analytical challenges in genetic association studies david meyre, associate professor, mcmaster...

Analytical challenges in genetic association studies

David Meyre, Associate Professor, McMaster University([email protected])

HRM 728 Graduate Course: Genetic Epidemiology – November, 7th 2014

Li & Meyre., Int J Obes 2013

single variant (100 SNPs)

detailed study of individual genes(102 SNPs)

regional studies (104 SNPs)

genome-wide association genome-wide association (5 10(5 1055 SNPs SNPs))

Whole-genome sequencingWhole-genome sequencing(3 10(3 1077 SNPs) SNPs)

1980

1990

2000

3,5 103,5 1066 SNPs (2007) SNPs (2007)

2006

The march of technology

2011

A storm of data to deal with!

Rankinen et al., Obesity 2006

. 426 positive findings in 127 genes but….

. only 22 genes associated with obesity-related phenotypes in > 5 studies

Replication is challenging in genetic epidemiology

Skepticism in the medical / scientific community


I.I. Analytical challenges to find a true association in a Analytical challenges to find a true association in a discovery study (risk of false positive result)discovery study (risk of false positive result)

II.II. Analytical challenges to replicate a true positive associationAnalytical challenges to replicate a true positive association

III.III. Guidelines for proper discovery and replication association Guidelines for proper discovery and replication association study designsstudy designs


Analytical challenges to find a true Analytical challenges to find a true association in a discovery studyassociation in a discovery study

Are you ready for the Episode 1 of the saga!

I.I. Lack of replication may occur because the original study Lack of replication may occur because the original study reports a false positive resultreports a false positive result

1) The phenotype is not heritable


Obesity is an heritable disease

. 2 obese parents 10-fold increased risk for childhood obesity

. Obesity has a strong genetic component: heritability 50- 85% (Stunkard et al., NEJM 1986; Wardle et al., AJCN 2008)



2) Insufficient sample size


Statistical power and sample size

Effect sizes for obesity-associated common genetic variants are small (OR < 2)

MAF in controls

0.01 0.05 0.1 0.2 0.3 0.4Allelic OR

1.1 443,854 92,868 49,252 27,974 21,518 19,010

1.2 116,354 24,434 13,018 7,460 5,792 5,162

1.3 54,110 11,404 6,102 3,526 2,760 2,480

1.5 21,208 4,498 2,426 1,424 1,132 1,032

2.0 6,386 1,374 754 458 376 354

Table 1. Sample sizes needed in a case control design to detect significant association with a power of 90% and a two-sided P-value of 0.001 by odds ratio and allele frequency for risk allele. Calculations assume multiplicative effect on disease risk. Sample sizes presented are total number of cases and controls needed, assuming an equal number of cases and controls.

Statistical power and sample size

.Association of the GAD2 promoter gene variant -243 A>G with morbid obesity (OR=1.05-1.58, P=0.01) using 575 cases and 646 controls

. No prior statistical power calculation in the princeps study

GAD2 or the importance of a well-powered study

.Lack of confirmation of the association of the GAD2 promoter gene variant -243 A>G with morbid obesity (OR=0.90-1.36, P=0.28) in a meta-analysis of 1,252 cases and 1,800 controls

Boutin et al., PLOS Biol 2003, Swarbrick et al., PLOS Biol 2005

Statistical power and rare variant analysis

“We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations

will require large sample sizes (>100,000) or studies in ethnically diverse populations.“

Fawcett et al., Diabetes 2010




3) Lack of correction for multiple testing


1 million 1 million polymorphisms!polymorphisms!

Multiple testing in the post-GWAS area

Bonferroni correction: Pcorrected = 0.05 / 1,000,000 = 5 x 10-8

2 SNP gene x gene interactions: Pcorrected = 1x 10-13

Multiple testing in the whole-exome/genome sequencing area

Bonferroni correction SNPs: Pcorrected = 0.05 / 30,000,000 = 1 x 10-9

Bonferroni correction genes: Pcorrected = 0.05 / 20,000 = 2.5 x 10-6

30 million polymorphisms30 million polymorphisms

20,000 genes20,000 genes

INSIG2: a GWA false positive association

Science April 2006

INSIG2 rs7566605 variant is associated with obesity (ORmeta-analysis=1.05-1.42, P =0.008), far from the threshold of significance after multiple testing correction (P=5 x 10-7)

Science January 2007

INSIG2: lack of association with obesity in 3 independent designs (N=22,381)





4) Geographical population substructure


Davey-Smith et al., EJHG 2009

LCT rs4988235 T allele frequency in UK

Lactase persistence and population substructure

Rare variants and founder effects

Croteau-Chonka et al., HMG 2012

-common SNP associated with adiponectin level in Fillipinos by GWAS

-exon resequencing identified a rare coding variant (R221S) in LD with the common SNP strongly associated with adiponectin level

-the mutation is found exclusively in Fillipinos






5) Technological biases, lack of quality control procedure


INS VNTR and association with childhood obesity, a technological bias?

