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Cited by 1,491Multiple Sclerosis Research StudiesGenetic Associations and EpidemiologyPsoriasis: Treatment and Pathogenesis

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

Open access
International Multiple Sclerosis Genetics Consortium, Patsopoulos, Nikolaos A., Baranzini, Sergio E., Santaniello, Adam, Shoostari, Parisa, Cotsapas, Chris, Wong, Garrett, Beecham, Ashley H. +150 more·2019·Science
DOI 10.1126/science.aav7188PMC PMC7241648

Published version of a preprint

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Abstract

Genetic roots of multiple sclerosis: The genetics underlying who develops multiple sclerosis (MS) have been difficult to work out. Examining more than 47,000 cases and 68,000 controls with multiple genome-wide association studies, the International Multiple Sclerosis Genetics Consortium identified more than 200 risk loci in MS (see the Perspective by Briggs). Focusing on the best candidate genes, including a model of the major histocompatibility complex region, the authors identified statistically independent effects at the genome level. Gene expression studies detected that every major immune cell type is enriched for MS susceptibility genes and that MS risk variants are enriched in brain-resident immune cells, especially microglia. Up to 48% of the genetic contribution of MS can be explained through this analysis. Science , this issue p. eaav7188 ; see also p. 1383

AI snapshot

Written from the paper’s full text.

TL;DR

This study meta-analyzed multiple sclerosis (MS) genome-wide association data from 47,429 cases and 68,374 controls, establishing a reference map of 200 autosomal non-MHC susceptibility variants, one chromosome X variant, and 32 within the extended MHC. It prioritizes 551 candidate genes and implicates peripheral immune cells and brain-resident microglia in MS susceptibility.

Key findings

  • The joint analysis identified 200 genome-wide significant autosomal non-MHC effects across 156 regions, including 62 secondary independent effects; odds ratios ranged from 1.06 to 2.06. One X-chromosome variant, rs2807267, reached genome-wide significance (OR=1.07, p=6.86×10−9), while no Y-chromosome variant had p<0.05. The MHC analysis found 31 independent genome-wide effects in the primary model and 32 in a sensitivity analysis; the abstract reports 32 within the extended MHC.
  • Heritability analyses in the discovery set estimated overall narrow-sense heritability at 19.2% (95% CI: 18.5–19.8%). The super-extended MHC explained 21.4% of h2g; prioritized effects explained 68.3%; genome-wide effects captured 18.3%. Together with the MHC, validated alleles explained ~39% of genetic predisposition, extending to ~48% when including suggestive effects.
  • Functional annotation showed enrichment of MS susceptibility loci in many immune cell types, including T cells, B cells, NK cells, and dendritic cells, and in thymus. Enrichment was seen in purified human microglia (p=5×10−14) but not astrocytes or neurons. The study prioritized 551 candidate MS genes; 39.6% (142/358) of overlapping IPA canonical pathways were enriched at FDR<5%, and 190/551 genes (34.5%) formed 13 connected PPI communities versus 9 from prior MS lists.
  • eQTL analyses found 36/200 genome-wide effects altered expression of 46 genes in naive CD4+ T cells, 36 affected 48 genes in monocytes, 60 affected 92 genes in MS PBMCs, and 66 affected 104 genes in dorsolateral prefrontal cortex. Across CNS and immune datasets, 104 effects were cis-eQTLs for 203 unique genes.

Methods

  • Discovery meta-analysis used 15 datasets totaling 14,802 MS cases and 26,703 controls, imputed with the 1000 Genomes European panel to an average of 7.8 million SNPs with MAF ≥1%. Meta-analysis covered 8,278,136 SNPs; genome partitioning and conditional modeling identified 4,842 statistically independent non-MHC autosomal effects.
  • Replication used the MS Chip in 20,360 MS cases and 19,047 controls across 9 datasets, plus ImmunoChip data from 12,267 MS cases and 22,625 controls across 11 datasets. Joint analysis included 47,429 cases and 68,374 controls; X/Y and extended MHC were analyzed jointly, with HLA allele and amino acid imputation.
  • Downstream analyses included heritability partitioning, enrichment against expression and epigenomic atlases, cis-eQTL analyses in immune cells, MS PBMCs, and cortex, ensemble gene prioritization, IPA pathway enrichment, and GeNets protein-protein interaction analysis.

Limitations

  • The paper states that many implicated molecular pathways are repurposed across cell types, leaving ambiguity about whether disease risk is driven by one or multiple cell types. It also notes that reference epigenomic data cover limited cell types and states, lack established inter-individual variability, and that the functional consequence of most MS variants requires further investigation.
  • The authors state that CNS analyses rely largely on human cortex, a complex tissue mixture, and that implicated cell types and subset-specific effects—such as in microglia or activated astrocytes—remain to be resolved. They also note that existing sex-chromosome data have not been fully leveraged and that relapsing-remitting and progressive MS were included as one disease spectrum.
  • The paper reports that non-replicated effects captured 38.8% of heritability but states the vast majority are likely false positives. It also shows that bulk cortical profiles underestimate microglial expression, e.g., CLECL1 expression was 20-fold greater in purified human cortical microglia than in bulk cortical tissue, and that many cortical eQTLs remain ambiguous across cell types.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1126/science.aav7188

Cites

68 of 68 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

13 of 23 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured

  • Findingown data

    Many elements of innate immunity, such as natural killer cells and dendritic cells, display strong enrichment for MS susceptibility genes.

