Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that results in significant neurodegeneration in the majority of those affected and is a common cause of chronic neurological disability in young adults1,2. Here, to provide insight into the potential mechanisms involved in progression, we conducted a genome-wide association study of the age-related MS severity score in 12,584 cases and replicated our findings in a further 9,805 cases. We identified a significant association with rs10191329 in the DYSF–ZNF638 locus, the risk allele of which is associated with a shortening in the median time to requiring a walking aid of a median of 3.7 years in homozygous carriers and with increased brainstem and cortical pathology in brain tissue. We also identified suggestive association with rs149097173 in the DNM3–PIGC locus and significant heritability enrichment in CNS tissues. Mendelian randomization analyses suggested a potential protective role for higher educational attainment. In contrast to immune-driven susceptibility3, these findings suggest a key role for CNS resilience and potentially neurocognitive reserve in determining outcome in MS.
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Genome-wide study of longitudinal brain imaging measures of multiple sclerosis progression across six clinical trials
Scientific Reports Open Access 31 August 2023
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The GWAS summary statistics generated in this study can be accessed through the International Multiple Sclerosis Genetics Consortium website (https://imsgc.net/). Individual-level genetic and phenotype data are deposited in the European Genome-phenome Archive for European centers (accession number EGAS00001007162) and in dbGAP (accession number phs002929.v1.p1) for other centres. Access restrictions are detailed in the Supplementary Note. Swedish participant metadata is available on Figshare (https://doi.org/10.6084/m9.figshare.22551355.v1) and access to genotype data can be requested by contacting the senior principal investigator at the Karolinska Institutet (currently firstname.lastname@example.org) and signing the required legal agreement regarding data sharing. Gene expression profiles of human tissues used in this study can be downloaded from the GTEx Portal v8 (https://gtexportal.org/home/datasets). The single-cell type expression profiles in human tissues can be downloaded from the Human Protein Atlas (https://www.proteinatlas.org/about/download). Additional CNS single-nucleus RNA expression and cell-type annotation data were obtained from the Gene Expression Omnibus under accession numbers GSE71585, GSE97942, GSE118257, and GSE180759. We used publicly available data from the eQTL Catalogue release 4 (https://www.ebi.ac.uk/eqtl/Data_access/), the LDSC GitHub repository (https://github.com/bulik/ldsc/) and the Gonçalo Castelo-Branco Group (https://ki.se/en/mbb/oligointernode/). Detailed information on the GWAS summary statistics used in the Mendelian randomization analysis is provided in Supplementary Table 18. The GRCh37 reference genome used for mapping was obtained from the 1000 Genomes Project (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/).
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We thank all study participants for their support and for making this work possible. This work was supported by funding from the NIH/NINDS (R01NS099240) to S.E.B. and S.J.S., and the European Union’s Horizon 2020 Research and Innovation Funding Programme (EU RIA 733161) to MultipleMS. We acknowledge support from the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. A.H. is supported by the NMSS-ABF Clinician Scientist Development Award (FAN-1808-32256) funded by the National Multiple Sclerosis Society (NMSS) and the Multiple Sclerosis Society of Canada (MSSC). We thank G. Liu for help with the longitudinal analyses. P.S. is supported by the Magretha af Ugglas foundation and Horizon 2020 EU grant (MultipleMS, 733161). S.E.B. holds the Professorship in Neurology I and the Heidrich Family and Friends Endowed Chair in Neurology. The UCSF DNA biorepository is supported by the NMSS (Si-2001-35701). J.L.M. acknowledges funding support from the NIH/NINDS (R01NS096212) and the Genentech Health Equity Innovation Fund (G-79758). L.A. has received academic grant support from the Swedish Research Council, the Swedish Research Council for Health, Working Life and Welfare and the Swedish Brain foundation. S.R.D. has received institutional research grant funding from the NMSS and the NIH/NINDS. T.O. has received academic grant support from the Swedish Research Council, the Swedish Brain foundation, Knut and Alice Wallenberg foundation and Margaretha af Ugglas foundation. M.J.F.