. Association of the INS VNTR variant with childhood obesity

. Genotyping by RFLP, a highly subjective method (Peters et al., CCM 2003)

. Lack of association of the INS VNTR variant with childhood obesity

. Genotyping by TaqMan, a highly reliable method

. Family-based design to enable a high-standard quality control procedure

Le Stunff et al., Nat Genet 2000, Bouatia-Naji et al., Obesity 2008

Next generation sequencing and false-positive mutations

. 10% of mutations are technological artifacts in next generation sequencing

. The rate of false positive mutations is higher in ‘old’ DNA libraries

Use of pedigrees, confirmation of mutations by Sanger resequencing

New methods (Rain Dance technology)Bonnefond et al., PLOS One 2012







6) Inappropriate statistical analysis


Association and adjustement for confounding factors

Frayling et al., Science 2007

. Association between FTO intron 1 SNP and type 2 diabetes (OR=1.09-1.23, P= 5x 10-8) if adjustment for sex and age

. Lack of association between FTO intron 1 SNP and type 2 diabetes (OR=0.96-1.10, P= 0.44) if adjustment for sex, age and BMI

FTO is an obesity gene

Inappropriate adjustment (or lack of adjustment) can lead to wrong conclusions







6) Inappropriate statistical analysis


Analytical challenges to replicate a true positive Analytical challenges to replicate a true positive associationassociation

Now the Episode 2 of the saga!

II. Replication may be challenging even when the original result II. Replication may be challenging even when the original result is a true positive associationis a true positive association

1) Willingness to replicate the original study


Lactase persistence and BMI variation

Despite a convincing initial evidence of association between the LCT rs4988235 T variant and BMI (P=8 x 10-5) in 31,720 European individuals…

Kettunen et al., HMG 2009

Lactase persistence and BMI variation

Replication studies showed-up after 2-4 years…

Correla et al., Obesity 2011



2) Winner’s curse effect and sample size in follow-up studies


Obesity loci from GIANT and replication

. Due to the small effect size of the SNPs on BMI variation, only a fraction of these associations replicates for obvious statistical power concerns (den Hoed et al., Diabetes 2010)




3) Gene x gene, gene x environment interactions


Interactions between FTO SNP and physical activity

Kilpelainen et al., PLOS Med 2012

.The effect of the rs9939609 SNP on obesity risk is decreased by 27% in physically active adults

. No genotype x physical activity interaction on obesity risk in children

Savage et al., Nat Genet 2002





4) Heterogeneity (ethnic heterogeneity, phenotype heterogeneity)


Ethnicity and linkage disequilibrium blocs

Distance (Kb)

Icelandic

French

Asian

African

Disease-associated LD block

SNP1 SNP2 SNP3 SNP4 SNP5

Causal SNP Proxy SNP

. Intronic variation (rs2237892) in a new locus (KCNQ1) was strongly associated with T2D in Asian (OR: 1.26-1.42, 10-40< P-value < 10-12)

. The association with T2D was nominally replicated in European descent populations (DIAGRAM: P=0.01), with similar OR but lower risk allele frequency (5-7% in European, 28-40% in Asian)

Ethnicity and SNP allele frequency

Obesity, waist and BMI have a partially overlapping genetic architecture





4) Heterogeneity (ethnic heterogeneity, phenotyp heterogeneity)

5) Inheritance model (parent of origin effects, de novo mutations…)







5) Inheritance model

6) Subjective interpretation of data


Is this glass half-full or half-empty?

Subjective interpretation of data






5) Inheritance model

6) Subjective interpretation of data


Guidelines for proper discovery and replication association study designs

Enough time for the Episode 3 of the saga?

III. Guidelines for proper discovery and replication association study designs

Discovery

1) Study designs


N

ObeseLean

Body mass index

General population

Gene discovery study designs

1) Case control studies from extremes of the BMI tails

2) Quantitative trait studies in the whole population

Correlation genotype / trait at a genetic locus

Best approach (GIANT / GIANT extreme): BMI study in the whole population + analysis of the extremes of the BMI tails (genetic variance, effect size…)

Berndt et al., Nat Genet 2013


3) Family-based association studies: allele transmission from parents to affected offsprings (imprinting, haplotypes….)

4) Cohort studies: correlation of a genotype with an incident disease event (gold standard)


N

ObeseLean

Body mass index

General population

Normal weight

5) The case control case design: discovery of gene variants associated with leanness or with obesity (applications in drug design)

exp(Effect)

Stu

dy

Re

fere

nce

0.03 0.10 0.32 1.00 3.16 10.00 31.62

French adults

French children

Italian children

Swiss adults

Ohshiro et al, 1999

Farooqi et al, 2000

Jacobson et al, 2002

Jacobson et al, 2002

Miraglia del Giudice et al, 2002

Hinney et al, 2003

Marti et al, 2003

Valli-Jaakola et al, 2004

Santini et al, 2004

Buono et al, 2005

Larsen et al, 2005

Summary

16 cohorts:5964 control and 6370 obese patients

OR = 0.53, p-value = 4.26.10-5

The gain-of-function V103I and I251L variants in The gain-of-function V103I and I251L variants in MC4R are associated with leanness MC4R are associated with leanness

-Meta-analysis in 39,879 subjects confirms an obesity-protective role of the V103I polymorphism (OR = 0.80; p-value = 0.002)

-V103I et I251L are infrequent (0.41-2.24%) and induce a gain of function effect on the melanocorin 4 receptor (Xiang et al., Biochemistry 2006)

Stutzmann et al., HMG 2007


6) Clinical trials, interventional studies: correlation of a genotype with response to intervention or treatment (lifestyle intervention, drug, surgery, smoking cessation, antipsychotic drug administration….)