    “Furthermore, while the adaptive immune system has been proposed to play a predominant role in MS onset,(26) we now demonstrate that many elements of innate immunity, such as natural killer (NK) cells and dendritic cells also display strong enrichment for MS susceptibility genes.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [26] Raj T, Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519– 523 (2014). 24786080 PMC4910825 source
  • Findingcitation

    One third (9 out of 31) of the identified MHC variants lie within either intergenic regions or a long-range haplotype that contains several non-classical HLA and other non-HLA genes.

    “One third (9 out of 31) of the identified variants lie within either intergenic regions or in a long-range haplotype that contains several non-classical HLA and other non-HLA genes.(17)”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [17] Patsopoulos NA, Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and non-HLA effects. PLoS Genet 9, e1003926 (2013). 24278027 PMC3836799 source

      The Results and Discussion address SNPs and the heritability of human height, not identified MHC variants. Searches for “9 out of 31,” intergenic regions, long-range haplotypes, and non-classical HLA genes found no passage supporting the cited count or location. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
  • Interpretationcitation

    The role of B cells, dendritic cells, and natural killer cells has emerged more clearly, broadening the prior narrative of T cell dysregulation from earlier studies.

    “the role of B cells, dendritic cells and natural killer cells has emerged more clearly, broadening the prior narrative of T cell dysregulation that emerged from earlier studies.(4)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [4] International Multiple Sclerosis Genetics Consortium, Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214– 219 (2011). 21833088 PMC3182531 source

      The paper’s pathway analysis (section labeled Abstract; page unavailable) reports enrichment of genes involved in T-cell activation and proliferation and implicates T-helper-cell differentiation in multiple sclerosis. That supports the T-cell part of the sentence, but the passages do not identify emerging roles for B cells, dendritic cells, or natural killer cells. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.
    • Rests on an assumption: Statistical enrichment of pathways among prioritized genes reflects MS pathophysiology.
  • Findingown data

    The genome-wide effects captured 18.3% of the overall heritability.

    “The genome-wide effects (GW) captured 18.3% of the overall heritability.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The 4,842 prioritized SNPs tag a true causal genetic effect.
  • Findingown data

    Interaction models of HLA-DRB1*15:01 with other variants in MHC explain the data better than the previously reported HLA-DRB1*15:01/HLA-DQA1*01:01 interaction term.

    “First, we found that there are interaction models of HLA-DRB1*15:01 with other variants in MHC that explain better the data than our previously reported HLA-DRB1*15:01/HLA-DQA1*01:01 interaction term (Fig. S5).”
    Quote in the paperSubstantial claim
  • May combine two claims. 34
  • Classified as probably not a substantial claim. 13
  • The quote only partly states this claim. 1

  • May combine two claims.

    Findingcitation

    rs2807267 lies within an enhancer peak specific for T cells and is 948bps downstream of the RNA U6 small nuclear 320 pseudogene (RNU6-320P).

    “This variant lies within an enhancer peak specific for T cells and is 948bps downstream of the RNA U6 small nuclear 320 pseudogene (RNU6-320P), a component of the U6 small nuclear ribonucleoprotein (snRNP) that is part of the spliceosome and is responsible for the splicing of introns from pre-mRNA (14) (Fig. 2B).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [14] Fortner DM, Troy RG, Brow DA, A stem/loop in U6 RNA defines a conformational switch required for pre-mRNA splicing. Genes Dev 8, 221– 233 (1994). 8299941 source

      The Abstract (page not provided) says U6 small nuclear RNA is an essential component of the spliceosome, which splices pre-mRNA. It does not identify RNU6-320P or establish that this pseudogene is a functional spliceosomal component. The variant’s enhancer and distance annotations are also not addressed in this paper. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingcitation

    Enrichment for MS susceptibility loci is observed not only in T cells but also in B cells.

    “An important finding is that the enrichment is observed not only in immune cells that have long been studied in MS, e.g. T cells, but also in B cells whose role has emerged more recently.(25)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [25] Hauser SL, B-cell depletion with rituximab in relapsing-remitting multiple sclerosis. N Engl J Med 358, 676– 688 (2008). 18272891 source

      The passage discussing disease-associated cis-eQTLs (indexed as Abstract; page unavailable) reports a preponderance of T-cell-specific effects among multiple-sclerosis susceptibility alleles. The study profiled T cells and monocytes, not B cells, so it does not support the claimed enrichment in B cells. (LLM, conf=0.90)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    Several non-genome-wide effects are eventually identified as genome-wide significant, given enough sample size and true effects.

    “More importantly, several such effects are eventually identified as genome-wide significant, given enough sample size and true effects.(3)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [3] International Multiple Sclerosis Genetics Consortium, Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 45, 1353– 1360 (2013). 24076602 PMC3832895 source

  • Classified as probably not a substantial claim.

    Premisecitation

    Functional and epigenomic annotation studies suggest that other elements of the immune system may be involved in MS.

    “While the T cell component plays an important role, functional and epigenomic annotation studies have begun to suggest that other elements of the immune system may be involved as well.(7, 8)”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [7] Farh KK, Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337– 343 (2015). 25363779 PMC4336207 source

      The section “Causal Autoimmunity Variants Map to Immune Cell-Specific Enhancers” (page unavailable) reports enrichment of autoimmune-disease variants in both B-cell and T-cell enhancers, supporting a possible role beyond T cells. The passage reports that result for autoimmune diseases collectively, however, rather than establishing a B-cell or other non-T-cell contribution specifically to MS. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [8] Maurano MT, Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190– 1195 (2012). 22955828 PMC3771521 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The role of the adaptive arm of the immune system, particularly its CD4+ T cell component, has become clearer, with multiple different T cell subsets being implicated in MS.

