Gene set analysis of genome-wide association studies: Methodological issues and perspectives

Lily Wang, Peilin Jia, Russell D. Wolfinger, Xi Chen, Zhongming Zhao

Research output: Contribution to journalReview articlepeer-review

119 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalGenomics
Volume98
Issue number1
DOIs
StatePublished - Jul 2011
Externally publishedYes

Keywords

  • Complex disease
  • Gene set
  • Gene-set enrichment analysis
  • Genome-wide association study
  • Pathway
  • Statistical significance

ASJC Scopus subject areas

  • Genetics

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