Meta-analysis for GWAS BST775 Fall 2013

DEMO

Replication Criteria for a successful GWAS P<5*10 -8 Replicate in independent cohorts Fine mapping

Statistical power of detection in GWAS for variants that explain 0.1– 0.5% of the variation at a type I error rate of 5 ×

Figure 2. Plots of power (solid lines) and coverage (dotted line) for increasing sample sizes of cases and controls (x-axis). Spencer CCA, Su Z, Donnelly P, Marchini J (2009) Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip. PLoS Genet 5(5): e doi: /journal.pgen

Meta vs Mega:How to combine Multiple GWAS for a same trait Meta-analysis Conduct analysis at individual cohorts Only share summary statistics for each variants Combine evidences using standard meta- analysis Mega-analysis Collect genotype and phenotype information from all participating cohorts. Conduct a single analysis in the pooled sample, adjusting individual cohorts

Why meta-analysis Achieving larger sample size Without sharing privacy geno/pheno data No power gain with pooled sample Adjusting heterogeneity among cohorts

Popularity of meta-analysis Most genetic risk variants discovered in the past few years have come from large-scale meta-analyses. Several hundreds GWAS meta published June 15, 2012: 139 meta each n>10,000 Largest, ~ n=500,000 for height (rumor)

Popularity of meta-analysis

Meta-analysis stages

Statistical approaches P-value Z-score Fixed effects Random effects Bayesian approaches Multivariate approaches

Fisher’s P-value The simplest genome-wide association study (GWAS) meta-analysis approach is to combine P values using Fisher's method. The formula for the statistic is where Pi is the P value for the i th study, and k is the number of studies in the meta-analysis. Under the null hypothesis, X 2 follows a χ 2 distribution with 2k degrees of freedom.

Stouffer’s Z-score The Z scores meta-analysis can be implemented using the equation where w i is the square root of sample size of the i th study and where Φ is the standard normal cumulative distribution function.

Fixed effects For fixed effects models, inverse variance weighting is widely used. The weighted average of the effect sizes can be calculated as where is the i th study normalized effect (for example, logarithm of odds ratio or β-coefficient for a logistic regression for a binary phenotype or mean difference or standardized mean difference for a continuous phenotype), and w i is the reciprocal of the estimated variance of the effect study.

Dealing with heterogeneity

Meta-analysis of multiple correlated variants An approximate conditional approach using summary data from a meta- analysis and LD correlations between SNPs estimated from a reference sample (here, a subset of the meta-analysis sample) was successfully applied in a meta-analysis for height and body mass index. This method identified 36 loci with multiple associated variants for height adding 49 additional SNPs on top of the already known variants. In this approach, a genome-wide stepwise selection procedure selects SNPs on the basis of conditional P values and estimates the joint effects of all selected SNPs after the model has been optimized. The method assumes that the reference sample is from the same population as the samples from which the genotype–phenotype associations are estimated (that is, that linkage correlation estimates in the reference sample are unbiased).

References Evangelou E, Ioannidis JP: Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 2013, 14(6): Yang J, et al: Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 2012, 44(4): , S Lin DY, Zeng D: Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genet Epidemiol 2010, 34(1):60-66.