Skip to main content
Log in

Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Objective

Many genomic loci have been identified for multiple sclerosis (MS) by genome-wide association studies (GWAS). Discrimination of the most functionally relevant genes in these loci remains challenging. The aim of this study was to highlight potential causal genes for MS.

Methods

We detected potential causal DNA methylations and gene expressions for MS by integrating data from large scale GWAS and quantitative trait locus (QTL) studies using the summary data-based Mendelian randomization method. Potential functional SNPs in the identified genes were searched.

Results

We found 178 DNA methylation sites and mRNA expressions of 29 genes that were causally associated with MS. The identified genes enriched in 21 specific KEGG pathways and 80 GO terms (e.g., antigen processing and presentation, interferon gamma mediated signaling pathway). Among the identified non-MHC genes, METTL21B, METTL1 and TSFM were strongly connected. MS-associated SNPs in DDR1 were strongly associated with plasma MHC class I polypeptide-related sequence B (MICB) and Granzyme A levels. And plasma MICB and Granzyme A levels were causally associated with MS. Many SNPs in the causal genes showed QTL effects. The association between m6A-SNPs rs923829 and METTL21B expression level was validated in 40 unrelated Chinese Han individuals.

Conclusions

This study identified many DNA methylations and genes as important risk factors for MS and provided novel evidence on the association between circulating MICB and Granzyme A and MS. We also showed that the interaction among DDR1, MICB and GZMA and interaction among METTL21B, METTL1 and TSFM may participate in the pathogenesis of MS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, Cotsapas C, Shah TS, Spencer C, Booth D, Goris A, Oturai A, Saarela J, Fontaine B, Hemmer B, Martin C, Zipp F, D'Alfonso S, Martinelli-Boneschi F, Taylor B, Harbo HF, Kockum I, Hillert J, Olsson T, Ban M, Oksenberg JR, Hintzen R, Barcellos LF, Agliardi C, Alfredsson L, Alizadeh M, Anderson C, Andrews R, Sondergaard HB, Baker A, Band G, Baranzini SE, Barizzone N, Barrett J, Bellenguez C, Bergamaschi L, Bernardinelli L, Berthele A, Biberacher V, Binder TM, Blackburn H, Bomfim IL, Brambilla P, Broadley S, Brochet B, Brundin L, Buck D, Butzkueven H, Caillier SJ, Camu W, Carpentier W, Cavalla P, Celius EG, Coman I, Comi G, Corrado L, Cosemans L, Cournu-Rebeix I, Cree BA, Cusi D, Damotte V, Defer G, Delgado SR, Deloukas P, di Sapio A, Dilthey AT, Donnelly P, Dubois B, Duddy M, Edkins S, Elovaara I, Esposito F, Evangelou N, Fiddes B, Field J, Franke A, Freeman C, Frohlich IY, Galimberti D, Gieger C, Gourraud PA, Graetz C, Graham A, Grummel V, Guaschino C, Hadjixenofontos A, Hakonarson H, Halfpenny C, Hall G, Hall P, Hamsten A, Harley J, Harrower T, Hawkins C, Hellenthal G, Hillier C, Hobart J, Hoshi M, Hunt SE, Jagodic M, Jelcic I, Jochim A, Kendall B, Kermode A, Kilpatrick T, Koivisto K, Konidari I, Korn T, Kronsbein H, Langford C, Larsson M, Lathrop M, Lebrun-Frenay C, Lechner-Scott J, Lee MH, Leone MA, Leppa V, Liberatore G, Lie BA, Lill CM, Linden M, Link J, Luessi F, Lycke J, Macciardi F, Mannisto S, Manrique CP, Martin R, Martinelli V, Mason D, Mazibrada G, McCabe C, Mero IL, Mescheriakova J, Moutsianas L, Myhr KM, Nagels G, Nicholas R, Nilsson P, Piehl F, Pirinen M, Price SE, Quach H, Reunanen M, Robberecht W, Robertson NP, Rodegher M, Rog D, Salvetti M, Schnetz-Boutaud NC, Sellebjerg F, Selter RC, Schaefer C, Shaunak S, Shen L, Shields S, Siffrin V, Slee M, Sorensen PS, Sorosina M, Sospedra M, Spurkland A, Strange A, Sundqvist E, Thijs V, Thorpe J, Ticca A, Tienari P, van Duijn C, Visser EM, Vucic S, Westerlind H, Wiley JS, Wilkins A, Wilson JF, Winkelmann J, Zajicek J, Zindler E, Haines JL, Pericak-Vance MA, Ivinson AJ, Stewart G, Hafler D, Hauser SL, Compston A, McVean G, De Jager P, Sawcer SJ, McCauley JL (2013) Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 45:1353–1360

