The Genetics of Multiple Sclerosis: From 0 to 200 in 50 Years

Abstract

Multiple sclerosis (MS) is a common autoimmune disease that targets myelin in the central nervous system (CNS). Multiple genome-wide association studies (GWAS) over the past 10 years have uncovered more than 200 loci that independently contribute to disease pathogenesis. As with many other complex diseases, risk of developing MS is driven by multiple common variants whose biological effects are not immediately clear. Here, we present a historical perspective on the progress made in MS genetics and discuss current work geared towards creating a more complete model that accurately represents the genetic landscape of MS susceptibility. Such a model necessarily includes a better understanding of the individual contributions of each common variant to the cellular phenotypes, and interactions with other genes and with the environment. Future genetic studies in MS will likely focus on the role of rare variants and endophenotypes.

Keywords: GWAS; gene regulatory networks; genetic pathways; multiple sclerosis.

Fig 1. Decreasing OR as study n increases

Box plots represent the median and quartiles of the odds ratio (OR) of studies performed at the indicated years. With exception of 2010, each new study revealed associations with smaller OR.

Fig 2. An autoimmunity gene network

All known associations to common autoimmune diseases were obtained from the GWAS Catalog [92] and mapped to the closest or most likely affected gene, as reported by the authors of each publication. Then, data was displayed graphically as a network using the software Cytoscape [93]. Nodes represent either diseases (circles) or genes (triangles), while edges indicate genetic associations. Genes associated with more than one disease are shown in blue and lie at the center of the network. Genes associated with only one disease are displayed with the same color as their corresponding disease and organized in outward fashion.

Figure 3. Strategy for cell specific PINBPA

A. Each of the 200 MS-associated SNPs and those in LD (regions) is queried for regulatory features such as eQTL, enhancer, DNase hypersensitivity region, histone modification, etc. from ENCODE, REP and IHEC. B. All signals are integrated in a cell specific manner. C. Regulatory potentials are computed for each gene in proximity to associated SNPs for different cell types. D. The regulatory potentials are incorporated as node attributes into a protein interaction network and the number of edges among genes with score > 0 is computed. E. The number of edges among genes for each cell type is displayed along with the distribution of edges from random networks of similar size.