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Lin, Deng, Lu, and Lei: Susceptibility Genes for Multiple Sclerosis Identified in a Gene-Based Genome-Wide Association Study
Original Article

Journal of Clinical Neurology 2015; 11(4): 311-318.

Published online: 21 August 2015

DOI: https://doi.org/10.3988/jcn.2015.11.4.311

Susceptibility Genes for Multiple Sclerosis Identified in a Gene-Based Genome-Wide Association Study

Xiang Lina, b, c, Fei-Yan Denga, b, Xin Lua, b, Shu-Feng Leia, b

aCenter for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, People's Republic of China.

bJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, School of Public Health, Soochow University, Suzhou, Jiangsu, People's Republic of China.

cDepartment of Tuberculosis Control, Ningbo Municipal Center for Disease Control & Prevention, Ningbo, Zhejiang, People's Republic of China.

Correspondence: Shu-Feng Lei, PhD. Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, No. 199, Ren'ai Road, Suzhou Industrial Park, Suzhou 215123, People's Republic of China. Tel +86-512-65883357, Fax +86-512-65883357, leisf@suda.edu.cn

Received 13 October 2014       Revised 22 November 2014       Accepted 26 November 2014

Copyright © 2015 Korean Neurological Association

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background and Purpose

Multiple sclerosis (MS) is a demyelinating and inflammatory disease of the central nervous system. The aim of this study was to identify more genes associated with MS.

Methods

Based on the publicly available data of the single-nucleotide polymorphism-based genome-wide association study (GWAS) from the database of Genotypes and Phenotypes, we conducted a powerful gene-based GWAS in an initial sample with 931 family trios, and a replication study sample with 978 cases and 883 controls. For interesting genes, gene expression in MS-related cells between MS cases and controls was examined by using publicly available datasets.

Results

A total of 58 genes was identified, including 20 "novel" genes significantly associated with MS (p<1.40×10-4). In the replication study, 44 of the 58 identified genes had been genotyped and 35 replicated the association. In the gene-expression study, 21 of the 58 identified genes exhibited differential expressions in MS-related cells. Thus, 15 novel genes were supported by replicated association and/or differential expression. In particular, four of the novel genes, those encoding myelin oligodendrocyte glycoprotein (MOG), coiled-coil alpha-helical rod protein 1 (CCHCR1), human leukocyte antigen complex group 22 (HCG22), and major histocompatibility complex, class II, DM alpha (HLA-DMA), were supported by the evidence of both.

Conclusions

The results of this study emphasize the high power of gene-based GWAS in detecting the susceptibility genes of MS. The novel genes identified herein may provide new insights into the molecular genetic mechanisms underlying MS.

Keywords: multiple sclerosis, gene-based GWAS, gene expression, human leukocyte antigen

INTRODUCTION

Multiple sclerosis (MS) is a chronic demyelinating and inflammatory disease of the the central nervous system (CNS).12 Genetic factors contribute markedly to the susceptibility to MS, since the children of affected parents have a tenfold higher risk of developing the condition than the general population.3
It has been demonstrated that the human leukocyte antigen (HLA) gene is closely related to MS susceptibility. The HLA gene contains a great many genes (HLA class I, II, and III) residing on chromosome 6, which is related to immune system function in humans. The proteins encoded by the HLA class I and II regions are involved in antigen processing and presentation, and play a major role in autoimmune events. MS is believed to be an immune-mediated disorder that leads to recurrent immune attacks on the CNS.4
Recent genome-wide association studies (GWASs) have identified many loci with modest effects.567 However, previous GWASs used single-nucleotide polymorphisms (SNPs) as a basic analysis unit,5689 and adopted stringent thresholds of significance to control for the false-positive rate. This approach resulted in a large number of SNPs with potential effects being filtered out and ignored.
The gene-based GWAS study strategy, involving analyzing all variants within a putative gene, has proved to be more powerful than regular single-SNP-based GWASs for detecting disease susceptibility genes.101112 To detect "novel" genes associated with MS, we performed a gene-based GWAS using the Knowledge-based mining system for Genome-wide Genetic studies (KGG; http://statgenpro.psychiatry.hku.hk/limx/kgg/index.html)10 in an initial study sample containing 931 family trios.8 We also performed other functional analyses to supplement the evidence regarding the relevance of the novel genes to MS.

METHODS

Samples

The initial GWAS sample included a total of 931 family trios, each of which consisted of an affected MS child and both parents. The replication sample contained 978 cases and 883 controls. The MS patient was diagnosed according to the McDonald criteria.13 Both the initial and replication study samples included all clinical subtypes and partial clinically isolated syndromes. However, the replication study had the priority to include patients with the relapsing onset form of MS. Institutional Review Board was exempted because this study used public available database of Genotypes and Phenotypes (dbGaP) which dose not involve any personal information. The study samples, genotyping, quality control, and SNP exclusion criteria have been described previously.89

Gene-based GWAS

The gene-based GWAS and replication analysis were based on the probability values generated in previous genome-wide SNP-based association studies and downloaded from the dbGaP (http://www.ncbi.nlm.nih.gov/gap/?term=multiple+sclerosis, accession number: phs000139 and phs000171). We used the Gene-Based Association Test Using Extended Simes Procedure analysis method modeled in KGG 2.5 (http://statgenpro.psychiatry.hku.hk/limx/kgg/index.html).10 The SNPs, ranging from the upstream 5 kb at the 5' end to the downstream 5 kb at the 3' end, were assigned to one gene. In total, 48% of SNPs across the whole genome were assigned to genes.

