Gongbu Pan1, Steve Simpson1, Ingrid van der Mei1, Jac C Charlesworth1, Robyn Lucas2, Anne-Louise Ponsonby3, Yuan Zhou1, Feitong Wu1, Bruce V Taylor1. 1. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia. 2. National Centre for Epidemiology and Population Health, Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia. 3. Murdoch Children's Research Institute, University of Melbourne, Melbourne, Victoria, Australia.
Abstract
BACKGROUND: The genetic drivers of multiple sclerosis (MS) clinical course are essentially unknown with limited data arising from severity and clinical phenotype analyses in genome-wide association studies. METHODS: Prospective cohort study of 127 first demyelinating events with genotype data, where 116 MS risk-associated single nucleotide polymorphisms (SNPs) were assessed as predictors of conversion to MS, relapse and annualised disability progression (Expanded Disability Status Scale, EDSS) up to 5-year review (ΔEDSS). Survival analysis was used to test for predictors of MS and relapse, and linear regression for disability progression. The top 7 SNPs predicting MS/relapse and disability progression were evaluated as a cumulative genetic risk score (CGRS). RESULTS: We identified 2 non-human leucocyte antigen (HLA; rs12599600 and rs1021156) and 1 HLA (rs9266773) SNP predicting both MS and relapse risk. Additionally, 3 non-HLA SNPs predicted only conversion to MS; 1 HLA and 2 non-HLA SNPs predicted only relapse; and 7 non-HLA SNPs predicted ΔEDSS. The CGRS significantly predicted MS and relapse in a significant, dose-dependent manner: those having ≥5 risk genotypes had a 6-fold greater risk of converting to MS and relapse compared with those with ≤2. The CGRS for ΔEDSS was also significant: those carrying ≥6 risk genotypes progressed at 0.48 EDSS points per year faster compared with those with ≤2, and the CGRS model explained 32% of the variance in disability in this study cohort. CONCLUSIONS: These data strongly suggest that MS genetic risk variants significantly influence MS clinical course and that this effect is polygenic. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
BACKGROUND: The genetic drivers of multiple sclerosis (MS) clinical course are essentially unknown with limited data arising from severity and clinical phenotype analyses in genome-wide association studies. METHODS: Prospective cohort study of 127 first demyelinating events with genotype data, where 116 MS risk-associated single nucleotide polymorphisms (SNPs) were assessed as predictors of conversion to MS, relapse and annualised disability progression (Expanded Disability Status Scale, EDSS) up to 5-year review (ΔEDSS). Survival analysis was used to test for predictors of MS and relapse, and linear regression for disability progression. The top 7 SNPs predicting MS/relapse and disability progression were evaluated as a cumulative genetic risk score (CGRS). RESULTS: We identified 2 non-human leucocyte antigen (HLA; rs12599600 and rs1021156) and 1 HLA (rs9266773) SNP predicting both MS and relapse risk. Additionally, 3 non-HLA SNPs predicted only conversion to MS; 1 HLA and 2 non-HLA SNPs predicted only relapse; and 7 non-HLA SNPs predicted ΔEDSS. The CGRS significantly predicted MS and relapse in a significant, dose-dependent manner: those having ≥5 risk genotypes had a 6-fold greater risk of converting to MS and relapse compared with those with ≤2. The CGRS for ΔEDSS was also significant: those carrying ≥6 risk genotypes progressed at 0.48 EDSS points per year faster compared with those with ≤2, and the CGRS model explained 32% of the variance in disability in this study cohort. CONCLUSIONS: These data strongly suggest that MS genetic risk variants significantly influence MS clinical course and that this effect is polygenic. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
Authors: Kayla C Jackson; Katherine Sun; Christopher Barbour; Dena Hernandez; Peter Kosa; Makoto Tanigawa; Ann Marie Weideman; Bibiana Bielekova Journal: Ann Hum Genet Date: 2019-08-08 Impact factor: 1.670
Authors: Claudia H Marck; Alysha M De Livera; Chelsea R Brown; Sandra L Neate; Keryn L Taylor; Tracey J Weiland; Emily J Hadgkiss; George A Jelinek Journal: PLoS One Date: 2018-05-23 Impact factor: 3.240
Authors: Anne I Boullerne; Mitchell T Wallin; William J Culpepper; Heidi Maloni; Elizabeth A Boots; Dagmar M Sweeney; Douglas L Feinstein Journal: Mult Scler Relat Disord Date: 2021-08-02 Impact factor: 4.808