also dependent on ethnicity
and potentially environmental factors. Thus, heterogeneity between mutation carriers may be an
important consideration when identifying modifiers of disease. Prospective, longitudinal
evaluation of carriers and further meta-analyses will be required for more precise penetrance
estimates, and provide the opportunity to inform therapeutic trials.
45
Figure 7. Comparison of SNCA and LRRK2 mutations.
SNCA mutations are illustrated as red lines and LRRK2 mutations are illustrated as black lines.
The cumulative incidence for distinct pathogenic mutations in each gene are shown in figure 2
and figure 4.
Figure 8. Cumulative Incidence of SNCA triplication carriers.
Dotted lines represent confidence intervals.
46
Figure 9. Cumulative Incidence of SNCA duplication carriers.
Dotted lines represent confidence intervals.
Figure 10. Cumulative Incidence of LRRK2 p.N1437H carriers.
Dotted lines represent confidence intervals.
47
Figure 11. Cumulative Incidence of LRRK2 p.R1441C carriers.
Dotted lines represent confidence intervals.
Figure 12. Cumulative Incidence of LRRK2 p.R1441G carriers.
Dotted lines represent confidence intervals.
48
Figure 13. Cumulative Incidence of LRRK2 p.Y1699C carriers.
Dotted lines represent confidence intervals.
Figure 14. Cumulative Incidence of LRRK2 p.G2019S carriers.
Dotted lines represent confidence intervals.
49
Figure 15. Cumulative Incidence of Ashkenazi Jewish LRRK2 p.G2019S carriers.
Dotted lines represent confidence intervals.
Figure 16. Cumulative Incidence of Tunisian Arab-Berber LRRK2 p.G2019S carriers.
Dotted lines represent confidence intervals.
50
Figure 17. Cumulative Incidence of Norwegian LRRK2 p.G2019S carriers.
Dotted lines represent confidence intervals.
Figure 18. Cumulative Incidence of EIF4G1 p.R1205H carriers.
Dotted lines represent confidence intervals.
51
Figure 19. Cumulative Incidence of VPS35 p.D620N carriers.
Dotted lines represent confidence intervals.
Figure 20. Cumulative Incidence of DNAJC13 p.N855S carriers.
Dotted lines represent confidence intervals.
52
Figure 21. World map with LRRK2 mutations
53
3. Chapter 3: A clinical comparison between LRRK2 parkinsonism and idiopathic PD
3.1. General clinical features of LRRK2 parkinsonism
PD is characterized by four cardinal signs: resting tremor with asymmetry at onset,
bradykinesia, rigidity, postural instability and positive response to Levodopa (Postuma et al.,
2015) . LRRK2 p.G2019S has the highest genotypic and population attributable risk. The
mutation was first shown to segregate with PD in a Norwegian family (Kachergus et al 2005).
Overall, the clinical presentation of idiopathic PD (iPD) is similar to LRRK2 parkinsonism
(Aasly et al., 2005) . However, there is heterogeneity in the cohorts, sample size and
methodology.
Some reports suggest a more severe phenotype in LRRK2 mutation carriers compared to
idiopathic PD. LRRK2 p.G2019S carriers were reported to have more severe motor symptoms
and dyskinesias (Nishioka, et al., 2009) (Oosterveld et al., 2015) . Depression, hallucinations,
sleep issues were reported to be more common in LRRK2 p.G2019S carriers (Pchelina, et al.,
2011) . Furthermore, postural instability and gait problems were more common in early-onset
LRRK2 carriers (Alcalay et al., 2009) (Alcalay et al., 2015; Marras et al., 2016) . On the other
hand, LRRK2 carriers have reported to have slower disease progression, less cognitive
impairment, lower depression, less autonomic dysfunction and UPDRS scores (Alcalay, et al.,
2009; Healy, et al., 2008) (Marras, et al., 2016) (Tijero et al., 2013) . A better characterization of
LRRK2 carriers is warranted.
Furthermore, collecting and analyzing a large database on clinical features of LRRK2
p.G2019S carriers can lead to a better understanding of the progression of the disease and study
of endophenotypes (both motor and non-motor). In the present study, we have expanded the
54
Tunisian Arab-Berber LRRK2 cohort over a period of six years to compare the disease onset,
clinical symptoms and disease progression.
3.2. Methods
All patients were recruited at the same neurological centre: Mongi Ben Hamida National
Institute of Neurology, Tunis. The center provides out-patient and in-patient services for
neurological disorders in Tunisia. Local on-site monitoring was independently performed by
PRN clinical research (www.prnservices.co.uk) every 18 months. Clinical examinations were
performed and questionnaires were administered by movement disorder specialists Dr. Faycal
Hentati, Dr. Samia Ben Sassi, Dr. Fatma Nabli, Dr. Emna Farhat. Diagnoses of PD were made
according to the UK PD Society brain bank criteria. Enrollment information including additional
family medical history and origin was also collected. Patients and control subjects completed
standardized clinical research forms (CRFs), all medical history of patients and families were
recorded. Clinical data and blood samples were collected for 778 patients and 580 unaffected
subjects (Table 5-6).
3.2.1. Motor symptom assessment
Movement disorders society unified Parkinson disease rating scale (MDS-UPDRS) were
performed on patients with symptoms of parkinsonism. The MDS-UPDRS consists of four parts:
Non-motor experiences of daily living, motor experiences of daily living, motor examination and
motor complications (Goetz, Nutt, & Stebbins, 2008; Goetz et al., 2008). All assessments in the
MDS-UPDRS have five responses: 0=normal, 1=slight, 2=mild, 3=moderate, 4=severe. “Slight”
refers to symptoms with low frequency or intensity that have no impact on function, “mild” has
55
modest impact on function, “moderate” has considerable impact on function and lastly, “severe”
refers to symptoms that prevent function. Medication status of L-dopa “on” and “off” stages
were recorded. Hoehn and Yahr staging (1-5), to objectively rate the patient’s disability at a
certain time (stage 0 means no signs of disease and stage 5 is wheelchair bound or bedridden),
was also recorded.
3.2.2. Non-motor symptom assessment
Multiple questionnaires were used for non-motor symptoms. The Schwab and England
questionnaire was used for activities of daily living (McRae, Diem, Vo, O'Brien, & Seeberger,
2000) . The rating was performed by the neurologist and ranges from 0% (only vegetative
functions are working, bedridden and helpless) to 100% (completely independent, able to do all
chores, no slowness or difficulty). Autonomic dysfunction was assessed with Scales for
Outcomes in Parkinson’s disease – Autonomic (SCOPA-AUT) (Visser, Marinus, Stiggelbout, &
Van Hilten, 2004). There are 25 items that assess gastrointestinal, urinary, cardiovascular,
thermoregulatory, and sexual dysfunction. Autonomic problems increase significantly with
disease severity (Visser, Marinus, Stiggelbout, et al., 2004; Visser, Marinus, van Hilten,
Schipper, & Stiggelbout, 2004) . Geriatric depression scale and Epworth daytime sleepiness
scale was used to assess depression and sleep function, respectively (Johns, 1991; Yesavage et
al., 1982). REM sleep behavior disorder was noted as 50% of all REM sleep disordered patients
by polysonography develop PD. A specially modified “sniffin’ test” was created with the help of
neurologist Dr. John Duda for odorant descriptors and distractors for the Arab Berber population.
“Sniffin tests” is a validated test consisting of odor detection, discrimination and sensitivity. It
was adopted a trial of 100 control subjects to be ‘culturally’ appropriate. Simplified, culturally
appropriate tests were created for largely illiterate population. Cognition was measured using the
56
mini-mental state examination (MMSE), Montreal cognitive assessment (MOCA), six picture
test or frontal assessment battery. The latter two were adapted for a largely illiterate population.
3.2.3. Genetic assessment and statistical analysis
DNA was extracted by standard procedures (Miller et al 1988) and LRRK2 c.6055G>A
(p.G2019S) was genotyped with a TaqMan probe on an ABI7900 analyzer and then verified by
sequencing, as previously described (Hulihan, et al., 2008) . Patients with pathogenic mutations
in PINK1 or Parkin were excluded from this study (Bonifati, et al., 2003; Valente, Salvi, et al.,
2004).
Multivariate regression models were used to investigate and compare different
questionnaires and clinical assessments, adjusted for age at onset, disease duration, gender and
medical state (on or off levodopa) (JMP software version 10). Cumulative incidence was
assessed with Kaplan Meier or kin-cohort analysis and significant differences were detected with
either log-rank or Wilcoxon tests. The log-rank test gives equal weight to all time points in a
cumulative incidence plot, whereas the Wilcoxon test gives more weight to earlier time points
and requires one group consistently have a higher risk than the other.
3.2.4. Michael J Fox Foundation (MJFF) database storage
Each patient and control subjects had a clinical research form ID, linked with a MJFF
family ID and individual ID. The data was imported into under six categories: 1) enrollment, 2)
UPDRS, 3) medications, 4) non-motor, 5) cognitive testing, 6) environment and lifestyle. The
collected data was stored in a database under the LRRK2 cohort consortium (https://www.inn-
tunisia.com/) webpage titled “Parkinson’s disease in Tunisia”. However, the database is no
57
longer maintained and is warehoused in Tunisia and UBC. It has also been submitted to the
MJFF LRRK2 cohort consortium to be made broadly accessible. However, it may be important
to make this publicly available.
58
Table 5. Demographics of unrelated patients and control subjects
Non-LRRK2 p.G2019S LRRK2 p.G2019S carriers
Patients Controls Patients Controls
N 350 399 220 (38%) 12 (3%)
Number of men (%) 187 (53%) 203 (51%) 124 (56%) 6 (50%)
Mean age (SD) years 66.6 (12.9) 61.1 (11.1) 67.6 (12.6) 56.7 (10.9)
Median age (IQR) 69 (59–76) 59 (53–69) 69 (48–90)
54.5 (38–
72)
Mean age of onset (SD) 55.3 (14.4) - 57.1 (11.6) -
Median age at onset (IQR) 58 (46–66) - 57 (40–74) -
Mean disease duration 8.10 (5.2) 9.07 (5.03)
Range disease duration 2-23 3-23
59
Table 6. Demographics of patients with a family history of parkinsonism within 1o
Affected
probands
Affected family
members
Unaffected family
members
N 162 126 169
Number of men (%) 89 (55%) 73 (58%) 74 (44%)
Mean age (SD) 67.0 (14.2) 76.6 (14.0) 59.4 (17.9)
Median age (IQR) 68 (46–90) 80 (66–95) 57 (26.5–87.5)
Mean age at onset (SD) 55.0 (14.0) 59.2 (14.4) -
Median age at onset
(IQR) 56 (38–74) 60 (42–79) -
LRRK2 p.G2019S
carriers 80 (49%) 46 (36%) 71 (42%)
60
3.3. Results
3.3.1. Motor features
The motor features were largely indistinguishable between iPD or LRRK2 carriers. Age and
age-at-onset were similar between LRRK2 p.G2019S homozygotes (n=32), heterozygotes
(n=177) and idiopathic PD (n=324) (Table 7). Comparison of first symptoms between idiopathic
PD and LRRK2 carriers was similar. Tremor was the most predominant first symptom across all
genotypes (range from 71.9% to 81.5%) (Table 7). When stratifying by gender, the results
remain comparable across LRRK2 parkinsonism and iPD (Table 8).
