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Genome-wide meta-analysis identifies new susceptibility loci for migraine

Abstract

Migraine is the most common brain disorder, affecting approximately 14% of the adult population, but its molecular mechanisms are poorly understood. We report the results of a meta-analysis across 29 genome-wide association studies, including a total of 23,285 individuals with migraine (cases) and 95,425 population-matched controls. We identified 12 loci associated with migraine susceptibility (P < 5 × 10−8). Five loci are new: near AJAP1 at 1p36, near TSPAN2 at 1p13, within FHL5 at 6q16, within C7orf10 at 7p14 and near MMP16 at 8q21. Three of these loci were identified in disease subgroup analyses. Brain tissue expression quantitative trait locus analysis suggests potential functional candidate genes at four loci: APOA1BP, TBC1D7, FUT9, STAT6 and ATP5B.

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Figure 1: Description of the studies comprising the International Migraine Genetics Meta-analysis Consortium and their sample contributions to each analysis.
Figure 2: Manhattan plot of the results of the meta-analysis.

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References

  1. 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  Google Scholar 

  2. 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  Google Scholar 

  3. Ligthart, L. et al. Meta-analysis of genome-wide association for migraine in six population-based European cohorts. Eur. J. Hum. Genet. 19, 901–907 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. International Headache Society. The International Classification of Headache Disorders: 2nd edition. Cephalalgia 24 (suppl. 1), 9–160 (2004).

  6. Fimia, G.M., De Cesare, D. & Sassone-Corsi, P. A family of LIM-only transcriptional coactivators: tissue-specific expression and selective activation of CREB and CREM. Mol. Cell. Biol. 20, 8613–8622 (2000).

    Article  CAS  Google Scholar 

  7. Dash, P.K., Hochner, B. & Kandel, E.R. Injection of the cAMP-responsive element into the nucleus of Aplysia sensory neurons blocks long-term facilitation. Nature 345, 718–721 (1990).

    Article  CAS  Google Scholar 

  8. Lee, Y.S. & Silva, A.J. The molecular and cellular biology of enhanced cognition. Nat. Rev. Neurosci. 10, 126–140 (2009).

    Article  CAS  Google Scholar 

  9. Sherman, E.A. et al. Genetic mapping of glutaric aciduria, type 3, to chromosome 7 and identification of mutations in c7orf10. Am. J. Hum. Genet. 83, 604–609 (2008).

    Article  CAS  Google Scholar 

  10. Schreiner, A. et al. Junction protein shrew-1 influences cell invasion and interacts with invasion-promoting protein CD147. Mol. Biol. Cell 18, 1272–1281 (2007).

    Article  CAS  Google Scholar 

  11. Lafleur, M.A., Xu, D. & Hemler, M.E. Tetraspanin proteins regulate membrane type-1 matrix metalloproteinase–dependent pericellular proteolysis. Mol. Biol. Cell 20, 2030–2040 (2009).

    Article  CAS  Google Scholar 

  12. Rozanov, D.V., Hahn-Dantona, E., Strickland, D.K. & Strongin, A.Y. The low density lipoprotein receptor–related protein LRP is regulated by membrane type-1 matrix metalloproteinase (MT1-MMP) proteolysis in malignant cells. J. Biol. Chem. 279, 4260–4268 (2004).

    Article  CAS  Google Scholar 

  13. Borrie, S.C., Baeumer, B.E. & Bandtlow, C.E. The Nogo-66 receptor family in the intact and diseased CNS. Cell Tissue Res. 349, 105–117 (2012).

    Article  CAS  Google Scholar 

  14. Schürks, M. et al. Migraine and cardiovascular disease: systematic review and meta-analysis. Br. Med. J. 339, b3914 (2009).

    Article  Google Scholar 

  15. Mizuguchi, T. et al. Heterozygous TGFBR2 mutations in Marfan syndrome. Nat. Genet. 36, 855–860 (2004).

    Article  CAS  Google Scholar 

  16. Biros, E., Walker, P.J., Nataatmadja, M., West, M. & Golledge, J. Downregulation of transforming growth factor, β receptor 2 and Notch signaling pathway in human abdominal aortic aneurysm. Atherosclerosis 221, 383–386 (2012).

    Article  CAS  Google Scholar 

  17. 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  Google Scholar 

  18. Terada, N. et al. The tetraspanin protein, CD9, is expressed by progenitor cells committed to oligodendrogenesis and is linked to β1 integrin, CD81, and Tspan-2. Glia 40, 350–359 (2002).

    Article  Google Scholar 

  19. Flavell, S.W. et al. Genome-wide analysis of MEF2 transcriptional program reveals synaptic target genes and neuronal activity–dependent polyadenylation site selection. Neuron 60, 1022–1038 (2008).