Differential expression analysis for MS-associated genes

We downloaded four gene-expression data sets from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo; GSE21942, GSE27694, GSE16461, and GSE52139). A case-control study design was used to investigate all four data sets, and multiple cell types were investigated, including peripheral blood mononuclear cells (PBMCs), CD34+ hematopoietic progenitor cells (HPCs), CD8+ T lymphocytes, and spinal cord. The study design data analysis are described in detail elsewhere.14151617 Differentially expressed genes between MS cases and controls were identified by comparing mean gene-expression signals in MS cases versus controls and analyzing the findings using t-tests.

Protein-protein interaction network

MS-associated gene interactions and associations were detected by protein-protein interaction analysis, conducted by searching the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (http://string-db.org/). The STRING database integrates known and predicted associations derived from a genomic context, high-throughput experiments, coexpression, and previous knowledge (text mining).18

Functional annotation clustering analysis

The probability of the identified MS-associated genes clustering in a Gene Ontology (GO) term or a particular biological pathway as defined by the GO project and Kyoto Encyclopedia of Genes and Genomes database was tested by performing a functional annotation clustering analysis using the Database for Annotation, Visualization and Integrated Discovery integrated database query tools (http://david.abcc.ncifcrf.gov/).1920 The enrichment was measured quantitatively using Fisher's exact test, and the Bonferroni method21 was adopted to correct for multiple testing.

RESULTS

Quantile-quantile plots of the association results for the original genome scan and the present gene-based GWAS are shown in Fig. 1, in which probability values (shown as -log10 values) for all 20,761 genes and all SNPs are plotted against the expected null distribution. The tail of the distribution of gene-based probability values deviated more significantly than those of SNPs inside or outside of the gene. The distribution of the probability values for the present gene-based GWAS is shown in Fig. 2.
Based on a false discovery rate threshold of 1.40×10-4, 58 genes were found to be significantly associated with MS in the initial gene-based GWAS (Supplementary Table 1 in the online-only Data Supplement). Among those 58 genes, 53 were located in the HLA region on chromosome 6. In contrast, according to the raw SNP-based probability values, 75 SNPs in 31 genes were found to be significantly associated with MS (threshold of p<1.49×10-7). Therefore, the present gene-based GWAS detected 27 novel genes that had not previously been detected in the original SNP-based GWAS. After searching the Phenotype-Genotype Integrator (www.ncbi.nlm.nih.gov/gap/phegeni/), a database that archives previous association results, we found that 7 of the 27 genes had already been reported for significant associations (threshold of p<5.0×10-5). Therefore, among the 58 MS-associated genes that we identified, 20 were considered as novel MS candidate genes. Of note, five of these novel genes were located outside the HLA region.
Among the total 58 identified MS-associated genes, 44 genes with genotype data in the replication sample were subjected to further association tests, which revealed that 35 of them (80%) were still significantly associated with MS (p<0.05). Furthermore, 21 of these 58 genes, including 8 novel genes, exhibited differential expression in the differential expression analysis (Table 1). Most interestingly, the genes encoding histone-lysine N-methyltransferase, H3 lysine-9 specific 3 (EHMT2), major histocompatibility complex, Class I, A (HLA-C), and negative elongation factor E (RDBP) exhibited significantly differential expressions between cases and controls in both PBMCs and CD34+ HPCs, and the gene encoding myelin oligodendrocyte glycoprotein (MOG) in PBMCs, CD8+ T lymphocytes, and spinal cord, simultaneously (Table 1).
The 58 identified MS-associated genes were retrieved from the STRING database. Only 36 genes, including 10 novel genes, were annotated in this database. The genes at the HLA regions were clearly enriched into two clusters: HLA class I and II clusters (Fig. 3). Two novel genes, those encoding major histocompatibility complex, class, I, A (HLA-A) and major histocompatibility complex, class II, DM alpha (HLA-DMA), were involved in HLA-class I and II clusters, respectively. Another four novel genes, MOG, and those encoding coiled-coil alpha-helical rod protein 1 (CCHCR1), bromodomain-containing protein 2 (BRD2), and chromosome 6 open reading frame 15 (C6orf15), were directly connected with the HLA clusters (Fig. 3).
Taking both the replication data and gene-expression data together, we found that 15 novel genes were supported by replicated association and/or differential expression (Table 2). Most interestingly, the significance of four genes—HLA-DMA, CCHCR1, MOG, and the gene encoding HLA complex group 22 (HCG22)—in MS was supported by both the replication and gene-expression studies. In addition, the significance of two non-HLA genes—those encoding family with sequence similarity 69, member A (FAM69A) and POU domain class 2 transcription factor 3 (POU2F3)—in MS was supported by evidence of differential expressions in PBMCs and CD8+ T lymphocytes, respectively.
The identified 58 MS-associated genes were found enriched in 54 GO terms, even after Bonferroni correction (p<0.05) (Supplementary Table 2 in the online-only Data Supplement). Most significantly, 12 genes were enriched in "antigen processing and presentation" (GO: 0019882; p=271.09×10-16), and 11 genes were enriched in "MHC protein complex" (GO: 0012611; p=1.06×10-16).

