title: A fast algorithm for learning epistatic genomic relationships.
abstract: Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes without employing a search strategy. Therefore, they do not scale to handling the high-dimensional data found in genome-wide association studies (GWAS). We represent genome-phenome interactions using a Bayesian network rule, which is a specialized Bayesian network. We develop an efficient search algorithm to learn from data a high scoring rule that may contain two or more interacting genes. Our experimental results using synthetic data indicate that this algorithm detects interacting genes as well as a Bayesian network combinatorial method, and it is much faster. Our results also indicate that the algorithm can successfully learn genome-phenome relationships using a real GWAS dataset.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
authors.forename: Richard E
authors.initials: RE
authors.lastname: Neapolitan
authors.forename: M Michael
authors.initials: MM
authors.lastname: Barmada
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D020641:Polymorphism, Single Nucleotide