Presentation is loading. Please wait.

Presentation is loading. Please wait.

AllerGen / Vancouver - 01/03//2009 Meta-Analysis of GABRIEL GWAS Asthma & IgE F. Demenais, M. Farrall, D. Strachan GABRIEL Statistical Group.

Similar presentations

Presentation on theme: "AllerGen / Vancouver - 01/03//2009 Meta-Analysis of GABRIEL GWAS Asthma & IgE F. Demenais, M. Farrall, D. Strachan GABRIEL Statistical Group."— Presentation transcript:

1 AllerGen / Vancouver - 01/03//2009 Meta-Analysis of GABRIEL GWAS Asthma & IgE F. Demenais, M. Farrall, D. Strachan GABRIEL Statistical Group

2 AllerGen / Vancouver - 01/03//2009 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 AllerGen / Vancouver - 01/03//2009 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 AllerGen – Vancouver – 01/03/2009 DATA AVAILABLE for Phase II GWAS - Most datasets are cases/controls - A few datasets include families: MRC (UK), EGEA (French), Canadian, Russian, GSK… Childhood onset Asthma 20 012 subjects Adult onset Asthma Industrial Asthma1356 subjects


6 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 Phase II GWAS: Overall Strategy 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 AllerGen – Vancouver – 01/03/2009 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 (log 10 ) IgE wadjusted on sex and age-at-measurement by study and by case-control status

8 AllerGen – Vancouver – 01/03/2009 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 AllerGen – Vancouver – 01/03/2009 Population stratification PCA on European controls from French National Genotyping Center Heath et al, Eur J Hum Genet, 2008

10 AllerGen – Vancouver – 01/03/2009 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 AllerGen – Vancouver – 01/03/2009 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 AllerGen / Vancouver - 01/03//2009 Fixed vs Random effect Models Example: Type 2 Diabetes (Ionnadis et al, PLoS one, 2007) Meta-analysis of FUSION, DGI, WTCC GeneSNPQ (p)I 2 (95% CI) Random pFixed p rs9300039 0.01975% (0-90)0.0154.3x10-7 FTO rs8050136 0.01377% (0-91)0.0151.3x10-12 CDKAL1 rs10946398 0.1646% (0-84)3.2x10-64.1x10-11 PPARG rs1801282 0.1547% (0-84)0.00031.7x10-6 CDKN2Brs5643980.480% (0-73)1.2x10-7 HHEXrs50154800.450% (0-73)5.7x10-10

13 AllerGen / Vancouver - 01/03//2009 Other Methods of Meta-analysis: Meta-Regression Bag & Nikolopoulos, Stat Appl Mol Biol, 2007 Study i = 1, 2..k Cases yij = 1 Controls yij =0 Genotype j =1,2,..r 138721 152782 16223 244381 227452 211173 Logit (pij) =  i +  2 z i2 +  3 z i3 if genotype effect cst between studies Logit (pij) =  i +  2 z i2 +  3 z i3 +  i2  i z i2 +  i3  i z i3 if gentoypexstudy int → Test for heterogeneity between studies using Multivariate Wald test Possible to include random effect + various covariates

14 AllerGen / Vancouver - 01/03//2009 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 AllerGen / Vancouver - 01/03//2009 Gene-Gene Interactions

18 AllerGen / Vancouver - 01/03//2009 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 AllerGen / Vancouver - 01/03//2009 Gene-Environment Interactions

20 AllerGen / Vancouver - 01/03//2009 2 Step-Analysis to identify genes involved in GxE Murcray 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 AllerGen / Vancouver - 01/03//2009 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 AllerGen / Vancouver - 01/03//2009 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

Download ppt "AllerGen / Vancouver - 01/03//2009 Meta-Analysis of GABRIEL GWAS Asthma & IgE F. Demenais, M. Farrall, D. Strachan GABRIEL Statistical Group."

Similar presentations

Ads by Google