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Not all roads lead to the immune system: The Genetic Basis of Multiple Sclerosis Severity Implicates Central Nervous System and Mitochondrial Involvement

Authors :
Vilija G. Jokubaitis
Omar Ibrahim
Jim Stankovich
Pavlina Kleinova
Fuencisla Matesanz
Daniel Hui
Sara Eichau
Mark Slee
Jeannette Lechner-Scott
Rodney Lea
Trevor J Kilpatrick
Tomas Kalincik
Philip L. De Jager
Ashley Beecham
Jacob L. McCauley
Bruce V. Taylor
Steve Vucic
Louise Laverick
Karolina Vodehnalova
Maria-Isabel García-Sanchéz
Antonio Alcina
Anneke van der Walt
Eva Kubala Havrdova
Guillermo Izquierdo
Nikolaos Patsopoulos
Dana Horakova
Helmut Butzkueven
Publication Year :
2022
Publisher :
Cold Spring Harbor Laboratory, 2022.

Abstract

Multiple sclerosis (MS) is a leading cause of neurological disability in adults. Heterogeneity in MS clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international MS Registry, MSBase. We assembled a cohort of deeply phenotyped individuals with relapse-onset MS. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined MS severity phenotypes in 1,813 individuals. Our results did not identify any variants of moderate to large effect sizes that met genome-wide significance thresholds. However, we demonstrate that clinical outcomes in relapse-onset MS are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62,000 variants and demographic variables available at MS disease onset, we could predict severity with an area under the receiver operator curve (AUROC) of 0.87 (95% CI 0.83 – 0.91). This approach, if externally validated, could quickly prove useful for clinical stratification at MS onset. Further, we find evidence to support central nervous system and mitochondrial involvement in determining MS severity.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........9587907a8d28532da0560bc11f04107d
Full Text :
https://doi.org/10.1101/2022.02.04.22270362