A Systems Biology approach to determine cell-specific gene regulatory effects of genetic associations in multiple sclerosis
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Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. To overcome these barriers, we have conducted a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS); which analyzed a total of 47,351 cases and 68,284 controls and found more than 200 non-MHC genome-wide associations. Our approach makes extensive use of gene regulatory data generated by the ENCODE and REP projects to build data-driven models of the predicted regulatory effects (PRE) of each associated variant and their flanking correlated variation over a wide range of linkage disequilibrium thresholds. A human protein interactome is used to compute the likelihood that genes with high PRE in a given cell belong to the same pathway. This analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.
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Medical Research Council (G1100125)
National Institutes of Health (NIH) (via University of California) (6399sc)