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Human Genetics

, Volume 134, Issue 11–12, pp 1143–1162 | Cite as

A review of genome-wide association studies for multiple sclerosis: classical and hypothesis-driven approaches

  • V. V. BashinskayaEmail author
  • O. G. Kulakova
  • A. N. Boyko
  • A. V. Favorov
  • O. O. Favorova
Review Paper

Abstract

Multiple sclerosis (MS) is a common complex neurodegenerative disease of the central nervous system. It develops with autoimmune inflammation and demyelination. Genome-wide association studies (GWASs) serve as a powerful tool for investigating the genetic architecture of MS and are generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. This review considers the main achievements and challenges of using GWAS to identify the genes involved in MS. It also describes hypothesis-driven studies with extensive genome coverage of the selected regions, complementary to GWASs. To date, over 100 MS risk loci have been identified by the combination of both approaches; 40 of them were found in at least two GWASs and meet genome-wide significance threshold (p ≤ 5 × 10−8) in at least one GWAS, whereas the threshold for the rest of GWASs was set in our review at p < 1 × 10−5. Yet, MS risk loci identified to date explain only a part of the total heritability, and the reasons of “missing heritability” are discussed. The functions of MS-associated genes are described briefly; the majority of them encode immune-response proteins involved in the main stages of MS pathogenesis.

Keywords

Multiple Sclerosis Multiple Sclerosis Patient Genetic Risk Score GWAS Data Genotype Imputation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the Russian Foundation for Basic Research (projects 13-04-40281-H, 13-04-40279-H, and 15-04-04866-A).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

439_2015_1601_MOESM1_ESM.pdf (341 kb)
Supplementary material 1 (PDF 341 kb)
439_2015_1601_MOESM2_ESM.pdf (171 kb)
Supplementary material 2 (PDF 170 kb)

References

  1. Aulchenko YS, Hoppenbrouwers IA, Ramagopalan SV et al (2008) Genetic variation in the KIF1B locus influences susceptibility to multiple sclerosis. Nat Genet 40:1402–1403. doi: 10.1038/ng.251 CrossRefPubMedGoogle Scholar
  2. Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene), Bahlo M et al (2009) Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 41:824–828. doi: 10.1038/ng.396 CrossRefGoogle Scholar
  3. Bahreini SA, Jabalameli MR, Saadatnia M, Zahednasab H (2010) The role of non-HLA single nucleotide polymorphisms in multiple sclerosis susceptibility. J Neuroimmunol 229:5–15CrossRefPubMedGoogle Scholar
  4. Ban M, Stewart GJ, Bennetts BH, Heard R, Simmons R, Maranian M, Compston A, Sawcer SJ (2002) A genome screen for linkage in Australian sibling-pairs with multiple sclerosis. Genes Immun 3:464–469CrossRefPubMedGoogle Scholar
  5. Baranzini SE, Wang J, Gibson RA et al (2009) Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 18:767–778. doi: 10.1093/hmg/ddn388 CrossRefPubMedGoogle Scholar
  6. Bomprezzi R, Kovanen PE, Martin R (2003) New approaches to investigating heterogeneity in complex traits. J Med Genet 40:553–559. doi: 10.1136/jmg.40.8.553 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bradl M, Lassmann H (2009) Progressive multiple sclerosis. Semin Immunopathol 31:455–465CrossRefPubMedGoogle Scholar
