Sample overlap in genome-wide association meta-analysis: practical comparisons across significance thresholds

Abstract

Meta-analysis is widely used in genome-wide association studies (GWAS) to combine evidence across studies without requiring individual-level data. However, as both the number of studies included in meta-analyses and their sample sizes continue to grow, the same participant may be included in more than one study. Such sample overlap induces dependence across studies and violates the independence assumptions underlying standard meta-analysis methods. HostSeq is a Canadian initiative in which 15 clinical and epidemiological studies were assembled with DNA samples from approximately 10,000 Canadians infected by the SARS-CoV-2 virus. We observed duplicate samples across different participating studies. This observation raises an important practical question: what is the impact of sample overlap on association results, and when should such overlap be a concern? Motivated by this real problem, we sought to compare five meta-analysis methods and evaluate their performance against a reference mega-analysis in which the true sample overlap is known. We show that when the degree of sample overlap is relatively small, its impact on association results is minimal, even at the genome-wide significance threshold of = 5*10 -8 . We demonstrate these findings through extensive simulation studies and an application to overlapping cohorts within HostSeq.