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Review

New Insights into Risk Genes and Their Candidates in Multiple Sclerosis

by
Remina Shirai
* and
Junji Yamauchi
*
Laboratory of Molecular Neurology, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji 192-0392, Japan
*
Authors to whom correspondence should be addressed.
Neurol. Int. 2023, 15(1), 24-39; https://doi.org/10.3390/neurolint15010003
Submission received: 6 October 2022 / Revised: 16 December 2022 / Accepted: 26 December 2022 / Published: 29 December 2022
(This article belongs to the Special Issue New Insights into Genetic Neurological Diseases)
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Abstract

:
Oligodendrocytes are central nervous system glial cells that wrap neuronal axons with their differentiated myelin membranes as biological insulators. There has recently been an emerging concept that multiple sclerosis could be triggered and promoted by various risk genes that appear likely to contribute to the degeneration of oligodendrocytes. Despite the known involvement of vitamin D, immunity, and inflammatory cytokines in disease progression, the common causes and key genetic mechanisms remain unknown. Herein, we focus on recently identified risk factors and risk genes in the background of multiple sclerosis and discuss their relationships.

1. Introduction

The myelin sheath is formed as a multilamellar membrane structure through the spiral wrapping of neuronal axons that act as insulators [1,2,3,4]. The transmission of each action potential on a limited membrane region is significantly promoted by the resulting saltatory conduction. Electrical signals are quickly derived to adjacent or distant neuronal cells and neuronal networks. If the myelin is damaged, however, fast signal transmission is not achieved, which causes defective neuronal function. This phenomenon is typically observed in demyelinating states. One well-known demyelinating disease is multiple sclerosis (MS) of the central nervous system (CNS). It is thought that MS is often caused by an abnormal autoimmune reaction in the CNS.
First defined by the National Multiple Sclerosis Society (in the United States) in 1996, MS is a chronic inflammatory disease that is characterized by demyelination mainly in the brain and, in turn, axonal degeneration [5]. The prevalence of MS is higher in Caucasians in the United States and Europe. The incidence rate is more than 100 patients per 100,000 population members in some areas of Northern Europe [6]. In Japan, the prevalence was estimated to be 1 to 5 patients per 100,000 population members, but this number has reportedly increased to 14 to 18 over a single decade [7]. The incidence of MS is increasing in both developed and developing countries [8]. The average age of onset of MS is middle-age and the disease is approximately twice as common in women than in men [9,10].
It is unclear why the number of MS patients has increased recently across countries and regions. There are various risks and possible reasons for the development of the disease, including smoking, vitamin D deficiency, obesity, and Epstein-Barr virus, which is a type of herpes virus [11]. MS also has genetic factors, as first-degree relatives and identical twins have a 25% chance of being affected [12]. The major histocompatibility complex (MHC) HLA-DRB1*15:01 allele was the first factor identified as a risk factor for MS [13]. Subsequent studies have shown that interleukin (IL) 2Rα and IL7R are also genetic factors [14].
MS symptoms likely depend on the tissues and regions where demyelination occurs. Some of the most common symptoms are optic neuritis and brainstem and spinal cord syndromes. Early clinical symptoms usually recover, but relapses are often followed by sequelae [15]. The onset is related to the location and size of the lesion, and even small lesions in the symptomatic zone are likely to cause symptoms, with magnetic resonance imaging showing typical “Dawson’s fingers” with periventricular lesions [16].
This narrative review will focus on the previously reported major risk factors of MS. We describe the disease and the possible therapeutic signaling pathways related to the risk factors as well as risk gene products in MS. We have selected the references using inclusion criteria that focus on reviews of MS and original papers. We have also included large-scale meta-analyses of original genetic studies.

