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 1813 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 CNS 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.

College of Medicine and Public Health Flinders University Adelaide SA 5042 Australia

CORe Department of Medicine University of Melbourne Melbourne VIC 3050 Australia

Department of Cell Biology and Immunology Instituto de Parasitología y Biomedicina López Neyra CSIC 18016 Granada Spain

Department of Medicine University of Melbourne Melbourne VIC 3050 Australia

Department of Neurology Alfred Health Melbourne VIC 3004 Australia

Department of Neurology and Centre of Clinical Neuroscience 1st Faculty of Medicine Charles University and General University Hospital 500 05 Prague Czech Republic

Department of Neurology and Division of Genetics Department of Medicine Brigham and Women's Hospital Harvard Medical School Brookline MA 02115 USA

Department of Neurology Fundación DINAC 41009 Sevilla Spain

Department of Neurology Hospital Universitario Virgen Macarena 41009 Sevilla Spain

Department of Neurology John Hunter Hospital Newcastle NSW 2305 Australia

Department of Neurology Melbourne Health Melbourne VIC 3050 Australia

Department of Neuroscience Central Clinical School Monash University Melbourne VIC 3004 Australia

Genomics Research Centre Centre of Genomics and Personalised Health Queensland University of Technology Brisbane QLD 4000 Australia

John P Hussman Institute for Human Genomics Miller School of Medicine University of Miami Miami FL 33136 USA

Melbourne Neuroscience Institute University of Melbourne Parkville VIC 3010 Australia

Menzies Institute for Medical Research University of Tasmania Hobart TAS 7000 Australia

Multiple Sclerosis Center and the Center for Translational and Computational Neuroimmunology Department of Neurology Columbia University New York NY 10027 USA

School of Medicine and Public Health University of Newcastle Newcastle NSW 2308 Australia

UGC Neurología Hospital Universitario Virgen Macarena Nodo Biobanco del Sistema Sanitario Público de Andalucía 41009 Sevilla Spain

Westmead Institute University of Sydney Sydney NSW 2145 Australia

Citace poskytuje Crossref.org

No evidence for association between rs10191329 severity locus and longitudinal disease severity in 1813 relapse-onset multiple sclerosis patients from the MSBase registry