The genomic map of multiple sclerosis involves the sensitivity of peripheral immune cells and microglia

Functional implications of MS loci, enriched pathways and gene sets

Then we started annotating the MS effects. To prioritize cell types or tissues in which the 200 non-MHC autosomal effects may exert their effect, we used two different approaches: one that exploits the atlases of gene expression patterns and the other that uses a catalog of epigenomic features such as Deoxyribonuclease (DHS) hypersensitivity sites (8, 9, 22–24). Significant enrichment in MS susceptibility loci was apparent in many types of immune cells and tissues, whereas there was no enrichment in the central nervous system (CNS) profiles at the tissue level (Fig. 5). Enrichment is observed not only in immune cells long studied in MS, such as T cells, but also in B cells, whose role appeared more recently (25). In addition, although the adaptive immune system has been proposed to play a predominant role in the onset of MS (26), we now demonstrate that many elements of innate immunity, such as NK cells and dendritic cells, also exhibit strong enrichment in MS susceptibility genes. At the tissue level, the role of the thymus is also highlighted, suggesting perhaps a role of genetic variation in the thymic selection of autoreactive T cells in MS (27). Public data on the central nervous system at the tissue level, which come from a complex mixture of cell types, do not show an excess of susceptibility variants to MS in the annotation analyzes. However, multiple sclerosis being a disease of the central nervous system, we extended the analysis of annotations by analyzing data generated from neurons derived from human iPSC, as well as astrocytes and purified primary human microglia (9). As shown in Figure 6, gene enrichment of MS is observed in human microglia (P = 5 × 10^-14) but not in astrocytes or neurons, suggesting that resident brain immune cells may also play a role in susceptibility to MS.

We repeated the enrichment analyzes for the S and NR effects, in order to verify if they have a similar enrichment profile with the effects of 200 GW. The S effects exhibited an enrichment pattern similar to that of GW effects, with only the expression of B cells reaching a statistical significance threshold (Fig. S7). This provides additional circumstantial evidence that this category of variants may harbor true causal associations. On the other hand, the results of NR enrichment seem to follow a rather random pattern, suggesting that most of these effects are not really related to MS (Figure S7).

The strong enrichment of the effects of GW in immune cell types has motivated us to prioritize susceptibility genes that are candidates for MS by identifying susceptibility variants that affect gene RNA expression. relatives.[Effetcisexpressionquantitativedelociexpression(cis[Cisexpressionquantitativetraitlocieffect(cis[effetcisexpressionquantitativedelociexpression(cis[cisexpressionquantitativetraitlocieffect(cis–eQTL)] [±500 kilobase pairs (kbp) around the effect SNP] (9). Thus, we questioned the potential function of MS susceptibility variants in naïve CD4.⁺ T-cells and monocytes from 211 healthy subjects as well as peripheral blood mononuclear cells (PBMCs) from 225 subjects with relapsing-remitting multiple sclerosis. Of the effects of 200 GW MS, 36 (18%) had at least one marking SNP (r² ≥ 0.5) having altered the expression of 46 genes [false discovery rate (FDR) < 5%] in CD4⁺ naive T lymphocytes (Tables S15 and S16) and 36 effects of MS (18%, 10 points in common with CD4⁺ naive T cells) influenced the expression of 48 genes in monocytes (11 genes common to T cells). In PBMCs of MS, 30% of GW effects (60 out of 200) were cis-eQTL for 92 genes in MS PBMC samples, with several loci shared with those found in T cells and healthy monocytes (26 effects and 27 genes). in T cells and 21 effects and 24 genes in monocytes, respectively) (Tables S15 and S16).

Since MS is a disease of the central nervous system, we also studied a large collection of Dorsolateral Prefrontal Rectal RNA Sequencing profiles from two longitudinal cohort studies on aging (not = 455 subjects), who recruit people who are not cognitively impaired (9). This cortical sample provides a tissue-level profile derived from a complex mixture of neurons, astrocytes, and other parenchymal cells, such as microglia and occasional peripheral immune cells. In these data, we found that 66 of the effects of GW MS (33% of the 200 effects) were cis-eQTL for 104 genes. On this central nervous system and the three sets of immune data, 104 GW effects were cis-eQTL for 203 different genes (not = 211 cis-eQTL), many seeming to be apparently specific to one type of cell or tissue (Table S16). Specifically, 21.2% (45 of 211 cis-eQTL) of these cortical cis-eQTLs showed no evidence of association.[[[[P > 0.05, for linear regression (9), with any SNP with r² > 0.1]in immune cells and PBMCs and are less likely to be related to the immune system (Tables S16 and S17).

