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Physiol Genomics. 2019 Nov 1;51(11):562-577. doi: 10.1152/physiolgenomics.00120.2018. Epub 2019 Sep 4.

Breakdown of multiple sclerosis genetics to identify an integrated disease network and potential variant mechanisms.

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Department of Biology, Athens State University, Athens, Alabama.
Graduate Biomedical Sciences, University of Alabama at Birmingham, Birmingham, Alabama.
HudsonAlpha Institute for Biotechnology, Huntsville, Alabama.
Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.
Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan.


Genetics of multiple sclerosis (MS) are highly polygenic with few insights into mechanistic associations with pathology. In this study, we assessed MS genetics through linkage disequilibrium and missense variant interpretation to yield a MS gene network. This network of 96 genes was taken through pathway analysis, tissue expression profiles, single cell expression segregation, expression quantitative trait loci (eQTLs), genome annotations, transcription factor (TF) binding profiles, structural genome looping, and overlap with additional associated genetic traits. This work revealed immune system dysfunction, nerve cell myelination, energetic control, transcriptional regulation, and variants that overlap multiple autoimmune disorders. Tissue-specific expression and eQTLs of MS genes implicate multiple immune cell types including macrophages, neutrophils, and T cells, while the genes in neural cell types enrich for oligodendrocyte and myelin sheath biology. There are eQTLs in linkage with lead MS variants in 25 genes including the multitissue eQTL, rs9271640, for HLA-DRB1/DRB5. Using multiple functional genomic databases, we identified noncoding variants that disrupt TF binding for GABPA, CTCF, EGR1, YY1, SPI1, CLOCK, ARNTL, BACH1, and GFI1. Overall, this paper suggests multiple genetic mechanisms for MS associated variants while highlighting the importance of a systems biology and network approach when elucidating intersections of the immune and nervous system.


GWAS; data integration; eQTL; multiple sclerosis; omics

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