Unified Parkinson disease rating scale (UPDRS) has also been assessed and comparisons
were performed with regression modeling. There were no remarkable differences between
LRRK2 parkinsonism and iPD (Table 7-13). However, early-onset LRRK2 carriers tend to suffer
more rigidity than late-onset carriers (UPDRS III rigidity score, p=0.05). Although this value is
not significant after Bonferroni correction.
61
Table 7. Clinical summary of patients
LRRK2 p.G2019S carrier status Homozygous Heterozygous iPD p-values
N 32 177 324
Mean age of onset (SD) 54.5 (12.4) 57.3 (11.9) 56.2 (13.8) 0.46
Median age of onset (IQR) 56 (40–72) 58 (41–75) 58 (38–78)
First symptom (%)
Tremor 23 (71.9%) 135 (76.3%) 265 (81.5%) 0.53
Gait or balance deterioration 4 (12.5%) 23 (13.0%) 26 (8.0%) Muscle cramping or dystonia 2 (6.2%) 5 (2.8%) 7 (2.2%)
Shoulder stiffness 0 5 (2.8%) 8 (2.5%) Other 3 (9.4%) 7 (4.0%) 10 (3.1%)
NA 0 2 (1.1%) 8 (2.5%) PD phenotype (%)
Mixed 22 (69%) 101 (57%) 192 (59.1%) 0.29
Akinetic rigid 3 (9.4%) 34 (19.2%) 68 (21.0%)
Tremor dominant 6 (19%) 31 (17.5%) 62 (19.1%) NA 1 (3.1%) 11 (6.2%) 2 (0.6%)
Hoehn and Yahr (SD)
On (SD, n) 2.5 (0.9, 20) 2.1 (0.8, 70) 1.9 (1.0, 126) 0.21
Off (SD, n) 1.6 (0.9, 8) 2.3 (1.1, 84) 2.3 (0.9, 123) UPDRS-III score (SD)
On (SD, n)
34.0 (21.1,
20) 36.7 (16.4, 70) 33.3 (19.5, 128) 0.45
Off (SD, n) 45.2 (19.8, 8) 49.0 (21.9, 84) 47.4 (17.2, 123)
62
Table 8. Parkinsonism in LRRK2 p.G2019S carriers by gender
LRRK2 p.G2019S iPD
Female Male Female Male
N 103 106 142 182
Mean age of onset (SD) 60.0 (12.0) 57.8 (12.0) 56.4 (13.4) 56.2 (14.1)
Median age of onset (IQR) 56 (40-72) 60 (43.5-
76.5)
58 (39.5-76.5) 58 (38-78)
First Symptom
Tremor 73 (70.9%) 85 (80.1%) 122 (85.9%) 143 (78.6%)
Gait or balance
deterioration 14 (13.6%) 13 (12.3%) 8 (5.6%) 18 (9.9%)
Muscle cramping or
dystonia 7 (6.8%) 0 4 (2.8%) 3 (1.6%)
Shoulder stiffness 1 (0.97%) 4 (3.8%) 2 (1.4%) 6 (3.3%)
Other 8 (0.78%) 2 (1.9%) 2 (1.4%) 8 (4.4%)
NA 0 2 (1.9%) 4 (2.8%) 4 (2.2%)
PD phenotype
Akinetic rigid 20 (19.4%) 17 (16.0%) 28 (19.7%) 40 (22.0%)
Mixed 58 (56.3%) 65 (61.3%) 84 (59.2%) 108 (59.3%)
Tremor dominant 17 (16.5%) 20 (18.9%) 29 (20.4%) 33 (18.1%)
NA 8 (7.8%) 4 (3.8%) 1 (0.70%) 1 (0.54%)
Hoehn &Yahr (SD)
On 2.3 (0.93)
(n=48)
2.0 (0.73)
(n=42)
1.9 (0.89)
(n=58)
2.0 (1.1)
(n=68)
Off 2.3 (1.2) (n=40) 2.2 (1.1)
(n=52)
2.5 (0.97)
(n=51)
2.1 (0.80)
(n=72)
UPDRS-III score (SD)
On 39.2 (19.4)
(n=48)
32.7 (14.4)
(n=43)
32.4 (18.2)
(n=60)
34.1 (20.7)
(n=68)
Off 50.2(21.2)
(n=40)
47.5(21.6)
(n=52)
51.6(16.9)
(n=51)
44.4(16.9)
(n=72)
NA = not available. Other = change in facial expression, change in speech or voice, decreased
dexterity, stooped posture or bradykinesia.
63
Table 9. UPDRS Part IA Mentation, Behaviour and Mood
LRRK2 p.G2019S iPD p-value
Cognitive impairment 0.28 (0.67) 0.33 (0.74) 0.66
Hallucinations and psychosis 0.14 (0.45) 0.23 (0.57) 0.35
Depressed mood 1.28 (1.00) 1.40 (1.03) 0.08
Anxious mood 1.06 (1.11) 1.02 (1.07) 0.32
Apathy 1.22 (1.03) 1.19 (1.01) 0.53
Features of dopamine
dysregulation syndrome
0.05 (0.25) 0.06 (0.35) 0.95
Quantitative scales are from 0-4: 0 (normal) – 4 (most severe). Mean values (standard deviation)
are given.
64
Table 10. UPDRS Part IB Mentation, Behaviour and Mood
LRRK2 p.G2019S iPD p-value
Sleep problems 0.96 (1.15) 0.87 (1.17) 0.30
Daytime sleepiness 0.96 (0.96) 0.85 (0.92) 0.14
Pain and other sensations 1.53 (1.21) 1.45 (1.12) 0.75
Urinary problems 1.20 (1.27) 1.19 (1.20) 0.68
Constipation problems 1.15 (1.21) 1.20 (1.14) 0.95
Light headedness on standing 0.92 (1.10) 1.01 (1.06) 0.11
Fatigue 1.86 (1.09) 1.74 (0.97) 0.57
Quantitative scales are from 0-4: 0 (normal) - 4 (most severe). Mean values (standard deviation)
are given.
65
Table 11. UPDRS Part II Activities of Daily Living
LRRK2 p.G2019S iPD p-value
Speech 1.01 (0.98) 1.00 (0.96) 0.34
Saliva and drooling 0.97 (1.13) 1.19 (1.24) 0.02
Chewing 0.72 (0.90) 0.70 (0.87) 0.95
Eating tasks 1.18 (0.89) 1.04 (0.82) 0.24
Dressing 1.60 (1.08) 1.48 (1.05) 0.79
Hygiene 1.71 (1.13) 1.59 (1.14) 0.34
Handwriting 1.54 (1.19) 1.53 (1.17) 0.76
Doing hobbies and other activities 1.86 (1.30) 1.71 (1.11) 0.99
Turning in bed 1.56 (1.17) 1.44 (1.10) 0.80
Tremor 2.00 (1.00) 1.98 (1.00) 0.74
Getting out of bed, a car, or a deep chair 1.67 (1.15) 1.32 (1.05) 0.01
Walking and balance 1.66 (1.04) 1.37 (0.98) 0.10
Freezing 0.85 (1.13) 0.72 (1.08) 0.92
Quantitative scales are from 0-4: 0 (normal) - 4 (most severe). Mean values (standard deviation)
are given.
66
Table 12. UPDRS Part III
Quantitative scales are from 0-4: 0 (normal) - 4 (most severe). Mean values (standard deviation)
are given.
LRRK2 p.G2019S iPD P-value
Mean (SD)
Part III. Total Rigidity Subscale 1.59 (0.71) 1.53 (0.76) 0.83
Part III. Total Bradykinesia Subscale 1.65 (0.96) 1.49 (0.98) 0.63
Part III. Total Tremor Subscale 0.76 (0.60) 0.65 (0.60) 0.31
Part III. Total Postural Instability and
Gait Disorder Subscale
1.33 (0.79) 1.27 (0.85) 0.40
67
Table 13. UPDRS Part IV Complications of Therapy
LRRK2 p.G2019S iPD p-value
Time spent with dyskinesias 0.47 (1.00) 0.30 (0.75) 0.83
Functional impact of dyskinesias 0.46 (1.02) 0.27 (0.80) 0.52
Time spent in the off state 1.34 (1.18) 1.14 (1.03) 0.34
Functional impact of fluctuations 1.51 (1.50) 1.34 (1.36) 0.62
Complexity of motor fluctuation 0.95 (0.97) 0.88 (0.91) 0.21
Painful off-state dystonia 0.30 (0.75) 0.23 (0.60) 0.42
Quantitative scales are from 0-4: 0 (normal) - 4 (most severe). Mean values (standard deviation)
are given.
68
3.3.2. Non-motor features
To assess the validity of the non-motor questionnaires we compared control subjects to iPD
for SCOPA-AUT, MOCA, Epworth sleepiness scale, and olfactory assessments (Table 14-17).
SCOPA-autonomic assessments can be clearly distinguished between control subjects and iPD
(p<0.0001). Interestingly, there is a trend for affected LRRK2 carriers with less gastrointestinal
dysfunction (mean score 0.64 in LRRK2 carriers compared to mean 0.74, p=0.04). For example,
the score for constipation is 0.64 for LRRK2 carriers compared to 0.74 in idiopathic PD.
Cognitive assessments were done using the Mini-Mental State Examination (MMSE) and
Montreal Cognitive Assessment (MOCA), six picture test and frontal assessment battery.
However, when looking into control subjects vs iPD these scores were comparable in the
Tunisian Arab Berber population. This suggests that cognitive assessments using these scales
may not be appropriate, and are not sensitive enough for measuring differences between patients
and control subjects (Table 16).
Of interest, LRRK2 carriers displayed less REM sleep behavior disorder compared to iPD
(16% for LRRK2 p.G2019S carriers compared to 29%, p<0.0001) (Table 17). Other sleep
disorders: Epworth sleepiness score, sleep apnea and restless legs syndrome were
indistinguishable between LRRK2 carriers and iPD.