To further explore the delicate and critical question of whether some of the variants of MS have an effect primarily exerted through a non-immune cell, we performed a secondary analysis of our cortical RNA sequencing data (RNA- seq) in which we attempted to assign a cis-eQTL brain to a particular cell type. Specifically, we assessed our tissue profile and adjusted each cis-eQTL analysis according to the proportion of estimated neurons, astrocytes, microglia, and oligodendrocytes in the tissue: The hypothesis was that the effect of a specific cell-specific e-CQTL-like SNP would be stronger if we adjust the proportion of the target cell type (Fig. 6 and Fig. S8). As expected, almost all MS variants present in the cortex remain ambiguous; it is likely that many of them influence the function of genes in many types of immune and non-immune cells. However, the SLC12A5 the locus is different; Here, the effect of the SNP is significantly stronger when we take into account the proportion of neurons (Fig. 6, A and B) and the CLECL1 the locus appears when we take into account the proportion of microglia. SLC12A5 is a potassium / chloride transporter whose expression is known in neurons and a rare variant in SLC12A5 causes a form of pediatric epilepsy (28, 29). Although this MS locus may therefore appear to be a good candidate for a predominantly neuronal effect, subsequent evaluation has shown that this susceptibility haplotype for MS also harbor susceptibility to rheumatoid arthritis (30) and a cis-eQTL in B cells for the CD40 uncomfortable (31). Thus, the same haplotype harbored different functional effects in very different contexts, illustrating the challenge of dissecting the functional consequences of autoimmune variants of immune function as opposed to targeted tissue in autoimmune disease. however, CLECL1 represents a simpler case of known susceptibility effect that has already been linked to an alteration CLECL1 Expression of RNA in monocytes (26, 32) Its enrichment in microglial cells, which share many molecular pathways with other myeloid cells, is simpler to understand. CLECL1 is expressed at low concentrations in our cortical profiles of RNA-seq because microglia represent only a small fraction of the cells at the cortical tissue level, and CLECL1The level of expression is 20-fold higher when we compare its level of expression in purified human cortical microglia to bulk cortical tissue (Fig. 6). CLECL1 therefore suggests a potential role for microglia in susceptibility to MS, which is underestimated in the bulk tissue profiles available in epigenomic and transcriptomic reference data. Overall, many genes that are eQTL targets of MS variants in the human cortex are most likely to affect multiple cell types. These cerebral eQTL findings and enrichment found in the analyzes of our data on purified human microglia thus underscore the need for more targeted, cell-specific, CNS-specific data to adequately determine the size of the cell. extent of its role in susceptibility to MS.

These eQTL studies begin to transform our genetic map into a resource describing the gene (s) of probable susceptibility to MS in a locus and the potential functional consequences of certain variants of MS. To link these results to a single locus in a higher order perspective of susceptibility to MS, we turned to pathway analyzes to evaluate how the extended list of genome-wide effects provides new information on the pathophysiology of the disease. Acknowledging that there was no available method to identify all causative genes after the findings of the Whole-Genome Association (GWAS) study, we prioritized the genes for all-channel analyzes. by allowing several hypotheses of mechanisms of action (9). In summary, we prioritized the (i) cis-eQTL genes in one of the eQTL datasets described above, (ii) had at least one exon variant at r² ≥ 0.1 with one of 200 effects, (iii) had a high regulator potential score using a cell-specific network approach, and (iv) had a coexpression pattern similar to that identified with DEPICT (33). Sensitivity analyzes comprising different combinations of the above categories and including genes with intronic variants to r² ≥ 0.5 with any of the 200 effects (9). Overall, we prioritized 551 MS candidate genes (Table S18, sensitivity analyzes are provided in Table S19) to test the statistical enrichment of known pathways. About 39.6% (142 out of 358) of the canonical pathways of ingenuity path analysis (34), which overlapped at least one of the identified genes, were enriched for MS genes at <5% FDR (Table S20). Sensitivity analyzes including different criteria for ranking genes revealed a similar pattern of pathway enrichment (Table S21) (9). The long list of susceptibility genes, which doubles more than previous knowledge of MS, captures the processes of development, maturation, and terminal differentiation of several immune cells that may interact to predispose to MS. In particular, the role of B cells, dendritic cells and NK cells has become clearer, broadening the previous story of T-cell dysregulation that had emerged from previous studies (4). Given the overrepresentation of the immune pathways in these databases, there remains an ambiguity on the place of action of certain variants: neurons, and in particular astrocytes, reorient the genes constituting many "immune" signaling pathways Such as ciliary neurotrophic factor, nerve growth factor, and very significant neuregulin signaling pathways in our analysis (Table S20). These results, as well as those related to microglia, highlight the need for further dissection of these pathways in specific cell types to resolve cases where a variant exerts its effect; it is possible that several types of cells are involved in the disease because they all experience the effect of the variant.