DISCUSSION

Gene-based association analysis is an efficient method for detecting associations between candidate genes and complex diseases, as it combines signals for all variants within a putative gene. By using this method, several studies have identified new associations between genes and diseases.2223 The present study again highlights the superior power of gene-based association analysis for detecting associations for MS. Specifically, we detected 58 genes significantly associated with MS, including 20 novel genes that were undetected in previous single SNP-based GWASs. Furthermore, associations for 80% of the identified MS-associated genes were replicated.
Most previous association studies have identified only the statistical relevance of genes to MS (at the DNA level), without dissecting the functional mechanisms underlying those associations. In contrast, in the present study we not only established statistical associations between genetic markers and MS at the DNA level, but also performed follow-up differential gene expression analyses and functional annotation clustering analyses as important supplementary methods to analyze the function of causal variants. This supplementary evidence strengthens the likelihood that the eight novel genes identified in this study with significantly differential expressions are directly involved in the pathogenesis of MS.
Recent GWASs have identified a great number of MS-associated genetic loci,59242526 most of which have been mapped to the HLA region. Most of the loci identified in the present study were also located in this region. As we know, the major HLAs are essential elements for immune function. Therefore, our findings also highlight the importance of the autoimmune system in the etiology of MS.
Most interestingly, evidence from association, replication, and differential expression studies strongly supports the significance of the following four genes to MS: MOG, HLA-DMA, CCHCR1, and HCG22. Up to now, the biological function of HCG22 in MS or immunity is unknown. HLA-DMA and CCHCR1 exhibit suggestive associations with rheumatoid arthritis (RA) and psoriasis,272829 respectively, suggesting that both genes are involved in the immune response. The protein encoded by MOG, myelin oligodendrocyte glycoprotein (MOG) is a membrane protein expressed on the surface of oligodendrocyte cells and myelin sheaths.30 MOG is an important candidate target antigen in MS.313233 Monoclonal antibodies against MOG were used to develop an animal model of MS.34 The functions of these genes in MS need further investigation.
In addition, three genes (POU2F3, BRD2, and FAM69A) were expressed differentially in MS-related cells. POU2F3, a non-HLA gene (chromosome 11), is associated with melanoma and cervical cancer.3536 BRD2 is associated with cancer, obesity, type 2 diabetes, RA, and inflammation,373839 and was differentially expressed in the PBMCs of patients with RA (p=2.31×10-3, data sets from GEO; www.ncbi.nlm.nih.gov/geo, GSE#: GSE15573). The associations with other autoimmune diseases suggested the possible relevance of BRD2 and POU2F3 to MS. The specific functions of FAM69A in MS are unknown. A previous study found that 21 SNPs located at the GFI-EVI5-RPL5-FAM69A locus were positively associated with MS.40 In-depth studies are needed to disclose the functional mechanism of these genes in MS.
It should be noted that since there is strong linkage disequilibrium (LD) at the HLA region, it is reasonable to infer that the significant signals for some of the genetic markers are partially due to their strong LD with true functional variants within the HLA region. Although the supplementary analysis could play a part in analyzing the function of causal variants, it is also necessary to research the functional mechanisms underlying the associations further, especially those between the HLAs and MS.
In conclusion, this gene-based GWAS has identified 20 novel MS-associated genes. The results highlight the advantages of gene-based association analysis over single SNP-based GWASs for detecting susceptibility genes for MS. The new findings may provide novel insights into the molecular mechanisms underlying MS.

Figures and Tables

Fig. 1

Quantile-quantile plots of the association results for the initial genome scan. The tail of the distribution of gene-based probability values deviated more significantly than those of SNPs inside or outside of the gene, which suggests that the power was higher for a gene-based association analysis than a single SNP-based GWAS in detecting associations. GWAS: genome-wide association study, SNP: single-nucleotide polymorphism.

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Fig. 2

Manhattan plot of gene probability values on chromosomes. Most of the genes significantly associated with MS are mapped to the HLA region (chromosome 6). HLA: human leukocyte antigen, MS: multiple sclerosis.

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Fig. 3

Protein-protein interactions between MS-associated genes. Only the connected genes are shown. The novel genes are labeled in red. Different line colors represent the types of evidence for the association. HLA class I cluster: major histocompatibility complex, class I, A (HLA-A), Major histocompatibility complex, class I, C (HLA-C), and major histocompatibility complex, class I, F (HLA-F); HLA class II cluster: major histocompatibility complex, class II, DQ alpha 1 (HLA-DQA1), major histocompatibility complex, class II, DQ alpha 2 (HLA-DQA2), major histocompatibility complex, class II, DQ beta 2 (HLA-DQB2), major histocompatibility complex, class II, DR alha (HLA-DRA), major histocompatibility complex, class II, DO beta (HLA-DOB), major histocompatibility complex, class II, DM alpha (HLA-DMA), and major histocompatibility complex, class II, DM beta (HLA-DMB). HLA: human leukocyte antigen, MS: Multiple sclerosis.