69
Table 14. Autonomic dysfunction (SCOPA-Aut) individual scores
Control
subjects
iPD p-value LRRK2
p.G2019S
iPD p-value
Swallowing/choking 0.20 (0.45) 0.45 (0.65) <0.0001 0.41 (0.63) 0.45 (0.65) 0.09
Sialorrea 0.13 (0.36) 0.84 (0.93) <0.0001 0.72 (0.90) 0.84 (0.93) 0.10
Dysphagia 0.14 (0.39) 0.47 (0.68) <0.0001 0.47 (0.57) 0.47 (0.68) 0.09
Early abdominal
fullness
0.33 (0.74) 0.82 (1.00) <0.0001 0.78 (0.91) 0.82 (1.00) 0.09
Constipation 0.48 (0.73) 1.40 (1.2) <0.0001 1.12 (1.09) 1.40 (1.2) 0.09
Straining for
defecation
0.41 (0.78) 1.10 (1.2) <0.0001 0.90 (1.1) 1.10 (1.2) 0.09
Faecal incontinence
0.0078
(0.09)
0.08 (0.32) 0.0035 0.08 (0.88) 0.08 (0.32) 0.09
Straining for
urination
0.33 (0.73) 0.90 (1.02) <0.0001 0.75 (0.87) 0.90 (1.02) 0.43
Urinary incontinence 0.20 (0.48) 0.65 (0.85) <0.0001 0.68 (0.92) 0.65 (0.85) 0.47
Incomplete emptying 0.23 (0.62) 0.78 (1.04) <0.0001 0.70 (0.97) 0.78 (1.04) 0.44
weak stream of urine 0.25 (0.65) 0.70 (1.00) <0.0001 0.68 (0.95) 0.70 (1.00) 0.45
frequency of urine
passing
0.33 (0.70) 0.98 (1.13) <0.0001 0.86 (1.1) 0.98 (1.13) 0.45
Nocturia 0.63 (0.88) 1.25 (1.13) <0.0001 1.11 (1.14) 1.25 (1.13) 0.45
Light headed when
standing up
0.48 (0.75) 0.85 (0.90) 0.0001 0.71 (0.87) 0.85 (0.90) 0.79
Light headed
standing some time
0.30 (0.61) 0.63 (0.82) <0.0001 0.61 (0.81) 0.63 (0.82) 0.81
Syncope 0.09 (0.28) 0.14 (0.40) 0.09 0.16 (0.45) 0.14 (0.40) 0.81
Hyperhidrosis during
day
0.53 (0.88) 1.06 (1.08) <0.0001 0.98 (1.1) 1.06 (1.08) 0.10
Hyperhidrosis during
night
0.46 (0.81) 0.90 (1.03) <0.0001 0.95 (1.1) 0.90 (1.03) 0.18
Oversensitive to
bright light
0.24 (0.67) 0.50 (0.85) <0.0001 0.55 (0.90) 0.50 (0.85) 0.56
Cold tolerance 0.49 (0.92) 0.67 (0.96) 0.0302 0.73 (0.92) 0.67 (0.96) 0.82
Heat tolerance 0.58 (0.92) 0.87 (1.00) 0.0013 0.93 (0.99) 0.87 (1.00) 0.34
Men: erection
problem
- 0.51 (1.01) 0.53 (1.02) 0.51 (1.01) 0.97
Men: ejaculation
problem
- 0.41 (0.91) 0.43 (0.94) 0.41 (0.91) 0.95
Medication for
erection disorder (%)
- 2 (0.8) 3 (1.8) 2 (0.8) 0.30
Women: vaginal
lubrication
- 1.50 (0.53) 1.4 (0.51) 1.50 (0.53) 0.38
Women: orgasm - 1.58 (0.67) 1.4 (0.51) 1.58 (0.67) 0.03
70
Control
subjects
iPD p-value LRRK2
p.G2019S
iPD p-value
Constipation
medications (%)
- 37 (16%) 23 (14%) 37 (16%) 0.89
Urinary medications
(%)
- 10 (4.3%) 4 (2.5%) 10 (4.3%) 0.39
PD medications (%) - 40 (17%) 34 (21%) 40 (17%) 0.17
L-Dopa ON State
(%)
- 119 (51%) 76 (47%) 119 (51%) 0.46
Scale: 0-4. 0=never, 1=sometimes, 2=regularly, 3=often, 4=use catheter. Mean values (standard
deviation or %) are given.
71
Table 15. Summary of autonomic assessments compared between LRRK2 parkinsonism
and iPD
LRRK2 p.G2019S iPD p-value
Gastrointestinal (SD) 0.64 (0.51) 0.74 (0.52) 0.04
Urinary (SD) 0.80 (0.80) 0.87 (0.83) 0.10
Cardiovascular (SD) 0.50 (0.59) 0.54 (0.54) 0.35
Thermoregulatory (SD) 0.89 (0.76) 0.87 (0.73) 0.92
SCOPA-Aut subdomain scores were compared between patients. Quantitative scales are from
from 0-4: 0 (normal) - 4 (most severe). SD: Standard deviation
72
Table 16. Summary of cognitive assessment compared between iPD, LRRK2 parkinsonism
and control subjects
Control
subjects
iPD p-value LRRK2
p.G2019S
iPD p-value
MMSE (SD) 27.1 (3.29) 25.4 (3.9) 0.6898 25.7 (3.6) 25.4 (3.9) 0.42
FAB (SD) 12.7 (4.60) 10.6 (4.5) 0.2728 10.8 (4.6) 10.6 (4.5) 0.27
MOCA (SD) 17.10 (10.8) 19.3 (8.5) 0.3550 21.7 (6.8) 19.3 (8.5) 0.03
Mini Mental State Examination (MMSE), Frontal Assessment Battery (FAB), Montreal
Cognitive Assessment (MoCA).
73
Table 17. Comparison of sleep scales among LRRK2 parkinsonism and iPD.
Only a subset of patients had levodopa state recorded .
LRRK2 p.G2019S iPD p-value
Epworth total score
On state (SD) 5.35 (4.67) 5.25 (4.90) 0.98
Off state (SD) 4.75 (4.48) 4.88 (5.19)
Restless legs
On state (%) 9/75 (12%) 15/118 (13%) 0.75
Off state (%) 6/81 (7.4%) 11/113 (9.7%)
REM sleep disorder
On state (%) 12/75 (16%) 34/118 (29%) 0.001
Off state (%) 14/81 (17%) 40/113 (35%)
Sleep apnea
On state (%) 7/75 (9.3%) 10/118 (8.5%) 0.65
Off state (%) 9/81 (11%) 15/113 (13%)
74
3.3.3. Disease progression
The rate of disease progression was determined by taking motor and autonomic scores
over the disease duration. Age at onset was highly correlated with motor and non-motor
progression scores in idiopathic PD (R=0.20-0.31, p<0.0001). However, LRRK2 parkinsonism
was more uniform in progression (Table 18).
Table 18. Rate of disease progression associated with age at onset in patients
iPD LRRK2 p.G2019S
Correlation to age at onset Correlation to age at onset
R p-value R p-value
Hoehn and Yahr progression 0.29 <0.0001* 0.00 0.99
GI progression 0.30 <0.0001* 0.09 0.27
Urinary progression 0.22 0.0020* 0.05 0.53
Cardiovascular progression 0.20 0.0054* 0.06 0.51
Thermoregulatory progression 0.24 0.0006* 0.08 0.33
Rigidity progression 0.30 <0.0001* 0.02 0.85
Bradykinesia progression 0.29 <0.0001* 0.07 0.45
Tremor progression 0.22 0.0017* 0.07 0.19
Postural instability and gait
disorder progression
0.31 <0.0001* 0.05 0.99
R =pearson’s correlation coefficient to age at onset. Disease progression is measured by severity
score/disease duration. *=significant after Bonferroni
75
3.4. Discussion
The main motor feature of LRRK2 p.G2019S parkinsonism in 220 sporadic patients and
126 familial patients was tremor-predominant parkinsonism with bradykinesia and rigidity that
responds to dopamine replacement therapy. Patients with LRRK2 p.G2019S parkinsonism are
generally indistinguishable from patients with iPD cross-sectionally, however, our data suggests
temporal distinction and trending differences in non-motor features. Earlier studies highlighted
tremor as the predominant feature of LRRK2 carriers which is supported by more recent meta-
analysis (‘dardarin’ the Basque word for tremor remains a colloquial term for LRRK2 protein)
(Aasly, et al., 2005; Healy, et al., 2008; Paisan-Ruiz, Lang, et al., 2005; Paisan-Ruiz, Saenz, et
al., 2005) . In this study, tremor was observed less in 220 Arab-Berber patients with LRRK2
p.G2019S than in iPD.
Non-motor features occurred at similar frequencies in LRRK2 p.G2019S patients and in
iPD. However, affected LRRK2 carriers have less REM sleep disorder and gastrointestinal
dysfunction. Less REM sleep disorder and olfactory impairment has also been seen in
Ashkenazi Jewish LRRK2 carriers (Saunders-Pullman et al., 2015; Saunders-Pullman et al.,
2014) . Patients with iPD have Lewy body disease in the periphery, most notably the dorsal
motor nucleus, vagal nerve, cardiac sympathetic and enteric nervous systems, as well as in the
olfactory bulb, brainstem (midbrain, pons, medulla) and cortex (Braak, et al., 2003). Nonmotor
features of cognitive impairment and hypotension has been correlated with presence of Lewy
bodies (Kalia et al., 2015) . While most LRRK2 p.G2019S patients have similar Lewy body
disease some develop alternative 4R-tauopathy or TDP43 proteinopathy (Marras, et al., 2016) .
Hence, we speculate marginal differences in REM sleep and gastrointestinal function in LRRK2
p.G2019S carriers may reflect less concomitant alpha-synucleinopathy. Less REM sleep
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behavioural disorder and peripheral dysfunction can also imply that the background of LRRK2
p.G2019S does not follow Braak staging (Goedert, et al., 2013a) .
The disease penetrance of LRRK2 p.G2019S ranges from 24% to 100% (Marder, et al.,
2015; Trinh, Amouri, et al., 2014) (Latourelle et al., 2008)(Latourelle et al., 2008)(Latourelle et
al., 2008)(Latourelle, Sun, et al.)(Latourelle et al., 2008)(Latourelle et al., 2008)(Latourelle et al.,
2008)(Latourelle et al., 2008)(Latourelle et al., 2008) (Latourelle et al., 2008) , the accuracy of
figures is disputed and some of the disparity may reflect differences in population ethnicity, bias
in patient recruitment, and differences in statistical analysis. Accurate penetrance estimates of
LRRK2 p.G2019S in Arab-Berbers are important given the highest frequency of LRRK2
p.G2019S carriers and the prevalence of parkinsonism in this region of the world (Hulihan, et al.,
2008; Lesage, Anheim, Letournel, Bousset, Honore, Rozas, Pieri, Madiona, Durr, Melki, Verny,
& Brice, 2013) . The population of Tunisia may also offer greater ethnic, genetic and
environmental homogeneity than prior North American, European and Israeli studies. The range
of age at onset in LRRK2 p.G2019S carriers is broad spanning 50 years. Ascertainment bias
appears an unlikely explanation as the kin-cohort analysis of LRRK2 p.G2019S pedigrees
supported the Kaplan Meier findings, which means the familial penetrance estimates are
comparable to the unrelated LRRK2 p.G2019S carriers. If there were an ascertainment bias, we
would expect higher penetrance estimates in LRRK2 families. Hence, penetrance modifiers that
modulate motor symptom onset in LRRK2 p.G2019S carriers appear likely but remain to be
defined.