    Article  CAS  Google Scholar 

  2. Hafler DA, Compston A, Sawcer S, Lander ES, Daly MJ, De Jager PL, de Bakker PI, Gabriel SB, Mirel DB, Ivinson AJ, Pericak-Vance MA, Gregory SG, Rioux JD, McCauley JL, Haines JL, Barcellos LF, Cree B, Oksenberg JR, Hauser SL (2007) Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357:851–862

    Article  CAS  Google Scholar 

  3. De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT, Piccio L, Raychaudhuri S, Tran D, Aubin C, Briskin R, Romano S, Baranzini SE, McCauley JL, Pericak-Vance MA, Haines JL, Gibson RA, Naeglin Y, Uitdehaag B, Matthews PM, Kappos L, Polman C, McArdle WL, Strachan DP, Evans D, Cross AH, Daly MJ, Compston A, Sawcer SJ, Weiner HL, Hauser SL, Hafler DA, Oksenberg JR (2009) Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 41:776–782

    Article  Google Scholar 

  4. Sawcer S, Hellenthal G, Pirinen M, Spencer CC, Patsopoulos NA, Moutsianas L, Dilthey A, Su Z, Freeman C, Hunt SE, Edkins S, Gray E, Booth DR, Potter SC, Goris A, Band G, Oturai AB, Strange A, Saarela J, Bellenguez C, Fontaine B, Gillman M, Hemmer B, Gwilliam R, Zipp F, Jayakumar A, Martin R, Leslie S, Hawkins S, Giannoulatou E, D'Alfonso S, Blackburn H, Martinelli Boneschi F, Liddle J, Harbo HF, Perez ML, Spurkland A, Waller MJ, Mycko MP, Ricketts M, Comabella M, Hammond N, Kockum I, McCann OT, Ban M, Whittaker P, Kemppinen A, Weston P, Hawkins C, Widaa S, Zajicek J, Dronov S, Robertson N, Bumpstead SJ, Barcellos LF, Ravindrarajah R, Abraham R, Alfredsson L, Ardlie K, Aubin C, Baker A, Baker K, Baranzini SE, Bergamaschi L, Bergamaschi R, Bernstein A, Berthele A, Boggild M, Bradfield JP, Brassat D, Broadley SA, Buck D, Butzkueven H, Capra R, Carroll WM, Cavalla P, Celius EG, Cepok S, Chiavacci R, Clerget-Darpoux F, Clysters K, Comi G, Cossburn M, Cournu-Rebeix I, Cox MB, Cozen W, Cree BA, Cross AH, Cusi D, Daly MJ, Davis E, de Bakker PI, Debouverie M, D'Hooghe MB, Dixon K, Dobosi R, Dubois B, Ellinghaus D, Elovaara I, Esposito F, Fontenille C, Foote S, Franke A, Galimberti D, Ghezzi A, Glessner J, Gomez R, Gout O, Graham C, Grant SF, Guerini FR, Hakonarson H, Hall P, Hamsten A, Hartung HP, Heard RN, Heath S, Hobart J, Hoshi M, Infante-Duarte C, Ingram G, Ingram W, Islam T, Jagodic M, Kabesch M, Kermode AG, Kilpatrick TJ, Kim C, Klopp N, Koivisto K, Larsson M, Lathrop M, Lechner-Scott JS, Leone MA, Leppa V, Liljedahl U, Bomfim IL, Lincoln RR, Link J, Liu J, Lorentzen AR, Lupoli S, Macciardi F, Mack T, Marriott M, Martinelli V, Mason D, McCauley JL, Mentch F, Mero IL, Mihalova T, Montalban X, Mottershead J, Myhr KM, Naldi P, Ollier W, Page A, Palotie A, Pelletier J, Piccio L, Pickersgill T, Piehl F, Pobywajlo S, Quach HL, Ramsay PP, Reunanen M, Reynolds R, Rioux JD, Rodegher M, Roesner S, Rubio JP, Ruckert IM, Salvetti M, Salvi E, Santaniello A, Schaefer CA, Schreiber S, Schulze C, Scott RJ, Sellebjerg F, Selmaj KW, Sexton D, Shen L, Simms-Acuna B, Skidmore S, Sleiman PM, Smestad C, Sorensen PS, Sondergaard HB, Stankovich J, Strange RC, Sulonen AM, Sundqvist E, Syvanen AC, Taddeo F, Taylor B, Blackwell JM, Tienari P, Bramon E, Tourbah A, Brown MA, Tronczynska E, Casas JP, Tubridy N, Corvin A, Vickery J, Jankowski J, Villoslada P, Markus HS, Wang K, Mathew CG, Wason J, Palmer CN, Wichmann HE, Plomin R, Willoughby E, Rautanen A, Winkelmann J, Wittig M, Trembath RC, Yaouanq J, Viswanathan AC, Zhang H, Wood NW, Zuvich R, Deloukas P, Langford C, Duncanson A, Oksenberg JR, Pericak-Vance MA, Haines JL, Olsson T, Hillert J, Ivinson AJ, De Jager PL, Peltonen L, Stewart GJ, Hafler DA, Hauser SL, McVean G, Donnelly P, Compston A (2011) Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476:214–219