    “The role of the adaptive arm of the immune system, particularly its CD4+ T cell component has become clearer, with multiple different T cell subsets being implicated.(4)”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [4] International Multiple Sclerosis Genetics Consortium, Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214– 219 (2011). 21833088 PMC3182531 source

      The paper’s pathway analysis reports MS-associated genes involved in T-cell activation and T-helper-cell differentiation, supporting a role for adaptive immunity and T cells. The cited passages do not identify multiple distinct T-cell subsets implicated in MS, so that part of the sentence is not supported. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    The previously reported MS susceptibility list identified 9 communities (81 connected genes out of 307), compared with 13 communities from the 551 prioritized genes.

    “This compares to 9 communities that could be identified by the previously reported MS susceptibility list (81 connected genes out of 307; Table S23).(3)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [3] International Multiple Sclerosis Genetics Consortium, Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 45, 1353– 1360 (2013). 24076602 PMC3832895 source

      The Abstract reports MS susceptibility variants and loci, while the Online Methods > Gene Ontology section describes annotating nearby genes; neither reports gene-network communities. Searches for “9 communities,” “81 connected genes out of 307,” and “551 prioritized genes” found no passage supporting the cited comparison. Page numbers were unavailable in our copy. (LLM, conf=0.74)

      Experimental — accuracy not yet measured
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.
    • Rests on an assumption: Protein-protein interaction connectivity among prioritized genes reflects functional relationships relevant to MS.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CLECL1 has previously been linked to altered CLECL1 RNA expression in monocytes.

    “On the other hand, CLECL1 represents a simpler case of a known susceptibility effect that has previously been linked to altered CLECL1 RNA expression in monocytes (26, 32); its enrichment in microglial cells, which share many molecular pathways with other myeloid cells, is more straightforward to understand.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [26] Raj T, Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519– 523 (2014). 24786080 PMC4910825 source

      The passages on monocyte-specific eQTLs and disease susceptibility variants (section labeled Abstract; page unavailable) discuss these effects generally but do not identify CLECL1. Searches for CLECL1 and altered CLECL1 RNA expression in monocytes found no passage addressing the specific link. (LLM, conf=0.72)

      Experimental — accuracy not yet measured
    • cites [32] Wallace C, Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum Mol Genet 21, 2815– 2824 (2012). 22403184 PMC3363338 source

      The Methods (Gene prioritization benchmark; page unavailable) discusses genes whose transcription is regulated in whole blood, but does not identify CLECL1 or report altered CLECL1 RNA expression in monocytes. Searches for CLECL1 and monocyte expression found no passage addressing that specific link. (LLM, conf=0.76)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In the authors' previous examination of ulcerative colitis, they observed enrichment of genetic variants mapping to colon tissue.

    “we observed enrichment of genetic variants mapping to colon tissue.(7)”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [7] Farh KK, Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337– 343 (2015). 25363779 PMC4336207 source

      The ‘Cell-Type Specific Signatures of Complex Diseases’ section reports that ulcerative colitis variants mapped to gastrointestinal tract regulatory elements. That supports enrichment in a bowel-related tissue, but the passage does not identify colon tissue specifically. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The study included a third category of effects for which there were no data for replication in any of the replication sets (no data, n=532).

    “We also included a third category: effects for which there were no data for replication in any of the replication sets (no data, ND, n=532).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Enrichment for MS genes is seen in human microglia (p=5×10−14) but not in astrocytes or neurons.

    “As seen in Fig. 6, enrichment for MS genes is seen in human microglia (p=5×10−14) but not in astrocytes or neurons, suggesting that the resident immune cells of the brain may also play a role in MS susceptibility.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Suggestive effects were sub-stratified into strongly suggestive (n=117) and underpowered suggestive (n=299).

    “Furthermore, to add granularity in each category, we sub-stratified the suggestive effects into 2 groups: (1a) strongly suggestive (5 ×10−8 > p-value <1×10−5; sS, n= 117; fixed effects inverse-variance meta-analysis) and (1b) underpowered suggestive (unS, n=299).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The reference map of MS genetic architecture includes 200 autosomal susceptibility variants outside the MHC, one chromosome X variant, and 32 within the extended MHC.

    “We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and establish a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 within the extended MHC.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The study replicates the presence of interactions among class II alleles, but the second interaction term besides HLA-DRB1*15:01 can vary depending on the other independent variants included in the model.

    “We replicate the presence of interactions among class II alleles but note that the second interaction term, besides HLA-DRB1*15:01, can vary depending on the other independent variants that are included in the model.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The allele frequencies of the risk alleles for the 200 genome-wide effects ranged from 2.1% to 98.4% in the European samples of the 1000 Genomes reference, with mean 51.3% and standard deviation 24.5%.

    “The odds ratios (ORs) of these genome-wide effects ranged from 1.06 to 2.06, and the allele frequencies of the respective risk allele from 2.1% to 98.4% in the European samples of the 1000 Genomes reference (mean: 51.3%, standard deviation: 24.5%; Table S8 and Fig. S4).”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: The 4,842 prioritized SNPs tag a true causal genetic effect.

  • May combine two claims.

    Findingown data

    The super extended MHC explained 21.4% of the narrow-sense heritability, with the remaining 78.6% captured by the second component.

    “The SE MHC explained 21.4% of the h2g, with the remaining 78.6% being captured by the second component (Fig. 4A).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Of the 4,842 prioritized effects, 82 were genome-wide significant in the discovery analysis and another 125 had a p-value < 1×10−5.

