Genetics of multiple sclerosis
Posted: Sat Nov 02, 2019 12:12 am
2019 Sep 4
Biology, Athens State University
Breakdown of Multiple Sclerosis Genetics to Identify an Integrated Disease Network and Potential Variant Mechanisms.
https://www.ncbi.nlm.nih.gov/pubmed/31482761
Abstract
Genetics of multiple sclerosis (MS) are highly polygenic with few insights into mechanistic associations with pathology. In this paper, 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 multi-tissue eQTL, rs9271640, for HLA-DRB1/DRB5. Using multiple functional genomic databases, noncoding variants were identified that disrupt TF binding for GABPA, CTCF, EGR1, YY1, SPI1, CLOCK, ARNTL, BACH1, and GFI1. Overall, this manuscript 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.
Biology, Athens State University
Breakdown of Multiple Sclerosis Genetics to Identify an Integrated Disease Network and Potential Variant Mechanisms.
https://www.ncbi.nlm.nih.gov/pubmed/31482761
Abstract
Genetics of multiple sclerosis (MS) are highly polygenic with few insights into mechanistic associations with pathology. In this paper, 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 multi-tissue eQTL, rs9271640, for HLA-DRB1/DRB5. Using multiple functional genomic databases, noncoding variants were identified that disrupt TF binding for GABPA, CTCF, EGR1, YY1, SPI1, CLOCK, ARNTL, BACH1, and GFI1. Overall, this manuscript 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.