Enrichment analyzes of pathways and gene sets can only identify statistically significant gene connections in previously reported and, in some cases, validated mechanisms of action. However, the function of many genes remains to be discovered and, even for well-studied genes, the complete repertoire of possible mechanisms is still incomplete. To complete the pathway analysis approach and explore the connectivity of our priority GW genes, we performed a protein-protein interaction analysis (PPI) using GeNets (9, 35). About one-third of the 551 priority genes (not = 190; 34.5%) were connected (P = 0.052; based on permutation P added value), and these could be organized into 13 communities – subnetworks with higher connectivity (P <0.002; based on permutation P value) (Table S22). This compares with nine communities that could be identified by the previously reported MS susceptibility list (81 genes out of 307) (Table S23) (3). Next, we exploited the GeNets to predict candidate genes based on network connectivity and pathway similarity, and checked to see if our previously known susceptibility list for MS could have predicted. One of the priority genes among recently identified effects. Of the 244 genes ranked by the new discoveries (out of the 551 global hierarchical genes), only five could be predicted given the old results (out of the 70 candidates from the extrapolation of previous data) (Figure S9 and table). S24). Similarly, we estimated that the list of 551 ranked genes could predict 102 new candidate genes, four of which can be prioritized because they are in the list of suggestion effects. (Fig. 1, Fig. S10 and Table S25).

International Consortium on the Genetics of Multiple Sclerosis

Nikolaos A Patsopoulos^1,2,3,4Sergio E. Baranzini⁵Adam Santaniello⁵Parisa Shoostari^4,6,7* Chris Cotsapas^4,6,7Garrett Wong^1.3Ashley H. Beecham⁸Tojo James⁹Joseph Replogle^2,3,4,10, Ioannis S. Vlachos^1,3,4Cristin McCabe⁴, Tune H. Pers¹¹Aaron Brandes⁴Charles White^4.10Brendan Keenan¹²Maria Cimpean^tenPhoebe Winn^ten, Ioannis-Pavlos Panteliadis^1.4Allison Robbins^ten, Up to F. M. Andlauer^13,14,15, Onigiusz Zarzycki^1.4Bénédicte Dubois¹⁶, A goris¹⁶, Helle Bach Søndergaard¹⁷Finn Sellebjerg¹⁷, Soelberg Sorensen¹⁷Henrik Ullum¹⁸, Lise Wegner Thørner¹⁸, Janna Saarela¹⁹Isabelle Cournu-Rebeix²⁰Vincent Damotte^20.21Bertrand Fontaine^20.22, Lena Guillot-Noel²⁰Mark Lathrop^23,24,25Sandra Vukusic^26,27,28Achim Berthele^14,15, Viola Pongratz^14,15, Dorothea Buck^14,15Christiane Gasperi^14,15Christiane Graetz^15,29Verena Grummel^14,15Bernhard Hemmer^14,15,30Muni Hoshi^14,15Benjamin Knier^14,15Thomas Korn^14,15,30Christina M. Lill^15,31,32Felix Luessi^15,31, Mark Mühlau^14,15Frauke Zipp^15,31Efthimios Dardiotis³³Cristina Agliardi³⁴Antonio Amoroso³⁵Nadia Barizzone³⁶Maria D. Benedetti^37,38, Luisa Bernardinelli³⁹Paola Cavalla⁴⁰Ferdinando Clarelli⁴¹Giancarlo Comi^41,42, Daniele Cusi⁴³Federica Esposito^41.44, Laura Ferrè⁴⁴Daniela Galimberti^45,46Clara Guaschino^41.44, Maurizio A. Leone⁴⁷, Vittorio Martinelli⁴⁴Lucia Moiola⁴⁴, Marco Salvetti^48,49Melissa Sorosina⁴¹, Domizia Vecchio⁵⁰Andrea Zauli⁴¹Silvia Santoro⁴¹Nicasio Mancini⁵¹Miriam Zuccalà⁵²Julia Mescheriakova⁵³Cornelia van Duijn^53.54, Steffan D. Bos⁵⁵Elisabeth G. Celius^55.56Anne Spurkland⁵⁷Manuel Comabella⁵⁸Xavier Montalban⁵⁸, Lars Alfredsson⁵⁹, Izaura L. Bomfim⁶⁰, David Gomez-Cabrero^60,61,62, Jan Hillert⁶⁰, Maja Jagodic⁶⁰Magdalena Lindén⁶⁰Fredrik Piehl⁶⁰, Ilijas Jelčić^63.64Roland Martin^63.64, Mirela Sospedra^63.64Amie Baker⁶⁵Maria Ban⁶⁶Clive Hawkins⁶⁶Pirro Hysi⁶⁷Seema Kalra⁶⁸Fredrik Karpe⁶⁸Jyoti Khadake⁶⁹Genevieve Lachance⁶⁷Paul Molyneux⁶⁷Matthew Neville⁶⁸John Thorpe⁷⁰Elizabeth Bradshaw^tenStacy J. Caillier⁵Peter Calabresi⁷¹Bruce C. C. Cree⁵Anne Cross⁷²Mary Davis⁷³, Paul