| Literature DB >> 31396954 |
Kayla C Jackson1, Katherine Sun1, Christopher Barbour1,2, Dena Hernandez3, Peter Kosa1, Makoto Tanigawa1, Ann Marie Weideman1, Bibiana Bielekova1.
Abstract
No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS-DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM-MSS). The GeM-MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis. GeM-MSS correlates with MS-DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.Entities:
Keywords: machine learning; multiple sclerosis; severity; single-nucleotide polymorphism
Year: 2019 PMID: 31396954 PMCID: PMC6898742 DOI: 10.1111/ahg.12342
Source DB: PubMed Journal: Ann Hum Genet ISSN: 0003-4800 Impact factor: 1.670