    Article  CAS  Google Scholar 

  5. Patsopoulos NA, Esposito F, Reischl J, Lehr S, Bauer D, Heubach J, Sandbrink R, Pohl C, Edan G, Kappos L, Miller D, Montalban J, Polman CH, Freedman MS, Hartung HP, Arnason BG, Comi G, Cook S, Filippi M, Goodin DS, Jeffery D, O'Connor P, Ebers GC, Langdon D, Reder AT, Traboulsee A, Zipp F, Schimrigk S, Hillert J, Bahlo M, Booth DR, Broadley S, Brown MA, Browning BL, Browning SR, Butzkueven H, Carroll WM, Chapman C, Foote SJ, Griffiths L, Kermode AG, Kilpatrick TJ, Lechner-Scott J, Marriott M, Mason D, Moscato P, Heard RN, Pender MP, Perreau VM, Perera D, Rubio JP, Scott RJ, Slee M, Stankovich J, Stewart GJ, Taylor BV, Tubridy N, Willoughby E, Wiley J, Matthews P, Boneschi FM, Compston A, Haines J, Hauser SL, McCauley J, Ivinson A, Oksenberg JR, Pericak-Vance M, Sawcer SJ, De Jager PL, Hafler DA, de Bakker PI (2011) Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol 70:897–912

    Article  CAS  Google Scholar 

  6. Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24

    Article  CAS  Google Scholar 

  7. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, Jansen R, de Geus EJ, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimaki T, Raitoharju E, Kahonen M, Seppala I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245–252

    Article  CAS  Google Scholar 

  8. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC, Nicolae DL, Cox NJ, Im HK (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47:1091–1098

    Article  CAS  Google Scholar 

  9. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48:481–487

    Article  CAS  Google Scholar 

  10. Pavlides JM, Zhu Z, Gratten J, McRae AF, Wray NR, Yang J (2016) Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med 8:84

    Article  Google Scholar 

  11. Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, Sarwath H, Thareja G, Wahl A, DeLisle RK, Gold L, Pezer M, Lauc G, El-Din Selim MA, Mook-Kanamori DO, Al-Dous EK, Mohamoud YA, Malek J, Strauch K, Grallert H, Peters A, Kastenmuller G, Gieger C, Graumann J (2017) Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun 8:14357

    Article  CAS  Google Scholar 

  12. Long T, Hicks M, Yu HC, Biggs WH, Kirkness EF, Menni C, Zierer J, Small KS, Mangino M, Messier H, Brewerton S, Turpaz Y, Perkins BA, Evans AM, Miller LA, Guo L, Caskey CT, Schork NJ, Garner C, Spector TD, Venter JC, Telenti A (2017) Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet 49:568–578

    Article  CAS  Google Scholar 

  13. Kerekes G, Nurmohamed MT, Gonzalez-Gay MA, Seres I, Paragh G, Kardos Z, Barath Z, Tamasi L, Soltesz P, Szekanecz Z (2014) Rheumatoid arthritis and metabolic syndrome. Nat Rev Rheumatol 10:691–696

