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10.1515/sagmb-2014-0050 http://scihub22266oqcxt.onion/10.1515/sagmb-2014-0050 C4788466!4788466!26426896     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Appl+Genet+Mol+Biol 2015 ; 14 (5): 443-64 Nephropedia Template TP {{tp|p=26426896|t=2015. A Robust Distribution-Free Test for Genetic Association Studies of Quantitative Traits |pdf=|usr=}}{{26426896}} gab.com Text A Robust Distribution-Free Test for Genetic Association Studies of Quantitative Traits JO: Stat Appl Genet Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=26426896 #FOAvip In association studies of quantitative traits, the association of each genetic marker with the trait of interest is typically tested using the F-test assuming an additive genetic model. In practice, the true model is rarely known, and specifying an incorrect model can lead to a loss of power. For case-control studies, the maximum of test statistics optimal for additive, dominant, and recessive models has been shown to be robust to model mis-specification. The approach has later been extended to quantitative traits. However, the existing procedures assume that the trait is normally distributed and may not maintain correct type-I error rates and can also have reduced power when the assumption of normality is violated. Here, we introduce a maximum (MAX3) test that is based on ranks and is therefore distribution-free. We examine the behavior of the proposed method using a Monte-Carlo simulation with both normal and non-normal data and compare the results to the usual parametric procedures and other nonparametric alternatives. We show that the rank-based maximum test has favorable properties relative to other tests, especially in the case of symmetric distributions with heavy tails. We illustrate the method with data from a real association study of symmetric dimethylarginine (SDMA). Twit Text FOAVip A Robust Distribution-Free Test for Genetic Association Studies of Quantitative Traits ????Stat Appl Genet Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=26426896 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26426896 #FOAvip English Wikipedia <ref>{{Cite pmid|26426896}}</ref> A Robust Distribution-Free Test for Genetic Association Studies of Quantitative Traits #MMPMID26426896 Kozlitina J; Schucany WR Stat Appl Genet Mol Biol 2015[Nov]; 14 (5): 443-64 PMID26426896show ga In association studies of quantitative traits, the association of each genetic marker with the trait of interest is typically tested using the F-test assuming an additive genetic model. In practice, the true model is rarely known, and specifying an incorrect model can lead to a loss of power. For case-control studies, the maximum of test statistics optimal for additive, dominant, and recessive models has been shown to be robust to model mis-specification. The approach has later been extended to quantitative traits. However, the existing procedures assume that the trait is normally distributed and may not maintain correct type-I error rates and can also have reduced power when the assumption of normality is violated. Here, we introduce a maximum (MAX3) test that is based on ranks and is therefore distribution-free. We examine the behavior of the proposed method using a Monte-Carlo simulation with both normal and non-normal data and compare the results to the usual parametric procedures and other nonparametric alternatives. We show that the rank-based maximum test has favorable properties relative to other tests, especially in the case of symmetric distributions with heavy tails. We illustrate the method with data from a real association study of symmetric dimethylarginine (SDMA). äA Robust Distribution-Free Test for Genetic Association Studies of Qua(epmc).pdf DeepDyve Pubget Overpricing