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10.1002/ajmg.b.32328 http://scihub22266oqcxt.onion/10.1002/ajmg.b.32328 C4638147!4638147 !26059482     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26059482 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Am+J+Med+Genet+B+Neuropsychiatr+Genet 2015 ; 168 (7 ): 517-27 Nephropedia Template TP {{tp|p=26059482 |t=2015. Gene set analysis: A step-by-step guide |pdf=|usr=}}{{26059482 }} gab.com Text Gene set analysis: A step-by-step guide JO: Am J Med Genet B Neuropsychiatr Genet http://www.kidney.de/pget.php?&tw=1&pmid=26059482 #FOAvip To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc. Twit Text FOAVip Gene set analysis: A step-by-step guide ????Am J Med Genet B Neuropsychiatr Genet http://www.kidney.de/pget.php?&tw=1&pmid=26059482 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26059482 #FOAvip English Wikipedia <ref>{{Cite pmid|26059482 }}</ref> Gene set analysis: A step-by-step guide #MMPMID26059482 Mooney MA ; Wilmot B Am J Med Genet B Neuropsychiatr Genet 2015[Oct]; 168 (7 ): 517-27 PMID26059482 show ga To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc. |Genetic Linkage [MESH] |Genetic Predisposition to Disease [MESH] |Genome-Wide Association Study/*methods [MESH] |Humans [MESH] |Polymorphism, Single Nucleotide [MESH] |Risk Factors [MESH]Gene set analysis: A step-by-step guide (epmc).pdf DeepDyve Pubget Overpricing