  8. Browne P, Chandraratna D, Angood C, Tremlett H, Baker C, Taylor BV, Thompson AJ (2014) Atlas of Multiple Sclerosis 2013: a growing global problem with widespread inequity. Neurology 83:1022–1024CrossRefPubMedPubMedCentralGoogle Scholar
  9. Burdett T, Hall PN, Hasting E, Hindorff LA, Junkins HA, Klemm AK, MacArthur J, Manolio TA, Morales J, Parkinson H, Welter D (2015) The NHGRI-EBI Catalog of published genome-wide association studies. http://www.ebi.ac.uk/gwas. Accessed 17 April 2015
  10. Bush WS, Moore JH (2012) Chapter 11: Genome-wide association studies. PLoS Comput Biol 8:e1002822. doi: 10.1371/journal.pcbi.1002822 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Comabella M, Craig DW, Carmiña-Tato M et al (2008) Identification of a novel risk locus for multiple sclerosis at 13q31.3 by a pooled genome-wide scan of 500,000 single nucleotide polymorphisms. PLoS One 3:e3490. doi: 10.1371/journal.pone.0003490 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Compston DA, Batchelor JR, McDonald WI (1976) B-lymphocyte alloantigens associated with multiple sclerosis. Lancet 2:1261–1265. doi: 10.1016/S0140-6736(76)92027-4 CrossRefPubMedGoogle Scholar
  13. Cotsapas C, Hafler DA (2013) Immune-mediated disease genetics: the shared basis of pathogenesis. Trends Immunol 34:22–26CrossRefPubMedGoogle Scholar
  14. Cree BA (2014) Multiple sclerosis genetics. Handb Clin Neurol 122:193–209CrossRefPubMedGoogle Scholar
  15. Cunningham C (2013) Microglia and neurodegeneration: the role of systemic inflammation. Glia 61:71–90. doi: 10.1002/glia.22350 CrossRefPubMedGoogle Scholar
  16. De Jager PL, Jia X, Wang J et al (2009a) Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 41:776–782. doi: 10.1038/ng.401.Meta-analysis CrossRefPubMedPubMedCentralGoogle Scholar
  17. De Jager PL, Baecher-Allan C, Maier LM et al (2009b) The role of the CD58 locus in multiple sclerosis. Proc Natl Acad Sci USA 106:5264–5269. doi: 10.1073/pnas.0813310106 CrossRefPubMedPubMedCentralGoogle Scholar
  18. De Jager PL, Chibnik LB, Cui J, Reischl J, Lehr S et al (2009c) Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score. Lancet Neurol 8:1111–1119. doi: 10.1016/S1474-4422(09)70275-3 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Denic A, Wootla B, Rodriguez M (2013) CD8(+) T cells in multiple sclerosis. Expert Opin Ther Targets 17:1053–1066CrossRefPubMedPubMedCentralGoogle Scholar
  20. Dore-Duffy P, Washington R, Dragovic L (1993) Expression of endothelial cell activation antigens in microvessels from patients with multiple sclerosis. Adv Exp Med Biol 331:243–248CrossRefPubMedGoogle Scholar
  21. Ebers GC, Kukay K, Bulman DE et al (1996) A full genome search in multiple sclerosis. Nat Genet 13:472–476. doi: 10.1038/ng0896-472 CrossRefPubMedGoogle Scholar
  22. Favorov AV, Andreewski TV, Sudomoina MA, Favorova OO, Parmigiani G, Ochs MF (2005) A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans. Genetics 171:2113–2121. doi: 10.1534/genetics.105.048090
  23. Favorova OO, Andreewski TV, Boiko AN et al (2002) The chemokine receptor CCR5 deletion mutation is associated with MS in HLA-DR4-positive Russians. Neurology 59:1652–1655. doi: 10.1212/01.WNL.0000035626.92372.0A CrossRefPubMedGoogle Scholar
  24. Favorova OO, Favorov AV, Boiko AN, Andreewski TV, Sudomoina MA, Alekseenkov AD, Kulakova OG, Gusev EI, Parmigiani G, Ochs MF (2006) Three allele combinations associated with multiple sclerosis. BMC Med Genet 7:63. doi: 10.1186/1471-2350-7-63 CrossRefPubMedPubMedCentralGoogle Scholar