2. MS and Environmental Factors (Vitamin D)

Epidemiological studies have shown that there are racial differences in developing MS, with a minimum prevalence at the equator and an increase with northern or southern latitudes [17]. Vitamin D is produced primarily by the action of ultraviolet B rays on the skin. This is supported by circumstantial evidence suggesting environmental factors of vitamin D deficiency due to a lack of sunlight as a predisposing factor for MS [18]. Indeed, vitamin D deficiency has been suggested as a possible cause of MS and/or contributor to the progression of MS, but it likely has limited pathological effects [19]. It has been reported that people with blood 25-OH-D levels of 40 ng/mL or higher have a 62% lower risk of MS than those with levels below 25 ng/mL [20], suggesting that having normal vitamin D levels reduces the risk of MS [9]. Serum 25-OH-D is a metabolite of vitamin D used to assess vitamin D levels in vivo (Figure 1) [20]. Adil et al. reported levels of vitamin D bioavailability and adipose tissue–secreted hormones such as adiponectin and leptin [21]. MS risk correlated with a genetic predisposition to the body mass index (BMI) but anti-correlated with the 25-OH-D level. Leptin and adiponectin have no effect on the increased risk of MS due to lowered vitamin D levels. Vitamin D supplementation modestly reverses the effect of obesity on MS [22]. In support of this study, Michaela et al. examined the association between 52 risk variants identified through genome-wide association studies (GWASs) and disease severity in MS and found that they were not associated with MS severity in terms of cohort, gender, age of onset, and HLA-DRB1*15:01 allele [21].
Scazzone et al. investigated the effects of vitamin D–related genes on MS susceptibility. Of the 12 vitamin D gene product pathways investigated, the most studied was the vitamin D receptor and the least studied were other vitamin D–related gene products. Scazzone et al. reported that it is not clear whether these mutations directly affect the risk of MS [23]. When vitamin D supplementation is used as a treatment, no statistically significant differences were found and its effectiveness could not be demonstrated [19,24]. Despite the lack of significant difference, vitamin D alters the transcriptome profile of macrophages and microglia. In addition, vitamin D activates T cells (Figure 1) [25]. MS risk and genetic abnormalities in vitamin D metabolism have been reported in several cases, with genetic abnormalities for CYP27B1 in the cytochrome P450 family gene products, which is a regulator of calcitriol synthesis, influencing MS risk [26].
Genetic polymorphisms in the gene-encoding molecules involved in vitamin D homeostasis are associated with vitamin D deficiency. However, Klotho, which is coded as a protein with vitamin D metabolism, has no genotypic frequencies that differ between MS patients and controls [27]. This finding means that the role of Klotho does not involve genetic susceptibility to MS.
Several studies have strengthened the candidacy of the environmental factors between vitamin D and the most major risk gene HLA-DRB1*15:01 allele. Vitamin D deficiency is also reported to be an important MS disease pathogenesis.

