title: Gene set analysis of genome-wide association studies: methodological issues and perspectives.
abstract: Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.
authors.affiliation: Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA. lily.wang@vanderbilt.edu
authors.forename: Lily
authors.initials: L
authors.lastname: Wang
authors.forename: Peilin
authors.initials: P
authors.lastname: Jia
authors.forename: Russell D
authors.initials: RD
authors.lastname: Wolfinger
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.forename: Zhongming
authors.initials: Z
authors.lastname: Zhao
mesh_terms: D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D005810:Multigene Family; D020641:Polymorphism, Single Nucleotide