  25. GAMES, Transatlantic Multiple Sclerosis Genetics Cooperative (2003) A meta-analysis of whole genome linkage screens in multiple sclerosis. J Neuroimmunol 143:39–46CrossRefGoogle Scholar
  26. Goodin DS, Khankhanian P (2014) Single nucleotide polymorphism (SNP)-strings: an alternative method for assessing genetic associations. PLoS One 9:e90034CrossRefPubMedPubMedCentralGoogle Scholar
  27. Gourraud PA, International Multiple Sclerosis Genetics Consortium (IMSGC) (2011) When is the absence of evidence, evidence of absence? Use of equivalence-based analyses in genetic epidemiology and a conclusion for the KIF1B rs10492972*C allelic association in multiple sclerosis. Genet Epidemiol 35:568–571. doi: 10.1002/gepi.20592 PubMedPubMedCentralGoogle Scholar
  28. Gourraud PA, McElroy JP, Caillier SJ, Johnson BA, Santaniello A, Hauser SL, Oksenberg JR (2011) Aggregation of multiple sclerosis genetic risk variants in multiple and single case families. Ann Neurol 69:65–74CrossRefPubMedPubMedCentralGoogle Scholar
  29. Gourraud PA, Harbo HF, Hauser SL, Baranzini SE (2012) The genetics of multiple sclerosis: an up-to-date review. Immunol Rev 248:87–103. doi: 10.1111/j.1600-065X.2012.01134.x CrossRefPubMedGoogle Scholar
  30. Gourraud PA, Sdika M, Khankhanian P, Henry RG, Beheshtian A, Matthews PM, Hauser SL, Oksenberg JR, Pelletier D, Baranzini SE (2013) A genome-wide association study of brain lesion distribution in multiple sclerosis. Brain 136:1012–1024CrossRefPubMedPubMedCentralGoogle Scholar
  31. Goverman J (2009) Autoimmune T cell responses in the central nervous system. Nat Rev Immunol 9:393–407. doi: 10.1038/nri2550 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Gregory SG, Schmidt S, Seth P et al (2007) Interleukin 7 receptor alpha chain (IL7R) shows allelic and functional association with multiple sclerosis. Nat Genet 39:1083–1091. doi: 10.1038/ng2103 CrossRefPubMedGoogle Scholar
  33. Gregory AP, Dendrou CA, Attfield KE et al (2012) TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis. Nature 488:508–511. doi: 10.1038/nature11307 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Guerini FR, Ferrante P, Losciale L, Caputo D, Lombardi ML, Pirozzi G, Luongo V, Sudomoina MA, Andreewski TV, Alekseenkov AD, Boiko AN, Gusev EI, Favorova OO (2003) Myelin basic protein gene is associated with MS in DR4- and DR5-positive Italians and Russians. Neurology 61:520–526CrossRefPubMedGoogle Scholar
  35. Hedegaard CJ, Krakauer M, Bendtzen K et al (2008) T helper cell type 1 (Th1), Th2 and Th17 responses to myelin basic protein and disease activity in multiple sclerosis. Immunology 125:161–169. doi: 10.1111/j.1365-2567.2008.02837.x CrossRefPubMedPubMedCentralGoogle Scholar
  36. Hermanowski J, Bouzigon E, Forabosco P, Ng MY, Fisher SA, Lewis CM (2007) Meta-analysis of genome-wide linkage studies for multiple sclerosis, using an extended GSMA method. Eur J Hum Genet 15:703–710. doi: 10.1038/sj.ejhg.5201818 CrossRefPubMedGoogle Scholar
  37. Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:9362–9367. doi: 10.1073/pnas.0903103106 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Hohlfeld R (2008) Neurotrophic cross-talk between the nervous and immune systems: relevance for repair strategies in multiple sclerosis? J Neurol Sci 265:93–96. doi: 10.1016/j.jns.2007.03.012 CrossRefPubMedGoogle Scholar