    Article  CAS  Google Scholar 

  20. Tsuzuki, K., Xing, H., Ling, J. & Gu, J.G. Menthol-induced Ca2+ release from presynaptic Ca2+ stores potentiates sensory synaptic transmission. J. Neurosci. 24, 762–771 (2004).

    Article  CAS  Google Scholar 

  21. Diniz, L.P. et al. Astrocyte-induced synaptogenesis is mediated by transforming growth factor β signaling through modulation of D-serine levels in cerebral cortex neurons. J. Biol. Chem. 287, 41432–41445 (2012).

    Article  CAS  Google Scholar 

  22. Allen, P.B., Greenfield, A.T., Svenningsson, P., Haspeslagh, D.C. & Greengard, P. Phactrs 1–4: s family of protein phosphatase 1 and actin regulatory proteins. Proc. Natl. Acad. Sci. USA 101, 7187–7192 (2004).

    Article  CAS  Google Scholar 

  23. Ferraro, G.B., Morrison, C.J., Overall, C.M., Strittmatter, S.M. & Fournier, A.E. Membrane-type matrix metalloproteinase-3 regulates neuronal responsiveness to myelin through Nogo-66 receptor 1 cleavage. J. Biol. Chem. 286, 31418–31424 (2011).

    Article  CAS  Google Scholar 

  24. Wilson, P.M., Fryer, R.H., Fang, Y. & Hatten, M.E. Astn2, a novel member of the astrotactin gene family, regulates the trafficking of ASTN1 during glial-guided neuronal migration. J. Neurosci. 30, 8529–8540 (2010).

    Article  CAS  Google Scholar 

  25. May, P. et al. Neuronal LRP1 functionally associates with postsynaptic proteins and is required for normal motor function in mice. Mol. Cell. Biol. 24, 8872–8883 (2004).

    Article  CAS  Google Scholar 

  26. Jha, K.N. et al. Biochemical and structural characterization of apolipoprotein A-I binding protein, a novel phosphoprotein with a potential role in sperm capacitation. Endocrinology 149, 2108–2120 (2008).

    Article  CAS  Google Scholar 

  27. Gouveia, R. et al. Expression of glycogenes in differentiating human NT2N neurons. Downregulation of fucosyltransferase 9 leads to decreased Lewisx levels and impaired neurite outgrowth. Biochim. Biophys. Acta 1820, 2007–2019 (2012).

    Article  CAS  Google Scholar 

  28. Lieberoth, A. et al. Lewisx and α2,3-sialyl glycans and their receptors TAG-1, Contactin, and L1 mediate CD24-dependent neurite outgrowth. J. Neurosci. 29, 6677–6690 (2009).

    Article  CAS  Google Scholar 

  29. Nishihara, S. α1,3-fucosyltransferase IX (Fut9) determines Lewis X expression in brain. Glycobiology 13, 445–455 (2003).

    Article  CAS  Google Scholar 

  30. Lawrence, T. & Natoli, G. Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat. Rev. Immunol. 11, 750–761 (2011).

    Article  CAS  Google Scholar 

  31. Park, S.J. et al. Astrocytes, but not microglia, rapidly sense H2O2 via STAT6 phosphorylation, resulting in cyclooxygenase-2 expression and prostaglandin release. J. Immunol. 188, 5132–5141 (2012).

    Article  CAS  Google Scholar 

  32. Dibble, C.C. et al. TBC1D7 is a third subunit of the TSC1-TSC2 complex upstream of mTORC1. Mol. Cell 47, 535–546 (2012).

    Article  CAS  Google Scholar 

  33. Han, J.M. & Sahin, M. TSC1/TSC2 signaling in the CNS. FEBS Lett. 585, 973–980 (2011).

    Article  CAS  Google Scholar 

  34. Russell, M.B. & Olesen, J. Increased familial risk and evidence of genetic factor in migraine. BMJ 311, 541–544 (1995).

    Article  CAS  Google Scholar 

  35. Frazer, K.A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    Article  CAS  Google Scholar 

  36. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  37. 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  Google Scholar 

  38. Altshuler, D., Daly, M.J. & Lander, E.S. Genetic mapping in human disease. Science 322, 881–888 (2008).

    Article  CAS  Google Scholar 

  39. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  Google Scholar 

  40. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc., B 57, 289–300 (1995).

    Google Scholar 

  41. Pruim, R.J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  43. Mägi, R. & Morris, A.P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

    Article  Google Scholar 

  44. Segrè, A.V., Groop, L., Mootha, V.K., Daly, M.J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

    Article  Google Scholar 

  45. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).