W. I. of Bakker^2,3,4†, Silvia Delgado⁷⁴Marieme Dembele⁷¹Keith Edwards⁷⁵Kate Fitzgerald⁷¹Irene Y. Frohlich^tenPierre-Antoine Gourraud^5.76, Jonathan L. Haines⁷⁷, Hakon Hakonarson^78.79Dorlan Kimbrough^3.80, Noriko Isobe^5.81Ioanna Konidari⁸Ellen Lathi⁸²Michelle H. Lee^ten, Taibo Li⁸³, David An⁸³Andrew Zimmer⁸³Lohith Madireddy⁵Clara P. Manrique⁸Mitja Mitrovic^4,6,7Marta Olah^tenEllis Patrick^10,84,85Margaret A. Pericak-Vance⁸Laura Piccio⁷¹Cathy Schaefer⁸⁶Howard Weiner⁸⁷, Kasper Lage⁸², ANZgene, IIBDGC, WTCCC2, Alastair Compston⁶⁴David Hafler^4.88, Hanne F. Harbo^54.55Stephen L. Hauser⁵Graeme Stewart⁸⁹, Sandra D'Alfonso⁹⁰Georgios Hadjigeorgiou³³Bruce Taylor⁹¹Lisa F. Barcellos⁹²David Booth⁹³Rogier Hintzen⁹⁴Ingrid Kockum⁹, Filippo Martinelli-Boneschi^41,42Jacob L. McCauley⁸, Jorge R. Oksenberg⁵Annette Oturai¹⁶Stephen Sawcer⁶², Adrian J. Ivinson⁹³Tomas Olsson⁹Philip L. De Jager^4.10

¹Computer and Systems Biology Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham & Women's Hospital, Boston, MA 02115, USA. ²Division of Genetics, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA. ³Harvard Medical School, Boston, MA 02115, USA. ⁴Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA. ⁵Department of Neurology, University of California at San Francisco, Sandler Neuroscience Center, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. ⁶Department of Neurology, School of Medicine, Yale University, New Haven, CT 06520, USA. ^sevenDepartment of Genetics, Yale School of Medicine, New Haven, CT 06520, USA. ⁸John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA. ⁹Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. ^tenTranslational and Computational Neuroimmunology Center, Multiple Sclerosis Center, Department of Neurology, Columbia University Medical Center, New York, NY, USA. ¹¹Novo Nordisk Foundation, Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark. ¹²Sleep and Circadian Neurobiology Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA. ¹³Max Planck Institute of Psychiatry, 80804 Munich, Germany. ¹⁴Department of Neurology, Klinikum Rechts der Isar, Technical University Munich, 81675 Munich, Germany. ¹⁵German skills network for multiple sclerosis. ¹⁶KU Leuven Department of Neuroscience, Neuroimmunology Laboratory, Herestraat 49 bus 1022, 3000 Leuven, Belgium. ¹⁷Danish Multiple Sclerosis Center, Department of Neurology, Rigshospitalet, University of Copenhagen, Section 6311, 2100 Copenhagen, Denmark. ¹⁸Department of Clinical Immunology, Rigshospitalet, University of Copenhagen, Section 2082, 2100 Copenhagen, Denmark. ¹⁹Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. ²⁰ICM-UMR 1127, INSERM, University of the Sorbonne, Pitié-Salpêtrière University Hospital 47 Boulevard de l'Hôpital, F-75013 Paris. ²¹UMR1167 University of Lille, Inserm, University Hospital of Lille, Pasteur Institute of Lille. ²²CRM-UMR974 Department of Neurology Pitié-Salpêtrière University Hospital 47 Boulevard de l'Hôpital F-75013 Paris. ²³Office of the Atomic Energy Commission, Genomic Institute, National Genotyping Center, Evry, France. ²⁴Jean Dausset Foundation – Center for the Study of Human Polymorphism, Paris, France. ²⁵McGill University and Génome Québec Innovation Center, Montreal, Canada. ²⁶Lyon Civil Hospices, Department of Neurology, Multiple Sclerosis, Myelin Pathologies and Neuro-inflammation, F-69677 Bron, France. ²⁷French Multiple Sclerosis Observatory, Lyon Neuroscience Research Center, INSERM 1028 and CNRS UMR 5292, F-69003 Lyon, France. ²⁸University of Lyon, Claude Bernard University Lyon 1, F-69000 Lyon, France; Eugène Devic EDMUS Foundation Against Multiple Sclerosis, F-69677 Bron, France. ²⁹Focus Translational Neuroscience Program (FTN), Main Network of Rhine Neuroscience (rmn2), Johannes