Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm

Abstract

An important goal of systems biology is to understand and identify mechanisms of the human body system. Genes play functional roles in the context of complex pathways. Analysis of genes as networks is therefore important to understand whole system mechanisms. Biological activities are governed by various signaling networks. The advent of high-throughput technologies has made it possible to obtain biological information on a genome-wide scale. Genetic interactions have been identified from high-throughput data such as microarray data using Bayesian networks. In this paper, we infer the disease-specific gene interaction network using a Bayesian network, which is robust to noise in the data. We apply a genetic algorithm to learn the Bayesian network. We use heterogeneous data, including microarray, protein-protein interaction (PPI), and HumanNET data to learn and score the network. We also exploit single nucleotide polymorphism (SNP) data to infer disease-specific genetic interaction network. We included SNPs as this data may help detect weak signals related to genetic variation In this paper, we reconstruct interactions between pathway genes using our method. We confirm that our method has statistically significant reconstruction power by applying it to Type II diabetes data. Importantly, using Alzheimer disease data, we infer an unreported interaction between a SNP and a disease-related gene. Copyright is held by the owner/author(s).