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

He Li,Fang Xu,Xiaodan Hou,Yan Liang,Gebeili Xing,Xiyue Zhang,Xuejiao Wang,Pan Cui,Wei Jiang

doi:10.3389/fnins.2021.614528

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.

Highlights

Multiple sclerosis (MS) is a neurodegenerative, inflammatory, demyelinating, and autoimmune disease of the central nervous system (CNS) that causes widespread tissue lesions and dysfunction (McFarland and Martin, 2007)
We used a large-scale MS-genome-wide association study (GWAS) dataset from the International Multiple Sclerosis Genetics Consortium (IMSGC), which consisted of 9,772 MS cases along with 17,376 control cases of European descent collected by 23 research teams from 15 countries (Sawcer et al, 2011)
Under the same null hypothesis, a set of p-values determined by independent tests were collected and calculated

Summary

INTRODUCTION

Multiple sclerosis (MS) is a neurodegenerative, inflammatory, demyelinating, and autoimmune disease of the central nervous system (CNS) that causes widespread tissue lesions and dysfunction (McFarland and Martin, 2007). GWAS have helped comprehend the genetic basis of human diseases, there are still some limitations, including the unprecedented potential to produce false-positive results, lack of information on gene function, insufficient sample size, lack of well-defined case and control groups, and insensitivity to rare, and structural variants (Pearson and Manolio, 2008). Gene-based tests have been considered as a complement to GWAS (Wang et al, 2011). Gene-based analysis prioritizes GWAS results and determines the association between diseases and functional genetic analysis (e.g., a shared biological pathway) (Wang et al, 2011). This study performed gene-based tests of an MSGWAS dataset that comprised 9,772 MS cases along with 17,376 control subjects using two methods. We analyzed two MS case-control gene expression datasets to investigate further the differential expression of MS risk genes identified by both methods

MATERIALS AND METHODS

RESULTS

DISCUSSION

CONCLUSION

ETHICS STATEMENT