Gutenberg University Medical Center, Mainz, Germany. ³⁰Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany. ³¹Department of Neurology, Specialized Translational Neuroscience (FTN) and Immunology (FZI) Program, Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany. ³²Group of Genetic and Molecular Epidemiology, Institute of Neurogenetics, University of Luebeck, Luebeck, Germany. ³³Department of Neurology, Laboratory of Neurogenetics, Larissa University Hospital, Greece. ³⁴Laboratory of Molecular Medicine and Biotechnology, Don C. Gnocchi Foundation ONLUS, IRCCS S. Maria Nascente, Milan, Italy. ³⁵Department of Medical Sciences, University of Turin, Turin, Italy. ³⁶Department of Health Sciences and Center for Interdisciplinary Research on Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy. ³⁷Regional Health Center, Health, Neurology B, AOUI Verona, Italy. ³⁸Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Italy. ³⁹Biostatistics Unit of the Medical Research Council, Robinson Way, Cambridge CB2 0SR, United Kingdom. ⁴⁰MS Center, Department of Neuroscience, A.O. Città della Salute and Science of Turin and University of Turin, Turin, Italy. ⁴¹Laboratory of Human Genetics of Neurological Complex Disorders, Institute of Experimental Neurology (INSPE), Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, 20132, Milan, Italy. ⁴²Department of Biomedical Sciences for Health, University of Milan, Milan, Italy. ⁴³University of Milan, Department of Health Sciences, San Paolo Hospital and Filarete Foundation, viale Ortles 22/4, 20139 Milan, Italy. ⁴⁴Department of Neurology, Institute of Experimental Neurology (INSPE), Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, 20132, Milan, Italy. ⁴⁵Unit of Neurology, Department of Physiopathology and Transplantation, University of Milan, Dino Ferrari Center, Milan, Italy. ⁴⁶Fondazione IRCCS Ca & # 39; Granda, Ospedale Policlinico, Milan, Italy. ⁴⁷Fondazione IRCCS Casa Sollievo della Sofferenza, Neurology Unit, San Giovanni Rotondo (FG), Italy. ⁴⁸Center for Experimental Neurological Therapies, Sant'Andrea Hospital, Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University, Rome, Italy. ⁴⁹Istituto Neurologico Mediterraneo (INM) Neuromed, Pozzilli, Isernia, Italy. ⁵⁰Department of Neurology, Ospedale Maggiore, Novara, Italy. ⁵¹Laboratory of Microbiology and Virology, San Raffaele Vita-Salute University, San Raffaele Hospital, Milan, Italy. ⁵²Department of Health Sciences and Center for Interdisciplinary Research on Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy. ⁵³Department of Neurology, Erasmus MC, Rotterdam, The Netherlands. ⁵⁴Nuffield Department of Population Health, Big Data Institute, Oxford University, Li Ka Shing Center for Discovery and Discovery of Health, Old Road Campus, Oxford OX3 7LF, UK. ⁵⁵Department of Neurology, Institute of Clinical Medicine, University of Oslo, Norway. ⁵⁶Department of Neurology, University Hospital of Oslo, Oslo, Norway. ⁵⁷Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway. ⁵⁸Servei de Neurologia-Neuroimmunologia, Center of Escleros Masters of Catalonia (Cemcat), Institute of Recerca Vall d'Hebron (VHIR), University Hospital Vall d'Hebron, Spain. ⁵⁹Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. ⁶⁰Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. ⁶¹Translational Bioinformatics Unit, NavarraBiomed, Complejo Hospitalario de Navarra (CHN), Public University of Navarra (UPNA), IdiSNA, Pamplona, ​​Navarre, Spain. ⁶²Division of Mucosal and Salivary Biology, King's College, London Dental Institute, London, UK. ⁶³Neuroimmunology and Multiple Sclerosis Research (NIMS), Neurology Clinic, Zurich University Hospital, Frauenklinikstrasse 26, 8091 Zurich, Switzerland. ⁶⁴Department of Neuroimmunology and