Unraveling the genetic architecture of Multiple Sclerosis and the underlying implicated pathways. NA. Patsopoulos1,2,3,4 for the International Multiple Sclerosis Genetics Consortium (IMSGC) 1) Department of Neurology, Brigham & Womens Hospital, Boston, MA; 2) Division of Genetics, Department of Medicine, Brigham & Womens Hospital, Boston, MA; 3) Harvard Medical School, Boston, MA; 4) The Broad Institute, Cambridge, MA.
Multiple sclerosis (MS) is a neurodegenerative disease with a genetic background. So far efforts to identify the genetic component using genome-wide association studies (GWAS) has a yielded ~50 loci. Here we present results of the International Multiple Sclerosis Genetics Consortiums (IMSGC) analysis of all available published GWAS enriched with new unpublished data and imputed in the 1000 genomes European panel. The final analysis was performed in 14,802 multiple sclerosis (MS) cases and 26,703 controls and ~8 million SNPs. We applied an exhaustive strategy to identify primary and secondary signals, identifying 78 primary and another 10 secondary genome-wide effects (p-value < 5x10-8). At the less conservative level of p-value < 10-5, we found another 96 primary effects and 30 secondary ones. By applying extended stepwise models we identified overall ~4.700 statistically independent effects at the nominal level (p-value<0.05). We replicated these in a custom designed iSelect chip (~80K SNPs) genotyping 18,000 cases and 18,000 controls and we present detailed results on the replication success rates. This large sample size allows us to quantify the exact variance explained and heritability by each susceptibility allele using multivariate models, and eventually estimate the overall contribution of common genetic variability to MS susceptibility. Furthermore, we describe the underlying mechanisms and pathways implicated by the newly identified loci and how these enrich and complete the current knowledge about the role of innate and humoral immunity. Finally, we leverage eQTL studies and the ENCODE and NIH Epigenomics Roadmap data to characterize the functional implications of the associated loci and nominate the most likely candidate causal variant in each locus. By performing the largest experiment to date in the genetic analysis of common variation in MS, we describe a holistic approach to identify and functionally annotate the most complete genetic map of MS susceptibility.
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