III. III. Guidelines for proper discovery and replication Guidelines for proper discovery and replication association study designsassociation study designs

DiscoveryDiscovery

1)1) Study designsStudy designs

2)2) PhenotypePhenotype


How to chose a relevant obesity phenotype?

Haworth et al., Obesity 2008, Almgren et al., Diabetologia 2011

Heritability for BMI:

-h² = 0.48 at age 4 y.

-h² = 0.78 at age 11 y.

Heritability for type 2 diabetes:

-h² = 0.69 (onset < 60 y.)

-h² = 0.31 (onset < 75 y.)

How to chose a relevant obesity phenotype?

-clinically and biologically relevant

-easy and inexpensive to measure

-relevant in diverse ethnicities

-minimal measurement error

-minimal misclassification and reporting biases

value of BMI to estimate the degree of adiposity questionable

body fat content, body adiposity index are more relevant

. Genome-wide association study for % fat mass in 36,000 subjects, replication of the best hits in 39,000 subjects

. Three % fat mass-associated loci : FTO, IRS1, SPRY2

. Only one locus (FTO) out of three has been conclusively associated with BMI body mass index in literature

Kilpelainen et al., Nat Genet 2011


DiscoveryDiscovery



3)3) Gene identification strategiesGene identification strategies


Gene identification strategies

CANDIDATE GENE APPROACHAGNOSTIC APPROACH

-highly successful

-novel disease causing mechanisms

-significance thresholds

-lack of biological relevance

-moderately successful

-previously known mechanisms

-strong selection criteria needed

-biological relevance

HIGH-THROUGHPOUT CANDIDATE GENE APPROACH

(pathway, expression, evolution…)


DiscoveryDiscovery




4)4) Genotyping methodology and quality control proceduresGenotyping methodology and quality control procedures


Genotyping methodology and quality control

-exclusion of low quality DNA (cases controls)

-highly reliable genotyping technology

-genotyping call rate (> 95%)\

-Hardy-Weinberg equilibrium (P > 0.005)\

-double genotyping concordance rate (> 99%)

-MAF comparison in public databases

-confirmation by a second method

-association of SNPs in linkage disequilibrium

-accurate experiments / data management and reporting (bar coding, automated processes, internal controls, flow charts….)

-sex inconsistencies, hidden relatedness, ethnic outliers….


DiscoveryDiscovery




4) Genotyping methodology and quality control procedures4) Genotyping methodology and quality control procedures

5) Statistical analysis5) Statistical analysis


Statistical analysis

-power calculation

-limited number of hypotheses tested

-multiple testing (FDR, Bonferroni…)

-adjustment for confounding factors

-caution with subgroup analyses

-best fitting inheritance model

-conditional analyses


DiscoveryDiscovery




4) Genotyping methodology and quality control procedures4) Genotyping methodology and quality control procedures

5) Statistical analysis5) Statistical analysis

6) Population stratification6) Population stratification


Population stratification

-correction for self-reported ethnicity

-exclusion of ethnic outliers

-genomic control (Ancestry Informative Markers)

-family-based association tests

-case control matched for age, sex, geography…


ReplicationReplication

1)1) Systematic replication and reporting of promising associationsSystematic replication and reporting of promising associations





2)2) Statistical power (Winner’s curse effect)Statistical power (Winner’s curse effect)





2)2) Statistical powerStatistical power

3)3) HeterogeneityHeterogeneity


How to lower heterogeneity in replication studies?

-same ethnicity / country

-same study design

-same ascertainment criteria

-same phenotype

-same genetic markers

-same age window, same sex ratio

-same inheritance model

-same statistical analysis

-same covariate adjustments






4)4) Meta-analysesMeta-analyses







4)4) Meta-analysesMeta-analyses

5)5) Additional studiesAdditional studies


Additional studies

-worldwide contribution

-extension to different study designs, ascertainment criteria

-association with obesity endophenotypes

-gene x environment interactions

-fine-mapping, causative gene variants

-functional experiments

-biological insights

FTO in 2007: ‘gene of unknown function in an unknown pathway’

2014: > 740 articles published

19971997: first identification of a monogenic obesity gene (LEP)

20072007: first gene variant in FTO conclusively associated with obesity

20122012: 40 monogenic (syndromic / non-syndromic) obesity genes, > 100 common gene variants conclusively associated with polygenic obesity

ANY QUESTIONS?ANY QUESTIONS?

The French fair-play!

analytical challenges in genetic association studies david meyre, associate professor, mcmaster...

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