Not all roads lead to the immune system: the genetic basis of multiple sclerosis severity.

Autor:	Jokubaitis VG; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia.; Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.; Department of Medicine, University of Melbourne, Melbourne, VIC 3050, Australia.; Department of Neurology, Melbourne Health, Melbourne, VIC 3050, Australia., Campagna MP; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia., Ibrahim O; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia., Stankovich J; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia., Kleinova P; Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 500 05 Prague, Czech Republic., Matesanz F; Department of Cell Biology and Immunology, Instituto de Parasitología y Biomedicina López Neyra, CSIC, 18016 Granada, Spain., Hui D; Department of Neurology and Division of Genetics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School, Brookline, MA 02115, USA., Eichau S; Department of Neurology, Hospital Universitario Virgen Macarena, 41009 Sevilla, Spain., Slee M; College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia., Lechner-Scott J; Department of Neurology, John Hunter Hospital, Newcastle, NSW 2305, Australia.; School of Medicine and Public Health, University of Newcastle, Newcastle, NSW 2308, Australia., Lea R; Genomics Research Centre, Centre of Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4000, Australia., Kilpatrick TJ; Department of Neurology, Melbourne Health, Melbourne, VIC 3050, Australia.; Melbourne Neuroscience Institute, University of Melbourne, Parkville, VIC 3010, Australia., Kalincik T; Department of Neurology, Melbourne Health, Melbourne, VIC 3050, Australia.; CORe, Department of Medicine, University of Melbourne, Melbourne, VIC 3050, Australia., De Jager PL; Multiple Sclerosis Center and the Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University, New York, NY 10027, USA., Beecham A; John. P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA., McCauley JL; John. P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA., Taylor BV; Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7000, Australia., Vucic S; Westmead Institute, University of Sydney, Sydney, NSW 2145, Australia., Laverick L; Department of Medicine, University of Melbourne, Melbourne, VIC 3050, Australia., Vodehnalova K; Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 500 05 Prague, Czech Republic., García-Sanchéz MI; UGC Neurología. Hospital Universitario Virgen Macarena, Nodo Biobanco del Sistema Sanitario Público de Andalucía, 41009 Sevilla, Spain., Alcina A; Department of Cell Biology and Immunology, Instituto de Parasitología y Biomedicina López Neyra, CSIC, 18016 Granada, Spain., van der Walt A; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia.; Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.; Department of Medicine, University of Melbourne, Melbourne, VIC 3050, Australia.; Department of Neurology, Melbourne Health, Melbourne, VIC 3050, Australia., Havrdova EK; Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 500 05 Prague, Czech Republic., Izquierdo G; Department of Neurology, Hospital Universitario Virgen Macarena, 41009 Sevilla, Spain.; Department of Neurology, Fundación DINAC, 41009 Sevilla, Spain., Patsopoulos N; Department of Neurology and Division of Genetics, Department of Medicine Brigham and Women's Hospital, Harvard Medical School, Brookline, MA 02115, USA., Horakova D; Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 500 05 Prague, Czech Republic., Butzkueven H; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia.; Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia.; Department of Medicine, University of Melbourne, Melbourne, VIC 3050, Australia.; Department of Neurology, Melbourne Health, Melbourne, VIC 3050, Australia.
Jazyk:	angličtina
Zdroj:	Brain : a journal of neurology [Brain] 2023 Jun 01; Vol. 146 (6), pp. 2316-2331.
DOI:	10.1093/brain/awac449
Abstrakt:	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. (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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