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10.1371/journal.pone.0193256 http://scihub22266oqcxt.onion/10.1371/journal.pone.0193256 C5832233!5832233!29494641     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29494641&cmd=llinks): Failed to open stream: HTTP request failed! 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Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies |pdf=|usr=}}{{29494641}} gab.com Text Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29494641 #FOAvip Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research. Twit Text FOAVip Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29494641 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29494641 #FOAvip English Wikipedia <ref>{{Cite pmid|29494641}}</ref> Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies #MMPMID29494641 Zhu Z; Anttila V; Smoller JW; Lee PH PLoS One 2018[]; 13 (3): � PMID29494641show ga Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research. �Statistical power and utility of meta-analysis methods for cross-pheno(epmc).pdf DeepDyve Pubget Overpricing