    Article  CAS  Google Scholar 

  14. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, Kochi Y, Ohmura K, Suzuki A, Yoshida S, Graham RR, Manoharan A, Ortmann W, Bhangale T, Denny JC, Carroll RJ, Eyler AE, Greenberg JD, Kremer JM, Pappas DA, Jiang L, Yin J, Ye L, Su DF, Yang J, Xie G, Keystone E, Westra HJ, Esko T, Metspalu A, Zhou X, Gupta N, Mirel D, Stahl EA, Diogo D, Cui J, Liao K, Guo MH, Myouzen K, Kawaguchi T, Coenen MJ, van Riel PL, van de Laar MA, Guchelaar HJ, Huizinga TW, Dieude P, Mariette X, Bridges SL Jr, Zhernakova A, Toes RE, Tak PP, Miceli-Richard C, Bang SY, Lee HS, Martin J, Gonzalez-Gay MA, Rodriguez-Rodriguez L, Rantapaa-Dahlqvist S, Arlestig L, Choi HK, Kamatani Y, Galan P, Lathrop M, Eyre S, Bowes J, Barton A, de Vries N, Moreland LW, Criswell LA, Karlson EW, Taniguchi A, Yamada R, Kubo M, Liu JS, Bae SC, Worthington J, Padyukov L, Klareskog L, Gregersen PK, Raychaudhuri S, Stranger BE, De Jager PL, Franke L, Visscher PM, Brown MA, Yamanaka H, Mimori T, Takahashi A, Xu H, Behrens TW, Siminovitch KA, Momohara S, Matsuda F, Yamamoto K, Plenge RM (2014) Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506:376–381

    Article  CAS  Google Scholar 

  15. Battle A, Brown CD, Engelhardt BE, Montgomery SB (2017) Genetic effects on gene expression across human tissues. Nature 550:204–213

    Article  Google Scholar 

  16. McRae AF, Marioni RE, Shah S, Yang J, Powell JE, Harris SE, Gibson J, Henders AK, Bowdler L, Painter JN, Murphy L, Martin NG, Starr JM, Wray NR, Deary IJ, Visscher PM, Montgomery GW (2018) Identification of 55,000 Replicated DNA Methylation QTL. Sci Rep 8:17605

    Article  Google Scholar 

  17. Kemppinen AK, Kaprio J, Palotie A, Saarela J (2011) Systematic review of genome-wide expression studies in multiple sclerosis. BMJ Open 1:e000053

    Article  CAS  Google Scholar 

  18. Zhang F, Shi Y, Wang L, Sriram S (2011) Role of HDAC3 on p53 expression and apoptosis in T cells of patients with multiple sclerosis. PLoS ONE 6:e16795

    Article  CAS  Google Scholar 

  19. Nakatsuji Y, Okuno T, Moriya M, Sugimoto T, Kinoshita M, Takamatsu H, Nojima S, Kimura T, Kang S, Ito D, Nakagawa Y, Toyofuku T, Takata K, Nakano M, Kubo M, Suzuki S, Matsui-Hasumi A, Uto-Konomi A, Ogata A, Mochizuki H, Sakoda S, Kumanogoh A (2012) Elevation of Sema4A implicates Th cell skewing and the efficacy of IFN-beta therapy in multiple sclerosis. J Immunol 188:4858–4865

    Article  CAS  Google Scholar 

  20. Zheng Y, Nie P, Peng D, He Z, Liu M, Xie Y, Miao Y, Zuo Z, Ren J (2018) m6AVar: a database of functional variants involved in m6A modification. Nucleic Acids Res 46:D139–D145

    Article  CAS  Google Scholar 

  21. Ren J, Jiang C, Gao X, Liu Z, Yuan Z, Jin C, Wen L, Zhang Z, Xue Y, Yao X (2010) PhosSNP for systematic analysis of genetic polymorphisms that influence protein phosphorylation. Mol Cell Proteom 9:623–634

    Article  CAS  Google Scholar 

  22. Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40:D930–934

    Article  CAS  Google Scholar 

  23. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, Robinson MR, McGrath JJ, Visscher PM, Wray NR, Yang J (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 9:224