-P. has received grant support from the Multiple Sclerosis Society of Western Australia (MSWA). M.V. is a PhD fellow (11ZZZ21N) and B.D. is a Clinical Investigator of the Research Foundation Flanders (FWO-Vlaanderen). B.D. and A.G. have received academic grant support from the Research Fund KU Leuven (C24/16/045) and the Research Foundation Flanders (FWO G.07334.15). S.L. holds research support from the Spanish Government (PI21/010189, PI18/01030, PI15/00587), funded by the Instituto de Salud Carlos III-Subdirección General de Evaluación and co-funded by the European Union, and the Red Española de Esclerosis Múltiple (REEM: RD16/0015/0002, RD16/0015/0003). S.B. and F.Z. have received funding from the German Research Foundation (CRC-TR-128). F.Z. also acknowledges support from the Progressive MS Alliance (BRAVEinMS PA-1604-08492) and the Federal Ministry of Education and Research (VIP+ HaltMS-03VP07030). A.M. is supported by Margaretha af Ugglas foundation. B.H. is associated with DIFUTURE (Data Integration for Future Medicine) (BMBF 01ZZ1804[A-I]), and received funding for the study by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology [EXC 2145 SyNergy – ID 390857198]. The study was supported by the Italian Foundation of Multiple Sclerosis (FISM, 2011/R/14 2015/R/10, 2019/R-Multi/033, grants), Ricerca finalizzata, Italian Ministry of Health (RF-2016-02361294 grant), the AGING Project for Department of Excellence at the Department of Translational Medicine (DIMET), Università del Piemonte Orientale, Novara, Italy. N.B. is partly supported by the MultipleMS project (Horizon 2020 European, grant 733161). N.A.P. was supported in part by the NMSS (grants JF-1808-32223 and RG-1707-28657). I. Kockum was partly supported by the MultipleMS project (Horizon 2020 European, Grant N. 733161), the Swedish Research Council (Grant N. 2020-01638), and the Swedish Brain foundation. L.F.B. is supported by the NIH (R01ES017080, R01AI076544, R01NS049510, and R01NR017431). We thank the late R. Q. Hintzen for his contributions to human genetic research.
T.O. has received compensation for advisory boards/lectures from Biogen, Novartis, Merck and Sanofi, as well as unrestricted MS research grants from the same companies, none of which are related to the current article. A.B. and his institution have received compensation for consultancy, lectures, and participation in clinical trials from Alexion, Biogen, Celgene, Merck, Novartis, Sandoz/Hexal, Sanofi and Roche, all outside the current work. S.R.D. has received compensation for serving on advisory boards from Novartis, and institutional research grant funding from EMD Serono and Novartis, all outside the current work. M.F. is Editor-in-Chief of the Journal of Neurology, Associate Editor of Human Brain Mapping, Associate Editor of Radiology, and Associate Editor of Neurological Sciences; received compensation for consulting services and/or speaking activities from Alexion, Almirall, Bayer, Biogen, Celgene, Eli Lilly, Genzyme, Merck-Serono, Neopharmed Gentili, Novartis, Roche, Sanofi, Takeda and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla and ARiSLA (Fondazione Italiana di Ricerca per la SLA). J.L.-S. received travel compensation from Biogen, Merck, Novartis; has been involved in clinical trials with Biogen, Novartis, Roche; her institution has received honoraria for talks and advisory board service from Biogen, Merck, Novartis, Roche, all outside the current work. M.J.F.-P. has received travel compensation from Merck outside the current work. A.G.K. has received speaker honoraria and Scientific Advisory Board fees from Bayer, BioCSL, Biogen Idec, Lgpharma, Merck, Novartis, Roche, Sanofi-Aventis, Sanofi Genzyme, Teva, NeuroScientific Biopharmaceuticals, Innate Immunotherapeutics and Mitsubishi Tanabe Pharma, all outside of the current work. F.Z. has recently received research grants and/or consultation funds from Biogen, Ministry of Education and Research (BMBF), Bristol-Meyers-Squibb, Celgene, German Research Foundation (DFG), Janssen, Max-Planck-Society (MPG), Merck-Serono, Novartis, Progressive MS Alliance (PMSA), Roche, Sanofi Genzyme and Sandoz, all outside of the current work. B.D. has received consulting fees and/or funding from Biogen Idec, BMS, Sanofi-Aventis and Teva. B.D. and A.G. have received consulting/travel fees and/or research funding from Novartis, Roche and Merck, all outside the current work. S.L. received compensation for consulting services and speaker honoraria from Biogen Idec, Novartis, TEVA, Genzyme, Sanofi and Merck, all outside the current work. S.B. has received honoraria from Biogen Idec, Bristol Meyer Squibbs, Merck Healthcare, Novartis, Roche, Sanofi Genzyme and TEVA; his research is funded by the German Research Foundation (DFG), Hertie Foundation and the Hermann and Lilly-Schilling Foundation. F.E. received compensation for consulting