Hoehn & Yahr scores are a composite measure of dysfunction encompassing activities of
daily living, motor and cognitive disability. In cross-sectional analysis LRRK2 parkinsonism and
iPD cannot be distinguished; the range and distributions of component symptoms and scores
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overlap. In iPD, mild progressors have an earlier onset age and faster progressors have a later
onset age. Patients with LRRK2 parkinsonism appear to have the same rate of progression
regardless of onset age. Age of onset has always been a reasonable predictor of disease
progression and morbidity in PD (Diamond, et al., 1989). Interestingly, onset age does not
predict progression for LRRK2 parkinsonism. Nevertheless, a more uniform rate of progression
in LRRK2 p.G2019S carriers may aid biomarker discovery and clinical trials focused on disease-
modification (neuroprotection). However, this might reflect a sample size and lack of test
sensitivity effect in the LRRK2 patient group.
Our objective was to compare the clinical features of iPD and LRRK2 parkinsonism and
estimate the risk in carriers as an aid for genetic counselling. Kaplan-Meier and kin-cohort
methods were used to estimate the risk of parkinsonism in sporadic and familial LRRK2 carriers.
Clinic-based and volunteer patient proband series may lead to an overestimate of the penetrance
of LRRK2 p.G2019S. However, the kin-cohort method, which does not take the proband into
consideration, gave similar results to Kaplan-Meier analyses. A weakness of our study is that
samples were only drawn from Tunisia; while LRRK2 p.G2019S carriers generally inherit the
same ancestral haplotype (Kachergus, et al., 2005) our penetrance findings may not be
universally applicable and comparative clinical and genetic studies in different ethnic
backgrounds are needed.
Presently, carrier status of this pathogenic mutation does not influence a patient’s choice
of treatment, although the discovery of LRRK2 biomarkers and specific molecular interventions
are actively sought (Cookson, 2010; R. J. Nichols et al., 2009). The range and severity of motor
and non-motor features in idiopathic PD and LRRK2 parkinsonism are comparable which
suggests therapies for LRRK2 parkinsonism might be readily generalizable to iPD. Future
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studies might include comparative whole genome sequencing (WGS) of LRRK2 patients with
divergent ages of onset in an attempt to find novel genetic variants that modulate
phenoconversion, to symptoms that warrant diagnosis and therapeutic interventions. WGS in
large multi-incident pedigrees with the p.G2019S mutation would be a good start in identifying
novel genetic modifiers as a rare variant segregating with age at onset in a family tree can be a
good indication of a modifier. Another possibility can be common polymorphisms influencing
age at onset, which can be identified in larger case cohorts. Similarly, environmental exposures
that influence risk of parkinsonism might be more readily identified in a relatively more
homogeneous patient sample.
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4. Chapter 4: Dynamin 3 modifies age at onset in LRRK2 parkinsonism
4.1. Introduction
Genetic variability in leucine-rich kinase 2 (LRRK2) has been linked to familial
parkinsonism and associated with idiopathic Parkinson disease (PD): LRRK2 c.6055G>A
(p.G2019S) confers the highest genotypic and population attributable risk (Kachergus, et al.,
2005; Ross, et al., 2011; Zimprich, et al., 2011) . Penetrance estimates are variable with a wide
range in age of onset (AOO) influenced by ethnicity (Healy, et al., 2008; Hentati et al., 2014;
Trojano, Moretta, Estraneo, & Santoro, 2010). The relatively homogeneous North-African Arab-
Berber population has the highest frequency of LRRK2 p.G2019S carriers, between 30-40% of
patients with PD (Lesage, et al., 2005; Trinh, Amouri, et al., 2014) and provides a unique
opportunity to identify genetic modifiers of AOO.
LRRK2 is a large multi-domain protein with GTPase (Roc) and kinase activities that appear
to modulate cytoskeletal outgrowth and vesicular dynamics, including synaptic transmission,
endosomal trafficking and lysosomal autophagy (Orenstein et al., 2013) . Although many
binding partners and substrates have been identified, it remains uncertain which are clinically
relevant to disease pathophysiology. Herein, a genome-wide approach was used to identify
genetic variability that directly influences LRRK2 p.G2019S penetrance.
4.2. Methods
4.2.1. Discovery cohort and replication series
Arab-Berber subjects were recruited between 2006 to 2012 by movement disorders
neurologists (FH, SBS, FN, EF) at the Mongi Ben Hamida National Institute of Neurology,
Tunis. Community-based samples consisted of 41 multi-incident LRRK2 p.G2019S families
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(150 affected and 103 unaffected LRRK2 carriers), and 232 unrelated LRRK2 p.G2019S carriers
(Table 19). All subjects were older than ≥18 years at neurological assessment and provided
informed consent prior to their participation. Specific approvals obtained from the local ethics
committee at the National Institute and Ministry of Health in Tunis were reviewed by
GlaxoSmithKline (GSK), the Institutional Review Board of Mayo Foundation and the Research
Ethics Board of the University of British Columbia. Additional replication cohorts included 263
LRRK2 p.G2019S carriers from Algeria (MT), France (AB), Norway (JAA) and North America
(PSG–Progeni GenePD Investigators (Latourelle et al., 2011) (Table 20). Human biological
samples were sourced ethically and their research use was in accord with the terms of the
informed consents. An overview of the discovery and replication samples is depicted in Figure
31.
4.2.2. Linkage analysis and STR genotyping
Genome wide linkage analysis was performed on 41 LRRK2 p.G2019S families from
Tunisia using deCODE’s 4cM density STR (short tandem repeat) marker set, with standard
approaches (Abecasis, Cherny, Cookson, & Cardon, 2002) . Allele frequencies derived from
Tunisian unrelated, non-carrier, control subjects were used for STRs. Consanguineous loops are
noted in ~1/3rd of the families but were split to maximize information content (Abecasis, et al.,
2002). Both non-parametric (NPL) and model-based linkage analyses were performed
considering early-onset and late-onset groups, dichotomized by median AOO: < or ≥ 56 years.
Linkage was performed with merlin. We had two categories: an early PD onset group (all
affected carriers with an age at onset <56 years) and a late PD onset group (all affected carriers
with age at onset ≥56 years and all unaffected carriers with an age at examination of ≥56 years).
Alternatively, AOO in patients and age at recruitment/examination of unaffected carriers were
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assessed as a quantitative trait. Model-based linkage used an additive model with incomplete
penetrance to provide LOD (logarithm of odds) and hLOD (heterogeneity LOD) scores.
4.2.3. Genome-wide SNP genotyping and association
Single nucleotide polymorphisms (SNP) were genotyped for the Tunisian Arab-Berber
cohort using Affymetrix 500K NspI and StyI (n=101) and Illumina Multi-Ethnic Genome Arrays
(MEGA) (n=131). Affymetrix genotypes were extracted from .cel intensity files using three
algorithms, BBRML, JAPL and CHIAMO, and only nominated when there was consensus as
previous (Trinh, Gustavsson, et al., 2014) ; GenomeStudio® was used to provide genotypes for
Ilumina data. Samples with genotype call rate below 99% were excluded from further analysis.
Genotype distributions for all SNPs within control subjects, and all cases combined, satisfied
Hardy-Weinberg equilibrium (HWE) expectations (p>0.001) . PLINK was used to assess IBS,
IBD and population stratification as quality measures for the MEGA and Affymetrix data
(Purcell et al., 2007). Extraction of the DNM3 locus region was performed with PLINK on
MEGA and Affymetrix merged datasets. Quality control of MEGA and Affymetrix data was
performed. A subset of consistent genotypes/individuals was assessed for population
stratification using Eigenstrat, as previously described. Prior to case-control association,
genome-wide IBS/IBD (identity by state/identify by descent) estimates were used to identify and
exclude sample contamination, duplicates and individuals with unknown relationship (e.g.
sibling-pairs in the unrelated case-control series). We assessed IBS and IBD in detail since the
unrelated carriers share the same G2019S haplotype. Within regions of linkage, PLINK
association analyses were performed (Howey & Cordell, 2014; Purcell, et al., 2007) . Quantile-
quantile plots of p-values were employed to highlight potential confounders (R package qqman).
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4.2.4. Whole genome sequencing and imputation
Whole genome sequencing (WGS) was accomplished for 14 Tunisian Arab-Berber
patients. All are LRRK2 p.G2019S carriers with a family history of parkinsonism, half had early-
onset disease (mean onset 34.9 years, SD±7.2, range 22-42) and the remainder are clinically
asymptomatic elderly carriers (mean age 77 years, SD±6.9, range 68-90). Sequencing was
carried out using Illumina 2x100 nucleotide paired-end reads, with minimum 50-fold mean depth
using standard methods for sequence alignment and variant calling (Figure 24).
SNP genotypes in the chromosome 1q23.3-24.3 region of linkage were imputed with
Beagle 4.0, (Browning & Browning, 2008, 2009) employing 14 Tunisian WGS and phased 1000
Genomes data as a reference for MEGA and Affymetrix data (n=232 LRRK2 carriers).
Subsequently, haplotype associations were assessed within the linked interval using a variable-
length Markov-chain Monte Carlo method (Browning & Browning, 2008, 2009). Affymetrix and
MEGA genotype calls were previously merged together. PLINK files were then converted into
VCF files with PLINK/SEQ and Beagle 4.0 was used for imputation. The genotype file (gt) was
designated as the merged file and 1000 Genomes data was used as the reference VCF file (ref).
There were 15075 reference markers, 1595 target markers and 2504 reference samples. Burn-in,
phase and imputation iterations were set at 10, to maximize genotype imputation accuracy. The
haplotype association was performed using Beagle 3.3 on affected unrelated individuals. Phasing
iterations and then haplotype association was performed on allelic, recessive, over-dominant and
dominant models. Corrected p-values for haplotype association and multiple-testing were
estimated by permutation analyses, randomizing case-control status. The beagle haplotype
association p-value was significant after permutation analyses, (p=0.002).
