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

In the paper

“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).”

What the sources say

• Partly supported

  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)

  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 sourceExperimental — accuracy not yet measured

In the paper

“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)”

What the sources say

• Partly supported

  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)

  cites [25] Hauser SL, B-cell depletion with rituximab in relapsing-remitting multiple sclerosis. N Engl J Med 358, 676– 688 (2008). 18272891 sourceExperimental — accuracy not yet measured

In the paper

“More importantly, several such effects are eventually identified as genome-wide significant, given enough sample size and true effects.(3)”

What the sources say

• Supported

  The Abstract (page not available in our copy) reports that 48 signals reached genome-wide significance in discovery alone, while 97 met the genome-wide threshold after replication and joint analysis with a larger sample. This supports the claim that several initially subthreshold effects became genome-wide significant, in the context of multiple sclerosis susceptibility signals. (LLM, conf=0.88)

  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

In the paper

“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)”

What the sources say

• Partly supported

  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)

  cites [7] Farh KK, Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337– 343 (2015). 25363779 PMC4336207 sourceExperimental — accuracy not yet measured
• Supported

  The “De novo identification of pathogenic cell types” section (page not provided) reports enrichment of MS-associated variants in both T cells and CD19+/CD20+ B cells. It identifies B cells as an immune component implicated alongside T cells, supporting the citing sentence’s hedged claim. (LLM, conf=0.93)

  cites [8] Maurano MT, Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190– 1195 (2012). 22955828 PMC3771521 source

In the paper

“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)”

What the sources say

• Partly supported

  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)

  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 sourceExperimental — accuracy not yet measured

In the paper

“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)”

What the sources say

• ?Could not verify

  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)

  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 sourceExperimental — accuracy not yet measured

In the paper

“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.”

What the sources say

• ?Could not verify

  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)

  cites [26] Raj T, Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519– 523 (2014). 24786080 PMC4910825 sourceExperimental — accuracy not yet measured
• ?Could not verify

  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)

  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 sourceExperimental — accuracy not yet measured

In the paper

“we observed enrichment of genetic variants mapping to colon tissue.(7)”

What the sources say

• Partly supported

  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)

  cites [7] Farh KK, Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337– 343 (2015). 25363779 PMC4336207 sourceExperimental — accuracy not yet measured

In the paper

“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).”

In the paper

“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.”

In the paper

“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).”

In the paper

“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.”

In the paper

“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.”

In the paper

“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).”

In the paper

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

In the paper

“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).”

In the paper

“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.”

In the paper

“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).”

In the paper

“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).”

What the sources say

• ?Not matched

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

  cites [9] See supplementary materials.

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“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).”

In the paper

“Overall, we prioritized 551 candidate MS genes (Table S18; Table S19 for sensitivity analyses) to test for statistical enrichment of known pathways.”

In the paper

“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.”

In the paper

“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).”

In the paper

“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).”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

“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)”

What the sources say

• ?Not checked — no full text of the sourcecites [18] Yang J, Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42, 565– 569 (2010). 20562875 PMC3232052 source
• ?Not checked — no full text of the sourcecites [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

In the paper

“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).”

In the paper

“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.”

In the paper

“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).”

What the sources say

• ?Not matched

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

  cites [9] See supplementary materials.

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”