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

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.

Using MS induced pluripotent stem cells to investigate MS aetiology

“…However recent mapping of established MS susceptibility genes onto multiple single cell RNA sequencing datasets from MS brain tissue, has revealed enrichment of MS-susceptibility genes in astrocytes, vascular cells and excitatory neurons (Absinta et al, 2021). Additionally, family-based studies and a novel machine learning approach implicate variants in CNS related genes in affecting MS susceptibility and progression, respectively (Fazia et al, 2021;Jokubaitis et al, 2022;Mascia et al, 2022). This supports the idea that cells of the CNS, not only immune cells, are involved in MS development.…”

Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis

“…The strengths of this study lies in the assumptions we made regarding the underlying disability process in MS (defined above), and the use of novel machine learning platforms capable of analysing the longitudinal changes in EDSS scores. By analysing the continuous-time evolution of EDSS transitions, the total genetic liability in progression rates attributable to the 28 candidate variants was substantially increased compared other studies 24 , 31 – 36 , 40 , 41 , 43 , 80 . In particular, the high intra-class correlations between the observed and ensemble-derived predicted probabilities of worsening revealed a good fit to the model.…”

“…There have been notable proponents for no effect of currently known risk variants on MS outcomes after onset 24 , 31 – 36 . There is, however, a plausible effect of genetic variants on MS progression, in particular relating to the severity of primary inflammation and/or relapses 37 , 38 .…”

“…Notwithstanding, machine learning models have recently been applied in studies of MS disability progression, including standard random forest (RF) and gradient boosting machines (GBM) 21 , 22 , 30 , 36 , 40 , 44 – 50 . Despite their continued use in predicting disability, past and recent studies 15 , 16 (not related to MS) have shown that these models have (1) limited clinical utility as they rely strictly on a discrete-time evolution of disease processes, meanwhile in MS, disability progression is characterised by a continuous-time evolution of expanded disability status scores (EDSS) 1 , 9 ; (2) weak predictive power as they do not account for correlated outcomes 51 (e.g.…”

Birth history is associated with whole-blood and T-cell methylation patterns in relapse onset multiple sclerosis

“…In this study, we confirm that genes related to these processes are differentially methylated between nulligravida and parous wwMS for up to 44.4 years post-pregnancy. Single nucleotide variants associated with mitochondrial and CNS function were recently shown to associate with MS severity outcomes 49 . Our study demonstrates a putative mechanism by which pregnancy may impact long-term legacy effects on outcomes in wwMS.…”