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4.2.5. Sequencing and genotyping
All subjects were screened for LRRK2 p.G2019S by Sanger sequencing or TaqMan SNP
assays-on-demand (Life Technologies, Inc, Foster City, CA), and excluded for other pathogenic
mutations implicated in PD (Gustavsson, Trinh, et al., 2015; Ishihara-Paul, et al., 2008).
Subsequent genotyping was carried out by a combination of Sequenom MassArray iPLEX
system (Sequenom, San Diego, CA) and TaqMan genotyping. Cumulative incidence plots
(Kaplan Meier) and hazard ratios (Cox proportional hazard regression models) were used to
stratify age of initial symptom by genotypes using JMP® software (SAS Institute Inc., Cary,
NC). These models were adjusted for family relatedness, gender and population series (Tunisia,
Algeria, France, Norway, and North America). Right censoring for asymptomatic carriers was
performed at age of examination. Meta-analyses of all populations was performed with R-
package ‘metafor’.
4.2.6. Brains, RNA, ampliseq transcriptome, antibodies
Brain tissue from 61 healthy control subjects without any neurological symptoms was
obtained from the Oxford Brain Bank, University of Oxford (LP); any with neurodegenerative
vascular pathology were excluded (LP). Full ethical approval (REC 07/Q2707/98) and written
informed consent are obtained for all participants. Gender, age-at-death and post mortem delay
was available for all subjects (Table 21). DNA was prepared from ~20mg of frozen tissue
samples of striatum with an Autogen NA1000 and quantified using standard methods. Prism 6.0
(GraphPad Software, Inc) was used for RNA/protein analysis. Total RNA was also extracted
(RNeasy Qiagen Minikit) from duplicate samples, and DNase I digested prior to assessing the
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concentration, quality and integrity (RIN) with an Agilent 2100 bioanalyzer, RNA 6000
LabChip kit and associated software (Agilent). After extraction, RNA integrity numbers for
samples was good quality (mean RIN = 8.9). Out of the 61 human control striatum, 38 have a
RIN > 7.0 and were used for TaqMan expression. A subset of the striatum was used for
AmpliseqTM whole human transcriptome analysis (n=17) was performed with an Ion Proton
(Life Technologies, Inc). High-quality, total RNA was reverse transcribed and amplified using
TaqMan One Step RT-PCR kit following manufacture’s protocol (ABI). Sequencing analysis
resulted in an average of over 12 million reads per sample and a read length of 114 bases.
AmpliseqRNA was used to map reads and generate absolute/normalized gene expression values
(reads per million, RPM). RNA expression analyses were adjusted by RIN quality. Expression
levels were quantified by dividing 2-Ct by the geometric mean of the expression levels of three
commonly used “housekeeping” genes: hypoxanthine phosphoribosyl-transferase (HPRT;
Hs02800695_m1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH; Hs02758991_g1) and
synaptophysin (SYP; Hs00300531_m1)). DNM3 expression was measured using Taqman probe
expression assay ID Hs00399015_m1 (all transcripts) and Hs00927940_m1 (NM_001136127.2
and NM_015569.4 only). Likewise, Ampliseq Transcriptome data was normalized with a variety
of housekeeping genes (GAPDH, HPRT, SYP, YWHAZ), including those primarily expressed in
neurons (TH, MAP2, ENO2, SV2A, SV2B, SYN1, SYN2) and the expression findings were
robust. For protein analysis, 20mg brain tissue (n=17) was lysed with buffer containing 1% NP-
40, 20mM HEPES, 125mM NaCl, 50mM NaF and protease inhibitor cocktail (Roche). The
lysates were put on ice for 1 hour. Blotting of dynamin-3 was done with a polyclonal rabbit
antibody (Synaptic Systems [115 302], 1:1000) and anti-for GAPDH a mouse monoclonal
antibody was used (Thermo Scientific [MA5-15738], 1:1000). MAP2 antibodies were used in
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immunofluorescence (Abcam, 1:1000).LRRK2 p.G2019S mice, primary neuronal littermate
cultures, immunostaining and image analysis were as previously described (Beccano-Kelly et al.,
2014) .
4.3. Results
4.3.1. Linkage and association of LRRK2 p.G2019S families
Linkage analysis of AOO in 41 Tunisian LRRK2 p.G2019S pedigrees identified
chromosome 12q12 using non-parametric (LOD NPL = 3.3, θ=0 at D12S85) and model-based
methods (maximum LOD = 7.6, θ =0 at D12S85 under a dominant model of inheritance), which
encompasses the LRRK2 locus. Genome-wide analysis using similar approaches, with allele-
dependent penetrances, also identified chromosome 1q23.3-24.3 (LOD NPL =2.90, maximum
LOD & hLOD = 4.99, θ =0 at D1S2768 with a recessive model, and LOD = 2.81 and hLOD=
3.81, θ =0 at D1S2768 with a dominant-additive model) (Figure 22). Significant linkage was
obtained using AOO as a dichotomous trait and was robust to subsequent ordered subset analyses
over a range of divisions (Hauser et al., 2004) and implications of significant familial
heterogeneity. The highest LOD score across all models was obtained on chromosome 1.
However, there was suggestive linkage on chromosome 6, 17 and 21 (Figure 26).
Evidence for association within the chromosome 1q23.3-24.3 linkage region (170.8-
172.5Mb, the maximum LOD -1 support interval) was assessed in unrelated LRRK2 p.G2019S
carriers (n=232). Only affected individuals were included in this association, the unaffected
carriers were excluded. Association with dichotomized AOO revealed three associated SNPs
(rs742510, rs2421947 and rs2206543, r2= 0·98; pnominal=2·6 x 10-5,) within the dynamin 3
locus (DNM3)(Table 23). Within the chromosome 1q23.3-24.3 linkage region, 634 SNPs were
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assessed. A Bonferroni correction was applied to account for multiple testing (corrected
p=0.016). A QQ-plot for association analyses on chromosome 1 deviated from the line of
equality but the DNM3 rs2421947 association was confirmed by TaqMan probe genotyping
(Figure 27).
Subsequently, all 21 coding exons of DNM3 gene were sequenced in LRRK2 p.G2019S
carriers with divergent AOO (n=25) and three rare (MAF<0.01) synonymous variants were
identified (p.A81A, p.H128H and p.V609V). Carriers of divergent AOO refer to LRRK2
p.G2019S carriers who have early onset PD (<45 onset year) or were elderly (>75years) without
motor signs of PD.
4.3.2. Higher resolution mapping
WGS of 14 LRRK2 p.G2019S Tunisian Arab-Berber subjects and 1000 Genomes data
provided references for SNP imputation, to improve haplotype analysis and identify specific
variability associated with AOO. Within and flanking the DNM3 locus (chr1:171,810,018-
172,382,057) a dense framework of informative markers (MAF>0.05) was imputed in all
unrelated LRRK2 carriers (n=232) with Affymetrix, MEGA and Sequenom iPLEX genotypes.
The shortest, most significant haplotype associated with AOO was subsequently defined between
chr1:171,832,491-171,833,094 (rs77565020 to rs2421947, 603bp), using variable-length
Markov-chain Monte Carlo methods (p=1.07 x 10-7, Text S1). Within the disease-associated
haplotype allelic association with rs2421947 was most significant (p=1.07 x 10-7) (Figure 22,
Table 24).
The Kaplan-Meier method was used to calculate median/IQR censoring at age of last
examination for unaffected carriers by DNM3 genotype. rs2421947 CC homozygous carriers had
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a median AOO of 64 years (IQR: 48-67); CG heterozygotes had a median 57 (IQR: 50.5-64
years), and GG homozygotes had a median 51.5 (IQR: 46-61.5 years) (Kaplan Meier log-rank p-
value=0.03) (Figure 23). The median onset of LRRK2 parkinsonism in DNM3 rs2421947 GG
homozygotes is 12.5 years younger than CC homozygotes. DNM3 rs2421947 has a minor allele
frequency (MAF) C= 0·42 in unrelated control subjects from Caucasian populations (HapMap-
CEU, n=226), and 0·42 in unrelated control subjects from Tunisia (n=321). In LRRK2 p.G2019S
carriers the MAF C=0·39 overall, irrespective of affection status, but increases to C=0·46 with
disease onset ≥56 years.
To fully estimate the effect of DNM3 rs2421947 in the Tunisian population, we
combined the unrelated individuals and families in a Cox proportional hazard model censoring
unaffected individuals while adjusting for family relatedness and gender (HR 1.63, CI=1.05-
2.63, p=0.03 for alternate homozygous genotypes).
4.3.3. DNM3 expression in brain
DNM3 rs2421947 was genotyped in striatal brain tissue (n=61) to assess any influence on
expression. The rs2421947 GG genotype was correlated with higher DNM3 mRNA levels
(r=0.25, p=0.006) (Figure 28), 1.25-fold higher for the GG genotype compared to CC. Results
were confirmed using Ampliseq whole transcriptome analysis in a subset of samples (n=17;
transcriptome data available on request). The findings were robust to normalization with a
variety of housekeeping genes. DNM3 total transcript expression was correlated with LRRK2
expression (r2=0.65 p=0.004)(Table 25). Dynamin-3 protein levels in striatum stratified by
rs2421947 genotype (n=17) are higher for the GG genotype (1.6 fold higher, p=0.08, Figure 29).
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4.3.4. Replication cohorts
The DNM3 association with AOO was examined in additional LRRK2 p.G2019S carriers
including subjects originating from Algeria (n=46), France (n=65), Norway (n=64) and North
America (n=88). DNM3 rs2421947 was imputed for the American series using 1000 Genomes as
a reference , or was otherwise genotyped. Of note, the MAF for LRRK2 carriers in each
population series is different. Cox proportional hazard ratios are provided for each population,
censoring unaffected individuals, adjusting for family relatedness and gender as covariates, and
combined within a meta-analysis also adjusting for population in the model (HR 1.46 CI=1.04-
2.04, p=0.02 for alternate homozygous genotypes) (Figure 23).
4.4. Discussion
Unbiased genome-wide linkage analyses and locus–specific association, with replication
of that association in an unrelated series, nominate DNM3 as a genetic modifier of AOO in
LRRK2 p.G2019S parkinsonism. The frequency of LRRK2 p.G2019S carriers is higher in North
Africa than in any other region reported to date (Kachergus, et al., 2005; Ross, et al., 2011).
Hence a strength of our study is the large number of patients and family members with LRRK2
p.G2019S originating from the same population. Clinical exams applied longitudinally by the
same team of movement disorder specialists ensure accurate diagnoses and consistent data
reporting. Inclusion of unrelated, incident cases at one site also avoids potential selection biases
in referrals from multiple centers. The Arab-Berber population of Tunisia provides ethnic,
genetic and environmental homogeneity to increase power for discovery. However, there are also
many study limitations. In general, AOO is broadly defined and subjective: its variance is large
even within LRRK2 families although highly correlated with age of a motor diagnosis.
