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Hum Mol Genet. 2016 May 1;25(9):1857-66. doi: 10.1093/hmg/ddw049. Epub 2016 Feb 21.

A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping.

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Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Republic of Korea,
Computer Science Department.
Department of Psychiatry and Biobehavioral Sciences, Semel Center for Informatics and Personalized Genomics, University of California, Los Angeles, CA 90095, USA.
Julius Center for Health Sciences and Primary Care, Department of Medical Genetics, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands.
Computer Science Department, Department of Human Genetics.
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.


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.

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