A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping
Year: 2016
Type: article
Abstract: Meta-analysis strategies have become critical to augment power of genome-wide association studies (GWAS). To reduce genotyping or sequencing cost, many studies today utilize shared controls, and these individuals can inadvertently overlap among multiple studies. If these overlapping individuals are not taken into account in meta-analysis, they can induce spurious associations. In this article, we propose a general framework for adjusting association statistics to account for overlapping subjects within a meta-analysis. The key idea of our method is to transform the covariance structure of the data, so it can be used in downstream analyses. As a result, the strategy is very flexible and allows a wide range of meta-analysis methods, such as the random effects model, to account for overlapping subjects. Using simulations and real datasets, we demonstrate that our method has utility in meta-analyses of GWAS, as well as in a multi-tissue mouse expression quantitative trait loci (eQTL) study where our method increases the number of discovered eQTL by up to 19% compared with existing methods.
Source: Human Molecular Genetics
DOI: 10.1093/hmg/ddw049
Authors: Buhm Han, Dat Duong, Jae Hoon Sul, Paul I. W. de Bakker, Eleazar Eskin, Soumya Raychaudhuri
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Asan Institute for Life Sciences, Asan Medical Center, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Human Genome Research Institute, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Awards: G1104670545, Defining the influence of RA genetic susceptibility factors on T cell antigen specificity and functional state, Substance Abuse & Behavioral Disinhibition: Integrating Genes & Environment, RA-SLE Molecular Deconstruction Leadership Center, III: Small: Inference of Causal Regulatory Relationships from Genetic Studies, G1972444143, G2355553396, Lipid and Lipoprotein Metabolism in Atherosclerosis, III: Medium: Private Identification of Relatives and Private GWAS: First Steps in the New Field of CryptoGenomics, G4440407748, Discovering the Genetic Basis of Hypertension, Collaborative Research: SEIII: Estimating Haplotype Frequencies, G491034554, Collaborative Research: SEIII: Estimating Haplotype Frequencies, Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays, G7329126573, G9000061537, G9000065387, G9000097946, G9000222054, G9000243492, G9000249559, Training Grant in Genomic Analysis and Interpretation, G932915660
OpenAlex ID: W1955623309