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Table 1

MS-associated genes with significantly differential expressions in MS-related cells/tissue

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Sample S1 S2 S3 S4
Target cells/tissue PBMC CD34+ HPC CD8+ T lymphocytes Spinal cord
Sample size 12:15 8:5 4:4 8:8
Platform [HG-U133_Plus_2] Affymetrix Human Agilent-014850 Whole Human [HG-U133_Plus_2] Affymetrix Human [HG-U133_Plus_2] Affymetrix Human
Genome U133 Plus 2.0 Array Genome Microarray 4×44K G4112F Genome U133 Plus 2.0 Array Genome U133 Plus 2.0 Array
PMID 22021740 22252466 21216829 24910450
GSE No. GSE21942 GSE27694 GSE16461 GSE52139
Gene Probe ID* t-test, p Gene Probe ID* t-test, p Gene Probe ID* t-test, p Gene Probe ID* t-test, p
EHMT2 207484_s_at 4.30×10-31 EHMT2 A_24_P801451 4.50×10-2 - - - - - -
HLA-C 211799_x_at 6.15×10-3 HLA-C A_24_P298409 2.74×10-2 - - - - - -
RDBP 209219_at 2.01×10-2 RDBP A_23_P122545 3.70×10-2 - - - - - -
MOG 214650_x_at 2.12×10-2 - - - MOG 214650_x_at 3.65×10-2 MOG 1555807_a_at 2.92×10-2
BRD2 208685_x_at 5.65×10-6 POU5F1 A_24_P144601 8.28×10-3 POU2F3 215355_at
HLA-DOB 205671_s_at 1.63×10-5 LOC100294145 A_32_P97739 4.21×10-2 HLA-DMB 203932_at
HLA-DRA 208894_at 5.82×10-5 TNXB A_24_P911362 4.45×10-2
HLA-DMA 217478_s_at 8.62×10-4 PSORS1C1 A_24_P24848 4.85×10-2
TAP2 204770_at 3.35×10-3
AIF1 209901_x_at 2.04×10-2
HLA-F 204806_x_at 2.64×10-2
HLA-DQA1 236203_at 3.51×10-2
HCG22 1560767_at 3.85×10-2
CCHCR1 37424_at 1.05×10-2
FAM69A 1556498_at 1.63×10-2

The ratio listed in the line of "Sample size" is the number of multiple sclerosis (MS) cases compared with that of controls. The genes listed here that are not defined in the main text are major histocompatibility complex, class II, DO beta (HLA-DOB), major histocompatibility complex, class II, DR alha (HLA-DRA), POU class 5 homeobox 1 (POU5F1), uncharacterized LOC100294145 (LOC100294145), tenascin XB (TNXB), psoriasis susceptibility 1 candidate 1 (PSORS1C1), major histocompatibility complex, class II, DM beta (HLA-DMB), transporter 2, ATP-binding cassette, sub-family B (TAP2), allograft inflammatory factor 1 (AIF1), major histocompatibility complex, class I, F (HLA-F), major histocompatibility complex, class II, DQ alpha 1 (HLA-DQA1), respectively.

*Only the most significant probe is listed, even if more than one probe was detected for a gene.

GSE No.: Gene Expression Omnibus number, www.ncbi.nlm.nih.gov/geo/, PMID: PubMed unique identifier.

Table 2

Fifteen novel MS-associated genes, supported by replication studies and/or expression studies

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Gene Chromosome Start position Length #SNP* Initial p Replication p Differential expression p, sample†
MOG 6 29619758 25391 2 1.21×10-5 5.09×10-4 2.12×10-2 S1
3.65×10-2 S3
2.92×10-2 S4
HLA-DMA 6 32911391 14508 2 3.15×10-7 4.08×10-2 8.62×10-4 S1
CCHCR1 6 31105216 25799 2 4.03×10-5 1.08×10-3 1.05×10-2 S1
HCG22 6 31016984 15669 6 1.21×10-4 2.29×10-2 3.85×10-2 S1
BRD2 6 32931437 22845 1 1.07×10-5 NS 5.65×10-6 S1
FAM69A 1 93302721 129358 11 4.47×10-5 NA 1.63×10-2 S1
LOC100294145 6 32856953 19582 3 2.27×10-5 NA 4.21×10-2 S2
POU2F3 11 120000000 89702 3 1.13×10-4 NS 8.41×10-3 S3
TRIM26P 6 30201078 13978 2 3.72×10-5 7.80×10-6 NA NA
HCP5P14 6 29733324 12339 1 1.33×10-4 9.94×10-5 NA NA
3.8-1.4 (HLA complex group 26) 6 29828692 11172 7 1.58×10-5 1.08×10-3 NA NA
C6orf15 6 31074000 11332 3 9.26×10-5 2.22×10-3 NS NS
MUC21 6 30946485 16190 4 4.40×10-5 3.42×10-3 NA NA
HLA-A 6 29905309 13352 4 1.61×10-5 8.38×10-3 NS NS
HLA-F-AS1 6 29689378 32448 7 1.83×10-6 2.52×10-2 NA NA

The genes listed here that are not defined in the main text are tripartite motif containing 26 B (TRIM26P), HLA complex P5 pseudogene 14 (HCP5P14), HLA complex group 26 pseudogene (3.8-1.4), mucin 21, cell surface associated (MUC21), HLA-F antisense RNA 1 (HLA-F-AS1), respectively.