3. MS and Environmental Factors (Immunity)

CNS fibers are covered with myelin sheaths whose composition contains abundant lipids and proteins. In MS, demyelinating plaques are involved in an immune response triggered by T cells. Proteolipid protein (PLP), myelin basic protein (MBP), and myelin oligodendrocyte glycoprotein (MOG) proteins have been well-studied as self-antigens involved in demyelination in MS [28]. The autoimmune disease against MOG is called MOG antibody (MOG-IgG)-associated disease [29]. The clinical features are considered to reflect a unique disease with a different etiology from MS and optic neuritis, related to aquaporin-4 (AQP-4)-IgG [30,31].
Immune responses to myelin-associated glycoprotein (MAG) have been primarily implicated in the development of MS. Increased MAG-recognizing T and B cells in MS patients have been observed [32]. However, the MAG peptide itself did not elicit disease-specific T and B cell responses, suggesting that this is secondary to demyelination rather than an attack on MAG by immune responses [33]. In addition to MAG proteins, environmental factors such as viral infection trigger demyelination somewhat. Then, differentiation into CD4-positive T cells and Th1-type cells results in one of the key events in the early stages of MS (Figure 1) [34].
Most of the more than 100 mutations in MS reported to date are related to the human leukocyte antigen (HLA) and the immune system, supporting the idea that MS is an immune disease. However, these mutations account for only 25% of heritability, leading to the new concept of “phantom heritability.” Sawcer et al. proposed insufficient non-redundant unnecessary sufficient (INUS), which describes the plurality of causation when a mutation cannot be found [35].
Experimental autoimmune encephalomyelitis (EAE) is a typical mouse model of MS and has also been the basis for its etiology and therapeutic development with regard to induced CNS inflammation. A few researchers have put forth that the debate should not be focused on EAE, arguing that the phenotype is weak [36]. Microglia, macrophages, and dendritic cells, which are potent antigen-presenting cells, have been reported to be increased in EAE mice [37]. Microglia and macrophages are present in MS lesions, myelin proteins MBP, and PLP as well as the minor myelin proteins [38]. Faber et al. compared gene expression in opticospinal EAE (OSE) and MOG EAE models. They demonstrated a more extensive enrichment of human MS risk genes among transcripts differentially expressed in OSE than in MOG EAE [39].
When Li et al. analyzed the transcriptional profiling data in the human brain in MS, 133 known and unknown genes were identified [40]. They included genes encoding a number of extracellular matrixes, such as collagen, signal-triggering receptor, and molecules involved in immune-related pathways and phosphatidylinositol-3 kinase (PI3K)-Akt pathways. Among them, four major extracellular and transmembrane proteins, IL17A, IL2, CD44, and IGF1, and 16 extracellular proteins interacting with IL17A have been associated with MS pathogenesis. Additionally, Del-1, which is an interacting protein with IL17A that may be associated with MS progression and relapse, has been identified as a probable biomarker.
Regulatory T cell (Treg) alteration has also been implicated in the pathogenesis of MS. X-linked forkhead box P3 (FoxP3) plays a crucial role in the development and stability of Tregs. However, FoxP3 and vitamin D3 did not have any association with MS [41].
The humoral immune response to Epstein-Barr virus nuclear antigen 1 (EBNA-1)-specific immunoglobulin γ (IgG) titers in families with MS was determined as a result of investigating the role of specific genetic loci on the antiviral IgG titers. The EBNA-1 IgG gradient being the highest in MS patients and the lowest in biologically unrelated spouses indicates a genetic contribution to EBNA-1 IgG levels that is only partially explained by HLA-DRB1*15:01 carriership [42].
Although it was previously known that non-coding RNAs (ncRNAs) create transcription noise, they are also now believed to be regulators of immune responses. Dysregulation of ncRNAs is one of the underlying mechanisms of immune disorders such as MS. Several studies have reported the aberrant expression of ncRNAs in the sera or blood cells of MS patients [43,44,45]. The results of these studies propose different classes of ncRNAs (long non-coding RNAs, microRNAs, and circular RNAs) as diagnostic or predictive markers in MS [46].
Demyelinating plaques are related to the autoimmune response in MS. The production of inflammatory cytokines caused by the immune response, such as PLP, MBP, and MOG, revealed MS as a chronic inflammatory disease.

