Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine

A Corrigendum to this article was published on 28 September 2016

This article has been updated

Abstract

Migraine is a debilitating neurological disorder affecting around one in seven people worldwide, but its molecular mechanisms remain poorly understood. There is some debate about whether migraine is a disease of vascular dysfunction or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we carried out a genetic study of migraine on 59,674 affected subjects and 316,078 controls from 22 GWA studies. We identified 44 independent single-nucleotide polymorphisms (SNPs) significantly associated with migraine risk (P < 5 × 10−8) that mapped to 38 distinct genomic loci, including 28 loci not previously reported and a locus that to our knowledge is the first to be identified on chromosome X. In subsequent computational analyses, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Manhattan plot showing results of the primary meta-analysis of all migraine samples (59,674 case and 316,078 control).
Figure 2: Expression enrichment of genes from the migraine loci in GTEx tissue samples.
Figure 3: Expression enrichment of genes from the migraine loci in 209 tissue or cell type annotations by DEPICT.
Figure 4: Enrichment of the migraine loci in sets of tissue- and cell-type-specific enhancers.
Figure 5: DEPICT network of the reconstituted gene sets that were significantly enriched (FDR < 0.05, determined empirically by permutation) for genes at the migraine loci (Online Methods).

Change history

  • 18 July 2016

    In the version of this article initially published online, the affiliations for Bertram Muller-Myhsok and Patricia Pozo-Rosich were incorrect or incomplete. These errors have been corrected for the print, PDF and HTML versions of this article.

References

  1. 1

    Vos, T. et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2163–2196 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2

    Vos, T. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386, 743–800 (2015).

    Google Scholar 

  3. 3

    Gustavsson, A. et al. Cost of disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol. 21, 718–779 (2011).

    Article  CAS  Google Scholar 

  4. 4

    Pietrobon, D. & Striessnig, J. Neurological diseases: neurobiology of migraine. Nat. Rev. Neurosci. 4, 386–398 (2003).

    Article  CAS  Google Scholar 

  5. 5

    Tfelt-Hansen, P.C. & Koehler, P.J. One hundred years of migraine research: major clinical and scientific observations from 1910 to 2010. Headache 51, 752–778 (2011).

    Article  Google Scholar 

  6. 6

    Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition (beta version). Cephalalgia 33, 629–808 (2013).

  7. 7

    Polderman, T.J.C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    Article  CAS  Google Scholar 

  8. 8

    Anttila, V. et al. Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nat. Genet. 42, 869–873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Chasman, D.I. et al. Genome-wide association study reveals three susceptibility loci for common migraine in the general population. Nat. Genet. 43, 695–698 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Freilinger, T. et al. Genome-wide association analysis identifies susceptibility loci for migraine without aura. Nat. Genet. 44, 777–782 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Anttila, V. et al. Genome-wide meta-analysis identifies new susceptibility loci for migraine. Nat. Genet. 45, 912–917 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Ophoff, R.A. et al. Familial hemiplegic migraine and episodic ataxia type-2 are caused by mutations in the Ca2+ channel gene CACNL1A4. Cell 87, 543–552 (1996).

    Article  CAS  Google Scholar 

  13. 13

    De Fusco, M. et al. Haploinsufficiency of ATP1A2 encoding the Na+/K+ pump α2 subunit associated with familial hemiplegic migraine type 2. Nat. Genet. 33, 192–196 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Dichgans, M. et al. Mutation in the neuronal voltage-gated sodium channel SCN1A in familial hemiplegic migraine. Lancet 366, 371–377 (2005).

    Article  CAS  Google Scholar 

  15. 15

    Nyholt, D.R. et al. A high-density association screen of 155 ion transport genes for involvement with common migraine. Hum. Mol. Genet. 17, 3318–3331 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    1000 Genomes Project Consortium et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  17. 17

    Chasman, D.I. et al. Selectivity in genetic association with sub-classified migraine in women. PLoS Genet. 10, e1004366 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Morton, M.J., Abohamed, A., Sivaprasadarao, A. & Hunter, M. pH sensing in the two-pore domain K+ channel, TASK2. Proc. Natl. Acad. Sci. USA 102, 16102–16106 (2005).

    Article  CAS  Google Scholar 

  20. 20

    Ramachandran, R. et al. TRPM8 activation attenuates inflammatory responses in mouse models of colitis. Proc. Natl. Acad. Sci. USA 110, 7476–7481 (2013).

    Article  Google Scholar 

  21. 21

    Hanna, M.G. Genetic neurological channelopathies. Nat. Clin. Pract. Neurol. 2, 252–263 (2006).

    Article  CAS  Google Scholar 

  22. 22

    Kraev, A. et al. Molecular cloning of a third member of the potassium-dependent sodium-calcium exchanger gene family, NCKX3. J. Biol. Chem. 276, 23161–23172 (2001).