*Number of SNPs included in the gene with p<0.05, †Multiple sclerosis (MS)-related cells/tissue sample used for the differential expression analysis; S1, PBMC; S2, CD34+ HPC; S3, CD8+ T lymphocytes; S4, spinal cord.

NA: not available, NS: not significant.

Acknowledgements

The study was supported by Natural Science Foundation of China (81473046, 81401343, 31401079, 31271336, 31071097, and 81373010), the Natural Science Foundation of Jiangsu Province (BK20130300), the Startup Fund from Soochow University (Q413900112, Q413900712), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, and a Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Notes

Conflicts of Interest The authors have no financial conflicts of interest.

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Supplementary Materials

The online-only Data Supplement is available with this article at http://dx.doi.org/10.3988/jcn.2015.11.4.311.
Supplementary Table 1

Results of gene-based and differential expression analyses of 58 MS-associated genes

jcn-11-311-s001
Gene Chromosome Start position Length #SNP* Initial p Genes significant in initial study based on SNP-based p Genes in PheGenI Replication p Differential expression p Sample†
C6orf10 6 32255475 89181 21 3.97×10-39 C6orf10 C6orf10 3.16×10-3 NS NS
NOTCH4 6 32157620 39224 7 2.51×10-31 NOTCH4 NOTCH4 NS NS NS
TNXB 6 32003932 78221 2 6.61×10-28 TNXB TNXB 3.37×10-3 4.45×10-2 S2
HLA-DRA 6 32402619 15204 9 3.73×10-24 HLA-DRA HLA-DRA 4.08×10-3 5.82×10-5 S1
AIF1 6 31577994 11804 1 3.15×10-23 AIF1 AIF1 NA 2.04×10-2 S1
SNORA38 6 31585856 10131 1 3.15×10-23 SNORA38 NA NA NA NA
BAT2 6 31583450 27104 3 1.18×10-22 BAT2 BAT2 3.42×10-2 NS NS
BAT3 6 31601805 23672 3 4.41×10-19 BAT3 BAT3 9.52×10-3 NS NS
PPIAP9 6 31481654 11525 1 1.46×10-15 PPIAP9 PPIAP9 4.67×10-2 NA NA
HLA-DQB2 6 32718875 17455 6 9.02×10-15 HLA-DQB2 HLA-DQB2 1.01×10-2 NS NS
HLA-DQA2 6 32704163 15501 6 1.25×10-14 HLA-DQA2 HLA-DQA2 3.93×10-3 NS NS
EHMT2 6 31842536 27928 1 8.91×10-14 EHMT2 EHMT2 NS 4.30×10-3 S1
4.50×10-2 S2
LOC100287272 6 31238352 13179 5 1.79×10-13 LOC100287272 NA 4.76×10-3 NA NA
MCCD1 6 31491739 11269 2 4.39×10-13 MCCD1 MCCD1 NA NS NS
LOC100462812 6 31340494 10311 5 4.71×10-13 LOC100462812 NA 8.07×10-5 NA NA
HCG27 6 31160537 16208 3 6.34×10-13 HCG27 HCG27 3.84×10-2 NS NS
HLA-DRB9 6 32422597 10269 6 9.42×10-13 HLA-DRB9 HLA-DRB9 1.60×10-3 NA NA
HLA-S 6 31344346 10918 6 4.89×10-12 HLA-S HLA-S 3.36×10-2 NA NA
HLA-DQA1 6 32600183 16246 1 1.68×10-11 HLA-DQA1 HLA-DQA1 5.96×10-4 3.51×10-2 S1
TAP2 6 32784610 26937 2 8.02×10-11 TAP2 TAP2 NS 3.35×10-3 S1
BTNL2 6 32357513 22387 7 1.43×10-10 BTNL2 BTNL2 8.76×10-5 NS NS
HLA-DOB 6 32775540 14285 4 1.78×10-10 HLA-DOB HLA-DOB NA 1.63×10-5 S1
RDBP 6 31914864 17000 2 4.31×10-10 RDBP RDBP 4.16×10-2 2.01×10-2 S1
3.70×10-2 S2
PSORS1C1 6 31077608 35261 10 8.59×10-10 PSORS1C1 PSORS1C1 NS 4.85×10-2 S2
HLA-DMB 6 32897406 16441 1 2.63×10-9 HLA-DMB HLA-DMB NS 4.45×10-2 S3
DHX16 6 30615896 29934 1 7.13×10-9 DHX16 DHX16 2.73×10-3 NS NS
POU5F1 6 31127114 16337 4 9.96×10-9 POU5F1 POU5F1 4.68×10-3 8.28×10-3 S2
TCF19 6 31121303 15689 2 2.02×10-7 TCF19 TCF19 1.78×10-2 NS NS
3.8-1.5 (HLA complex group 26) 6 29727893 11180 4 2.40×10-7 3.8-1.5 NA 1.27×10-3 NA NA
HLA-F 6 29686117 13956 7 4.67×10-7 HLA-F NA 1.17×10-2 2.64×10-2 S1
LOC100507436 6 31362561 76025 8 4.98×10-7 LOC100507436 NA NS NA NA
HLA-X 6 31424623 10644 1 1.55×10-6 NA HLA-X NA NA NA
HCG4P4 6 29917982 10428 1 8.60×10-6 NA HCG4P4 6.98×10-4 NA NA
RPL3P2 6 31243108 11240 2 1.66×10-5 NA RPL3P2 NA NA NA
HLA-C 6 31231529 13326 2 1.72×10-5 NA HLA-C 4.29×10-2 6.15×10-3 S1
2.74×10-2 S2
DHFRP2 6 31326244 13498 2 2.89×10-5 NA DHFRP2 NA NA NA
MICE 6 29704234 17646 1 4.63×10-5 NA MICE NA NA NA
TRIM31 6 30065674 20193 1 1.18×10-4 NA TRIM31 1.31×10-4 NS NS
MOG 6 29619758 25391 2 1.21×10-5 NA NA 5.09×10-4 2.12×10-2 S1
3.65×10-2 S3
2.92×10-2 S4
HLA-DMA 6 32911391 14508 2 3.15×10-7 NA NA 4.08×10-2 8.62×10-4 S1
CCHCR1 6 31105216 25799 2 4.03×10-5 NA NA 1.08×10-3 1.05×10-2 S1
HCG22 6 31016984 15669 6 1.21×10-4 NA NA 2.29×10-2 3.85×10-2 S1
BRD2 6 32931437 22845 1 1.07×10-5 NA NA NS 5.65×10-6 S1
FAM69A 1 93302721 129358 11 4.47×10-5 NA NA NA 1.63×10-2 S1
LOC100294145 6 32856953 19582 3 2.27×10-5 NA NA NA 4.21×10-2 S2
POU2F3 11 120000000 89702 3 1.13×10-4 NA NA NS 8.41×10-3 S3
TRIM26P 6 30201078 13978 2 3.72×10-5 NA NA 7.80×10-6 NA NA
HCP5P14 6 29733324 12339 1 1.33×10-4 NA NA 9.94×10-5 NA NA
3.8-1.4(HLA complex group 26) 6 29828692 11172 7 1.58×10-5 NA NA 1.08×10-3 NA NA
C6orf15 6 31074000 11332 3 9.26×10-5 NA NA 2.22×10-3 NS NS
MUC21 6 30946485 16190 4 4.40×10-5 NA NA 3.42×10-3 NA NA
HLA-A 6 29905309 13352 4 1.61×10-5 NA NA 8.38×10-3 NS NS
HLA-F-AS1 6 29689378 32448 7 1.83×10-6 NA NA 2.52×10-2 NA NA
MXD3 5 177000000 16791 2 7.60×10-7 NA NA NS NS NS
LOC100287247 22 - - 1 2.71×10-5 NA NA NA NA NA
LOC100127934 1 93392136 10509 1 6.18×10-5 NA NA NA NA NA
RPL15P4 6 31490853 10645 1 2.08×10-7 NA NA NA NA NA
HCG2P8 6 29767896 13543 1 3.96×10-5 NA NA NA NA NA