services and speaker honoraria from Novartis, Sanofi Genzyme, Almirall, Teva and Merck-Serono. J. Smolders received consultancy and/or lecture fee from Biogen, Merck, Novartis and Sanofi Genzyme, his institution received research funding by Biogen, GSK, Idorsia and Merck, all outside the current work. B.H. has served on scientific advisory boards for Novartis; he has served as DMSC member for AllergyCare, Polpharma, Sandoz and TG therapeutics; his institution received research grants from Regeneron and Roche for MS research. He holds part of two patents; one for the detection of antibodies against KIR4.1 in a subpopulation of patients with MS and one for genetic determinants of neutralizing antibodies to interferon. J. Saarela received speaker honoraria and a research grant for rare diseases from Sanofi Genzyme, and is a founder and minority shareholder of the University of Helsinki spin-off company VEIL.AI. J.L.M. has participated in advisory board meetings for Sanofi Genzyme and received research funding from Genentech, Biogen Idec and the Bristol-Myers Squibb Foundation. N.A.P. is currently an employee of Novartis Institutes for BioMedical Research (NIBR). K. Stefánsson and I.J. are employees of the biotechnology company deCODE genetics/AMGEN. L. Amezcua reports personal compensation for consulting and serving on steering committees or advisory boards for Biogen Idec, Novartis, Genentech, EMD Serono, and research funding from the Bristol-Myers Squibb Foundation, NMSS, Race to Erase MS and NIH NINDS. P.C. reports consulting fees from Biogen, Nervgen, Idorsia, Avidea (now Vaccitech) and Disarm Therapeutics (now Lilly); research grant support from Genentech. A.R.C. reports personal compensation for participating as active speaker, consulting and serving on steering committees or advisory boards for Biogen Idec, Novartis, Genentech, EMD Serono, Bristol-Myers Squib, Sanofi Genzyme, Banner Life Sciences, Alexion and Horizon. The other authors declare no competing interests.
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Extended data figures and tables
a, Discovery population (n = 12,584). b, Replication population (n = 9,805). Bars represent the proportion of patients in each category. Centers are ordered as in the box plot legend (bottom right subpanel). Box plots show median, first, and third quartiles; whiskers represent the smallest and largest values within 1.5-times the interquartile range; outliers are depicted as dots. The countries corresponding to the abbreviations in the box plot legend are shown in Supplementary Table 1. ARMSS, age-related multiple sclerosis severity; EDSS, expanded disability status scale; Primary prog., primary progressive; yrs, years.
MS cases were recruited from 13 countries for the discovery (a) and 8 for the replication (b). After removing population outliers, all remaining cases were of European ancestry. The first two principal components respectively captured the north-to-south and east-to-west gradients of European genetic structure. US and Canadian participants overlapped with those from other countries. Based on self-reported ancestry, East European and Ashkenazi Jewish individuals constituted the majority of the predominantly US subcluster located at the bottom right of the discovery population (a). The scree plots for our principal component analysis in the discovery (c) and replication (d) populations confirm that the first few principal components capture most of the variance attributable to the minimal population structure remaining after quality control.
a, Genome-wide significant lead variant rs10191329. b, Suggestive lead variant rs149097173. Forest plots show successful replication of the two variants with minimal heterogeneity between centers as indicated by the Cochran’s Q and I2 statistics (n = 9,805 participants). ARMSS scores are rank-based inverse-normal transformed. Error bars represent 95% CIs. ARMSS, age-related multiple sclerosis severity; CI, confidence interval.
a, Adjusted mean EDSS scores over time by carrier status for rs149097173 predicted from LMM analysis. Shaded ribbons indicate the standard error of the mean over time; P value from LMM. b, Covariate-adjusted cumulative incidence of 24-week confirmed disability worsening for the same groups of individuals. c, Covariate-adjusted cumulative incidence of requiring a walking aid; carriers had a 2.2-year shorter median time to require a walking aid. HR and two-sided P values were obtained from Cox proportional hazards models using imputed allele dosage (b–c; Methods). Results were not significant after adjusting for multiple testing across two variants (see Fig. 3 for rs10191329 associations) and three outcomes (P < 0.05/6), although the latter are not expected to be independent. CI, confidence interval; HR, hazard ratio.
