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10.1159/000360161 http://scihub22266oqcxt.onion/10.1159/000360161 C4115296!4115296!24969398     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24969398&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Hum+Hered 2014 ; 78 (1): 17-26 Nephropedia Template TP {{tp|p=24969398|t=2014. Analysis of Gene-Gene Interactions Using Gene-Trait Similarity Regression |pdf=|usr=}}{{24969398}} gab.com Text Analysis of Gene-Gene Interactions Using Gene-Trait Similarity Regression JO: Hum Hered http://www.kidney.de/pget.php?&tw=1&pmid=24969398 #FOAvip Objective: Gene-Gene interactions (GxG) are important to study because of their extensiveness in biological systems and their potential in explaining missing heritability of complex traits. In this work, we propose a new similarity-based test to assess GxG at gene level, which permits the study of epistasis at biologically functional units with amplified interaction signals. Methods: Under the framework of gene-trait similarity regression (SimReg), we propose a gene-based test for detecting gene-gene interactions. SimReg uses a regression model to correlate trait similarity with genotypic similarity across a gene. Unlike existing gene-level methods based on leading principal components (PCs), SimReg summarizes all information on genotypic variation within a gene and can be used to assess the joint/interactive effects of two genes as well as the effect of one gene conditional on another. Results: Using simulations and a real data application on warfarin study, we show that the SimReg GxG tests have satisfactory power and robustness under different genetic architecture when compared to existing gene-based interaction tests such as PC analysis or partial least squares (PLS). A genomewide association study with ~20,000 genes may be completed on a parallel computing system in 2 weeks. Twit Text FOAVip Analysis of Gene-Gene Interactions Using Gene-Trait Similarity Regression ????Hum Hered http://www.kidney.de/pget.php?&tw=1&pmid=24969398 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24969398 #FOAvip English Wikipedia <ref>{{Cite pmid|24969398}}</ref> Analysis of Gene-Gene Interactions Using Gene-Trait Similarity Regression #MMPMID24969398 Wang X; Epstein MP; Tzeng JY Hum Hered 2014[]; 78 (1): 17-26 PMID24969398show ga Objective: Gene-Gene interactions (GxG) are important to study because of their extensiveness in biological systems and their potential in explaining missing heritability of complex traits. In this work, we propose a new similarity-based test to assess GxG at gene level, which permits the study of epistasis at biologically functional units with amplified interaction signals. Methods: Under the framework of gene-trait similarity regression (SimReg), we propose a gene-based test for detecting gene-gene interactions. SimReg uses a regression model to correlate trait similarity with genotypic similarity across a gene. Unlike existing gene-level methods based on leading principal components (PCs), SimReg summarizes all information on genotypic variation within a gene and can be used to assess the joint/interactive effects of two genes as well as the effect of one gene conditional on another. Results: Using simulations and a real data application on warfarin study, we show that the SimReg GxG tests have satisfactory power and robustness under different genetic architecture when compared to existing gene-based interaction tests such as PC analysis or partial least squares (PLS). A genomewide association study with ~20,000 genes may be completed on a parallel computing system in 2 weeks. äAnalysis of Gene-Gene Interactions Using Gene-Trait Similarity Regress(epmc).pdf DeepDyve Pubget Overpricing