    “Of these, 82 effects were genome-wide significant in the discovery analysis, and another 125 had a p-value < 1×10−5 (fixed effects inverse-variance meta-analysis).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the EVI5 region, the study identified four statistically independent genome-wide effects, three of which were found under the same association peak.

    “In this locus, we identified four statistically independent genome-wide effects, three of which were found under the same association peak (Fig. 2A), illustrating how our approach and the large sample size clarifies associations described in smaller studies and can facilitate functional follow-up of complex loci.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The 4,842 prioritized SNPs tag a true causal genetic effect.

  • May combine two claims.

    Findingown data

    In the discovery analysis, 26,395 SNPs reached genome-wide significance and another 576,204 SNPs had at least nominal evidence of association.

    “Of these, 26,395 SNPs reached genome-wide significance (p-value < 5×10−8; fixed effects inverse-variance meta-analysis) and another 576,204 SNPs had at least nominal evidence of association (5×10−8 > p-value < 0.05; fixed effects inverse-variance meta-analysis).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    The study stratified 4,111 non-genome-wide effects with replication data into suggestive (n=416) and non-replicated (n=3,695) categories.

    “Specifically, we decided to stratify these 4,111 effects into 2 main categories (9): (1) suggestive effects (S, n=416), and (2) non-replicated effects (NR, n= 3,695).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] See supplementary materials.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Findingown data

    Significant enrichment for MS susceptibility loci was apparent in many different immune cell types and tissues, whereas there was an absence of enrichment in tissue-level CNS profiles.

    “Significant enrichment for MS susceptibility loci was apparent in many different immune cell types and tissues, whereas there was an absence of enrichment in tissue-level central nervous system (CNS) profiles (Fig. 5).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    156 regions had at least one genome-wide effect, and overall 200 prioritized effects reached genome-wide significance in these 156 regions.

    “156 regions had at least one genome-wide effect, and, overall, 200 prioritized effects reached a level of genome-wide significance (GW) in these 156 regions (Fig. 1).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The 4,842 prioritized SNPs tag a true causal genetic effect.

  • May combine two claims.

    Findingown data

    The study confirms prior MHC susceptibility variants, including a non-classical HLA effect located in the TNFA/LST1 long haplotype.

    “We confirm prior MHC susceptibility variants (including a non-classical HLA effect located in the TNFA/LST1 long haplotype), and we extend the association map to uncover a total of 31 statistically independent effects at the genome-wide level within the MHC (Fig. 3, Table S11).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The main differences between the two MHC approaches were the inclusion of interaction of HLA-DRB1*15:01 and rs1049058 in step 3 and the stronger association of HLA*DPB1/2 effects over HLA*DRB1 effects in the sensitivity model.

    “The main differences between the results of the two approaches were the inclusion of interaction of HLA-DRB1*15:01 and rs1049058 in step 3 and the stronger association of HLA*DPB1/2 effects over HLA*DRB1 effects in the sensitivity model (Tables S12–S13 and Fig. S6).”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: The 4,842 prioritized SNPs tag a true causal genetic effect.

  • May combine two claims.

    Findingown data

    Thirty-six MS effects (18%; 10 common with CD4+ naïve T cells) influenced the expression of 48 genes in monocytes (11 genes in common with T cells).

    “Thirty-six out of the 200 GW MS effects (18%) had at least one tagging SNP (r2>=0.5) that altered the expression of 46 genes (false discovery rate; FDR<5%) in CD4+ naïve T cells (Table S15–S16), and 36 MS effects (18%; 10 common with the CD4+ naïve T cells) influenced the expression of 48 genes in monocytes (11 genes in common with T cells).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    CLECL1 is expressed at low level in cortical RNAseq profiles because microglia represent just a small fraction of cells at the cortical tissue level, and its expression level is 20-fold greater in purified human cortical microglia compared to bulk cortical tissue.

    “CLECL1 is expressed at low level in our cortical RNAseq profiles because microglia represent just a small fraction of cells at the cortical tissue level, and its expression level is 20-fold greater when we compare its level of expression in purified human cortical microglia to the bulk cortical tissue (Fig. 6).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    About a third of the 551 prioritized genes (n=190; 34.5%) were connected (p=0.052; permutation-based p-value) and could be organized into 13 communities with higher connectivity (p<0.002; permutation-based p-value).

    “About a third of the 551 prioritized genes (n=190; 34.5%) were connected (p-value = 0.052; permutation-based p-value) and these could be organized into 13 communities, i.e. sub-networks with higher connectivity (p-value: < 0.002; permutation-based p-value; Table S22).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.
    • Rests on an assumption: Protein-protein interaction connectivity among prioritized genes reflects functional relationships relevant to MS.

  • May combine two claims.

    Findingown data

    Over the CNS and three immune sets of data, 104 GW effects were cis-eQTLs for 203 unique genes (n=211 cis-eQTLs), with several appearing to be seemingly specific for one cell/tissue type.

    “Over this CNS and the three immune sets of data, 104 GW effects were cis-eQTLs for 203 unique genes (n=211 cis-eQTLs), with several appearing to be seemingly specific for one of the cell/tissue type (Table S16).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In MS PBMC, 30% of the GW effects (60 out of 200) were cis-eQTLs for 92 genes, with several loci shared with healthy T cells and monocytes (26 effects and 27 genes in T cells, and 21 effects and 24 genes in monocytes).

