Learning Genetic Epistasis Using Bayesian Network Scoring Criteria
Xia Jiang, Richard E. Neapolitan, M. Michael Barmada, Shyam Visweswaran · Bioinformatics · 2014
XIA JIANG, RICHARD E. NEAPOLITAN, M. MICHAEL BARMADA, AND SHYAM VISWESWARAN 10.1 BACKGROUND The advent of high-throughput genotyping technology has brought the promise of identifying genetic variations that underlie common diseases such as hypertension, diabetes mellitus, cancer and Alzheimer&s;s disease. However, our knowledge of the genetic architecture of common diseases remains limited; this is in part due to the complex relationship between the genotype and the phenotype. One likely reason for this complex relationship arises from gene-gene and gene-environment interactions. So an important challenge in the analysis of high-throughput genetic data is the development of computational and statistical methods to identify genegene interactions. In this paper we apply Bayesian network scoring criteria to identifying gene-gene interactions from genome-wide association study (GWAS) data.