  39. Holman DW, Klein RS, Ransohoff RM (2011) The blood–brain barrier, chemokines and multiple sclerosis. Biochim Biophys Acta Mol Basis Dis 1812:220–230CrossRefGoogle Scholar
  40. http://www.genecards.org. Accessed 20 April 2015
  41. http://www.ncbi.nlm.nih.gov/gene. Accessed 20 April 2015
  42. http://revigo.irb.hr/. Accessed 25 April 2015
  43. International Multiple Sclerosis Genetics Consortium (2013) Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls. Am J Hum Genet 92:854–865. doi: 10.1016/j.ajhg.2013.04.019 CrossRefGoogle Scholar
  44. International Multiple Sclerosis Genetics Consortium (IMSGC), Bush WS, Sawcer SJ, de Jager PL, Oksenberg JR, McCauley JL, Pericak-Vance MA, Haines JL (2010) Evidence for polygenic susceptibility to multiple sclerosis—the shape of things to come. Am J Hum Genet 86:621–625. doi: 10.1016/j.ajhg.2010.02.027 CrossRefGoogle Scholar
  45. International Multiple Sclerosis Genetics Consortium (IMSGC), Beecham AH, Patsopoulos NA, Xifara DK et al (2013) Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 45:1353–1360. doi: 10.1038/ng.2770 CrossRefGoogle Scholar
  46. International Multiple Sclerosis Genetics Consortium, David A, Hafler MD, Compston A et al (2007) Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357:2373–2383. doi: 10.1056/NEJMoa1407764 Google Scholar
  47. International Multiple Sclerosis Genetics Consortium, Wellcome Trust Case Control Consortium, Sawcer S et al (2011) Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476:214–219. doi: 10.1038/nature10251 CrossRefGoogle Scholar
  48. International Schizophrenia Consortium, Purcell SM, Wray NR, Stone JL et al (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748–752. doi: 10.1038/nature08185 PubMedCentralGoogle Scholar
  49. Isobe N, Madireddy L, Khankhanian P, Matsushita T, Caillier SJ, Moré JM, Gourraud PA, McCauley JL, Beecham AH, International Multiple Sclerosis Genetics Consortium, Piccio L, Herbert J, Khan O, Cohen J, Stone L, Santaniello A, Cree BA, Onengut-Gumuscu S, Rich SS, Hauser SL, Sawcer S, Oksenberg JR (2015) An ImmunoChip study of multiple sclerosis risk in African Americans. Brain 138:1518–1530CrossRefPubMedPubMedCentralGoogle Scholar
  50. Iwanowski P, Losy J (2015) Immunological differences between classical phenotypes of multiple sclerosis. J Neurol Sci 349:10–14CrossRefPubMedGoogle Scholar
  51. Jafari N, Broer L, van Duijn CM, Janssens AC, Hintzen RQ (2011) Perspectives on the use of multiple sclerosis risk genes for prediction. PLoS One 6:e26493. doi: 10.1371/journal.pone.0026493 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Jakkula E, Leppä V, Sulonen AM et al (2010) Genome-wide association study in a high-risk isolate for multiple sclerosis reveals associated variants in STAT3 gene. Am J Hum Genet 86:285–291. doi: 10.1016/j.ajhg.2010.01.017 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Kaufman DW, Reshef S, Golub HL, Peucker M, Corwin MJ, Goodin DS, Knappertz V, Pleimes D, Cutter G (2014) Survival in commercially insured multiple sclerosis patients and comparator subjects in the US. Mult Scler Relat Disord 3:364–371. doi: 10.1016/j.msard.2013.12.003 CrossRefPubMedGoogle Scholar
  54. Khankhanian P, Gourraud PA, Lizee A, Goodin DS (2015) Haplotype-based approach to known MS-associated regions increases the amount of explained risk. J Med Genet 52:587–594CrossRefPubMedPubMedCentralGoogle Scholar
