Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits

• Type of Material: Article
• Authors: Karen N. Conneely, Emory University; Michael Boehnke, University of Michigan
• Language: English
• Date: 2010-11
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2010 Wiley-Liss, Inc.
• Final Published Version (URL): http://onlinelibrary.wiley.com/doi/10.1002/gepi.20538/abstract
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 34
• Issue: 7
• Start Page: 739
• End Page: 746
• Grant/Funding Information: This research was supported by National Institutes of Health (NIH) grant HG000376 (to M.B.).
• Keywords: association study; meta-analysis; multiple testing; SNPs
• Research Categories: Biology, Genetics; Biology, Biostatistics; Health Sciences, Epidemiology

Abstract

Meta-analysis has become a key component of well-designed genetic association studies due to the boost in statistical power achieved by combining results across multiple samples of individuals and the need to validate observed associations in independent studies. Meta-analyses of genetic association studies based on multiple SNPs and traits are subject to the same multiple testing issues as single-sample studies, but it is often difficult to adjust accurately for the multiple tests. Procedures such as Bonferroni may control the type I error rate but will generally provide an overly harsh correction if SNPs or traits are correlated. Depending on study design, availability of individual-level data, and computational requirements, permutation testing may not be feasible in a meta-analysis framework. In this paper we present methods for adjusting for multiple correlated tests under several study designs commonly employed in meta-analyses of genetic association tests. Our methods are applicable to both prospective meta-analyses in which several samples of individuals are analyzed with the intent to combine results, and retrospective meta-analyses, in which results from published studies are combined, including situations in which 1) individual-level data are unavailable, and 2) different sets of SNPs are genotyped in different studies due to random missingness or two-stage design. We show through simulation that our methods accurately control the rate of type I error and achieve improved power over multiple testing adjustments that do not account for correlation between SNPs or traits.