Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1534/genetics.114.165704 http://scihub22266oqcxt.onion/10.1534/genetics.114.165704 C4174925!4174925 !25236454     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25236454 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Genetics 2014 ; 198 (1 ): 129-37 Nephropedia Template TP {{tp|p=25236454 |t=2014. Multiple quantitative trait analysis using bayesian networks |pdf=|usr=}}{{25236454 }} gab.com Text Multiple quantitative trait analysis using bayesian networks JO: Genetics http://www.kidney.de/pget.php?&tw=1&pmid=25236454 #FOAvip Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness. Twit Text FOAVip Multiple quantitative trait analysis using bayesian networks ????Genetics http://www.kidney.de/pget.php?&tw=1&pmid=25236454 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25236454 #FOAvip English Wikipedia <ref>{{Cite pmid|25236454 }}</ref> Multiple quantitative trait analysis using bayesian networks #MMPMID25236454 Scutari M ; Howell P ; Balding DJ ; Mackay I Genetics 2014[Sep]; 198 (1 ): 129-37 PMID25236454 show ga Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness. |*Models, Genetic [MESH] |*Quantitative Trait, Heritable [MESH] |Bayes Theorem [MESH] |Genome, Plant [MESH]Multiple quantitative trait analysis using bayesian networks (epmc).pdf DeepDyve Pubget Overpricing