Entropy Based Approach to Finding Interacting Genes Responsible for Complex Human Disease

Abstract

A challenging problem in human genetics is the identification and characterization of susceptibility genes for complex human diseases such as cardiovascular disease, cancer, hypertension and obesity. These conditions are likely due to the efiects of high-order interactions among multiple genes and environmental factors. Genome-wide association studies, where hundreds of thousands of single-nucleotide polymorphisms (SNPs) are genotyped in samples of cases and controls, offer a powerful approach for mapping of complex disease genes. The classical statistical methods, parametric and nonparametric, are usually limited to small number of SNPs. Here we propose a new method based on a classical search algorithm - "sequential forward oating search", utilizing entropy based criterion function. Using simulated case-control data we demonstrate that the method has a high discovery rate under different models of gene-gene interaction, including pure interaction without main effects of the genes. The performance of the proposed method is also compared to a method recently advocated in the literature: multifactor dimensionality reduction (MDR).