Multiple Sclerosis Research, Neurology Clinic, University Hospital Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland. ⁶⁵University of Cambridge, Department of Clinical Neuroscience, Addenbrooke's Hospital, BOX 165, Hills Road, Cambridge CB2 0QQ, UK. ⁶⁶Keele University Medical School, North Staffordshire University Hospital, Stoke-on-Trent ST4 7NY, UK. ⁶⁷Department of Twinned Research and Genetic Epidemiology, King's College London, London, SE1 7EH, United Kingdom. ⁶⁸NIHR Oxford Biomedical Research Center, Diabetes and Metabolism Theme, OCDEM, Churchill Hospital, Oxford, United Kingdom. ⁶⁹NIHR BioResource, Box 299, University of Cambridge and Cambridge University Hospitals, NHS Trust Foundation Hills Road, Cambridge CB2 0QQ, UK. ⁷⁰Département de neurologie, hôpital municipal de Peterborough, campus Edith Cavell, Bretton Gate, Peterborough PE3 9GZ, Royaume-Uni. ⁷¹Département de neurologie, faculté de médecine de l&#39;Université Johns Hopkins, Baltimore MD. ⁷²Centre de sclérose en plaques, Département de neurologie, École de médecine, Université Washington de St Louis, St Louis MO. ⁷³Centre de recherche sur la génétique humaine, Centre médical de l&#39;Université Vanderbilt, 525 Light Hall, 2215 Garland Avenue, Nashville, TN 37232, États-Unis. ⁷⁴Division de la sclérose en plaques, Département de neurologie, Université de Miami, Miller School of Medicine, Miami, FL 33136, USA. ⁷⁵Centre MS du nord-est de l&#39;État de New York 1205 Troy Schenectady Rd, Latham, NY 12110, États-Unis. ⁷⁶Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ATIP-Avenir, Equipe 5, Nantes, France. ⁷⁷Sciences de la santé de la population et quantitatives, Département d&#39;épidémiologie et de biostatistique, Université Case Western Reserve, 10900 Euclid Avenue, Cleveland, OH 44106-4945 USA. ⁷⁸Centre de génomique appliquée, Hôpital pour enfants de Philadelphie, 3615 Civic Center Blvd., Philadelphie, PA 19104, États-Unis. ⁷⁹Département de pédiatrie, École de médecine Perelman, Université de Pennsylvanie, Philadelphie, PA, États-Unis. ⁸⁰Département de neurologie, Brigham & Women&#39;s Hospital, Boston, 02115 MA, USA. ⁸¹Départements de neurologie et de thérapeutique neurologique, Institut neurologique, École supérieure de sciences médicales, Université de Kyushu, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japon. ⁸²Centre Elliot Lewis, 110, rue Cedar, Wellesley, MA, 02481, États-Unis. ⁸³Broad Institute of Harvard University et MIT, Cambridge, 02142 MA, États-Unis. ⁸⁴École de mathématiques et de statistique, Université de Sydney, Sydney, NSW 2006, Australie. ⁸⁵Institut de recherche médicale Westmead, Université de Sydney, Westmead, NSW 2145, Australie. ⁸⁶Kaiser Permanente Division de la recherche, Oakland, Californie, États-Unis. ⁸⁷Centre Ann Romney pour les maladies neurologiques, Département de neurologie, Brigham & Women&#39;s Hospital, Boston, 02115 MA, États-Unis. ⁸⁸Départements de neurologie et d&#39;immunobiologie, faculté de médecine de l&#39;Université de Yale, New Haven, CT 06520, États-Unis. ⁸⁹Westmead Millennium Institute, Université de Sydney, Nouvelle-Galles du Sud, Australie. ⁹⁰Département des sciences de la santé et Centre de recherche interdisciplinaire sur les maladies auto-immunes (IRCAD), Université du Piémont oriental, Novara, Italie. ⁹¹Menzies Research Institute Tasmania, Université de Tasmanie, Australie. ⁹²École de santé publique UC Berkeley et Centre de biologie computationnelle, États-Unis. ⁹³Westmead Millennium Institute, Université de Sydney, Nouvelle-Galles du Sud, Australie. ⁹⁴Département de neurologie et Département d&#39;immunologie, Erasmus MC, Rotterdam, Pays-Bas. ⁹⁵Institut britannique de recherche sur la démence, University College London, Gower Street, Londres WC1E 6BT, Royaume-Uni.

