Genetic landscape of multiple sclerosis susceptibility by leveraging multi-omics data

Abstract: The main objective of the research studies presented in this thesis is to study the genetic variants and the expression of genes that relate to Multiple Sclerosis (MS). MS is a polygenic disease with HLA-DRB1'15:01 allele as a strong risk factor. Currently there are more than 200 non-HLA regions identified for MS. However, most of the risk loci identified in those studies are primarily driven by the relapsing-remitting form of MS (RRMS). To identify risk factors specific for the primary progressive form of MS (PPMS) which is a smaller group of MS patients, we have examined the exomes of PPMS and RRMS patients matching to population based controls in a case-control study setting and reported risk variants and mutations that are associated to PPMS and RRMS. The context of this study is during the ‘post-GWAS’ era, when researchers are primarily focused to understand the functional consequences of the genetic risk factors. Using the possibilities of transcriptomic and genotyping data, genes that correlate to the risk loci are identified in relevant cell types of MS. Several statistical methods are implemented to characterize the risk loci and replicate the findings in the context of disease. MicroRNAs (miRNAs), small non-coding RNAs which regulate gene expression at post-transcriptional level, have been identified dysregulated in autoimmune diseases, including MS. We used experimental autoimmune encephalomyelitis (EAE), a commonly used animal model for MS to understand the role of miRNA in the immune activation of EAE. Next generation sequencing (NGS) methods were widely applied in all of these studies specifically at transcriptomic and genomic level of the disease. NGS methods are data intensive but have higher reliability. To test the reliability, we compared reported gene expression measurements for ostensibly similar tissue samples collected from different RNA-seq studies. We found an overall consistency on expression data obtained from different studies and identified the factors contributing to systematic differences. This thesis gives an overview of progresses happening in the area of MS genetics, EAE model for neuroinflammation and omics data analysis to address genetic regulation of disease.