title: Gene set analysis: A step-by-step guide.
abstract: 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.
authors.affiliation: Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon.|OHSU Knight Cancer Institute, Portland, Oregon.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Mooney
authors.affiliation: Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon.|OHSU Knight Cancer Institute, Portland, Oregon.|Oregon Clinical and Translational Research Institute, Portland, Oregon.
authors.forename: Beth
authors.initials: B
authors.lastname: Wilmot
mesh_terms: D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors; D012984:Software
keywords: complex traits; gene set analysis; genome-wide association studies; polygenic effects