title: Functional and genomic context in pathway analysis of GWAS data.
abstract: 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 single-nucleotide polymorphisms (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.
authors.affiliation: Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Mooney
authors.affiliation: Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Joel T
authors.initials: JT
authors.lastname: Nigg
authors.affiliation: Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA. Electronic address: mcweeney@ohsu.edu.
authors.forename: Shannon K
authors.initials: SK
authors.lastname: McWeeney
authors.affiliation: Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA.
authors.forename: Beth
authors.initials: B
authors.lastname: Wilmot
mesh_terms: D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015398:Signal Transduction
keywords: GWAS; complex traits; gene set analysis; polygenic effects