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Nevertheless, the variance in AOO in families is less than the variance in unrelated LRRK2
p.G2019S carriers suggestive of penetrance modifiers . AOO is a fixed albeit temporal measure
of disease pathophysiology. Hence, in our initial linkage and association analyses a dichotomized
approach was used, using AOO about 56 years as a categorical variable. Key findings were
assessed using Cox proportional hazards regression models censoring unaffected individuals,
adjusting for family relatedness, gender and population series. It would be worthwhile to
examine disease onset and progression in other ways. Longitudinal follow up of these families,
additional patients and asymptomatic carriers is warranted.
In general, AOO is broadly defined and subjective: its variance is large even within
LRRK2 families in this study although highly correlated with age of a motor diagnosis.
Nevertheless, the variance in AOO in families is less than the variance in unrelated LRRK2
p.G2019S carriers suggestive of penetrance modifiers (Table 19, median interquartile range).
AOO is a fixed albeit temporal measure of disease pathophysiology. Hence, it would be
worthwhile to assess onset and disease progression in other ways. Longitudinal follow up of
these families, additional patients and asymptomatic carriers is warranted.
Genome-wide linkage analysis to AOO was performed in large LRRK2 p.G2019S
pedigrees employing informative STRs. The highest linkage peak identified is on chromosome
12 and explained by p.G2019S; however, there was no evidence of genetic variability in cis or
trans within this region influencing AOO. In Tunisia, several heterozygous ‘married in’ relatives
and homozygous carriers are observed in highly consanguineous multi-incident pedigrees, and
within the families 150 (59%) of carriers are affected (Table 19). While the incidence of
idiopathic PD is generally low (~2% at >65 years), and biologically carrier status and AOO may
be independent, in our dataset this does not appear to be the case. Hence, we took a careful look
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at both the cis/trans effects of the LRRK2 haplotype and association between AOO and common
polymorphisms. Nevertheless, no significant effects were identified after a Bonferroni correction
(data available on request). However, lack of evidence for association to AOO should not be
considered evidence against. The second highest linkage peak is on chromosome 1q23.3-24.3
and remained robust when considering different models and allele frequencies. Other linkage
peaks were also present on chromosome 6, 17 and 21; while none showed evidence for
association further investigation is warranted in larger datasets. LRRK2 p.G2019S is a relatively
rare, pathogenic mutation for disease. Thus our study was limited by the number of LRRK2
p.G2019S carriers available, in families and in population-based series of idiopathic PD. As a
continuous trait the distribution of affected carriers was too sparse for AOO analysis; unaffected
carriers were not included and there was insufficient information for linkage analysis. However,
as a dichotomized trait, we were able to include unaffected carriers’ age greater than or equal to
the median AOO. In addition, unaffected carriers younger than the median AOO for the
pedigrees were marked as ‘unknown’ status in pedigree analyses and thus contribute their
genotype information. Hence, the significance of the DNM3 finding may be driven by the
inclusion of unaffected carriers older than the median AOO, not only affected carriers. Overall,
rs2421947 appears to have an effect on AAO of LRRK2 p.G2019S parkinsonism. Nevertheless,
confidence intervals are wide and span 1.0 for several replication series, albeit relative to sample
size, and the effect appears to be in the opposite direction for the French series. In addition, in
replication series, a major caveat is that convenience samples suffer an intrinsic ascertainment
bias – as they are from patients with PD of which a subset were found to be p.G2019S carriers.
Worldwide LRRK2 p.G2019S is generally inherited from the same ancestral haplotype
(Kachergus, et al., 2005) but the influence of modifiers and their associated allele frequencies
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may be population specific. There is suggestive linkage (LOD = 2.43) for AOO on chromosome
1q32.1 in predominantly North American LRRK2 p.G2019S families, albeit with no evidence
for association in that region in those samples (Latourelle, et al., 2011) . Nevertheless, genome-
wide association analysis of idiopathic PD in Japan robustly implicates PARK16 within 1q32
(Satake et al., 2009), which is reproducibly observed albeit with low effect size (OR ~1.1) in a
mega meta-analysis of Caucasian samples (Nalls, Pankratz, Lill, Do, Hernandez, Saad,
DeStefano, Kara, Bras, Sharma, Schulte, Keller, Arepalli, Letson, Edsall, Stefansson, Liu, Pliner,
Lee, Cheng, Ikram, et al., 2014) . PARK16 includes RAB29 (formerly RAB7L1) investigated as
a candidate gene and associated with reduced risk of idiopathic and monogenic parkinsonism
(LRRK2 p.G2019S and GBA p.N370S) in Ashkenazi (Gan-Or et al., 2008) . Functional studies
also support an interaction between RAB7L1 and LRRK2 (Beilina et al., 2014; D. A. MacLeod,
et al., 2013) . Nevertheless, chromosome 1 linkage results in this and the previous study appear
independent. Patterns of linkage disequilibrium in Tunisian Arab-Berber and Israeli Jewish
population samples are also different thus additional tagging SNPs may be required to evaluate
DNM3 or other loci as penetrance modifiers.
Non-synonymous variability in DNM3 was not observed in LRRK2 carriers which
allowed us to focus on polymorphic non-coding eQTLs (expression quantitative trait loci).
Variability in DNM3 expression correlates with genotype whether quantified by Ampliseq
transcriptome or TaqMan methods. A specific isoform (Dyn3b) co-localizes with clathrin (Cao,
Garcia, & McNiven, 1998) and appears more highly expressed in DNM3 rs2421947 GG
homozygotes (Figure 28, Figure 29). In striatum DNM3 and LRRK2 expression are correlated
suggesting they are involved in the same process. DNM3 rs2421947 does not appear to
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contribute to risk of idiopathic PD, neither susceptibility nor AOO, but the influence of DNM3
rare variability has yet to be explored.
LRRK2 has been implicated in neurite outgrowth (D. MacLeod et al., 2006; Parisiadou et
al., 2009) , synaptic vesicle trafficking and neurotransmitter release , and via kinase-dependent
mechanisms (Arranz et al., 2015) . Much of the underlying mechanistic biology in these
processes remains enigmatic, as does their clinical relevance to PD. However, our genetic study
shows DNM3 is an AOO modifier of LRRK2 p.G2019S parkinsonism. LRRK2 co-
immunoprecipitates with the dynamin family GTPases that drive membrane fission (DNM1-3
and dynamin-related proteins). LRRK2 co-immunoprecipitates with the dynamin family
GTPases that drive membrane fission (DNM1-3, and dynamin-related proteins) (Stafa et al.,
2014) . Amphiphysin recruits dynamin and endophilin A (a LRRK2 kinase substrate) , and
recruits synaptojanin (SYNJ1) for endocytic vesicle fission (S. M. Ferguson & De Camilli, 2012)
. Recessive mutations in SYNJ1 have been implicated in seizure disorders and early-onset
parkinsonism (Krebs et al., 2013; Quadri, et al., 2013) . In neurons, dynamin 3 localizes to the
endocytic machinery of dendritic spines to modulate receptor recycling and excitatory synaptic
transmission (Gray, Kruchten, Chen, & McNiven, 2005) . In this process ‘Dyn3b’ isoform
expression is also centrally involved in the regulation of actin polymerization, filopodia and
spine formation (Cao, et al., 1998; Gray, et al., 2005) . Intriguingly, a significant reduction and
redistribution of dendritic dynamin 3 staining is observed in LRRK2 p.G2019S murine cortical
cultures (Figure 30), although it may also reflect elevated glutamateric synaptic transmission
(Beccano-Kelly, et al., 2014) .
We postulated LRRK2 p.G2019S activates kinase activity (Kachergus, et al., 2005), an
outcome of which has been the pursuit of competitive LRRK2 inhibitors. Based on similarly
93
unbiased genetic data, we postulate lower levels of DNM3, and perhaps specific dynamin 3
isoforms, will delay the onset of LRRK2 p.G2019S parkinsonism. The crystal structure of the
dynamin tetramer has just been elucidated (Reubold et al., 2015) and might accelerate the
development of dynamin GTPase inhibitors (dynasores). These anticonvulsants repress synaptic
transmission in seizure disorder (Li et al., 2015) and delay alpha-synuclein uptake by neuronal
and oligodendroglial cells (Konno et al., 2012) . At autopsy, most LRRK2 p.G2019S carriers
have alpha-synucleinopathy and Lewy body disease (Ross et al., 2006) . Thus DNM3 expression
represents a target for neuroprotection in LRRK2 p.G2019S carriers, and potentially for disease-
modification in LRRK2 parkinsonism.
94
Figure 22. Chromosome 1 linkage peak
a. (LOD score = 4.99). b. Region of association within the LOD -1 linkage interval: Plink SNPs
(10-5
) and Beagle haplotype (p=1.06 x 10-7
) associations
95
Figure 23. Age-associated cumulative incidence of LRRK2 p.G2019S carriers.
a. Replication cohorts: Algerian, French, Norwegian and North American, stratified by
rs2421947 genotype (log rank p=0.0001). b. All populations combined (Algerian, French,
Norwegian, North American and Tunisian Arab Berber) stratified by rs2421947 genotype (log
rank p<0.0001).
