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10.1093/hmg/ddw049 http://scihub22266oqcxt.onion/10.1093/hmg/ddw049 C4986332!4986332 !26908615     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26908615 &cmd=llinks): Failed to open stream: HTTP request failed! 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A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping |pdf=|usr=}}{{26908615 }} gab.com Text A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping JO: Hum Mol Genet http://www.kidney.de/pget.php?&tw=1&pmid=26908615 #FOAvip 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. Twit Text FOAVip A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping ????Hum Mol Genet http://www.kidney.de/pget.php?&tw=1&pmid=26908615 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26908615 #FOAvip English Wikipedia <ref>{{Cite pmid|26908615 }}</ref> A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping #MMPMID26908615 Han B ; Duong D ; Sul JH ; de Bakker PI ; Eskin E ; Raychaudhuri S Hum Mol Genet 2016[May]; 25 (9 ): 1857-66 PMID26908615 show ga 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. |*Meta-Analysis as Topic [MESH] |Animals [MESH] |Case-Control Studies [MESH] |Disease/*genetics [MESH] |Gene Expression Profiling [MESH] |Genome-Wide Association Study/*methods [MESH] |Humans [MESH] |Mice [MESH] |Models, Theoretical [MESH] |Polymorphism, Single Nucleotide/*genetics [MESH]A general framework for meta-analyzing dependent studies with overlapp(epmc).pdf DeepDyve Pubget Overpricing