    Article  CAS  Google Scholar 

  23. 23

    Ismailov, I.I. et al. A biologic function for an 'orphan' messenger: D-myo-inositol 3,4,5,6-tetrakisphosphate selectively blocks epithelial calcium-activated chloride channels. Proc. Natl. Acad. Sci. USA 93, 10505–10509 (1996).

    Article  CAS  Google Scholar 

  24. 24

    De Bock, M. et al. Connexin channels provide a target to manipulate brain endothelial calcium dynamics and blood-brain barrier permeability. J. Cereb. Blood Flow Metab. 31, 1942–1957 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Kathiresan, S. et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. 41, 334–341 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Debette, S. et al. Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection. Nat. Genet. 47, 78–83 (2015).

    Article  CAS  Google Scholar 

  27. 27

    Law, C. et al. Clinical features in a family with an R460H mutation in transforming growth factor β receptor 2 gene. J. Med. Genet. 43, 908–916 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Bown, M.J. et al. Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1. Am. J. Hum. Genet. 89, 619–627 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Arndt, A.K. et al. Fine mapping of the 1p36 deletion syndrome identifies mutation of PRDM16 as a cause of cardiomyopathy. Am. J. Hum. Genet. 93, 67–77 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Fujimura, M. et al. Genetics and biomarkers of Moyamoya disease: significance of RNF213 as a susceptibility gene. J. Stroke 16, 65–72 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31

    McElhinney, D.B. et al. Analysis of cardiovascular phenotype and genotype-phenotype correlation in individuals with a JAG1 mutation and/or Alagille syndrome. Circulation 106, 2567–2574 (2002).

    Article  Google Scholar 

  32. 32

    Bezzina, C.R. et al. Common variants at SCN5A–SCN10A and HEY2 are associated with Brugada syndrome, a rare disease with high risk of sudden cardiac death. Nat. Genet. 45, 1044–1049 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Sinner, M.F. et al. Integrating genetic, transcriptional, and functional analyses to identify five novel genes for atrial fibrillation. Circulation 130, 1225–1235 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Neale, B.M. et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc. Natl. Acad. Sci. USA 107, 7395–7400 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35

    Desch, M. et al. IRAG determines nitric oxide- and atrial natriuretic peptide-mediated smooth muscle relaxation. Cardiovasc. Res. 86, 496–505 (2010).

    Article  CAS  Google Scholar 

  36. 36

    Lang, N.N., Luksha, L., Newby, D.E. & Kublickiene, K. Connexin 43 mediates endothelium-derived hyperpolarizing factor-induced vasodilatation in subcutaneous resistance arteries from healthy pregnant women. Am. J. Physiol. Heart Circ. Physiol. 292, H1026–H1032 (2007).

    Article  CAS  Google Scholar 

  37. 37

    Dong, H., Jiang, Y., Triggle, C.R., Li, X. & Lytton, J. Novel role for K+-dependent Na+/Ca2+ exchangers in regulation of cytoplasmic free Ca2+ and contractility in arterial smooth muscle. Am. J. Physiol. Heart Circ. Physiol. 291, H1226–H1235 (2006).

    Article  CAS  Google Scholar 

  38. 38

    Yamaji, M., Mahmoud, M., Evans, I.M. & Zachary, I.C. Neuropilin 1 is essential for gastrointestinal smooth muscle contractility and motility in aged mice. PLoS One 10, e0115563 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Lu, X. et al. Genome-wide association study in Han Chinese identifies four new susceptibility loci for coronary artery disease. Nat. Genet. 44, 890–894 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Hager, J. et al. Genome-wide association study in a Lebanese cohort confirms PHACTR1 as a major determinant of coronary artery stenosis. PLoS One 7, e38663 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    The Coronary Artery Disease (C4D) Genetics Consortium. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat. Genet. 43, 339–344 (2011).

  42. 42

    O'Donnell, C.J. et al. Genome-wide association study for coronary artery calcification with follow-up in myocardial infarction. Circulation 124, 2855–2864 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Porcu, E. et al. A meta-analysis of thyroid-related traits reveals novel loci and gender-specific differences in the regulation of thyroid function. PLoS Genet. 9, e1003266 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Soler Artigas, M. et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat. Genet. 43, 1082–1090 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Lu, T. et al. REST and stress resistance in ageing and Alzheimer disease. Nature 507, 448–454 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Kar, R., Riquelme, M.A., Werner, S. & Jiang, J.X. Connexin 43 channels protect osteocytes against oxidative stress-induced cell death. J. Bone Miner. Res. 28, 1611–1621 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Dixit, D., Ghildiyal, R., Anto, N.P. & Sen, E. Chaetocin-induced ROS-mediated apoptosis involves ATM-YAP1 axis and JNK-dependent inhibition of glucose metabolism. Cell Death Dis. 5, e1212 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Chuikov, S., Levi, B.P., Smith, M.L. & Morrison, S.J. Prdm16 promotes stem cell maintenance in multiple tissues, partly by regulating oxidative stress. Nat. Cell Biol. 12, 999–1006 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Castellano, J. et al. Hypoxia stimulates low-density lipoprotein receptor-related protein-1 expression through hypoxia-inducible factor-1α in human vascular smooth muscle cells. Arterioscler. Thromb. Vasc. Biol. 31, 1411–1420 (2011).