    “In MS PBMC, 30% of the GW effects (60 out of the 200) were cis-eQTLs for 92 genes in the PBMC MS samples, with several loci being shared with those found in healthy T cells and monocytes (26 effects and 27 genes in T cells, and 21 effects and 24 genes in monocytes, respectively; Table S15–S16).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingown data

    The authors prioritized 551 candidate MS genes to test for statistical enrichment of known pathways.

    “Overall, we prioritized 551 candidate MS genes (Table S18; Table S19 for sensitivity analyses) to test for statistical enrichment of known pathways.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.

  • May combine two claims.

    Findingown data

    The list of 551 prioritized genes could predict 102 new candidate genes, four of which can be prioritized because they are in the list of suggestive effects.

    “we estimated that the list of 551 prioritized genes could predict 102 new candidate genes, four of which can be prioritized since they are in the list of suggestive effects.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.
    • Rests on an assumption: Protein-protein interaction connectivity among prioritized genes reflects functional relationships relevant to MS.

  • May combine two claims.

    Findingown data

    The S effects exhibited a pattern of enrichment similar to the GW effects, with only B cell expression reaching a threshold of statistical significance.

    “The “S” effects exhibited a pattern of enrichment that is similar to the “GW” effects, with only B cell expression reaching a threshold of statistical significance (Fig. S7).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Non-prioritized SNPs in the 1,961 regions explained 11.6% of the heritability, suggesting residual LD with prioritized effects or true effects that have not yet been identified.

    “The “Non-prioritized” SNPs in the 1,961 regions explained 11.6% of the heritability, which suggests that there may be residual LD with prioritized effects or true effects that have not yet been identified (Fig. 4B).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    MS is most likely initially triggered by perturbation of peripheral immune responses.

    “Using expression profiles from purified human microglia, we observe enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    Few of the 3,695 non-replicated effects would be falsely non-replicated; the vast majority of these effects are likely to be false positive results.

    “However, few of the 3,695 NR effects would fall in either of the above two cases; the vast majority of these effects are likely to be false positive results.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The study's approach and large sample size clarify associations described in smaller studies and can facilitate functional follow-up of complex loci.

    “In this locus, we identified four statistically independent genome-wide effects, three of which were found under the same association peak (Fig. 2A), illustrating how our approach and the large sample size clarifies associations described in smaller studies and can facilitate functional follow-up of complex loci.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The 4,842 prioritized SNPs tag a true causal genetic effect.

  • May combine two claims.

    Interpretationown data

    The group of HLA-DRB1*15:01-interacting variants is consistently identified regardless of the marginal effects of other statistically independent variants added to the model, implying that these interaction terms capture a different subset of phenotypic variance.

    “This group of HLA-DRB1*15:01-interacting variants is consistently identified regardless of the marginal effects of other statistically independent variants that are added in the model, implying that these interaction terms capture a different subset of phenotypic variance and can be explored after the identification of the marginal effects.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Several studies have demonstrated that non-genome-wide effects explain an important proportion of the effect of genetic variation on disease susceptibility.

    “The commonly used threshold of genome-wide significance (p-value = 5×10−8) has played an important role in making human genetic study results robust; however, several studies have demonstrated that non-genome-wide effects explain an important proportion of the effect of genetic variation on disease susceptibility. (18, 19)”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [18] Yang J, Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42, 565– 569 (2010). 20562875 PMC3232052 source
    • cites [19] Shi H, Kichaev G, Pasaniuc B, Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data. Am J Hum Genet 99, 139– 153 (2016). 27346688 PMC5005444 source

  • May combine two claims.

    Interpretationown data

    The study yielded a detailed genome-wide genetic map that includes the first successful evaluation of the X chromosome in MS.

    “This effort has yielded a detailed genome-wide genetic map that includes the first successful evaluation of the X chromosome in MS, and provides a powerful platform for the creation of a detailed genomic map, outlining the functional consequences of most variants and their assembly into susceptibility networks (Fig. S1).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    CLECL1 suggests a potential role of microglia in MS susceptibility, which is under-estimated in bulk tissue profiles.

    “CLECL1 therefore suggests a potential role of microglia in MS susceptibility, which is under-estimated in bulk tissue profiles that are available in epigenomic and transcriptomic reference data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    21.2% (45 out of 212) of cortical cis-eQTLs displayed no evidence of association in immune cell/tissue results and are less likely to be immune-related.

    “Specifically, 21.2% (45 out of 212 cis-eQTLs) of these cortical cis-eQTLs displayed no evidence of association (p-value>0.05, for linear regression(9), with any SNP with r2>0.1) in the immune cell/tissue results and are less likely to be immune-related (Table S16–S17).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] See supplementary materials.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationreasoning

    The study suggests that while MS origin may lie primarily within the peripheral immune compartment where dysregulation of all branches of the immune system leads to organ-specific autoimmunity, there is a subset of loci with a key role in directing the tissue-specific autoimmune response.

    “our study suggests that while MS is a disease whose origin may lie primarily within the peripheral immune compartment where dysregulation of all branches of the immune system leads to organ specific autoimmunity, there is subset of loci with a key role in directing the tissue specific autoimmune response.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.
    • Rests on an assumption: Statistical enrichment of pathways among prioritized genes reflects MS pathophysiology.

  • May combine two claims.

    Interpretationown data

    The extensive list of MS susceptibility genes, which more than doubles previous knowledge in MS, captures processes of development, maturation, and terminal differentiation of several immune cells that potentially interact to predispose to MS.

