A Knowledge-Driven Multi-Locus Analysis of Multiple Sclerosis Susceptiblity

Abstract

Evaluating epistasis in whole-genome association studies is an important challenge in human genetics, as many common diseases are thought to have complex underlying genetic architectures that include small independent effects and interactions between many genes. In this project, I applied a simple bioinformatics approach for generating and ranking biologically supported multi-locus models of multiple sclerosis (MS) susceptibility, using data sources implying interaction of molecules, sources implying gene relationship to disease, and literature-based information. Putative gene-gene interaction models were constructed based on these relationships. These models were evaluated in whole-genome association dataset consisting of 931 MS case/pseudo-control pairs, 2,950 population-based controls, and a replication sample of 808 MS cases and 1,720 controls. Using this approach, I highlight the potential utility of this knowledge-driven analysis technique, and propose a potential role for inositol-based signaling molecules in multiple sclerosis susceptibility.