    Article  CAS  Google Scholar 

  50. 50

    Schlossmann, J. et al. Regulation of intracellular calcium by a signalling complex of IRAG, IP3 receptor and cGMP kinase Iβ. Nature 404, 197–201 (2000).

    Article  CAS  Google Scholar 

  51. 51

    Nalls, M.A. et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat. Genet. 46, 989–993 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Lambert, J.C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  54. 54

    Wood, A.R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Bulik-Sullivan, B.K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  58. 58

    Magi, R., Lindgren, C.M. & Morris, A.P. Meta-analysis of sex-specific genome-wide association studies. Genet. Epidemiol. 34, 846–853 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59

    Maller, J.B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Nicolae, D.L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  63. 63

    Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Chi, J.T. et al. Gene expression programs of human smooth muscle cells: tissue-specific differentiation and prognostic significance in breast cancers. PLoS Genet. 3, 1770–1784 (2007).

    Article  CAS  Google Scholar 

  65. 65

    Bernstein, B.E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  67. 67

    Winsvold, B.S. et al. Genetic analysis for a shared biological basis between migraine and coronary artery disease. Neurol. Genet. 1, e10 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Malik, R. et al. Shared genetic basis for migraine and ischemic stroke: a genome-wide analysis of common variants. Neurology 84, 2132–2145 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Ferrari, M.D., Klever, R.R., Terwindt, G.M., Ayata, C. & van den Maagdenberg, A.M.J.M. Migraine pathophysiology: lessons from mouse models and human genetics. Lancet Neurol. 14, 65–80 (2015).

    Article  CAS  Google Scholar 

  70. 70

    Olesen, J., Burstein, R., Ashina, M. & Tfelt-Hansen, P. Origin of pain in migraine: evidence for peripheral sensitisation. Lancet Neurol. 8, 679–690 (2009).

    Article  Google Scholar 

  71. 71

    Hadjikhani, N. et al. Mechanisms of migraine aura revealed by functional MRI in human visual cortex. Proc. Natl. Acad. Sci. USA 98, 4687–4692 (2001).

    Article  CAS  Google Scholar 

  72. 72

    Lauritzen, M. Pathophysiology of the migraine aura. The spreading depression theory. Brain 117, 199–210 (1994).

    Article  Google Scholar 

  73. 73

    Olesen, J. The role of nitric oxide (NO) in migraine, tension-type headache and cluster headache. Pharmacol. Ther. 120, 157–171 (2008).

    Article  CAS  Google Scholar 

  74. 74

    Ashina, M., Hansen, J.M. & Olesen, J. Pearls and pitfalls in human pharmacological models of migraine: 30 years' experience. Cephalalgia 33, 540–553 (2013).

    Article  Google Scholar 

  75. 75

    Read, S.J. & Parsons, A.A. Sumatriptan modifies cortical free radical release during cortical spreading depression: a novel antimigraine action for sumatriptan? Brain Res. 870, 44–53 (2000).

    Article  CAS  Google Scholar 

  76. 76

    Anderson, C.A. et al. Data quality control in genetic case-control association studies. Nat. Protoc. 5, 1564–1573 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Winkler, T.W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  78. 78

    Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

    Article  CAS  Google Scholar 

  79. 79

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Browning, S.R. & Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  82. 82

    Fuchsberger, C., Abecasis, G.R. & Hinds, D.A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    The International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

  84. 84

    Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).

    Article  Google Scholar 

  85. 85

    Wright, F.A. et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46, 430–437 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86

    Richards, A.L. et al. Schizophrenia susceptibility alleles are enriched for alleles that affect gene expression in adult human brain. Mol. Psychiatry 17, 193–201 (2012).

    Article  CAS  Google Scholar 

  87. 87

    Nyholt, D.R. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am. J. Hum. Genet. 74, 765–769 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  Google Scholar 

  89. 89

    Mi, H., Muruganujan, A., Casagrande, J.T. & Thomas, P.D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Fehrmann, R.S.N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 91

    Frey, B.J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the numerous individuals who contributed to sample collection, storage, handling, phenotyping, and genotyping for each of the individual cohorts. We also acknowledge the important contribution to research made by the study participants. We are grateful to H. Zhao (QIMR Berghofer Medical Research Institute) for helpful correspondence on the pathway analyses. We acknowledge the GTEx Consortium for support and contribution of pilot data. A list of study-specific acknowledgments and funding information can be found in the Supplementary Note.

