A fast algorithm for learning epistatic genomic relationships

AMIA Annu Symp Proc. 2010 Nov 13;2010:341-5.

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.

MeSH terms

  • Algorithms
  • Bayes Theorem*
  • Epistasis, Genetic
  • Genome-Wide Association Study*
  • Genomics
  • Humans
  • Polymorphism, Single Nucleotide