PheGenI, Phenotype-Genotype Integrator.

*The number of SNP included in a gene with p-value<0.05, †Ms-related cells sample used to differential expression ananlysis. S1: PBMC, S2: CD34+ HPC, S3: CD8+ T lymphocytes, S4, spinal cord.

The genes listed here that are not defined in the main text are chromosome 6 open reading frame 10 (C6orf10), notch 4 (NOTCH4), small nucleolar RNA, H/ACA box 38 (SNORA38), proline-rich coiled-coil 2A (BAT2), BCL2-associated athanogene 6 (BAT3), peptidylprolyl isomerase A pseudogene 9 (PPIAP9), major histocompatibility complex, class II, DQ beta 2 (HLA-DQB2), major histocompatibility complex, class II, DQ alpha 2 (HLA-DQA2), ubiquitin specific peptidase 8 pseudogene 1 (LOC100287272), mitochondrial coiled-coil domain 1 (MCCD1), fibroblast growth factor receptor 3 pseudogene 1 (LOC100462812), HLA complex group 27 (HCG27), major histocompatibility complex, class II, DR beta 9 (HLA-DRB9), major histocompatibility complex, class I, S (HLA-S), butyrophilin-like 2 (BTNL2), DEAH (Asp-Glu-Ala-His) box polypeptide 16 (DHX16) transcription factor 19 (TCF19), HLA complex group 26 pseudogene (3.8-1.5), MHC class I polypeptide-related sequence A (LOC100507436), major histocompatibility complex, class I, x (HLA-X), HLA complex group 4 pseudogene 4 (HCG4P4), ribosomal protein L3 pseudogene 2 (RPL3P2), dihydrofolate reductase pseudogene 2 (DHFRP2), MHC class I polypeptide-related sequence E (MICE), ripartite motif containing 31 (TRIM31), MAX dimerization protein 3 (MXD3), similar to hCG1987428 (LOC100287247), meiotic nuclear divisions 1 homolog (LOC100127934), ribosomal protein L15 pseudogene 4 (RPL15P4), HLA complex group 2 pseudogene 8 (HCG2P8) respectively.