Author information

Affiliations

Authors

Consortia

Contributions

P.G., V. Anttila, G.W.M., M.I.K., M. Kals, R. Mägi, K.P., E.H., E.L., A.G.U., L.C., E.M., L.M., A.-L.E., A.F.C., T.F.H., A.J.A., D.I.C., and D.R.N. performed the experiments. P.G., V. Anttila, B.S.W., P.P., T.E., T.H.P., K.-H.F., M. Muona, N.A.F., A.I., G.M., L.L., S.G.G., S. Steinberg, L.Q., H.H.H.A., D.A.H., J.-J.H., R. Malik, A.E.B., E.S., C.M.v.D., E.M., D.P.S., N.E., B.M.N., D.I.C., and D.R.N. performed the statistical analyses. P.G., V. Anttila, B.S.W., P.P., T.E., T.H.P., K.-H.F., E.C.-L., N.A.F., A.I., G.M., L.L., M. Kallela, T.M.F., S.G.G., S. Steinberg, M. Koiranen, L.Q., H.H.H.A., T.L., J.W., D.A.H., S.M.R., M.F., V. Artto, M. Kaunisto, S.V., R. Malik, M.I.K., M. Kals, R. Mägi, K.P., H.H., A.E.B., J.H., E.S., C.S., C.W., Z.C., K.H., E.L., L.M.P., A.-L.E., A.F.C., T.F.H., J.K., A.J.A., O.R., M.A.I., M.-R.J., D.P.S., M.W., G.D.S., N.E., M.J.D., B.M.N., J.O., D.I.C., D.R.N., and A.P. participated in data analysis and/or interpretation. P.G., V. Anttila, B.S.W., T.H.P., K.-H.F., E.C.-L., T.K., G.M.T., M. Kallela, C.R., A.H.S., G.B., M. Koiranen, T.L., M.S., M.G.H., M.F., V. Artto, M. Kaunisto, S.V., R. Malik, A.C.H., P.A.F.M., N.G.M., G.W.M., H.H., A.E.B., L.F., J.H., P.H.L., C.S., C.W., Z.C., B.M.-M., S. Schreiber, T.M., J.G.E., V.S., A.G.U., C.M.v.D., A.S., C.S.N., H.G., A.-L.E., A.F.C., T.F.H., T.W., A.J.A., O.R., M.-R.J., C.K., M.D.F., A.C.B., M.D., M.W., J.-A.Z., B.M.N., J.O., D.I.C., D.R.N., A.-P.S., J.E.B., P.M.R., and A.P. contributed materials and/or analysis tools. T.E., T.K., T.L., H.S., B.W.J.H.P., A.C.H., P.A.F.M., N.G.M., G.W.M., L.F., A.H., A.S., C.S.N., M. Männikkö, T.W., J.K., O.R., M.A.I., T.S., M.-R.J., A.M., C.K., D.P.S., M.D.F., A.M.J.M.v.d.M., J.-A.Z., D.I.B., G.D.S., K.S., N.E., B.M.N., J.O., D.I.C., D.R.N., and A.P. supervised the research. T.K., G.M.T., G.B., T.L., J.E.B., M.S., P.M.R., H.S., B.W.J.H.P., A.C.H., P.A.F.M., N.G.M., G.W.M., L.F., V.S., A.H., L.C., A.S., C.S.N., H.G., J.K., A.J.A., O.R., M.A.I., M.-R.J., A.M., C.K., D.P.S., M.D., A.M.J.M.v.d.M., D.I.B., G.D.S., N.E., M.J.D., B.M.N., D.I.C., D.R.N., and A.P. conceived and designed the study. P.G., V. Anttila, B.S.W., P.P., T.E., T.H.P., E.C.-L., H.H., B.M.N., J.O., D.I.C., D.R.N., and A.P. wrote the paper. All authors contributed to the final version of the manuscript.

Corresponding author

Correspondence to Aarno Palotie.

Ethics declarations

Competing interests

T.W. has acted as a lecturer and consultant for H. Lundbeck A/S, Valby, Denmark. M.S. is a full-time employee of Bayer HealthCare, Germany.

Additional information

A full list of members and affiliations appears at the end of the paper and in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Figures 3–15, and Supplementary Tables 1–25 (PDF 7376 kb)

Supplementary Figure 1

Forest plots of the 44 independently associated SNPs (PDF 2623 kb)

Supplementary Figure 2

Regional plots of the 44 independently associated SNPs (PDF 3517 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gormley, P., Anttila, V., Winsvold, B. et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat Genet 48, 856–866 (2016). https://doi.org/10.1038/ng.3598

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing