Gene-Based Tests of a Genome-Wide Association Study Dataset Highlight Novel Multiple Sclerosis Risk Genes

Abstract

Multiple sclerosis (MS) is an autoimmune disorder influenced by genetic and environmental factors. Many studies have provided insights into genetic factors’ contribution to MS via large-scale genome-wide association study (GWAS) datasets. However, genetic variants identified to date do not adequately explain genetic risks for MS. This study hypothesized that novel MS risk genes could be identified by analyzing the MS-GWAS dataset using gene-based tests. We analyzed a GWAS dataset consisting of 9,772 MS cases and 17,376 healthy controls of European descent. We performed gene-based tests of 464,357 autosomal single nucleotide polymorphisms (SNPs) using two methods (PLINK and VEGAS2) and identified 28 shared genes satisfied p-value < 4.56 × 10–6. In further gene expression analysis, ten of the 28 genes were significantly differentially expressed in the MS case-control gene expression omnibus (GEO) database. GALC and HLA-DOB showed the most prominent differences in gene expression (two- and three-fold, respectively) between MS patients and healthy controls. In conclusion, our results reveal more information about MS hereditary characteristics and provide a basis for further studies.