Detailed materials and methods are listed in the Supplementary Material (9 ). In brief, we analyzed genetic data from 15 GWAS of MS. For the autosomal non-MHC genome, we applied a partitioning approach to create regions of ±1Mbps around the most statistically significant SNP. Then we performed stepwise conditional analyses within each region to identify statistically independent effects (n=4,842). We replicated these effects in two large-scale replication cohorts: i) nine (9 ) data sets genotyped with the MS Replication Chip, and ii) eleven (11 (link)) data sets genotyped with the ImmunoChip. Chromosomes X and Y were analyzed jointly across all the data sets, i.e. the discovery and replication. The extended MHC region was also analyzed jointly across all data sets. We further imputed HLA class I and II alleles and corresponding amino acids. Statistically independent effects in the autosomal non-MHC genome were group into 4 categories post-replication: i) genome-wide effects (GW), ii) suggestive effects (S), iii) non-replicated (NR), and iv) no replication data (ND). Narrow sense heritability was estimated for various combinations of these effects, and the extended MHC region, to quantify the amount of the heritability our findings could explain. Next, we leveraged enrichment methods and tissue/cell reference data sets to characterize the potential involvement of the identified MS effects in the immune and central nervous system, at the tissue and cellular level. We developed an ensemble approach to prioritize genes putative associated with the identify effects, leveraging cell-specific eQTL studies, network approaches, and genomic annotations. Pathway analyses were performed to characterize canonical pathways statistically enriched for the putative causal genes. Finally, we leveraged protein-protein interaction networks to quantify the degree of connectivity of the putative causal genes and identify new mechanisms of action.