ABSTRACT
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 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.
