title: A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping.
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.
authors.affiliation: Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Republic of Korea, buhm.han@amc.seoul.kr.
authors.forename: Buhm
authors.initials: B
authors.lastname: Han
authors.affiliation: Computer Science Department.
authors.forename: Dat
authors.initials: D
authors.lastname: Duong
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, Semel Center for Informatics and Personalized Genomics, University of California, Los Angeles, CA 90095, USA.
authors.forename: Jae Hoon
authors.initials: JH
authors.lastname: Sul
authors.affiliation: Julius Center for Health Sciences and Primary Care, Department of Medical Genetics, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands.
authors.forename: Paul I W
authors.initials: PI
authors.lastname: de Bakker
authors.affiliation: Computer Science Department, Department of Human Genetics.
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
authors.affiliation: Division of Genetics, Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA and Faculty of Medical and Human Sciences, University of Manchester, Manchester M13 9PL, UK.
authors.forename: Soumya
authors.initials: S
authors.lastname: Raychaudhuri
mesh_terms: D000818:Animals; D016022:Case-Control Studies; D004194:Disease / Q000235:genetics*; D020869:Gene Expression Profiling; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015201:Meta-Analysis as Topic; D051379:Mice; D008962:Models, Theoretical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D040641:Quantitative Trait Loci / Q000235:genetics*