    Article  CAS  Google Scholar 

  46. Lage, K. et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc. Natl. Acad. Sci. USA 105, 20870–20875 (2008).

    Article  CAS  Google Scholar 

  47. Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    Article  CAS  Google Scholar 

  48. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Article  Google Scholar 

  49. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  Google Scholar 

  50. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    Article  CAS  Google Scholar 

  51. Li, C. & Wong, W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98, 31–36 (2001).

    Article  CAS  Google Scholar 

  52. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  Google Scholar 

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Acknowledgements

Study-specific acknowledgments appear in the Supplementary Note. We wish to thank A. Coffey, S. Hunt, R. Gwillian, P. Whittaker, S. Potter and A. Tashakkori-Ghanbarian for their invaluable help with this study, and we collectively thank everyone who has contributed to the collection, genotyping and analysis of the individual cohorts, as well as all the study participants.

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A. Palotie, D.I.C., D.R.N., M.J.D., K.S., G.D.S., M.W., M.D., A.M.J.M.v.d.M., M.D.F., C.K., T.K., D.P.S., L.C., J.-A.Z., M.-R.J., C.v.D., D.I.B., J.K., L.Q. and G.T. jointly supervised research. A. Palotie, D.I.C., D.R.N., V. Anttila, B.S.W., M.J.D., M.W., M.D., A.M.J.M.v.d.M., C.K., L.C., J.-A.Z., M.-R.J., C.v.D., D.I.B., J.K., T.K., M. Kallela, R.M., B.d.V., G.T., L.Q., M.A.I., L.L., E.H., M.S., H.S., K.S., F.J., T.F. and B.M.-M. conceived and designed the study. V. Anttila, B.S.W., A. Palotie, D.R.N., G.D.S., M.W., M.D., A.M.J.M.v.d.M., C.K., J.-A.Z., M.-R.J., O.R., C.v.D., D.I.B., J.K., E.B., M. Kallela, B.d.V., G.T., E.H., T.F., R.R.F., N.G.M., A.G.U., T.M. and J.G.E. performed the experiments. V. Anttila, B.S.W., P.G., D.I.C., D.R.N., M.J.D., D.P.S., E.B., J.R.G., S.B.R.J., T.K., F.B., G.M., R.M., B.d.V., L.Q., M.A.I., L.L., I.D., P.P., M.S., S. Steinberg, T.F. and B.M.-M. performed statistical analysis. V. Anttila, B.S.W., P.G., A. Palotie, D.I.C., D.R.N., L.C., J.R.G., S.B.R.J., K.L., T.K., F.B., G.M., R.M., B.d.V., S.E.M., L.Q., M.A.I., L.L., J.W., P.P., M.S., S. Steinberg, H.S., T.F., N.A., B.M.-M. and D.T. analyzed the data. D.I.C., D.R.N., M.J.D., A. Palotie, G.D.S., M.W., M.D., A.M.J.M.v.d.M., M.D.F., C.K., D.P.S., L.C., J.-A.Z., M.-R.J., O.R., C.v.D., D.I.B., B.W.P., J.K., E.B., J.R.G., K.L., E.R., V. Anttila, B.S.W., P.G., T.K., F.B., G.M., M. Kallela, R.M., B.d.V., G.T., U.T., W.L.M., L.Q., M. Koiranen, M.A.I., T.L., A.H.S., L.L., I.D., B.M.N., M.S., L.M.R., J.E.B., P.M.R., S. Steinberg, H.S., F.J., D.A.L., D.M.E., S.M.R., M.F., V. Artto, M.A.K., T.F., J.S., R.R.F., N.P., C.M.W., R.Z., A.C.H., P.A.F.M., G.W.M., N.G.M., G.B., H.G., A. Heinze, K.H.-K., F.M.K.W., A.-L.H., A. Pouta, J.v.d.E., A.G.U., A. Hofman, J.-J.H., J.M.V., K.H., M.A., B.M.-M., S. Schreiber, T.M., H.E.W., A.A., J.G.E., B.J.T. and D.T. contributed reagents and/or materials and/or analysis tools. V. Anttila, B.S.W., A. Palotie, D.I.C., D.R.N., A.M.J.M.v.d.M. and C.K. wrote the manuscript. All authors contributed to the final version of the manuscript.

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Correspondence to Verneri Anttila or Aarno Palotie.

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Anttila, V., Winsvold, B., Gormley, P. et al. Genome-wide meta-analysis identifies new susceptibility loci for migraine. Nat Genet 45, 912–917 (2013). https://doi.org/10.1038/ng.2676

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