    “the extensive list of susceptibility genes, that more than doubles the previous knowledge in MS, captures processes of development, maturation, and terminal differentiation of several immune cells that potentially interact to predispose to MS.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.
    • Rests on an assumption: Statistical enrichment of pathways among prioritized genes reflects MS pathophysiology.

  • May combine two claims.

    Interpretationcitation

    The same haplotype harbors different functional effects in very different contexts, illustrating the challenge in dissecting the functional consequences of autoimmune variants in immune function as opposed to the tissue targeted in autoimmune disease.

    “Thus, the same haplotype harbors different functional effects in very different contexts, illustrating the challenge in dissecting the functional consequences of autoimmune variants in immune function as opposed to the tissue targeted in autoimmune disease.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationown data

    The eQTL studies begin to transition the genetic map into a resource outlining the likely MS susceptibility gene(s) in a locus and the potential functional consequences of certain MS variants.

    “These eQTL studies begin to transition our genetic map into a resource outlining the likely MS susceptibility gene(s) in a locus and the potential functional consequences of certain MS variants.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.

  • May combine two claims.

    Interpretationown data

    Almost all MS variants present in cortex remain ambiguous; it is likely that many of them influence gene function in multiple immune and non-immune cell types.

    “As anticipated, almost all of the MS variants present in cortex remain ambiguous: it is likely that many of them influence gene function in multiple immune and non-immune cell types.”
    Quote in the paperSubstantial claim
45 of 45

  • The authors state this as an assumption.

    The 4,842 prioritized SNPs tag a true causal genetic effect.

    “We refer to these 4,842 prioritized SNPs as “effects”, assuming that these SNPs tag a true causal genetic effect.”

  • The paper relies on this without support.

    The 551 prioritized genes are the true causal MS susceptibility genes at the associated loci.

    “we prioritized 551 candidate MS genes”
    “Pathway analyses were performed to characterize canonical pathways statistically enriched for the putative causal genes.”

  • The paper relies on this without support.

    Statistical enrichment of pathways among prioritized genes reflects MS pathophysiology.

    “we turned to pathway analyses to evaluate how the extended list of genome-wide effects provides new insights into the pathophysiology of the disease.”
    “captures processes of development, maturation, and terminal differentiation of several immune cells that potentially interact to predispose to MS.”

  • The paper relies on this without support.

    Protein-protein interaction connectivity among prioritized genes reflects functional relationships relevant to MS.

    “we performed a protein-protein interaction (PPI) analysis using GeNets.(9, 35)”
    “these could be organized into 13 communities, i.e. sub-networks with higher connectivity”

  • The paper relies on this without support.

    Available reference epigenomic data are representative of the relevant cell types and states for MS susceptibility.

    “We have been thorough in our evaluation of available reference epigenomic data, but many different cell types and cell states remain to be characterized and could alter our summary.”
5 of 5

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Extending the analysis to include data generated from human induced pluripotent stem cell-derived neurons, astrocytes and microglia revealed the enrichment of MS susceptibility genes in microglia but not astrocytes or neurons.

    “Extending the analysis to include data generated from human induced pluripotent stem cell-derived neurons, astrocytes and microglia, revealed the enrichment of MS susceptibility genes in microglia but not astrocytes or neurons [161].”

    From Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function (2022) · cites this paper as [161]

    The section on functional implications reports enrichment of MS genes in human microglia (p=5×10−14), but not astrocytes or neurons. It specifies that only the neurons were iPSC-derived; the astrocytes and microglia were purified primary human cells, not iPSC-derived as the citing sentence says. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • These findings indicate a key role of CNS resilience and neurocognitive reserve in determining outcome in MS.

    “In contrast to immune-driven susceptibility3, these findings indicate a key role of CNS resilience and neurocognitive reserve in determining outcome in MS.”

    From Locus for severity implicates CNS resilience in progression of multiple sclerosis (2023) · cites this paper as [3]

  • Repeating the heritability enrichment analysis for MS susceptibility revealed strong enrichment in lymphoid organs, immune lymphoid and myeloid cells, and tissues with immunological functions.

    “In contrast, repeating the same analysis for MS susceptibility3 revealed strong enrichment in lymphoid organs, immune lymphoid and myeloid cells, as well as in tissues with recognized immunological functions and microbiota interactions (pharynx, lung, terminal ileum and endocervix; Fig. 1 and Supplementary Table 8).”

    From Locus for severity implicates CNS resilience in progression of multiple sclerosis (2023) · cites this paper as [3]

  • The lead variant rs10191329 (MAF = 0.17) was not close to or in LD with any of the lead MS susceptibility variants.

    “The lead variant rs10191329 (MAF = 0.17) was not close to (> 3 Mb) or in LD with (r2 ≤ 0.006) any of the lead MS susceptibility variants3.”

    From Locus for severity implicates CNS resilience in progression of multiple sclerosis (2023) · cites this paper as [3]

    The Discovery and replication of genetic associations section reports MS susceptibility variants, but the passages do not identify rs10191329 or compare its distance or linkage disequilibrium with those variants. Searches for rs10191329 and the stated distance and LD values found no passage addressing that comparison. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

  • The lead variant rs149097173 did not overlap with known MS susceptibility loci.

    “The lead variant (rs149097173) did not overlap with known MS susceptibility loci3.”

    From Locus for severity implicates CNS resilience in progression of multiple sclerosis (2023) · cites this paper as [3]

    The Discovery and replication of genetic associations section describes MS susceptibility regions and prioritized SNPs, but the retrieved passages do not mention rs149097173 or locate it relative to previously known loci. Searches for the variant identifier and for overlap with known loci did not establish the claimed non-overlap. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Cis-eQTL totals conflict

    Minor

    The text reports 211 cis-eQTLs across the CNS and three immune datasets, then uses 212 cis-eQTLs as the denominator for cortical cis-eQTLs alone.