4. Gene Risk and Signaling Pathway

Environmental cues associated with the increased risk of developing MS have been established, and over 200 risk loci with moderate to subtle effects have been described. To dissect the influence of genetic predisposition and environmental factors, Florian et al. investigated the peripheral immune signatures of 61 monozygotic twin pairs discordant for MS. They revealed an inflammatory shift in a monocyte cluster of twins with MS, coupled with the emergence of a population of naive helper T cells that have a transient response IL2 as MS-related immune alterations [47]. The research on genetically identical (monozygotic) twins shows that the concordance rate for MS is approximately 30%. This indicates that genetic and environmental factors interact with MS. Baranzini et al. examined DNA methylation and gene expression across the genome in three monozygotic twins discordant for MS; however, there were no consistent differences in DNA sequence [48]. It is surprising that the environment strongly indicated epigenetic modifications to germline susceptibility based on studies of adoptees, half-siblings, and avuncular pairs. The fact that complete explanations for disease heritability were unachieved after whole-genome association studies warrants consideration of all the factors contributing to disease risk, such as genetic, epigenetic, and environmental factors [49].
As many as 200 single-nucleotide polymorphisms (SNPs) are associated with MS risk (Table 1) [50,51,52,53]. Gresle et al. analyzed MS risk expression quantitative trait loci associations for 129 distinct genes in MS patients [54]. They identified the MS risk SNPs, rs2256814 Myelin transcription factor 1 (MYT1) in CD4 cells and rs12087340 RF00136 in monocyte cells. IL7 receptor (IL7R) is a member of the type I cytokine receptor family and is a primary pleiotropic receptor in immune cells (Figure 1). Two GWASs of MS reported that three SNPs outside of the MHC region were associated with MS: rs6897032 within the IL7R gene and two SNPs (rs2104286 and rs12722489) in the IL2R gene [14,55]. Omraninava et al. revealed that the IL7RA gene rs6897932 SNP decreases MS susceptibility (Figure 1) [56]. Infection with the herpes virus and Mycoplasma pneumonia create grounds for MS. The T allele in the IFNγ gene (+874) and the genotypes of AA and AG at the TNFα gene (-308) at position−308 were considered potential risk factors for MS (Figure 1) [57]. Despite GWASs explaining that there are common SNPs associated with various diseases, known common variants only account for part of the estimated heritability of common complex diseases. Nadia et al. identified the rare functional variants analyzed within a large Italian MS multiplex family with five affected members [58]. Another recent study showed that up to 5% of MS inheritability may be accounted for by rare variations in the gene coding sequence, with four novel low genes driving MS risk independently of common variant signals [59]. Based on the research of a large cohort of Italian individuals, researchers identified three SNPs (rs4267364, rs8070463, rs67919208) that were involved in the regulation of TBK1 Binding Protein 1 (TBKBP1) and prioritized them as functionally relevant in MS [60]. Recent GWAS research in MS that has analyzed up to 47,000 MS patients and 68,000 healthy controls has determined more than 200 non-MHC genome-wide associations. The results show that immune cells, such as T cells, B cells, and monocytes, have susceptible gene specificity [61]. The International Multiple Sclerosis Consortium analyzed the large-scale GWAS data of 47,000 MS patients and 68,000 healthy controls and established a reference genetic map of MS. Their findings demonstrate the enrichment of MS genes in these brain-resident immune cells, suggesting that they may have a role in targeting an autoimmune process to the CNS, although MS is most likely initially triggered by a perturbation in peripheral immune responses [52].
