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10.1534/genetics.114.169573 http://scihub22266oqcxt.onion/10.1534/genetics.114.169573 C4256758!4256758!25271303     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genetics 2014 ; 198 (4): 1377-93 Nephropedia Template TP {{tp|p=25271303|t=2014. Mapping eQTL Networks with Mixed Graphical Markov Models |pdf=|usr=}}{{25271303}} gab.com Text Mapping eQTL Networks with Mixed Graphical Markov Models JO: Genetics http://www.kidney.de/pget.php?&tw=1&pmid=25271303 #FOAvip Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene?gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. Twit Text FOAVip Mapping eQTL Networks with Mixed Graphical Markov Models ????Genetics http://www.kidney.de/pget.php?&tw=1&pmid=25271303 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25271303 #FOAvip English Wikipedia <ref>{{Cite pmid|25271303}}</ref> Mapping eQTL Networks with Mixed Graphical Markov Models #MMPMID25271303 Tur I; Roverato A; Castelo R Genetics 2014[Dec]; 198 (4): 1377-93 PMID25271303show ga Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene?gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. äMapping eQTL Networks with Mixed Graphical Markov Models (epmc).pdf DeepDyve Pubget Overpricing