  55. Kilpinen H, Barrett JC (2013) How next-generation sequencing is transforming complex disease genetics. Trends Genet 29:23–30CrossRefPubMedGoogle Scholar
  56. Kleinewietfeld M, Hafler DA (2014) Regulatory T cells in autoimmune neuroinflammation. Immunol Rev 259:231–244. doi: 10.1111/imr.12169 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Kuhle J, Disanto G, Dobson R, Adiutori R et al (2015) Conversion from clinically isolated syndrome to multiple sclerosis: a large multicentre study. Mult Scler 21:1013–1024. doi: 10.1177/1352458514568827 CrossRefPubMedGoogle Scholar
  58. Lambert CA, Tishkoff SA (2009) Genetic structure in African populations: implications for human demographic history. Cold Spring Harb Symp Quant Biol 74:395–402CrossRefPubMedGoogle Scholar
  59. Lassmann H, Brück W, Lucchinetti CF (2007) The immunopathology of multiple sclerosis: an overview. Brain Pathol 17:210–218CrossRefPubMedGoogle Scholar
  60. Leray E, Vukusic S, Debouverie M, Clanet M et al (2015) Excess mortality in patients with multiple sclerosis starts at 20 years from clinical onset: data from a large-scale French observational study. PLoS One 10:e0132033. doi: 10.1371/journal.pone.0132033 CrossRefPubMedPubMedCentralGoogle Scholar
  61. Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10:387–406. doi: 10.1146/annurev.genom.9.081307.164242 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Lill CM (2014) Recent advances and future challenges in the genetics of multiple sclerosis. Front Neurol 5:130CrossRefPubMedPubMedCentralGoogle Scholar
  63. Lin R, Charlesworth J, van der Mei I, Taylor BV (2012) The genetics of multiple sclerosis. Pract Neurol 12:279–288CrossRefPubMedGoogle Scholar
  64. Lovett-Racke AE, Yang Y, Racke MK (2011) Th1 versus Th17: are T cell cytokines relevant in multiple sclerosis? Biochim Biophys Acta Mol Basis Dis 1812:246–251. doi: 10.1016/j.bbadis.2010.05.012 CrossRefGoogle Scholar
  65. Lu YF, Goldstein DB, Angrist M, Cavalleri G (2014) Personalized medicine and human genetic diversity. Cold Spring Harb Perspect Med 4:a008581CrossRefPubMedGoogle Scholar
  66. Lundmark F, Duvefelt K, Iacobaeus E et al (2007) Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis. Nat Genet 39:1108–1113. doi: 10.1038/ng2106 CrossRefPubMedGoogle Scholar
  67. Lvovs D, Favorova OO, Favorov AV (2012) A polygenic approach to the study of polygenic diseases. Acta Naturae 4:59–71PubMedPubMedCentralGoogle Scholar
  68. Maier LM, Anderson DE, Severson CA, Baecher-Allan C, Healy B, Liu DV, Wittrup KD, De Jager PL, Hafler DA (2009) Soluble IL-2RA levels in multiple sclerosis subjects and the effect of soluble IL-2RA on immune responses. J Immunol 182:1541–1547CrossRefPubMedPubMedCentralGoogle Scholar
  69. Manolio TA, Collins FS, Cox NJ et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753. doi: 10.1038/nature08494 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Marian AJ (2012) Molecular genetic studies of complex phenotypes. Transl Res 159:64–79. doi: 10.1016/j.trsl.2011.08.001 CrossRefPubMedGoogle Scholar
  71. Marigorta UM, Lao O, Casals F et al (2011) Recent human evolution has shaped geographical differences in susceptibility to disease. BMC Genom 12:55. doi: 10.1186/1471-2164-12-55 CrossRefGoogle Scholar
