title: Bayesian inference of epistatic interactions in case-control studies.
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.
authors.affiliation: Department of Statistics, the Pennsylvania State University, Thomas Building 422A, University Park, Pennsylvania 16802, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jun S
authors.initials: JS
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods*; D004843:Epistasis, Genetic; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D016015:Logistic Models; D008268:Macular Degeneration / Q000235:genetics; D008957:Models, Genetic; D009010:Monte Carlo Method