Supplementary Table 2

Enrichment of GO terms and KEGG pathways of the 58 MS-associated genes

jcn-11-311-s002
Category Term Count % Genes List total Relative enrichment Bonferroni*
GOTERM_BP_FAT GO:0019882~antigen processing and presentation 12 21.43 3112, 285830, 3117, 3118, 6891, 3107, 3109, 3108, 3134, 3105, 3132, 3122 25 78.23 1.09×10-16
GOTERM_CC_FAT GO:0042611~MHC protein complex 11 19.64 3112, 285830, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3132, 3122 24 102.78 1.06×10-16
SP_PIR_KEYWORDS mhc ii 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3105, 3132, 3122 34 164.25 1.84×10-14
UP_SEQ_FEATURE Region of interest:Connecting peptide 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 33 137.17 3.39×10-13
UP_SEQ_FEATURE Domain:Ig-like C1-type 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 33 130.32 5.42×10-13
KEGG_PATHWAY hsa05330:Allograft rejection 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 14 90.80 7.23×10-14
KEGG_PATHWAY hsa05332:Graft-versus-host disease 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 14 83.82 1.47×10-13
KEGG_PATHWAY hsa04940:Type I diabetes mellitus 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 14 77.83 2.80×10-13
KEGG_PATHWAY hsa04612:Antigen processing and presentation 10 17.86 3112, 3117, 3118, 6891, 3107, 3109, 3108, 3134, 3105, 3122 14 43.76 7.53×10-13
KEGG_PATHWAY hsa05320:Autoimmune thyroid disease 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 14 64.10 1.50×10-12
INTERPRO IPR003597:Immunoglobulin C1-set 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 29 76.03 1.16×10-11
UP_SEQ_FEATURE Region of interest:Alpha-1 7 12.50 3117, 3118, 3107, 3108, 3134, 3105, 3122 33 202.71 1.56×10-10
UP_SEQ_FEATURE Region of interest: Alpha-2 7 12.50 3117, 3118, 3107, 3108, 3134, 3105, 3122 33 202.71 1.56×10-10
INTERPRO IPR014745:MHC class II, alpha/beta chain, N-terminal 7 12.50 3112, 3117, 3118, 3109, 3108, 3132, 3122 29 182.78 7.35×10-11
INTERPRO IPR003006:Immunoglobulin/major histocompatibility complex, conserved site 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 29 59.43 8.98×10-11
KEGG_PATHWAY hsa05416:Viral myocarditis 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 14 46.04 2.47×10-11
SMART SM00407:IGc1 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 24 50.07 7.27×10-11
GOTERM_CC_FAT GO:0042613~MHC class II protein complex 7 12.50 3112, 3117, 3118, 3109, 3108, 3132, 3122 24 128.55 5.56×10-10
GOTERM_BP_FAT GO:0002504~antigen processing and presentation of peptide or polysaccharide antigen via MHC class II 7 12.50 3112, 3117, 3118, 3109, 3108, 3132, 3122 25 114.78 3.42×10-9
GOTERM_MF_FAT GO:0032395~MHC class II receptor activity 6 10.71 3112, 3117, 3118, 3107, 3108, 3122 23 178.26 7.85×10-9
SP_PIR_KEYWORDS Immune response 10 17.86 3112, 3117, 3118, 6891, 3107, 3109, 3108, 3134, 3105, 3122 34 25.26 8.42×10-9
KEGG_PATHWAY hsa04514:Cell adhesion molecules (CAMs) 9 16.07 3112, 3117, 3118, 3107, 3109, 3108, 3134, 3105, 3122 14 24.76 4.04×10-9
GOTERM_CC_FAT GO:0042825~TAP complex 5 8.93 3112, 6891, 3109, 3108, 3122 24 380.42 2.00×10-8
GOTERM_CC_FAT GO:0042824~MHC class I peptide loading complex 5 8.93 3112, 6891, 3109, 3108, 3122 24 295.88 7.18×10-8
GOTERM_BP_FAT GO:0048002~antigen processing and presentation of peptide antigen 6 10.71 6891, 3107, 3108, 3134, 3105, 3122 25 115.95 2.17×10-7
PIR_SUPERFAMILY PIRSF001991:Class II histocompatibility antigen 6 10.71 3112, 3117, 3118, 3109, 3108, 3122 18 94.82 2.79×10-8
INTERPRO IPR007110:Immunoglobulin-like 11 19.64 3112, 3117, 3118, 4340, 3107, 3109, 3108, 3134, 56244, 3105, 3122 29 12.61 3.53×10-7
KEGG_PATHWAY hsa05310:Asthma 6 10.71 3112, 3117, 3118, 3109, 3108, 3122 14 75.15 1.10×10-7
