Genetic model of MS severity predicts future accumulation of disability. (8th August 2019)

• Record Type: Journal Article
• Authors: Jackson, Kayla C.; Sun, Katherine; Barbour, Christopher; Hernandez, Dena; Kosa, Peter; Tanigawa, Makoto; Weideman, Ann Marie; Bielekova, Bibiana
• Is Part Of: Annals of human genetics. Volume 84:Number 1(2020:Jan.)
• Page Start: 1
• Page End: 10
• Publication Date: 2019-08-08
• Subjects: machine learning -- multiple sclerosis -- severity -- single‐nucleotide polymorphism; Human genetics -- Periodicals; 599.935
• DOI: 10.1111/ahg.12342
• Languages: English
• ISSNs: 0003-4800

Abstract

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.