Functional and genomic context in pathway analysis of GWAS data

Research output: Contribution to journalArticle

55 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)390-400
Number of pages11
JournalTrends in Genetics
Volume30
Issue number9
DOIs
StatePublished - 2014

Fingerprint

Genome-Wide Association Study
Genes
Single Nucleotide Polymorphism
Information Storage and Retrieval
Disease Susceptibility
Guidelines

Keywords

  • Complex traits
  • Gene set analysis
  • GWAS
  • Polygenic effects

ASJC Scopus subject areas

  • Genetics

Cite this

Functional and genomic context in pathway analysis of GWAS data. / Mooney, Michael; Nigg, Joel; McWeeney, Shannon; Wilmot, Beth.

In: Trends in Genetics, Vol. 30, No. 9, 2014, p. 390-400.

Research output: Contribution to journalArticle

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