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10.1016/j.tig.2014.07.004 http://scihub22266oqcxt.onion/10.1016/j.tig.2014.07.004 C4266582!4266582!25154796     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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Trends+Genet 2014 ; 30 (9): 390-400 Nephropedia Template TP {{tp|p=25154796|t=2014. Functional and Genomic Context in Pathway Analysis of GWAS Data |pdf=|usr=}}{{25154796}} gab.com Text Functional and Genomic Context in Pathway Analysis of GWAS Data JO: Trends Genet http://www.kidney.de/pget.php?&tw=1&pmid=25154796 #FOAvip Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA?such as how gene sets are defined, how SNPs are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects?will improve the interpretability and comparability of results across methods and studies. In this review, we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results. Twit Text FOAVip Functional and Genomic Context in Pathway Analysis of GWAS Data ????Trends Genet http://www.kidney.de/pget.php?&tw=1&pmid=25154796 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25154796 #FOAvip English Wikipedia <ref>{{Cite pmid|25154796}}</ref> Functional and Genomic Context in Pathway Analysis of GWAS Data #MMPMID25154796 Mooney MA; Nigg JT; McWeeney SK; Wilmot B Trends Genet 2014[Sep]; 30 (9): 390-400 PMID25154796show ga Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA?such as how gene sets are defined, how SNPs are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects?will improve the interpretability and comparability of results across methods and studies. In this review, we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results. äFunctional and Genomic Context in Pathway Analysis of GWAS Data (epmc).pdf DeepDyve Pubget Overpricing