The Janus kinase and signal transducer and activator of the transcription (JAK/STAT) pathway is essential for both innate and acquired immunity. It has also been reported to be associated with several neuroinflammatory diseases (Figure 2) [62]. In EAE mice, Th1 cells produce interferon-gamma (IFNγ) via STAT4 and inflammatory macrophages, which promote macrophage activation. Similarly, Th17 produces granulocyte-macrophage colony-stimulating factor (GM-CSF) in the CNS and promotes macrophage polarization to inflammation via JAK/STAT5 (Figure 2) [63].
A comprehensive analysis of genes in the brain of MS patients has shown increased levels of immune cell populations and decreased ones of endothelial cells, Th1 cells, and Treg cells in MS lesions [64]. Toll-like receptors (TLRs) have a variety of roles, including axonal pathway formation and dorsoventral patterning in the CNS. TLR ligands, such as pathogen-associated molecular patterns (PAMPs), have been identified as T cell promoters in MS. In particular, TLR2 expression is high in MS lesions and TLR2 activation induces the expression of pro-inflammatory cytokines such as IL-6, IL-8, and TNF-α, which are implicated in exacerbated inflammation (Figure 3) [65]. The HLA signal in the Italian population maps to a glycoprotein involved in dendritic cell (DC) maturation, such as TNFSF14 gene encoding LIGHT. Miriam et al. reported that the TNFSF14 intronic SNP rs1077667 was the main MS-associated variant in the region. That means that the intronic variant rs1077667 alters the expression of TNFSF14 in DCs, which may play a role in MS pathogenesis [66]. A variant in TNHSH13B, encoding the cytokine and drug target B-cell activating factor (BAFF), was associated with upregulated humoral immunity through increased levels of soluble BAFF, B lymphocytes, and immunoglobulins in MS [67]. Leptin (LEP) and leptin receptor (LEPR) overexpression are related to MS activity and progression, and peroxisome proliferator-activated receptor gamma co-activator 1-alpha (PGC1A) is able to affect the reactive oxygen species production in the pathogenesis of MS. LEP rs7799039 and LEPR rs1137101 genetic variants modify the serum LEP levels and PGC1A rs8192678 alters the PGC1A activity. Ivana et al. revealed that the PGC1A rs8192678 minor allele had an increased risk for the occurrence of MS, and LEP rs7799039 affected the LEP gene expression in relapsing-remitting patients [68].
Furthermore, in relapsing MS, reduced suppression of cytokine signaling-3 (SOCS3) expression in the CNS and immune cells may induce LEP-mediated overexpression of pro-inflammatory cytokines (Figure 3) [69]. Pattern recognition receptors, which are triggered by both microbe-associated molecular patterns and damage-associated molecular patterns, have been reported to regulate innate immune responses in MS and an EAE model. Pattern recognition receptor signaling promotes inflammatory-producing cytokine production in CNS autoimmune diseases (Figure 3) [70]. NF-κB is involved in a wide range of vital processes, including inflammation, cell proliferation, and differentiation. Abnormal NF-κB activation has been reported to be closely associated with the development of MS and EAE [71].
In MS, the altered Foxp3-E2 variant-associated inhibitory activity of Treg cells is associated with defective signaling via IL-2 and glycolysis, which modulates Treg cell induction and function in autoimmunity [72]. The expression of vascular endothelial growth factors and matrix metallopeptidases involved in angiogenesis is increased in MS. These genes are also involved in basement membrane degradation and blood–brain barrier disruption, which allows immune cells to infiltrate the CNS in EAE and MS (Figure 4) [73]. Programmed cell death 1 (PD-1) is known as an immune checkpoint that is associated with several autoimmune diseases. Research on the frequency of PD-1 genotypes and alleles in MS patients shows that PD-1 gene polymorphisms may be associated with MS [74]. Phosphorylation of receptor-interacting protein kinase 1 (RIPK1) in astrocytes and microglia triggers a detrimental neuroinflammatory program that contributes to the neurodegenerative environment in MS (Figure 4) [75].
Risk genes have been well studied by meta-analyses and many SNPs have been identified. MYT1, IL2R, IL7R, IFNγ, and TNFα, among others, are considered to be the major risk genes in MS. The related major signaling in MS is the JAK/STAT pathway.