  72. Marrie RA, Elliott L, Marriott J, Cossoy M, Blanchard J, Leung S, Yu N (2015) Effect of comorbidity on mortality in multiple sclerosis. Neurology 85:240–247. doi: 10.1212/WNL.0000000000001718 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Martinelli-Boneschi F, Esposito F, Brambilla P et al (2012) A genome-wide association study in progressive multiple sclerosis. Mult Scler 18:1384–1394. doi: 10.1177/1352458512439118 CrossRefPubMedGoogle Scholar
  74. Matesanz F, González-Pérez A, Lucas M, et al. (2012) Genome-wide association study of multiple sclerosis confirms a novel locus at 5p13.1. PLoS One 7:e36140. doi: 10.1371/journal.pone.0036140
  75. Matsushita T, Madireddy L, Sprenger T, Khankhanian P, Magon S, Naegelin Y, Caverzasi E, Lindberg RL, Kappos L, Hauser SL, Oksenberg JR, Henry R, Pelletier D, Baranzini SE (2015) Genetic associations with brain cortical thickness in multiple sclerosis. Genes Brain Behav 14:217–227CrossRefPubMedPubMedCentralGoogle Scholar
  76. Naito S, Namerow N, Mickey MR, Terasaki PI (1972) Multiple sclerosis: association with HL-A3. Tissue Antigens 2:1–4CrossRefPubMedGoogle Scholar
  77. Nischwitz S, Cepok S, Kroner A et al (2010) Evidence for VAV2 and ZNF433 as susceptibility genes for multiple sclerosis. J Neuroimmunol 227:162–166. doi: 10.1016/j.jneuroim.2010.06.003 CrossRefPubMedGoogle Scholar
  78. Nylander A, Hafler DA (2012) Multiple sclerosis. J Clin Invest 122:1180–1188CrossRefPubMedPubMedCentralGoogle Scholar
  79. Oksenberg JR (2013) Decoding multiple sclerosis: an update on genomics and future directions. Expert Rev Neurother 13:11–19. doi: 10.1586/14737175.2013.865867 CrossRefPubMedGoogle Scholar
  80. Oksenberg JR, Baranzini SE, Sawcer S, Hauser SL (2008) The genetics of multiple sclerosis: sNPs to pathways to pathogenesis. Nat Rev Genet 9:516–526CrossRefPubMedGoogle Scholar
  81. Ortiz GG, Pacheco-Moisés FP, Macías-Islas MÁ, Flores-Alvarado LJ, Mireles-Ramírez MA, González-Renovato ED, Hernández-Navarro VE, Sánchez-López AL, Alatorre-Jiménez MA (2014) Role of the blood–brain barrier in multiple sclerosis. Arch Med Res 45:687–697CrossRefPubMedGoogle Scholar
  82. Parkes M, Cortes A, van Heel DA, Brown MA (2013) Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat Rev Genet 14:661–673. doi: 10.1038/nrg3502 CrossRefPubMedGoogle Scholar
  83. Patsopoulos NA, Bayer Pharma MS Genetics Working Group, Steering Committees of Studies Evaluating IFN[beta]-1b and a CCR1-Antagonist, ANZgene Consortium, GeneMSA, International Multiple Sclerosis Genetics Consortium, Esposito F, Reischl J et al (2011) Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol 70:897–912. doi: 10.1002/ana.22609 CrossRefPubMedPubMedCentralGoogle Scholar
  84. Pavlopoulos GA, Oulas A, Iacucci E et al (2013) Unraveling genomic variation from next generation sequencing data. BioData Min 6:13. doi: 10.1186/1756-0381-6-13 CrossRefPubMedPubMedCentralGoogle Scholar
  85. Pèer I, Yelensky R, Altshuler D, Daly MJ (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 32:381–385. doi: 10.1002/gepi.20303 CrossRefGoogle Scholar
  86. Ramagopalan SV, Ebers GC (2009) Multiple sclerosis: major histocompatibility complexity and antigen presentation. Genome Med 1:105. doi: 10.1186/gm105 CrossRefPubMedPubMedCentralGoogle Scholar
  87. Rasmussen HB, Kelly MA, Clausen J (2001) Genetic susceptibility to multiple sclerosis: detection of polymorphic nucleotides and an intron in the 3′ untranslated region of the major histocompatibility complex class II transactivator gene. Hum Immunol 62:371–377. doi: 10.1016/S0198-8859(01)00215-4 CrossRefPubMedGoogle Scholar