    Article  Google Scholar 

  24. Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693–698

    Article  CAS  Google Scholar 

  25. Li X, Xiao B, Chen XS (2017) DNA methylation: a new player in multiple sclerosis. Mol Neurobiol 54:4049–4059

    Article  CAS  Google Scholar 

  26. Casaccia-Bonnefil P, Pandozy G, Mastronardi F (2008) Evaluating epigenetic landmarks in the brain of multiple sclerosis patients: a contribution to the current debate on disease pathogenesis. Prog Neurobiol 86:368–378

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Huynh JL, Casaccia P (2013) Epigenetic mechanisms in multiple sclerosis: implications for pathogenesis and treatment. Lancet Neurol 12:195–206

    Article  CAS  Google Scholar 

  28. Annibali V, Ristori G, Angelini DF, Serafini B, Mechelli R, Cannoni S, Romano S, Paolillo A, Abderrahim H, Diamantini A, Borsellino G, Aloisi F, Battistini L, Salvetti M (2011) CD161(high)CD8 + T cells bear pathogenetic potential in multiple sclerosis. Brain 134:542–554

    Article  Google Scholar 

  29. Davey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23:R89–98

    Article  CAS  Google Scholar 

  30. Smith GD, Ebrahim S (2003) 'Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32:1–22

    Article  Google Scholar 

  31. Park SM, Choi EY, Bae M, Choi JK, Kim YJ (2017) A long-range interactive DNA methylation marker panel for the promoters of HOXA9 and HOXA10 predicts survival in breast cancer patients. Clin Epigenetics 9:73

    Article  Google Scholar 

  32. Flavahan WA, Drier Y, Liau BB, Gillespie SM, Venteicher AS, Stemmer-Rachamimov AO, Suva ML, Bernstein BE (2016) Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529:110–114

    Article  CAS  Google Scholar 

  33. Schoenfelder S, Fraser P (2019) Long-range enhancer-promoter contacts in gene expression control. Nat Rev Genet. https://doi.org/10.1038/s41576-41019-40128-41570

    Article  PubMed  Google Scholar 

  34. Fernandez-Morera JL, Rodriguez-Rodero S, Lahoz C, Tunon A, Astudillo A, Garcia-Suarez O, Martinez-Borra J, Lopez-Vazquez A, Rodrigo L, Gonzalez S, Lopez-Larrea C (2008) Soluble MHC class I chain-related protein B serum levels correlate with disease activity in relapsing-remitting multiple sclerosis. Hum Immunol 69:235–240

    Article  CAS  Google Scholar 

  35. Gandhi KS, McKay FC, Cox M, Riveros C, Armstrong N, Heard RN, Vucic S, Williams DW, Stankovich J, Brown M, Danoy P, Stewart GJ, Broadley S, Moscato P, Lechner-Scott J, Scott RJ, Booth DR (2010) The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis. Hum Mol Genet 19:2134–2143

    Article  CAS  Google Scholar 

  36. Malecki J, Aileni VK, Ho AYY, Schwarz J, Moen A, Sorensen V, Nilges BS, Jakobsson ME, Leidel SA, Falnes PO (2017) The novel lysine specific methyltransferase METTL21B affects mRNA translation through inducible and dynamic methylation of Lys-165 in human eukaryotic elongation factor 1 alpha (eEF1A). Nucleic Acids Res 45:4370–4389

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Hamey JJ, Wienert B, Quinlan KGR, Wilkins MR (2017) METTL21B is a novel human lysine methyltransferase of translation elongation factor 1A: discovery by CRISPR/Cas9 Knockout. Mol Cell Proteom 16:2229–2242

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The study was supported by Natural Science Foundation of China (81773508), the Key Research Project (Social Development Plan) of Jiangsu Province (BE2016667), the Startup Fund from Soochow University (Q413900313, Q413900412), Project funded by China Postdoctoral Science Foundation (2013M530269 and 2014M551649, 2014T70547), and a Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huan Zhang.

Ethics declarations

Conflicts of interest

The authors have declared no conflicts of interest.

Ethical standard

All aspects of the study were performed in accordance with the Declaration of Helsinki, and the study protocol was approved by the local Ethics Board of Soochow university.

Informed consent

Written informed consent was obtained from every participant.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2217 kb)

Supplementary file2 (XLSX 85 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mo, XB., Lei, SF., Qian, QY. et al. Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis. J Neurol 266, 2699–2709 (2019). https://doi.org/10.1007/s00415-019-09476-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-019-09476-w

Keywords

Navigation