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10.1016/j.ajhg.2016.01.017 http://scihub22266oqcxt.onion/10.1016/j.ajhg.2016.01.017 C4800053!4800053!26942286     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Hum+Genet 2016 ; 98 (3): 525-40 Nephropedia Template TP {{tp|p=26942286|t=2016. A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants |pdf=|usr=}}{{26942286}} gab.com Text A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants JO: Am J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=26942286 #FOAvip Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. Twit Text FOAVip A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants ????Am J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=26942286 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26942286 #FOAvip English Wikipedia <ref>{{Cite pmid|26942286}}</ref> A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants #MMPMID26942286 Broadaway K; Cutler D; Duncan R; Moore J; Ware E; Jhun M; Bielak L; Zhao W; Smith J; Peyser P; Kardia S; Ghosh D; Epstein M Am J Hum Genet 2016[Mar]; 98 (3): 525-40 PMID26942286show ga Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. äA Statistical Approach for Testing Cross-Phenotype Effects of Rare Var(epmc).pdf DeepDyve Pubget Overpricing