Meta-Analysis of GABRIEL GWAS Asthma & IgE

1. Meta-Analysis of GABRIEL GWASAsthma & IgE F. Demenais, M. Farrall, D. Strachan GABRIEL Statistical Group

2. GABRIEL Phase I GWAS GWAS (Illumina 300K) of UK & German data → 17q21 locus (ORMDL3) associated with asthma Moffat et al, Nature, 2007 Replication of this association by several studies Genetic heterogeneity at 17q21 locus (French EGEA data) → Effect of 17q21 variants restricted to early-onset asthma and enhanced by early-life exposure to ETS Bouzigon et al, New Engl J Med, 2008

3. Aim of Phase II Gabriel GWAS To identify associations of genetic variants with: - susceptibility to asthma (childhood onset, adult onset, industrial) - total IgE levels across populations of European ancestry using Illumina Human 610-Quad beadchip by conducting a meta-analysis of all studies

4. DATA AVAILABLE for Phase II GWAS • Most datasets are cases/controls • A few datasets include families: • MRC (UK), EGEA (French), Canadian, Russian, GSK…

5. GWAS Phase II DATA

6. Phase II GWAS: Overall Strategy Genotyping at CNG (Y. Gut, M. Lathrop, Evry, France) Using Illumina Human 610-Quad beadchip Initial QC processing at CNG (S.Heath, CNG) • % genotype calls - by individuals (< 95%: individuals excluded) • Relationship analysis to confirm known & identify cryptic relationships • Sex checks based on X-chromosome SNPs • Principal components analysis to identify cryptic non-European ancestry Analysis study by study (M Farrall, Oxford) From Phenotypic data (each group) & Genotypic Data (CNG) Meta-analysis of all studies: Phase II + Phase I (imputation) Asthma (F Demenais, Paris) IgE (D Strachan, London) childhood onset, adult onset, all controls & cases separately industrial asthma

7. Phenotypes • Asthma : • Cases : doctor-diagnosed asthma or self-reported • + age onset of asthma • Controls: unaffecteds (not selected as « hypernormal » • and may include other forms of wheezing) • → Childhood Onset / Adult Onset Asthma using a cutoff of 16yrs • Controls drawn at random for childhood onset/ adult onset cases • IgE (log10) • IgE wadjusted on sex and age-at-measurement • by study and by case-control status

8. Method used for Study by Study Analysis • Single SNP analysis based on logistic regression models (linear regression for IgE) allowing for familial clustering using STATA • Different models considered: • - additive model (1df) • - additive and non-additive effects ( 2 x 1 df) • - genotype association model (2 df) • Population Stratification: • Eigenvectors from PCA included in regression model • PCA uses HapMap data + CNG data (European controls)

9. Population stratificationPCA on European controls from French National Genotyping CenterHeath et al, Eur J Hum Genet, 2008

10. Meta-Analysis for Asthma & IgE • From the study-by-study analysis, tables generated • including for each SNP: • QC metrics (MAF, SNP Call Rate, HW..) • Number of cases / controls by genotype • Regression coefficients & Standard errors • Various test statistics QC Filtering based on MAF (1% or 5%), SNP Call Rate (≥ 97%) HW (p > 10-4) Meta-analysis using different methods

11. Methods used for Primary Meta-Analysis • • Fixed-effect (inverse variance weighted) models • assumes that observed effects are estimates of a single effect • average effect computed by weighting each study’s log OR according to the inverse of their sampling variance • →Test of homogeneity for SNP effect across studies • using Cochran Q test • •Random-effect models (DerSimonian & Laird, 1986) • allows for effects to vary across studies • variance = between study variation + intra-study variation • preferred if # of study-specific estimates ≥ 5

12. Fixed vs Random effect ModelsExample: Type 2 Diabetes (Ionnadis et al, PLoS one, 2007)Meta-analysis of FUSION, DGI, WTCC

13. Other Methods of Meta-analysis: Meta-RegressionBag & Nikolopoulos, Stat Appl Mol Biol, 2007 Logit (pij) = i + 2zi2 + 3zi3if genotype effect cst between studies Logit (pij) = i + 2zi2 + 3zi3 + i2 izi2 +i3 izi3if gentoypexstudy int → Test for heterogeneity between studies using Multivariate Wald test Possible to include random effect + various covariates

14. Other Approaches of Meta-Analysis ● Combining p-values or Z scores ● Local Score method (Guedj et al, 2006; Aschard et al, 2007) can detect aggregation of association signals flexible approach which can use any test statistic

16. Outcome of Meta-Analysis Identify Top SNPs (genome-wide significant) Phase III Gabriel Genotype top SNPs in 40 000 individuals

17. Gene-Gene Interactions

18. Various Methods to investigate GxG • Regression-based methods (one stage, 2 stages…) • Bayesian based approaches • Data Reduction based-methods / Machine Learning ‘Combinatorial Partitioning Method (CPM), MDR) - Pattern recognition models (neural networks) • Combination of test statistics (meta-statistics)  Gabriel provides opportunity to compare these methods by pooling data or in the context of meta-analysis

19. Gene-Environment Interactions

20. 2 Step-Analysis to identify genes involved in GxEMurcray et al, Am J Epidemiol, 2008 Step 1: Screening test: case only analysis (combined case/control sample ) For each of N SNPs: LR Test for association between G and E → Select m SNPs with P < 1 Step 2 : Case- Control analysis LR Test for GxE applied to m SNPs selected at step 1 →Significance based on P <  /m Comparison with classical one-step approach applied to case-controls →Significance based on P <  /N

21. Power for one-step and two-step analyses to detect GxE for varying levels of interaction effect size 10,000 markers and 500 cases/500 controls

22. GABRIEL Working Groups GW search for G X smoking in asthma M Boezen, D Postma, The Netherlands Childood Asthma(M Kabesch)& Adult Asthma(D. Jarvis) to summarize data available in each study (phenotypes, environment) Main areas of interest for collaborations: Phenotypes Environmental exposures : GxE Pathways: GxG Other types of variation: CNVs Methodological issues • New opportunities that are going to emerge from the AllerGen meeting