DOI: 10.5061/dryad.hx3ffbghk
title: Not all roads lead to the immune system: The genetic basis of multiple sclerosis severity

description: Multiple sclerosis is a leading cause of neurological disability in
 adults. Heterogeneity in multiple sclerosis 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 multiple
 sclerosis Registry, MSBase. We assembled a cohort of deeply phenotyped
 individuals of European ancestry with relapse-onset multiple sclerosis. We
 used unbiased genome-wide association study and machine learning
 approaches to assess the genetic contribution to longitudinally defined
 multiple sclerosis severity phenotypes in 1,813 individuals. Our primary
 analyses did not identify any genetic variants of moderate to large effect
 sizes that met genome-wide significance thresholds. The strongest signal
 was associated with rs7289446 (β=-0.4882, P = 2.73 × 10−7), intronic to
 SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in
 relapse-onset multiple sclerosis are associated with multiple genetic loci
 of small effect sizes. Using a machine learning approach incorporating
 over 62,000 variants together with clinical and demographic variables
 available at multiple sclerosis disease onset, we could predict severity
 with an area under the receiver operator curve of 0.84 (95% CI 0.79–0.88).
 Our machine learning algorithm achieved positive predictive value for
 outcome assignation of 80% and negative predictive value of 88%. This
 outperformed our machine learning algorithm that contained clinical and
 demographic variables alone (area under the receiver operator curve 0.54,
 95% CI 0.48–0.60). Secondary, sex-stratified analyses identified two
 genetic loci that met genome-wide significance thresholds. One in females
 (rs10967273; βfemale =0.8289, P = 3.52 × 10-8), the other in males
 (rs698805; βmale =  -1.5395, P = 4.35 × 10-8), providing some evidence for
 sex dimorphism in multiple sclerosis severity. Tissue enrichment and
 pathway analyses identified an overrepresentation of genes expressed in
 central nervous system compartments generally, and specifically in the
 cerebellum (P = 0.023). These involved mitochondrial function, synaptic
 plasticity, oligodendroglial biology, cellular senescence, calcium and
 g-protein receptor signalling pathways. We further identified six variants
 with strong evidence for regulating clinical outcomes, the strongest
 signal again intronic to SEZ6L (adjusted hazard ratio 0.72,
 P = 4.85 × 10-4). Here we report a milestone in our progress towards
 understanding the clinical heterogeneity of multiple sclerosis outcomes,
 implicating functionally distinct mechanisms to multiple sclerosis risk.
 Importantly, we demonstrate that machine learning using common single
 nucleotide variant clusters, together with clinical variables readily
 available at diagnosis can improve prognostic capabilities at diagnosis,
 and with further validation has the potential to translate to meaningful
 clinical practice change.
description: <strong>Data
 collection:</strong> Participants of European descent were recruited
 from eight tertiary-referral multiple sclerosis-specialist centres, from
 three countries (Australia, Spain, and Czech Republic), participating in
 the MSBase Registry. MSBase is an international, prospective,
 observational, multiple sclerosis clinical outcomes registry, registered
 with the World Health Organization International Clinical Trials Registry
 Platform, ID ACTRN12605000455662. Clinical data used to derive phenotypic
 information were entered by neurologists in, or near real-time including:
 participant demographics, disease phenotype, expanded disability status
 scale (EDSS) scores, relapse information, and disease-modifying therapy
 use. Clinical assessments
 occur on average every 6 months. This study was approved by the Melbourne Health
 Human Research Ethics Committee, and by institutional review boards at all
 participating centres. All participants gave written informed consent for
 participation in the MSBase Registry, together with additional informed consent to participate in
 genetic research (HREC/13/MH/189 and per local approvals
 elsewhere). <strong>Data
 processing:</strong> Peripheral blood samples
 were taken from participants, and genomic DNA extracted. Samples were
 genotyped at the John. P. Hussman
 Institute for Human Genomics (University of Miami, FL, USA) using the
 Ilumina MegaEx Beadchip array, and imputed on the Michigan Server (Das et
 al., Nature Genetics 2016; 48: (1284–1287) using the Haplotype Reference Consortium panel.
 QC and data processing was performed in PLINK 1.9 using standard quality
 control procedures. Association testing was also performed in PLINK 1.9
 and 2.0, adjusting for the first 5 principal components and other
 imbalanced covariates as per our manuscript.
 Weighted genetic risk scores
 were calculated according to De Jager (Lancet Neurol. 2009 Dec;8(12):1111-9)
 and based on directly
 genotyped SNVs described by the International multiple sclerosis Genetics
 Consortium (Science. 2019
 Sep 27;365(6460)). Survival analyses were
 performed in Stata v 17 (Stata Corp,
 College Station, TX) using the cox or weibull packages as appropriate and
 noted in the data tables.
description: Data files can be opened with Excel.