* Adresse actuelle: Centre de médecine informatique, Centre Peter Gilgan de recherche et d&#39;apprentissage, Hôpital pour enfants malades (SickKids), Toronto, ON M5G 0A4, Canada.

† Adresse actuelle: Vertex Pharmaceuticals, 50 Northern Avenue, Boston, MA 02210, États-Unis.

Remerciements: Nous remercions la Harvard Aging Brain Study (HABS; P01AG036694). Nous remercions le personnel de laboratoire du centre de bio-exposition et du laboratoire de technologie du génome (en particulier S. West, S. Clarke, D. Martinez et P. Whitehead) de l’Institut John P. Hussman de génomique humaine de l’Université de Miami pour l’ADN centralisé. manipulation et génotypage pour ce projet. L&#39;IMSGC remercie W. Edgerly et L. Edgerly, J. Carlos et E. Carlos, M. Crowninshield et W. Fowler et C. Fowler, dont les engagements durables ont été essentiels à la création du consortium. Nous remercions les volontaires de la Biobanque Oxford (www.oxfordbiobank.org.uk) et de la Bioressource du Institut national de recherche sur la santé (NIHR) pour leur participation. Les points de vue exprimés sont ceux des auteurs et pas nécessairement ceux du NHS, de l&#39;INDH ou du département de la santé du ministère allemand de l&#39;éducation et de la recherche, réseau de compétences allemand MS (BMBF KKNMS). Le financement: Cette enquête a été financée en partie par des subventions de la National MS Society (NMSS) à A.J.I. pour le compte du consortium international MS Genetics (RG 4198-A-1 et AP-3758-A-16). Les autres subventions comprennent une bourse Harry Weaver en neurosciences décernée par le NMSS à P.L.D. (JF2138A1) as well as NIH grants RC2 GM093080 and R01AG036836. This work was also supported by a postdoctoral fellowship from the National Multiple Sclerosis Society (FG 1938-A-1) and a Career Independence Award from the National Multiple Sclerosis Society (TA 3056-A-2) to N.A.P. and National Multiple Sclerosis Society award AP3758-A-16. N.A.P. has been supported by Harvard NeuroDiscovery Center and an Intel Parallel Computing Center award, the U.S. National Multiple Sclerosis Society (grants RG 4680-A-1), and the NIH/NINDS (grant R01NS096212). T.A. was supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent, under the auspices of the e:Med Programme (01ZX1614J), Swedish Medical Research Council; Swedish Research Council for Health, Working Life and Welfare, Knut and Alice Wallenberg Foundation, AFA insurance, Swedish Brain Foundation, and the Swedish Association for Persons with Neurological Disabilities. This study makes use of data generated as part of the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z), including data from the British 1958 Birth Cohort DNA collection (funded by the Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02) and the UK National Blood Service controls (funded by the Wellcome Trust). The study was supported by the Cambridge NIHR Biomedical Research Centre, UK Medical Research Council (G1100125), and the UK MS society (861/07); NIH/NINDS: R01 NS049477, NIH/NIAID: R01 AI059829, NIH/NIEHS: R01 ES0495103; Research Council of Norway grant 196776 and 240102; NINDS/NIH R01NS088155; Oslo MS association and the Norwegian MS Registry and Biobank and the Norwegian Bone Marrow Registry; Research Council KU Leuven, Research Foundation Flanders; AFM, AFM-Généthon, CIC, ARSEP, ANR-10-INBS-01 and ANR-10-IAIHU-06; Research Council KU Leuven, Research Foundation Flanders; Inserm ATIP-Avenir Fellowship and Connect-Talents Award; German Ministry for Education and Research, German Competence Network MS (BMBF KKNMS); and Dutch MS Research Foundation. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the NIHR-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. The recall process was supported by the NIHR Oxford Biomedical Research Centre Programme; Italian Foundation of Multiple Sclerosis (FISM grants, Special Project “Immunochip” 2011/R/1, 2015/R/10); NMSS (RG 4680A1/1); and the MultipleMS EU project. We acknowledge the Lundbeck Foundation and Benzon Foundation for support (THP). This research was supported by grants from the Danish Multiple Sclerosis Society, the Danish Council for Strategic Research [grant 2142-08-0039], Novartis, Biogen (Denmark) A/S, the Sofus Carl Emil Friis og Hustru Olga Doris Friis Foundation, and the Foundation for Research in Neurology. The Observatoire Français de la Sclérose en Plaques (OFSEP) is supported by a grant provided by the French government and handled by the Agence Nationale de la Recherche, within the framework of