Learning genetic epistasis using Bayesian network scoring criteria

Background: Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a num...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Xia Jiang, Richard E. Neapolitan, M. Michael Barmada, Shyam Visweswaran
• Keywords: Artificial Intelligence, BMCBI 2011, Data Sets, Epistatic, Epistatic Interactions