A fast algorithm for learning epistatic genomic relationships

Jiang X, Neapolitan RE, Barmada M, Visweswaran S, Cooper GF.   A fast algorithm for learning epistatic genomic relationships.  In: Proceedings of the Annual Symposium of the American Medical Informatics Association (2010) 341-345. PMID: 21346997 PMC3041370

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.

Publication Year: 
2010
Publication Credits: 
Jiang X, Neapolitan RE, Barmada M, Visweswaran S, Cooper GF.
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