GOTERM_BP_FAT GO:0006955~immune response 12 21.43 3112, 285830, 3117, 3118, 6891, 3107, 3109, 3108, 3134, 3105, 3132, 3122 25 9.41 1.53×10-6
GOTERM_MF_FAT GO:0042288~MHC class I protein binding 5 8.93 3112, 6891, 3109, 3108, 3122 23 176.40 8.88×10-7
INTERPRO IPR013783:Immunoglobulin-like fold 11 19.64 3112, 3117, 3118, 4340, 3107, 3109, 3108, 3134, 56244, 3105, 3122 29 11.43 9.06×10-7
GOTERM_MF_FAT GO:0042287~MHC protein binding 5 8.93 3112, 6891, 3109, 3108, 3122 23 112.90 6.11×10-6
KEGG_PATHWAY hsa04672:Intestinal immune network for IgA production 6 10.71 3112, 3117, 3118, 3109, 3108, 3122 14 44.48 1.72×10-6
SP_PIR_KEYWORDS Heterodimer 6 10.71 3117, 3118, 3107, 3134, 3105, 3122 34 32.96 6.98×10-5
SP_PIR_KEYWORDS Transmembrane protein 10 17.86 3112, 3117, 3118, 4340, 6891, 3107, 3108, 3134, 3105, 3122 34 8.81 7.59×10-5
INTERPRO IPR001003:MHC class II, alpha chain, N-terminal 4 7.14 3117, 3118, 3108, 3122 29 191.48 7.32×10-5
KEGG_PATHWAY hsa05322:Systemic lupus erythematosus 6 10.71 3112, 3117, 3118, 3109, 3108, 3122 14 22.01 6.04×10-5
GOTERM_BP_FAT GO:0002474~antigen processing and presentation of peptide antigen via MHC class I 4 7.14 6891, 3107, 3134, 3105 25 127.32 6.60×10-4
GOTERM_CC_FAT GO:0044459~plasma membrane part 15 26.79 4855, 3112, 3117, 3118, 6891, 394263, 285830, 199, 3107, 3109, 3108, 3134, 3105, 3132, 3122 24 3.63 2.43×10-4
INTERPRO IPR001039:MHC class I, alpha chain, alpha1, and alpha2 4 7.14 285830, 3107, 3134, 3105 29 85.10 9.56×10-4
INTERPRO IPR011161:MHC class I-like antigen recognition 4 7.14 285830, 3107, 3134, 3105 29 82.06 1.07×10-3
GOTERM_CC_FAT GO:0042612~MHC class I protein complex 4 7.14 285830, 3107, 3134, 3105 24 76.08 1.17×10-3
BIOCARTA h_mhcPathway:Antigen processing and presentation 3 5.36 6891, 3105, 3122 3 143.70 5.67×10-4
UP_SEQ_FEATURE Sequence variant 31 55.36 170679, 3112, 4855, 11074, 54535, 6891, 56244, 7148, 10919, 6941, 199, 6046, 83463, 5460, 3107, 3109, 3108, 3134, 3105, 401250, 3117, 3118, 4340, 29113, 8449, 10665, 7916, 394263, 25833, 3122, 7917 33 1.50 1.94×10-2
SP_PIR_KEYWORDS Polymorphism 31 55.36 170679, 3112, 4855, 11074, 54535, 6891, 56244, 7148, 10919, 6941, 199, 6046, 83463, 5460, 3107, 3109, 3108, 3134, 3105, 401250, 3117, 3118, 4340, 29113, 8449, 10665, 7916, 394263, 25833, 3122, 7917 34 1.52 7.47×10-3
UP_SEQ_FEATURE Region of interest:Alpha-3 3 5.36 3107, 3134, 3105 33 193.06 2.92×10-2
SP_PIR_KEYWORDS mhc i 3 5.36 3107, 3134, 3105 34 169.72 1.05×10-2
GOTERM_BP_FAT GO:0002478~antigen processing and presentation of exogenous peptide antigen 3 5.36 6891, 3108, 3122 25 147.58 3.25×10-2
PIR_SUPERFAMILY PIRSF001990:Class I histocompatibility antigen 3 5.36 3107, 3134, 3105 18 136.96 2.30×10-3
GOTERM_BP_FAT GO:0019884~antigen processing and presentation of exogenous antigen 3 5.36 6891, 3108, 3122 25 115.95 5.30×10-2
GOTERM_MF_FAT GO:0042277~peptide binding 5 8.93 3112, 6891, 3109, 3108, 3122 23 13.90 2.69×10-2
INTERPRO IPR000353:MHC class II, beta chain, N-terminal 3 5.36 3112, 3109, 3132 29 101.37 2.84×10-2
GOTERM_MF_FAT GO:0032393~MHC class I receptor activity 3 5.36 3107, 3134, 3105 23 99.61 2.89×10-2
GOTERM_CC_FAT GO:0005886~plasma membrane 16 28.57 4855, 3112, 3117, 3118, 4340, 6891, 394263, 285830, 199, 3107, 3109, 3108, 3134, 3105, 3132, 3122 24 2.26 2.98×10-2

Count, the number of genes enriched in particular GO terms or KEGG pathways; Genes, potential genes enriched in particular GO terms or KEGG pathways.

*p value with Bonferroni correction for multiple tests. Only significant results with p<0.05 are listed.

GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes, MHC: major histocompatibility complex, MS: mltiple sclerosis.

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