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10.3390/genes11111286 http://scihub22266oqcxt.onion/10.3390/genes11111286 33138126!7692801!33138126     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genes+(Basel) 2020 ; 11 (11): ä Nephropedia Template TP {{tp|p=33138126|t=2020. Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study |pdf=|usr=}}{{33138126}} gab.com Text Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study JO: Genes (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33138126 #FOAvip In genome-wide association studies, linear mixed models (LMMs) have been widely used to explore the molecular mechanism of complex traits. However, typical association approaches suffer from several important drawbacks: estimation of variance components in LMMs with large scale individuals is computationally slow; single-locus model is unsatisfactory to handle complex confounding and causes loss of statistical power. To address these issues, we propose an efficient two-stage method based on hybrid of restricted and penalized maximum likelihood, named HRePML. Firstly, we performed restricted maximum likelihood (REML) on single-locus LMM to remove unrelated markers, where spectral decomposition on covariance matrix was used to fast estimate variance components. Secondly, we carried out penalized maximum likelihood (PML) on multi-locus LMM for markers with reasonably large effects. To validate the effectiveness of HRePML, we conducted a series of simulation studies and real data analyses. As a result, our method always had the highest average statistical power compared with multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA). More importantly, HRePML can provide higher accuracy estimation of marker effects. HRePML also identifies 41 previous reported genes associated with development traits in Arabidopsis, which is more than was detected by the other methods. Twit Text FOAVip Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study ????Genes (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33138126 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33138126 #FOAvip English Wikipedia <ref>{{Cite pmid|33138126}}</ref> Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study #MMPMID33138126 Ren W; Liang Z; He S; Xiao J Genes (Basel) 2020[Oct]; 11 (11): ä PMID33138126show ga In genome-wide association studies, linear mixed models (LMMs) have been widely used to explore the molecular mechanism of complex traits. However, typical association approaches suffer from several important drawbacks: estimation of variance components in LMMs with large scale individuals is computationally slow; single-locus model is unsatisfactory to handle complex confounding and causes loss of statistical power. To address these issues, we propose an efficient two-stage method based on hybrid of restricted and penalized maximum likelihood, named HRePML. Firstly, we performed restricted maximum likelihood (REML) on single-locus LMM to remove unrelated markers, where spectral decomposition on covariance matrix was used to fast estimate variance components. Secondly, we carried out penalized maximum likelihood (PML) on multi-locus LMM for markers with reasonably large effects. To validate the effectiveness of HRePML, we conducted a series of simulation studies and real data analyses. As a result, our method always had the highest average statistical power compared with multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA). More importantly, HRePML can provide higher accuracy estimation of marker effects. HRePML also identifies 41 previous reported genes associated with development traits in Arabidopsis, which is more than was detected by the other methods. |Algorithms[MESH] |Arabidopsis/genetics/growth & development[MESH] |Computer Simulation[MESH] |Databases, Genetic[MESH] |Genetic Markers[MESH] |Genome-Wide Association Study/*methods/statistics & numerical data[MESH] |Humans[MESH] |Likelihood Functions[MESH] |Linear Models[MESH] |Models, Genetic[MESH] |Polymorphism, Single Nucleotide[MESH] |Quantitative Trait, Heritable[MESH]Hybrid of Restricted and Penalized Maximum Likelihood Method for Effic(epmc).pdf DeepDyve Pubget Overpricing