5. Conclusions and Perspective

We have examined and discussed the genetic risks in the background of MS. The major risks include (1) the genes related to vitamin D deficiency, (2) the genes involved in the immune response, and (3) the genes responsible for inflammatory cytokines and the related signaling molecules. Nucleotide sequence analyses with advancing technologies have clarified that there are an increasing number of other possible categories of risk genes besides these in MS. In the future, molecules related to these risk gene products may be promising therapeutic target candidates.

Author Contributions

Conceptualization, R.S. and J.Y.; methodology, R.S.; software, R.S.; validation, R.S. and J.Y.; formal analysis, R.S.; investigation, R.S.; resources, R.S.; data curation, R.S. and J.Y.; writing—original draft preparation, R.S.; writing—review and editing, R.S. and J.Y.; visualization, R.S. and J.Y.; supervision, J.Y.; project administration, J.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We thank Takako Morimoto and Yoichi Seki (Tokyo University of Pharmacy and Life Sciences) for their insightful comments.

Conflicts of Interest

The authors have declared that no competing interest exist.

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Figure 1. Schematic diagram of some putative major factors associated with multiple sclerosis. Deficiency of vitamin D results in demyelination. The levels of 25-OH-D, a metabolite from vitamin D, is one of the risks of MS. MAG proteins, as well as signaling molecules around immune cells, are also related to MS demyelination.
Figure 1. Schematic diagram of some putative major factors associated with multiple sclerosis. Deficiency of vitamin D results in demyelination. The levels of 25-OH-D, a metabolite from vitamin D, is one of the risks of MS. MAG proteins, as well as signaling molecules around immune cells, are also related to MS demyelination.
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Figure 2. JAK/STAT signaling pathway associated with MS. Cytokines, through JAK/STAT signaling, especially in Th1 and Th17 cells, are putatively considered responsible for the progression of MS.
Figure 2. JAK/STAT signaling pathway associated with MS. Cytokines, through JAK/STAT signaling, especially in Th1 and Th17 cells, are putatively considered responsible for the progression of MS.
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Figure 3. Interaction of some receptors with their cognate ligands induces the expression of pro-inflammatory cytokines in immune cells. Pathogen-associated molecular patterns interaction with TLRs, SOCS3 activation by cytokine receptors, microbe-associated molecular patterns or damage-associated molecular patterns binding to pattern recognition receptors, and/or activation through NF-κB are involved in the regulation of the expression of inflammatory cytokines, which are responsible for MS, in immune cells.
Figure 3. Interaction of some receptors with their cognate ligands induces the expression of pro-inflammatory cytokines in immune cells. Pathogen-associated molecular patterns interaction with TLRs, SOCS3 activation by cytokine receptors, microbe-associated molecular patterns or damage-associated molecular patterns binding to pattern recognition receptors, and/or activation through NF-κB are involved in the regulation of the expression of inflammatory cytokines, which are responsible for MS, in immune cells.
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Figure 4. Abnormal autoimmune reaction and neuroinflammation in MS. Altered Foxp3 expression in Treg cells induces an abnormal autoimmune reaction. Expression levels of vascular endothelial growth factors and matrix metallopeptidases are increased, probably disrupting the blood–brain barrier. This disruption allows immune cells to infiltrate. Phosphorylation of RIPK1 in astrocytes and microglia is involved in the promotion of the neuroinflammatory program.
Figure 4. Abnormal autoimmune reaction and neuroinflammation in MS. Altered Foxp3 expression in Treg cells induces an abnormal autoimmune reaction. Expression levels of vascular endothelial growth factors and matrix metallopeptidases are increased, probably disrupting the blood–brain barrier. This disruption allows immune cells to infiltrate. Phosphorylation of RIPK1 in astrocytes and microglia is involved in the promotion of the neuroinflammatory program.
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Table
Table 1. The risk allele and its possible role for the 200 autosomal non-MHC genome-wide effects. This list shows the 200 SNP regions and the possible roles of probable genes associated with MS risks, as identified by SNP analyses [50,51,52,53].
Table 1. The risk allele and its possible role for the 200 autosomal non-MHC genome-wide effects. This list shows the 200 SNP regions and the possible roles of probable genes associated with MS risks, as identified by SNP analyses [50,51,52,53].
SNP RegionGeneProteinPossible Role of Nearest Gene
rs6742rtel1RTEL1DNA helicase
rs32658fam170aFAM170ADNA binding activator
rs137955rpl3RPL3ribosomal protein
rs140522hdac10HDAC10deacetylase
rs198398mtorMTORrapamycin kinase
rs244656ppp2caPPP2CAcatalytic subunit of protein phosphatase
rs249677arhgap26ARHGAP26GTPase activating protein
rs354033znf862ZNF862zinc finger protein
rs405343axin1AXIN1cytoplasmic protein
rs438613eomesEOMESDNA binding domain
rs483180notch2NOTCH2notch receptor
rs531612relaRELAproto-oncogene transcription factor
rs631204tnfaip3TNFAIP3cytokine
rs701006arhgap9ARHGAP9GTPase activating protein
rs719316atxn1ATXN1DNA binding protein
rs735542mycMYCproto-oncogene transcription factor
rs760517lgals1LGALS1galactoside binding protein
rs802730ptprkPTPRKprotein tyrosine phosphatase receptor
rs883871raraRARAretinoic acid receptor
rs962052rnd3RND3Rho family GTPase
rs983494cd48CD48immune response regulator
rs1014486il12aIL12Acytokine
rs1026916hoxa13HOXA13homeobox
rs1076928pim1PIM1proto-oncogene kinase
rs1077667c3C3complement component
rs1087056znf438ZNF438zinc finger protein
rs1112718ideIDEinsulin enzyme
rs1177228commd1COMMD1copper metabolism
rs1250551zmiz1ZMIZ1zinc finger protein
rs1323292rgs1RGS1G Protein Signaling
rs1365120traf6TRAF6adaptor protein
rs1399180gata3GATA3transcription factor
rs1415069bcar3BCAR3anti-estrogen resistance protein
rs1465697atf5ATF5transcription factor
rs1738074synj2SYNJ2inositol polyphosphate 5-phosphatase
rs1800693cd9CD9immune response regulator
rs2084007ppp2caPPP2CAcatalytic subunit of protein phosphatase
rs2150879rps6kb1RPS6KB1ribosomal protein
rs2248137znf217ZNF217zinc finger protein
rs2269434celf1CELF1alternative splicing
rs2286974litafLITAFcytokine
rs2289746alcamALCAMimmunoglobulin receptor
rs2317231cd1eCD1Eimmune response regulator
rs2327586sgk1SGK1serine/threonine kinase
rs2331964cd86CD86immune response regulator
rs2364485cd9CD9immune response regulator
rs2469434cd226CD226immune response regulator
rs2546890il12bIL12Bcytokine
rs2585447znf217ZNF217zinc finger protein
rs2590438bcl6BCL6immune signaling receptor
rs2705616mapk10MAPK10MAPK
rs2726479cxxc4CXXC4zinc finger protein
rs2836438ets2ETS2transcription factor
rs2986736camta1CAMTA1transcription activator
rs3184504arpc3ARPC3cell polymerization
rs3737798cd48CD48immune response regulator
rs3809627mapk3MAPK3MAPK
rs3923387plecPLECcytoskeleton
rs4262739ets1ETS1proto-oncogene transcription factor
rs4325907rpl24RPL24ribosomal protein
rs4409785maml2MAML2cytoplasmic protein
rs4728142smoSMOG protein-coupled receptor
rs4796224acacaACACAacetyl-CoA carboxylase
rs4808760ifi30IFI30lysosomal thiol reductase
rs4812772mybl2MYBL2proto-oncogene transcription factor
rs4820955lifLIFcytokine
rs4896153bclaf1BCLAF1BCL transcription factor
rs4939490fads1FADS1fatty acid desaturase
rs4940730malt1MALT1caspase-like protease
rs5756405rac2RAC2GTP binding protein
rs6020055cebpbCEBPBtranscriptional activator protein
rs6032662cd40CD40immune response regulator
rs6072343plcg1PLCG1phospholipase
rs6427540cd48CD48immune response regulator
rs6496663iqgap1IQGAP1GTPase activating protein
rs6533052nfkb1NFKB1cytokine
rs6564681mafMAFproto-oncogene kinase
rs6589706kmt2aKMT2ALysine Methyltransferase
rs6589939clmpCLMPtransmembrane protein
rs6670198prdm16PRDM16zinc finger protein
rs6672420runx3RUNX3transcription factor
rs6738544stat1STAT1transcription activator
rs6789653zbtb38ZBTB38zinc finger protein
rs6837324tecTECtyrosine kinase
rs6911131hivep2HIVEP2zinc finger protein
rs6990534mycMYCproto-oncogene transcription factor
rs7222450crhr1CRHR1G-protein coupled receptor
rs7260482apoeAPOEapoprotein
rs7731626map3k1MAP3K1MAPK kinase
rs7855251anp32bANP32BRNA polymerase binding protein
rs7975763mphosph9MPHOSPH9M phase phosphoprotein
rs7977720olr1OLR1low density lipoprotein receptor
rs8062446nlrc5NLRC5cytokine receptor
rs9308424batf3BATF3basic leucine zipper protein
rs9568402rnaseh2bRNASEH2Bribonuclease
rs9591325rnaseh2bRNASEH2Bribonuclease
rs9610458ube2l3UBE2L3ubiquitin conjugating enzyme
rs9808753ifnar2IFNAR2interferon receptor
rs9843355cd80CD80immune response regulator
rs9863496satb1SATB1matrix protein
rs9878602rybpRYBPDNA binding protein
rs9900529grb2GRB2growth factor receptor
rs9909593raraRARAretinoic acid receptor
rs9955954malt1MALT1caspase-like protein
rs9992763rpl34RPL34ribosomal protein
rs10063294slc1a3EAA1transporter
rs10191360cxcr4CXCR4chemokine receptor
rs10230723ikzf1IKAROSDNA binding protein
rs10245867hoxa13HOXA13homeobox
rs10271373tbxas1TBXAS1lipid synthase
rs10801908atp1a1ATP1A1transporting subunit
