On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs

TL;DR

Combining data from multiple GWAS improves the detection power for disease-associated SNPs, with meta-analytic methods outperforming global tests in practical scenarios.

Contribution

This paper compares various methods for combining GWAS data, highlighting the superior performance of meta-analytic approaches over traditional global tests.

Findings

Meta-analytic methods have higher power and detection probability.

Single degree of freedom tests outperform global chi-square and Fisher's methods.

Meta-analytic approaches are more effective in practical GWAS data combinations.

Abstract

Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies. We compared several procedures to combine GWA study data both in terms of the power to detect a disease-associated SNP while controlling the genome-wide significance level, and in terms of the detection probability ($\mathit{DP}$). The $\mathit{DP}$ is the probability that a particular disease-associated SNP will be among the $T$ most promising SNPs selected on the basis of low $p$-values. We studied both fixed effects and random effects models in which associations varied across studies. In settings of practical relevance, meta-analytic approaches that focus on a single degree of freedom had higher power and $\mathit{DP}$ than global tests such as summing chi-square test-statistics across studies, Fisher's combination of $p$-values, and forming a combined list of the best SNPs from within each study.