the Investments for the Future program, under the reference ANR-10-COHO-002, by the Eugène Devic EDMUS Foundation against multiple sclerosis, and by the ARSEP Foundation. Competing interests: H.F.H. has received modest travel support and honoraria for advice or lecturing from Norwegian branches of Biogen Idec, Sanofi-Genzyme, Merck, Novartis, Roche, and Teva and a modest unrestricted research grant from Novartis, as well as research grants from the South Eastern Norwegian Health Authorities and the Research Council of Norway. D.H. has been a consultant or SAB for the following companies: Compass Therapeutics, EMD Serono, Genentech, Novartis Pharmaceuticals, Proclara Bioscience, Sanofi Genzyme, and Versant Venture. We believe none of these consulting activities or grants represent a conflict of interest for this work. M.S. received consulting fees or speaking honoraria from Biogen, Merck, Novartis, Sanofi, Teva, Roche. K.L. is a paid consultant and Chair of the Genetics Advisory Panel of Biogen (Cambridge, USA). K.L. is a cofounder and co-owner of Intomics (Copenhagen, DK). K.L. has been a paid consultant to Merck (Boston, USA) and GoldFinch Bio (Cambridge, USA). H.W. receives grant funding from the National Institutes of Health, National Multiple Sclerosis Society, Verily Life Sciences, EMD Serono, Biogen, Teva Pharmaceuticals, Sanofi, Novartis, Genentech, and Tilos Therapeutics as well as consulting fees from Genentech, Tiziana Life Sciences, IM Therapeutics, Magnolia Therapeutics, MedDay Pharmaceuticals, and vTv Therapeutics. P.d.B. owns stock in Vertex Pharmaceuticals. H.F.H. receives funds from the South Eastern Norwegian Health Authorities. Data availability: The GWASs used in the discovery phase are available in the following repositories: (i) dbGAP, phs000275.v1.p1, phs000139.v1.p1, phs000294.v1.p1, and phs000171.v1.p1; and (ii) European Genome-phenome Archive database, EGAD00000000120, EGAD00000000022, and EGAD00000000021. The MS Chip and ImmunoChip data are available from the respective EGA accession nos.: EGAS00001003216 and EGAS00001003219. The ImmVar data are available in the Gene Expression Omnibus (GEO): GSE56035. The MS PBMC data are available in GEO: GSE16214. Human Gene Atlas: http://snpsea.readthedocs.io/en/latest/data.html#geneatlas2004-gct-gz. ImmGen: http://snpsea.readthedocs.io/en/latest/data.html#immgen2012-gct-gz. The brain-related data are available in Synapse: www.synapse.org/#!Synapse:syn2580853/wiki/409844. The list of putative associated MS genes is available for public access in GeNets. (https://apps.broadinstitute.org/genets). A subset of the data are available for MS-related studies only because the parent studies consent does not permit deposition into a repository of genetic data: (i) The ANZGENE GWAS data are available by means of request to the ANZGENE Consortium; a direct request can be made through https://msra.org.au. The request should state the purpose of the study, its relation to MS, and a list of the data being requested. (ii) Sharing of individual participant data was not included in the informed consent of the Rotterdam MS study because there is potential risk of revealing participants’ identities, and it is not possible to completely anonymize the data. The Rotterdam MS data are available by email to [email protected]. The request should state the purpose of the study, its relation to MS, and a list of the data being requested. (iii) The Rotterdam Study control data are available to interested researchers upon request. Requests can be directed to the study’s data manager Frank J. A. van Rooij ([email protected]). The following website contains more information about this cohort: www.ergo-onderzoek.nl/wp/contact. Sharing of individual participant data was not included in the informed consent of the Rotterdam MS study, and there is potential risk of revealing participants’ identities because it is not possible to completely anonymize the data. (iv) The genetic data from MS case and controls recruited through Kaiser Permanente Division of Research and University of California, Berkeley, data are available from the Institutional Data Access/Ethics Committee at University of California, Berkeley (contact R. Harris, [email protected], for researchers who meet the criteria for access to confidential data. Please reference the manuscript title and corresponding author in your communication). Corresponding summary statistics for these three GWAS studies (ANZGENE, Rotterdam, and Berkeley) are available upon request.