B
A
96
Table 19. Demographics of discovery cohorts: Tunisian Arab-Berber LRRK2 p.G2019S
carriers
Unrelated
patients
Unrelated
control
subjects
Familial
patients
Unaffected
family
members
N 220 12 150 103
Number of men
(%)
124 (56%) 6 (50%) 77 (51.3%) 48 (46.6%)
Mean age (SD)
years
67.6 (12.6) 56.7 (10.9) 68.6 (15.8) 56.1 (17.5)
Median age
(IQR)
69 (48-90) 54.5 (38-72) 70.5 (57-81) 53 (43-72.5)
Mean age of
onset (SD)
57.1 (11.6) - 56.1 (12.8) -
Median age of
onset (IQR)
57 (40-74) - 56 (47-65) -
97
Table 20 . Demographics of LRRK2 p.G2019S carriers: replication series
Norway France Algeria North America Total
Patient Unaffected Patient Unaffected Patient Unaffected Patient Unaffected
N 19 45 48 17 45 1 88 - 263
Number
of men
(%)
8 (42%) 18 (40%) 26 (60%) 7 (39%) 19 (42%) - 41(47%) -
Mean
age
(SD)
years
67.6
(17.5)
63.6 (12.4) 57.7 (13.8) 67.4 (11.8) 55.5
(11.3)
54 NA -
Median
age
(IQR)
73 (52-82) 62 (54.5-
70)
59 (46.8-
67.3)
67 (59.5-
76.8)
55 (45.3-
63)
54 NA -
Mean
age of
onset
(SD)
62.6
(13.0)
- 52.1 (13.5) - 49.6
(10.3)
- 61.5
(10.1)
-
Median
age of
onset
(IQR)
65 (49-74) - 51 (41.3-62) - 50 (43-
56)
- 63 (56-
70)
-
98
Table 21. Demographics of healthy control brains for expression analysis
Control subjects
N 61
Number of men (%) 30 (49.2%)
Mean age at death (SD) years 80.6 (12.1)
Median age at death (IQR) 85 (71-89)
Tissue type Striatal
Average RIN (RNA integrity
number) (SD)
8.6 (1.2)
Average PMI (Post-mortem
interval) (SD)
48.8 (33.2)
99
Table 22. Primer pairs and custom TaqMan probe design for different DNM3 transcript
isoforms in human striatum
Names 5'->3' Primers Amino acid sequence
Region 1
DNM3_Reg1F aaacggaaaggattgttgc
DNM3_Reg1Fprobe tctcttacatcaacaccaacc
DNM3_Reg1BR cccttgcgaatcacaatttg GTNLPPSRQI
DNM3_Reg1AR ttgcgaatcacctgatttc
DNM3_Reg1C_F gcaaattgtacgagctaagttc VRAKFCKLYCCFFI
DNM3_Reg1_R ttcaggttgtccaagggaag
Region 2
DNM3_Reg2A_F tatcctgacaaatctgtagctg SVAEN
DNM3_Reg2_R ggtcctctgaagaatacaac
DNM3_Reg2B_F tctgtagggaacaacaaagc SVGNNKAEN
DNM3_Reg2_R ggtcctctgaagaatacaac
Region 3
DNM3_Reg3_F aaaggaggccaacactaag SRRPPPSPTRPTIIRP
DNM3_Reg3B_R attatagtgggacgagttgg
DNM3_Reg3_F aaaggaggccaacactaag RFGAMKDEAAEP
DNM3_Reg3A_R cagcagcttcatccttcatgg
Probe
TaqMan Probe design attggcttcgcaaatgctcagcagag
100
Table 23. PLINK association underneath linkage regions
CHR SNP BP (hg19) A1 F_A F_U A2 CHISQ P OR
1 rs742510 171858930 A 0.5 0.1974 G 18.13 2.06 x 10-5
** 4.067
1 rs2421947 171833094 C 0.5 0.1974 G 18.13 2.06 x 10-5
** 4.067
1 rs2206543 171835493 G 0.5 0.1974 A 18.13 2.06 x 10-5
** 4.067
** Values significant after Bonferroni correction for all SNP association tests within the LOD-1 linkage interval on chr 1
101
Table 24. DNM3 haplotypes associated with AAO
rs77565020
rs75848807
rs192895361
rs74673993
rs142760983
rs559149705
rs185844670
rs541736672
rs563254497
rs530428455
rs190417579
rs74777828
rs192302781
rs146042960
rs566301333
rs376575981
rs183688167
rs114979811
rs56237038
rs72713714
rs2421947
count p-value
Major haplotypes:
G G G T A G G A A C A T A T G A G T A G G 238 1.07E-07**
G G G T A G G A A C A T A T G A G T A G C 138 0.122
Minor haplotypes:
G A G T A G G A A C A T A T G A G T A G C 1 NA
G G G T A G G A A C A A A T G A G T A G C 5 NA
G G G T A G G A A C A T A T G A G T A C C 3 NA
G A G T A G G A A C A T A T G A G T A C C 1 NA
G A G T A G G A A C A T A T G A G T A G G 2 0.515
G G G T A G G A A C A T A T G A G T A C G 1 0.411
** Values significant after Bonferroni correction all haplotype associations within the LOD-1 linkage interval on chr 1
102
Table 25. DNM3 transcript levels correlate with LRRK2, VPS35 and SYNJ1 expression in
striatal tissue transcriptome data from normal controls (n=17).
Gene DNM3 expression
level correlation
coefficient
p-value
Genes implicated in
Late-onset autosomal dominant
LRRK2 0.65 0.004**
VPS35 0.65 0.008
SNCA 0.65 0.04
DNAJC13 0.21 0.14
Genes implicated in
Early-onset recessive
SYNJ1 0.41 0.008
PINK1 0.53 0.02
PARK2 0.21 0.04
FBXO7 0.51 0.12
**Values significant after Bonferroni correction
103
Table 26. Sensitivity analysis for different age cut-offs on chromosome 1q23.3-24.3 using
non-parametric linkage
Age at onset dichotomization
Chromosome 1q23.3-24.3
NPL LOD score
45 years 2.3
50 years 2.5
55 years 2.9
60 years 1.8
65 years 1.2
104
Clinical characteristics of subjects with WGS
7 early onset LRRK2 p.G2019S carriers 7 asymptomatic LRRK2 p.G2019S carriers
Mean Age of Onset (SD): 34.8 (7.2) Mean Age (SD): 77 (6.9) Mean Sequencing Depth
50X
↓
Align to NCBI hg19 Build38
↓
Compare SNPs against dbSNP
(91% of variants represented in dbSNP)
↓
Average number of SNP variants ~3,000,000
Average number of insertion variants ~325,750
Average number of deletion variants ~349,189
↓
Imputation of chromosome 1 linkage region (DNM3 locus)
↓
Use imputed data for Beagle haplotype association
.Figure 24. Whole genome sequencing and imputation workflow
105
Figure 25. A schematic of the thirteen dynamin isoforms.
Refer to table 22 for primer designs to capture different amino acid sequences. Figure adapted
from (Cao, et al., 1998).
106
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
A. Multipoint model-based linkage LOD (blue) and HLOD (black) dominant model
0
2
4
6
8
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
B. Multipoint model-based linkage LOD (blue) and HLOD (black) recessive model
0
2
4
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
C. Multipoint non-parametric linkage (NPL) LOD (cumulative)
LO
D
score
LO
D s
core
Chromosome
Chromosome
LO
D
score
Chromosome
107
Figure 26. Multipoint model-based and non-parametric linkage analysis of Tunisian Arab-Berber LRRK2 p.G2019S families. A. Parametric linkage with divergent ages at onset (<56 or ≥56 years) , using a dominant model with incomplete penetrance; B
Parametric linkage hLOD cumulative scores; C. Non-parametric linkage D. Continuous trait analysis
0
1
2
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
D. Continuous trait linkage analysis (NPL) LOD
LO
D s
core
108
Figure 27 Chromosome 1 Q-Q plot values
109
.
Figure 28. DNM3 transcript levels normalized by geometric mean of housekeeping genes
Total DNM3 RNA levels
DNM3 rs2421947
DN
M3/G
eo
metr
ic M
ean
CC
CG
GG
0
1
2
3
4 ** p=0.006
CC CG GG
Dyn3A/Dyn3B 1.53 1.46 0.98
No
rmal
ize
d D
NM
3
110
Figure 29. Dynamin 3 protein levels normalized by GAPDH
DNM3 protein levels
DNM3 rs2421947 genotypes
DN
M3/G
AP
DH
CC
GG
0.0
0.5
1.0
1.5
2.0 p=0.08
111
Figure 30. Dynamin 3 staining in cortical neurons
A. representative confocal microscopic images of dynamin-3 (red) and MAP2 (blue) staining in
wild-type (WT) and GKI (LRRK2 p.G2019S) murine cortical neurons, cultured as previously
described (Beccano-Kelly, et al., 2014)Left: 60X 2-times zoom of individual neuron staining.
Right: expanded region of interest with and without MAP2; B. Quantification of dynamin-3
intensity in cortical cultures (DIV=21). Scale bars, 50um, n=3 cultures per group; C.
Quantification of dynamin-3 cluster density in cortical cultures (DIV=21) **=p<0.05.
A
B
C
112
Figure 31. Flow diagram of discovery and replication cohorts
113
5. Chapter 5: Elucidating mechanisms of reduced penetrance in Mendelian disease
5.1. The importance of reduced penetrance
It has been over 10 years since the discovery of LRRK2 mutations in PD. The penetrance
of LRRK2 p.G2019S parkinsonism is complex and varies across ethnicities and environments.
Other pathogenic mutations in LRRK2 also show variable penetrance estimates. The penetrance
estimates for p.G2019S are relevant for genetic counseling, but treatment and prognosis for
these patients are the same as typical PD. The lack of a definitive cure for PD drives the search
for modifier genes that are informative for genetic counseling, disease severity and potential new
avenues for therapeutics. However, discovering penetrance modifiers in monogenic forms of
disease (albeit genetic or environmental) requires large numbers of mutation carriers (both in
families and sporadic unrelated patients) for sufficient power. Here we have a large
homogeneous Tunisian population with a high frequency of the LRRK2 p.G2019S mutation. Our
study was limited by the rarity of this Mendelian form of parkinsonism, in families and in
population-based series of idiopathic PD, and it has taken 10 years of research to build this
valuable resource of clinical and genetic data.
In PD, homozygous or compound heterozygous mutations in Parkin and PINK1 are
highly penetrant genotypes in early onset parkinsonism, heterozygous mutations in LRRK2,
VPS35, EIF4G1, have reduced penetrance estimates and heterozygous states of Parkin/PINK1
mutations may be regarded as pathogenic but with very low penetrance, although this is
debatable (Klein & Ziegler, 2011) . Thus far, there has not been a genetic modifier identified for
genes implicated in monogenic forms of PD. Although few genetic modifiers have been
identified and validated, there are many biological candidates. Mutations of GBA are an
important risk factor for PD which reduce enzyme activity leading to ER associated degradation.
114
The GBA enzyme, GCase, interacts with alpha synuclein. Reduced GCase in GBA mutation is
associated with increased SNCA (Schapira, 2015) .
There are several examples of genetic modifiers in movement disorders and
neurodegeneration. Mutations in SGCE lead to development of myoclonus-dystonia. However,
maternal imprinting of SGCE does not lead to disease in the offspring when transmitted through
the mother (Guettard et al., 2008) . The finding is extremely relevant for young female patients
with an SGCE mutation, as their children will not suffer from the disease. Likewise, DYT1
dystonia is caused by a TOR1A GAG deletion. However, when p.D216H polymorphism in
TOR1A is present in trans, there is reduced penetrance of the TOR1A GAG deletion to 3%
(Bruggemann et al., 2009; Kamm et al., 2008; Klein, 2014).
Herein, we have the unique opportunity of a large homogeneous cohort with one identical
mutation to study age-at-onset genetic modifiers. The work in this thesis has made use of
collected detailed clinical research forms to study and characterize endophenotypes in LRRK2
parkinsonism. The work has demonstrated the usefulness of families in linkage analysis,
withsubsequent use of whole-genome sequencing and haplotype analysis. We have identified a
potential age-at-onset modifier for the most common mutation in familial PD.
