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10.1038/hdy.2014.57 http://scihub22266oqcxt.onion/10.1038/hdy.2014.57 C4274615!4274615 !24984606     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 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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24984606 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Heredity+(Edinb) 2014 ; 113 (6 ): 526-32 Nephropedia Template TP {{tp|p=24984606 |t=2014. Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix |pdf=|usr=}}{{24984606 }} gab.com Text Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix JO: Heredity (Edinb) http://www.kidney.de/pget.php?&tw=1&pmid=24984606 #FOAvip Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle. Twit Text FOAVip Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix ????Heredity (Edinb) http://www.kidney.de/pget.php?&tw=1&pmid=24984606 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24984606 #FOAvip English Wikipedia <ref>{{Cite pmid|24984606 }}</ref> Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix #MMPMID24984606 Gao H ; Wu Y ; Zhang T ; Wu Y ; Jiang L ; Zhan J ; Li J ; Yang R Heredity (Edinb) 2014[Dec]; 113 (6 ): 526-32 PMID24984606 show ga Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle. |*Genetic Association Studies [MESH] |*Models, Genetic [MESH] |*Principal Component Analysis [MESH] |*Quantitative Trait, Heritable [MESH] |Algorithms [MESH] |Animals [MESH] |Cattle/*genetics [MESH] |Meat [MESH] |Multivariate Analysis [MESH] |Phenotype [MESH]Multiple-trait genome-wide association study based on principal compon(epmc).pdf DeepDyve Pubget Overpricing