  88. Sadee W, Hartmann K, Seweryn M, Pietrzak M, Handelman SK, Rempala GA (2014) Missing heritability of common diseases and treatments outside the protein-coding exome. Hum Genet 133:1199–1215CrossRefPubMedPubMedCentralGoogle Scholar
  89. Sanna S, Pitzalis M, Zoledziewska M et al (2010) Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis. Nat Genet 42:495–497. doi: 10.1038/ng.584 CrossRefPubMedPubMedCentralGoogle Scholar
  90. Sawcer S, Jones HB, Feakes R et al (1996) A genome screen in multiple sclerosis reveals susceptibility loci on chromosome 6p21 and 17q22. Nat Genet 13:464–468. doi: 10.1038/ng0896-464 CrossRefPubMedGoogle Scholar
  91. Sawcer S, Ban M, Maranian M et al (2005) A high-density screen for linkage in multiple sclerosis. Am J Hum Genet 77:454–467. doi: 10.1086/444547 CrossRefPubMedGoogle Scholar
  92. Sawcer S, Franklin RJ, Ban M (2014) Multiple sclerosis genetics. Lancet Neurol 13:700–709CrossRefPubMedGoogle Scholar
  93. Spencer CC, Su Z, Donnelly P, Marchini J (2009) Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5:e1000477. doi: 10.1371/journal.pgen.1000477 CrossRefPubMedPubMedCentralGoogle Scholar
  94. Stys PK (2005) General mechanisms of axonal damage and its prevention. J Neurol Sci 233:3–13CrossRefPubMedGoogle Scholar
  95. Supek F, Bošnjak M, Škunca N, Šmuc T (2011) Revigo summarizes and visualizes long lists of gene ontology terms. PLoS One 6:e21800. doi: 10.1371/journal.pone.0021800 CrossRefPubMedPubMedCentralGoogle Scholar
  96. Tauber SC, Nau R, Gerber J (2007) Systemic infections in multiple sclerosis and experimental autoimmune encephalomyelitis. Arch Physiol Biochem 113:124–130CrossRefPubMedGoogle Scholar
  97. The ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. doi: 10.1038/nature11247 CrossRefPubMedCentralGoogle Scholar
  98. Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24CrossRefPubMedPubMedCentralGoogle Scholar
  99. von Büdingen H-C, Bar-Or A, Zamvil SS (2011) B cells in multiple sclerosis: connecting the dots. Curr Opin Immunol 23:713–720CrossRefGoogle Scholar
  100. Wang JH, Pappas D, De Jager PL et al (2011) Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data. Genome Med 3:3. doi: 10.1186/gm217 CrossRefPubMedPubMedCentralGoogle Scholar
  101. Wang L, Mousavi P, Baranzini SE (2015) iPINBPA: an integrative network-based functional module discovery tool for genome-wide association studies. Pac Symp Biocomput 2015:255–266Google Scholar
  102. Wellcome Trust Case Control Consortium, Australo-Anglo-American Spondylitis Consortium (TASC), Burton PR et al (2007) Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat Genet 39:1329–1337. doi: 10.1038/ng.2007.17 CrossRefGoogle Scholar
  103. Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA 109:1193–1198. doi: 10.1073/pnas.1119675109 CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • V. V. Bashinskaya
    • 1
    • 2
    Email author
  • O. G. Kulakova
    • 1
    • 2
  • A. N. Boyko
    • 1
  • A. V. Favorov
    • 3
    • 4
  • O. O. Favorova
    • 1
    • 2
  1. 1.Pirogov Russian National Research Medical UniversityMoscowRussia
  2. 2.Russian Cardiology Research and Production ComplexMoscowRussia
  3. 3.Vavilov Institute of General GeneticsMoscowRussia
  4. 4.Oncology Biostatistics and BioinformaticsJohn Hopkins School of MedicineBaltimoreUSA

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