5.2. Factors that influence penetrance
The phenotypic manifestation of mutations in neurodegenerative diseases are age-
dependent, e.g. c9orf72 in FTD/ALS, LRRK2 mutations in PD, HTT expansion in HD. The risk
of developing the disease increases with age. The mutation type can also influence penetrance.
Some mutations are more penetrant than others. For example, we have found that SNCA point
115
mutations, duplications and triplications are more highly penetrant in comparison to LRRK2
point mutations in PD(Trinh, Guella, et al., 2014). This may be due to mutation type (duplication
or triplications can severely influence the patient compared to point mutations). It can also be
due to gene-specific differences. Perhaps perturbations in SNCA have a larger effect on disease
processes compared to LRRK2. But even within the same gene, the penetrance estimates are
vastly different. For example, LRRK2 p.G2019S is more penetrant compared to the
R1441G/C/H mutations. Perhaps penetrance correlates with the kinase or GTPase activity in
LRRK2. This phenomena is not specific to PD. Cis and trans elements that control gene
expression can also influence the penetrance of mutations. If there is unequal expression of the
wild-type and the mutant allele, then there would be an influence on expression levels. The
ethnic or environmental background can influence penetrance. In our study, we have shown that
cumulative incidence estimates are significantly different between Norwegians and Tunisians
with the LRRK2 p.G2019S mutation (Hentati, et al., 2014) . Furthermore, new studies with
larger sample sizes show that there are differences between Ashkenazi Jews from New York
(n=90 LRRK2 p.G2019Scarriers) and Tunisian Arab Berbers (n=220 LRRK2 p.G2019S
carriers)with the same LRRK2 p.G2019S mutation (Marder, et al., 2015; Trinh, Guella, et al.,
2014). Gender can also influence penetrance, although controversial, females may have higher
risk of disease compared to males in LRRK2 p.G2019S (Cilia et al., 2014) (Trinh, Amouri, et al.,
2014) . This is in contrast to the higher risk of idiopathic PD in males compared to females (de
Lau & Breteler, 2006) .
116
5.3. Methods and approaches to identify genetic modifiers
The most obvious candidates for genetic modifiers may be the top GWAS hits. Genes
such as SNCA, MAPT, RAB7L1 that contribute to risk of PD could also modify age-at-onset in
idiopathic PD. In fact, there are a few polymorphisms in SNCA and the TMEM175/GAK loci that
may influence age-at-onset (Lill et al., 2015; Ritz, Rhodes, Bordelon, & Bronstein, 2012). A
combined genetic risk score for age-at-onset of all significantly associated SNPs revealed that
the signal was mostly driven by SNCA and the TMEM175/GAK. There was a reduction of the
effect when these two top SNPs were removed and thus other PD risk loi besides SNCA and
TMEM175/GAK have a relatively small contribution to AAO variability (Lill, et al., 2015) .
Another study has shown that SNCA rs356165 and rs356219 modifies age-at-onset in idiopathic
PD (Brockmann et al., 2013) . However, we have found that SNCA polymorphisms do not have
an effect on disease risk or onset age in LRRK2 p.G2019S carriers (Trinh, Gustavsson, et al.,
2014), suggesting that PD GWAS risk loci may have a relatively small contribution to modifying
endophenotypes in Mendelian forms of PD.
A combination of linkage analyses, genome-wide association studies, meta-analyses and
exome sequencing of ‘extreme’ cases have been used to identify modifiers of disease severity
and comorbidities in the field of complex genetic disorders (Wright et al., 2011) (Emond et al.,
2012) which requires good phenotyping, especially in the context of movement disorders and
longitudinal follow up on families and patients. It also requires large Mendelian families or sib-
pairs segregating with disease. Many modifiers of endophenotypes in cystic fibrosis have been
explored with linkage analysis of phenotypes such as lung disease severity (Corvol et al., 2015;
Emond, et al., 2012; Wright, et al., 2011). A genome-wide study on 486 sib-pairs identified
linkage on chromosome 20q13.2 that modifies lung disease severity (Wright, et al., 2011) .
117
Another method is looking for ‘protective’ alleles. These are alleles that lower risk of
getting disease. One example is in the context of cholesterol low-density lipoprotein, loss of
function variants in PCSK9 were found in individuals with low levels of LDL cholesterol (Cohen
et al., 2005) . Other examples include inactivating mutations in NPC1L1 and APOC3 protecting
from coronary heart disease (Crosby et al., 2014; "Inactivating mutations in NPC1L1 and
protection from coronary heart disease," 2014; Jorgensen, Frikke-Schmidt, Nordestgaard, &
Tybjaerg-Hansen, 2014) . Protein inactivating mutations in NPC1L1 such as p.Arg406X were
more associated with lower LDL cholesterol levels and lower risk of coronary heart disease.
Protective alleles may also exist in the LRRK2 p.G2019S carriers. The Norwegian population
may carry ‘protective’ alleles that the Tunisian Arab-Berber population does not carry.
Interestingly, the DNM3 rs2421947 GG genotype is almost absent in the Norwegian LRRK2
p.G2019S carriers.
Penetrance of mutations can be modified by expression levels. Using translational models
of human stem cells or other mammalian models to look for transcriptomic differences may be
one important step to test potential candidate modifiers or look for novel modifiers. THAP1
mutations can cause early onset primary torsion dystonia, with an autosomal-dominant
inheritance and 40% penetrance (T. Fuchs et al., 2009) . THAP1 encodes a transcription factor
that regulates expression of TOR1A and also autoregulates its own expression levels (Erogullari
et al., 2014) .
Based on this multiplicity of mechanisms and their conceivable interactions, it appears
unlikely that a single approach will suffice to arrive at a comprehensive understanding of the
molecular mechanisms underlying reduced penetrance of movement disorders. In this respect, a
118
number of DNA and RNA based genetic methods, complemented by functional models may be
required for thorough investigations of potential modifier genes.
5.4. Dynamin 3 as potential therapeutic target of LRRK2 parkinsonism
LRRK2 has been found to interact with various presynaptic proteins: AP3, clathrin,
dynamin-1 (Schreij et al., 2015; Stafa, et al., 2014; Waschbusch et al., 2014) .These presynaptic
proteins are important for maintaining reserve vesicle pools and membrane fusion. LRRK2 binds
to purified synaptic vesicles and perhaps regulates exocytosis, modulating vesicle pool
mobilization (Piccoli et al., 2014) . We identified a genetic modifier of LRRK2 parkinsonism
that is heavily involved in synaptic vesicle fission and release of clathrin (S. M. Ferguson & De
Camilli, 2012; Raimondi et al., 2011; Wu et al., 2014) . DNM3 rs2421947 GG is associated with
earlier age at onset and higher gene expression in human control striatum. The dynamin 3b
isoform which is involved in regulation of actin polymerization, filopodia and spine formation is
also more highly expressed. Lastly, a significant redistribution of dendritic dynamin 3 staining is
observed in LRRK2 p.G2019S murine cortical culture. The discovery directs therapeutic
development to dynamin 3, as a neuroprotective strategy for LRRK2 parkinsonism or subjects
with Parkinson disease. Diagnostics/therapeutics targeting (a) DNM3 nucleic acid , (b) reducing
the levels of DNM3 GTPase activity, protein or mRNA may help delay the onset of and prevent
symptom progression since the higher gene expression is associated with earlier age of onset.
The therapeutic target may even be generalizable to treat other neurodegenerative disorders with
similar pathogenesis such as Alzheimer's disease, Huntington disease, immune and inflammatory
disorders. Suppression of dynamin GTPase decreases a-synuclein uptake by neuronal and
oligodendroglial cells and amyloid-beta internalization (Konno, et al., 2012; Yu, Nwabuisi-
119
Heath, Laxton, & Ladu, 2010) . Drp-1 (dynamin-related protein like 1) inhibitors were shown to
protect against ischemic neuronal injury through inhibiting mitochondrial calcium uptake (Tian
et al., 2014). Preliminary evidence has shown that small molecule dynamin inhibitors can also be
an anticonvulsant drug, acting to control synaptic transmission as a novel target for epilepsy.
A limitation is the therapeutic potential of dynamin 3. Thus far, there has not been a
specific drug to readily target dynamin 3, although non-specific inhibitors such as dynasore
(which inhibits GTPase activity of dynamin I and dynamin II but not dynamin III) exist. Other
more potent series include: dimeric tyrphostins, long chain amines and ammonium salts
(myristyl trimethyl ammonium bromides), dynoles, iminodyns and pthaladyns are other drugs
that inhibit dynamin. These drugs have been considered in cancer treatments to induce apoptosis
following cytokinesis failure in a concentration-dependent manner (Chircop et al., 2011; Joshi,
Braithwaite, Robinson, & Chircop, 2011) . Another disadvantage is that pharmacological
inhibition of dynamin in mice has reduced long-term potentiation and resulted in memory loss
(Fa, Staniszewski, Saeed, Francis, & Arancio, 2014) . This limitation can be overcome with
careful monitoring of dynamin inhibitor levels. Perhaps a moderate to low level of inhibitor will
be beneficial and neuroprotective whereas more potent levels lead to apoptosis. Alternatively,
allosteric modulators of dynamin GTPase activity might be considered.
5.5. Conclusion
The identification of risk loci, genes and mutations in PD has provided new insights into
disease aetiology and highlighted new study approaches. Several biological processes involved
in PD pathogenesis have been highlighted, and the discovery of novel PD-associated genes in
families with Mendelian disease has been particularly informative in this regard. Historically,
120
each major discovery has defined a major theme for translational neuroscience. For example, the
discovery of α‑synuclein as a key component of Lewy bodies highlighted protein aggregation
and propagation, and the discovery of parkin highlighted protein ubiquitination and the
proteosome. Each discovery generally led to a change and/or replacement of focus. Recently,
some pathways have emerged that relate to mitochondrial metabolism (PINK1, PARK2) and
lysosomal-autophagy (ATP13A2, GBA, LRRK2). Nevertheless, the ultimate focus must be on
late-onset Lewy body PD as, clinically and pathologically, this phenotype describes the vast
majority of patients.
On the basis of the finding of DNM3 as a penetrance modifier of LRRK2 parkinsonism,
we postulate a unifying synthesis whereby deficits in synaptic exocytosis and endocytosis
involving DNAJC6, DNAJC13, VPS35, SNCA and LRRK2 are relevant for the clinical
phenotype of disease onset. With the advent of next-generation sequencing, we anticipate
genetic advances in PD will continue to flourish, and our understanding of the molecular
mechanisms underlying susceptibility, progression and response to treatment will continue to
evolve.
121
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