Bayesian inference of epistatic interactions in case-control studies

Yu Zhang, Jun S. Liu

Research output: Contribution to journalArticle

332 Scopus citations

Abstract

Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.

Original languageEnglish (US)
Pages (from-to)1167-1173
Number of pages7
JournalNature Genetics
Volume39
Issue number9
DOIs
StatePublished - Sep 1 2007

All Science Journal Classification (ASJC) codes

  • Genetics

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