Gene expression profiles were obtained from GTEx73 (version 8). Transcripts were collapsed to the gene level and expressed in natural log-transformed transcript per million (TPM) units. DYSF, ZNF638, DNM3 and PIGC are expressed in the brain. Box plots show median, first, and third quartiles; whiskers represent the smallest and largest values within 1.5-times the interquartile range; outliers are depicted as dots. Bold x-axis labels identify CNS tissues. Colors represent tissue types as defined in GTEx.
Single-cell RNA sequencing data from 25 human tissues and peripheral blood mononuclear cells were obtained from the Human Protein Atlas77. Transcript expression levels were summarized per gene and reported as average normalized transcripts per million (nTPM) in 76 cell types. Asterisks mark cell type specificity for the gene, defined as at least fourfold higher expression in a cell type compared to the mean of others. We note that three of the genes show specificity for oligodendrocyte lineage cells. PIGC expression in brain neuronal and glial cells, missing here, is demonstrated in Extended Data Fig. 8. Colors represent cell type categories; bold x-axis labels identify neuronal and glial cell categories.
Single nuclear RNA expression from 4 progressive MS patients and 5 non-neurological controls26 confirms PIGC expression in neuronal and glial cells including oligodendrocyte lineage cells. COPs, committed oligodendrocyte precursors; ImOLGs, immune oligodendroglia; Oligo, oligodendrocyte; OPCs, oligodendrocyte precursor cells; Vasc, vascular.
Shared genetic contribution obtained from cross-trait LDSC. Colors correspond to genetic correlation (rg) estimates (blue, negative; red, positive). An asterisk indicates a correlation that is significantly different from zero, based on two-sided P values calculated using LDSC (*FDR < 0.05, **FDR < 0.01). Full results are in Supplementary Table 17. Aging-GIP1 was constructed using principal component analysis to capture GWASs of healthspan, father lifespan, mother lifespan, longevity, frailty, and self-rated health85.
Extended Data Fig. 9 Association of individual MS susceptibility variants (n = 209) with longitudinal disability outcomes.
a, Distribution of P values from adjusted LMM analysis of EDSS change across all study visits. Distribution of two-sided P values from adjusted Cox proportional hazards analyses of (b) time to 24-week confirmed disability worsening and (c) time to require a walking aid. The dashed orange line represents the Bonferroni-corrected significance threshold adjusted for the number of susceptibility variants. d, Venn diagram of nominal associations (Punadjusted < 0.05) between individual MS susceptibility variants and all disability outcomes considered; no variant showed consistent association across three or more outcomes. The labels in this panel correspond to the following outcomes: ARMSS, association with ARMSS scores following rank-based inverse normal transformation; Disability worsening, time to 24-week confirmed disability worsening; Walking aid, time to require a walking aid (EDSS 6.0); EDSS rate, rate of EDSS change across all study visits.
a, Adjusted mean EDSS scores over time by PGS quartile predicted from LMM analysis. Shaded ribbons indicate the standard error of the mean over time; P value from LMM. b, Covariate-adjusted cumulative incidence of 24-week confirmed disability worsening comparing individuals in the highest versus those in the lowest quartile of MS susceptibility PGS. c, Covariate-adjusted cumulative incidence of requiring a walking aid for the same groups of individuals. HR and two-sided P values were obtained from Cox proportional hazards models using imputed allele dosage (b–c; Methods). Across all analyses, the MS susceptibility PGS had no influence on longitudinal outcomes.
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International Multiple Sclerosis Genetics Consortium., MultipleMS Consortium. Locus for severity implicates CNS resilience in progression of multiple sclerosis. Nature 619, 323–331 (2023). https://doi.org/10.1038/s41586-023-06250-x