Genetic and gene expression signatures in multiple sclerosis. (April 2020)

• Record Type: Journal Article
• Authors: Patsopoulos, Nikolaos A; De Jager, Philip L
• Other Names: Chitnis Tanuja guest-editor.; Prat Alexandre guest-editor.
• Is Part Of: Multiple sclerosis. Volume 26:Number 5(2020)
• Page Start: 576
• Page End: 581
• Publication Date: 2020-04
• Subjects: Genetics -- genetic risk score -- gene expression -- gene signatures -- single cell; Central nervous system -- Diseases -- Periodicals; Myelin sheath -- Diseases -- Periodicals; Inflammation -- Periodicals; Multiple sclerosis -- Periodicals; Central Nervous System Diseases -- Periodicals; Demyelinating Diseases -- Periodicals; Inflammation -- Periodicals; Multiple Sclerosis -- Periodicals; Système nerveux central -- Maladies -- Périodiques; Gaine de myéline -- Maladies -- Périodiques; Inflammation (Pathologie) -- Périodiques; Sclérose en plaques -- Périodiques; Electronic journals; 616.834005
• DOI: 10.1177/1352458519898332
• Languages: English
• ISSNs: 1352-4585

Abstract

Multiple sclerosis (MS) exhibits a well-documented increased incidence in individuals with respective family history, that is, is a heritable disease. In the last decade, genome-wide association studies have enabled the agnostic interrogation of the whole genome at a large scale. To date, over 200 genetic associations have been described at the strict level of genome-wide significance. Our current understanding of MS genetics can explain up to half of the disease's heritability, raising the important question of whether this is enough information to leverage toward improving diagnosis in MS. Parallel advancements in technologies that allow the characterization of the full transcriptome down to the single-cell level have enabled the generation of an unprecedented wealth of information. Transcriptional changes of putative causal cells could be utilized to identify early signs of disease onset. These recent findings in genetics and genomics, coupled with new technologies and deeply phenotyped cohorts, have the potential to improve the diagnosis of MS.