1 of 1

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Authors
International Multiple Sclerosis Genetics Consortium, Patsopoulos, N. A., Baranzini, S. E., Santaniello, A., Shoostari, P., Cotsapas, C., Wong, G., Beecham, A. H., James, T., Replogle, J., Vlachos, I. S., McCabe, C., Pers, T. H., Brandes, A., White, C., Keenan, B., Cimpean, M., Winn, P., Panteliadis, I.-P., Robbins, A., Andlauer, T. F. M., Zarzycki, O., Dubois, B., Goris, A., Søndergaard, H. B., Sellebjerg, F., Sorensen, P. S., Ullum, H., Thørner, L. W., Saarela, J., Cournu-Rebeix, I., Damotte, V., Fontaine, B., Guillot-Noel, L., Lathrop, M., Vukusic, S., Berthele, A., Pongratz, V., Buck, D., Gasperi, C., Graetz, C., Grummel, V., Hemmer, B., Hoshi, M., Knier, B., Korn, T., Lill, C. M., Luessi, F., Mühlau, M., Zipp, F., Dardiotis, E., Agliardi, C., Amoroso, A., Barizzone, N., Benedetti, M. D., Bernardinelli, L., Cavalla, P., Clarelli, F., Comi, G., Cusi, D., Esposito, F., Ferrè, L., Galimberti, D., Guaschino, C., Leone, M. A., Martinelli, V., Moiola, L., Salvetti, M., Sorosina, M., Vecchio, D., Zauli, A., Santoro, S., Mancini, N., Zuccalà, M., Mescheriakova, J., van Duijn, C., Bos, S. D., Celius, E. G., Spurkland, A., Comabella, M., Montalban, X., Alfredsson, L., Bomfim, I. L., Gomez-Cabrero, D., Hillert, J., Jagodic, M., Lindén, M., Piehl, F., Jelčić, I., Martin, R., Sospedra, M., Baker, A., Ban, M., Hawkins, C., Hysi, P., Kalra, S., Karpe, F., Khadake, J., Lachance, G., Molyneux, P., Neville, M., Thorpe, J., Bradshaw, E., Caillier, S. J., Calabresi, P., Cree, B. A. C., Cross, A., Davis, M., de Bakker, P. W. I., Delgado, S., Dembele, M., Edwards, K., Fitzgerald, K., Frohlich, I. Y., Gourraud, P.-A., Haines, J. L., Hakonarson, H., Kimbrough, D., Isobe, N., Konidari, I., Lathi, E., Lee, M. H., Li, T., An, D., Zimmer, A., Madireddy, L., Manrique, C. P., Mitrovic, M., Olah, M., Patrick, E., Pericak-Vance, M. A., Piccio, L., Schaefer, C., Weiner, H., Lage, K., ANZgene, IIBDGC, WTCCC2, Compston, A., Hafler, D., Harbo, H. F., Hauser, S. L., Stewart, G., D’Alfonso, S., Hadjigeorgiou, G., Taylor, B., Barcellos, L. F., Booth, D., Hintzen, R., Kockum, I., Martinelli-Boneschi, F., McCauley, J. L., Oksenberg, J. R., Oturai, A., Sawcer, S., Ivinson, A. J., Olsson, T., & De Jager, P. L.
Affiliations
  • Broad Institute (US)
  • University of California, San Francisco (US)
  • Brigham and Women's Hospital (US)
  • University of Miami (US)
  • Karolinska Institutet (SE)
  • Columbia University Irving Medical Center (US)
  • University of Copenhagen (DK)
  • Munich Cluster for Systems Neurology (DE)
  • KU Leuven (BE)
  • University of Helsinki (FI)
  • Inserm (FR)
  • Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
  • TUM Klinikum (DE)
  • Johannes Gutenberg University Mainz (DE)
  • University Hospital of Larissa (GR)
  • Don Carlo Gnocchi Foundation (IT)
  • University of Turin (IT)
  • Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT)
  • Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
  • MRC Biostatistics Unit (GB)
  • Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT)
  • IRCCS Ospedale San Raffaele (IT)
  • University of Milan (IT)
  • Ospedale Maggiore (IT)
  • Sapienza University of Rome (IT)
  • Erasmus MC (NL)
  • Oslo University Hospital (NO)
  • University of Oslo (NO)
  • Vall d'Hebron Institut de Recerca (ES)
  • University of Zurich (CH)
  • University of Cambridge (GB)
  • Royal Stoke University Hospital (GB)
  • King's College London (GB)
  • University of Oxford (GB)
  • Peterborough City Hospital (GB)
  • Johns Hopkins University (US)
  • Washington University in St. Louis (US)
  • Vanderbilt University Medical Center (US)
  • Children's Hospital of Philadelphia (US)
  • Kaiser Permanente (US)
  • The University of Sydney (AU)
  • University of Tasmania (AU)
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2019-09-27
Volume
365
Issue
6460
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.aav7188
PMC
PMC7241648
OpenAlex
W2981477552
Cited by
1,471
References
69
Citations by year
2026: 146 · 2025: 191 · 2024: 272 · 2023: 211 · 2022: 232 · 2021: 183 · 2020: 140 · 2019: 55 · 2018: 36 · 2017: 3
FWCI
95.42
Type
journal article
Language
en
Open access
Licence
cc-by-nd
Locations
  • Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
  • ResearchOnline at James Cook University (James Cook University) (submittedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
  • PubMed Central (submittedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion, other-oa)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
  • HAL (Le Centre pour la Communication Scientifique Directe) (submittedVersion)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
  • Otago University Research Archive (University of Otago) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Figshare (submittedVersion)
  • IrInSubria (University of Insubria) (submittedVersion)
  • Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
  • Research Portal (King's College London) (submittedVersion)
  • Lirias (KU Leuven) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • Griffith Research Online (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
  • NOVA (University of Newcastle Australia) (submittedVersion)
  • Apollo
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • National Multiple Sclerosis Society (JF2138A1, RG 4680A1/1)
  • Harvard NeuroDiscovery Center
  • Yale University (CT 06520)
  • Biogen
  • Kaiser Permanente
  • Children's Hospital of Philadelphia
  • Vanderbilt University
  • University of Miami
  • University of Pennsylvania
  • Harvard University
  • Fondation pour l'Aide à la Recherche sur la Sclérose en Plaques (ANR-10-COHO-002)
  • Strategiske Forskningsråd (2142-08-0039)
  • Helsingin Yliopisto
  • Johns Hopkins University
  • Case Western Reserve University
  • Scleroseforeningen
  • McGill University
  • Westmead Millennium Institute for Medical Research
  • Wellcome Trust (085475/Z/08/Z, 068545/Z/02), 085475, G0000934, 068545, 085475/B/08/Z)
  • Vertex Pharmaceuticals
  • Università degli Studi di Milano
  • Vanderbilt University Medical Center
  • Broad Institute
  • Université de Lille
  • National Institute for Health and Care Research (G0000934)
  • Multiple Sclerosis Society (861/07)
  • University of Cambridge
  • King's College London
  • University College London
  • University of Oxford
  • European Commission (ANR-10-IAIHU-06)
  • Menzies Research Institute Tasmania
  • University of Tasmania
  • Agence Nationale de la Recherche (ANR-10-IAIHU06, 10-IAIHU-06, ANR-10-COHO-002, ANR-10, ANR-10-COHO-0002)
  • Institut National de la Santé et de la Recherche Médicale (ANR-10-IAIHU-06)
  • Bundesministerium für Bildung und Forschung (01ZX1614J)
  • AFA Försäkring
  • Cambridge University Hospitals
  • Fonds Wetenschappelijk Onderzoek
  • Lundbeckfonden
  • Johannes Gutenberg-Universität Mainz
  • KU Leuven
  • Karolinska Institutet
  • Knut och Alice Wallenbergs Stiftelse
  • Novo Nordisk
  • Vetenskapsrådet (20132)
  • Centre National de la Recherche Scientifique (UMR 5292)
  • Keele University
  • Rigshospitalet
  • Universitetet i Oslo
  • Norges Forskningsråd (240102, 196776)
  • Università degli Studi del Piemonte Orientale
  • Technische Universität München
  • Hospital for Sick Children
  • Hospices Civils de Lyon
  • Forskningsrådet om Hälsa, Arbetsliv och Välfärd
  • Université Claude Bernard Lyon 1
  • Università degli Studi di Torino
  • Universidad Pública de Navarra
  • Faculty of Health and Medical Sciences, University of Western Australia
  • Université de Nantes
  • Universitätsspital Zürich
  • Novo Nordisk Fonden
  • Université de Lyon
  • Novo Nordisk Foundation Center for Basic Metabolic Research
  • Vlaamse regering
  • Fundació Institut de Recerca Hospital Universitari Vall d’Hebron
  • H. Lundbeck A/S
  • Menzies Institute for Medical Research
  • UK Dementia Research Institute
  • Sorbonne Université
  • National Institutes of Health (G0000934, R01AG03-6836, 068545/Z/02, P01 AG036694, R01NS096212, R01NS088155, NS049477, ANR-10-IAIHU-06)
  • Perelman School of Medicine, University of Pennsylvania
  • Leonard M. Miller School of Medicine
  • NIHR BioResource
  • Medical Research Council (G1100125, 068545/Z/02, G0000934, WC1E 6BT)
  • Menzies Centre for Australian Studies, King's College London, University of London
  • Japan Society for the Promotion of Science (18K07529)
  • Det Sundhedsvidenskabelige Fakultet, Københavns Universitet
  • NIHR Oxford Biomedical Research Centre
  • National Institute on Aging (P01AG036694, R01AG036836)
  • National Institute of General Medical Sciences (RC2-GM093080)
  • National Institute of Allergy and Infectious Diseases (R01 AI059829)
  • National Institute of Neurological Disorders and Stroke (R01 NS096212, R01 NS 026799, R01NS049477, NS049477, R35 NS111644, R01 NS088155)
  • National Institute of Environmental Health Sciences (G0000934, 068545/Z/02)
Topics
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
Keywords
multiple sclerosis, genetic susceptibility, genome-wide association study, major histocompatibility complex, peripheral immune cells, microglia, innate immunity, adaptive immunity, autoimmune disease, central nervous system autoimmunity, susceptibility genes
MeSH terms
RNA-Seq, Chromosome Mapping, Gene Frequency, Humans, Major Histocompatibility Complex, Multiple Sclerosis, Case-Control Studies, Microglia, Cell Cycle Proteins, Polymorphism, Single Nucleotide, GTPase-Activating Proteins, Genomics, Inheritance Patterns, Quantitative Trait Loci, Chromosomes, Human, X, Genome-Wide Association Study, Genetic Loci, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-13.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-13.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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