rs10936182il12aIL12Acytokine
rs10936602mecomMDS1 And EVI1 Complex Locuszinc finger protein
rs10951042mad1l1MAD1cell cycle controller
rs10951154hoxa4HOXA4homeobox
rs11079784npeppsNPEPPSpeptidase
rs11083862c5ar1C5AR1complement component receptor
rs11125803adcy3ADCY3adenylate cyclase
rs11161550bcl10BCL10immune signaling receptor
rs11231749esrraESRRAestrogen related receptor
rs11256593pfkfb3PFKFB3phosphofructo kinase
rs11578655extl2EXTL2glycosyltransferase
rs11749040dab2DAB2adaptor protein
rs11809700rpl5RPL5ribosomal protein
rs11852059ptger2PTGER2prostaglandin receptor
rs11899404lpin1LPIN1lipid phosphohydrolase
rs11919880cnot10CNOT10transcription complex
rs12133753cdc7CDC7cell cycle kinase
rs12147246rcor1RCOR1transcription factor
rs12211604rreb1RREB1binding protein
rs12365699kmt2aKMT2Amethyltransferase
rs12434551zfp36l1ZFP36L1zinc finger protein
rs12478539zfp36l2ZFP36L2zinc finger protein
rs12588969rcor1RCOR1chromatin binding
rs12609500tyk2TYK2tyrosine kinase
rs12614091cd28CD28immune response regulator
rs12622670aplfAPLFcomponent of the cellular response
rs12722559pfkfb3PFKFB3glycolysis-related biphosphatase
rs12832171cd9CD9immune response regulator
rs12925972mafMAFproto-oncogene kinase
rs12971909map2k2MAP2K2MAPK kinase
rs13066789bcl6BCL6immune signaling receptor
rs13136820uchl1UCHL1ubiquitin hydrolase
rs13327021eomesEOMESDNA binding domain
rs13385171sertad2SERTAD2transcription activator
rs13414105alkALKtyrosine kinase
rs17051321qrfprQRFPRpyroglutamylated receptor
rs17724508mafMAFproto-oncogene kinase
rs17741873camk2gCAMK2GCAM kinase
rs17780048tnfaip3TNFAIP3cytokine
rs28703878pkiaPKIAprotein kinase inhibitor
rs28834106dnm2DNM2GTP binding protein
rs34026809kmt2aKMT2Amethyltransferase
rs34536443tyk2TYK2tyrosine kinase
rs34681760adcy2ADCY2adenylate cyclase
rs34695601fosFOSproto-oncogene transcription factor
rs34723276extl2EXTL2glycosyltransferase
rs34947566litafLITAFcytokine
rs35218683deaf1DEAF1zinc finger protein
rs35486093bcl10BCL10adaptor protein
rs35540610sp110SP110nuclear body protein
rs35703946irf8IRF8cytokine
rs55858457mad1l1MAD1L1cell cycle controller
rs56095240maml2MAML2transcriptional activator
rs57116599il1bIL1Bcytokine
rs58166386rasal3RASAL3Ras GTPase activating protein
rs58394161rpl5RPL5ribosomal protein
rs59655222znf281ZNF281zinc finger protein
rs60600003elmo1ELMO1adaptor protein
rs61708525plxnc1PLXNC1transmembrane receptor
rs61863928egr2EGR2transcription factor
rs61884005arntlARNTLtranscriptional activator
rs62013236acsbg1ACSBG1acyl-CoA synthetase
rs62420820tnfaip3TNFAIP3cytokine
rs67111717nsd1NSD1transcriptional regulator
rs67934705rpl11RPL11ribosomal protein
rs71329256cd86CD86immune response regulator
rs72922276pde4bPDE4Bphosphodiesterase
rs72928038rragdRRAGDRas related GTPase binding protein
rs72989863march1MARCH1ubiquitin protein ligase
rs73414214pik3cgPIK3CGPhosphoinositide 3-kinase
chr1:154983036arhgef2ARHGEF2Rho/Rac guanine nucleotide exchanger
chr1:32738415hdac1HDAC1histone deacetylase
chr2:112492986anapc1ANAPC1anaphase-promoting complex
chr3:100848597rpl24RPL24ribosomal protein
chr3:112693983cd200CD200immune response regulator
chr3:121783015cd86CD86immune response regulator
chr5:40429250dab2DAB2DAB adaptor protein
chr6:119215402mcm9MCM9ATP hydrolysis activity
chr6:130348257arhgap18ARHGAP18Ras GTPase activating protein
chr6:14691215jarid2JARID2transcriptional repressor
chr7:50328339ikzf1IKZF1zinc finger protein
chr8:129177769mycMYCproto-oncogene transcription factor
chr8:95851818rad54bRAD54BDEAD-like helicase
chr11:118783424kmt2aKMT2Alysine methyltransferase
chr11:14868316pde3bPDE3Bphosphodiesterase
chr13:100026952dock9DOCK9Cdc42 guanine nucleotide exchanger
chr14:88523488kcnk10KCNK10potassium channel protein
chr16:11213951litafLITAFcytokine
chr16:11353879litafLITAFcytokine
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Shirai, R.; Yamauchi, J. New Insights into Risk Genes and Their Candidates in Multiple Sclerosis. Neurol. Int. 2023, 15, 24-39. https://doi.org/10.3390/neurolint15010003

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Shirai R, Yamauchi J. New Insights into Risk Genes and Their Candidates in Multiple Sclerosis. Neurology International. 2023; 15(1):24-39. https://doi.org/10.3390/neurolint15010003

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Shirai, Remina, and Junji Yamauchi. 2023. "New Insights into Risk Genes and Their Candidates in Multiple Sclerosis" Neurology International 15, no. 1: 24-39. https://doi.org/10.3390/neurolint15010003

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Shirai, R., & Yamauchi, J. (2023). New Insights into Risk Genes and Their Candidates in Multiple Sclerosis. Neurology International, 15(1), 24-39. https://doi.org/10.3390/neurolint15010003

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