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Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors

Abstract

Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10−9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10−10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10−8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10−11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10−10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10−9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10−10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10−10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10−9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10−8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10−10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10−11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10−9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.

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Figure 1: Genome-wide discovery-phase meta-analysis P-values (−log10P) plotted against their chromosomal positions.
Figure 2: Relative impact of SNP associations at known and newly identified risk loci for GBM and non-GBM tumors.

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References

  1. Bondy, M.L. et al. Brain tumor epidemiology: consensus from the Brain Tumor Epidemiology Consortium. Cancer 113, 1953–1968 (2008).

    Article  PubMed  Google Scholar 

  2. Ostrom, Q.T. et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro-oncol. 17 (Suppl. 4), iv1–iv62 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Louis, D.N. et al. The 2007 WHO classification of tumors of the central nervous system. Acta Neuropathol. 114, 97–109 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ostrom, Q.T. et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007–2011. Neuro-oncol. 16, iv1–iv63 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ostrom, Q.T. et al. The epidemiology of glioma in adults: a 'state of the science' review. Neuro-oncol. 16, 896–913 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hemminki, K., Tretli, S., Sundquist, J., Johannesen, T.B. & Granstrom, C. Familial risks in nervous-system tumors: a histology-specific analysis from Sweden and Norway. Lancet Oncol. 10, 481–488 (2009).

    Article  PubMed  Google Scholar 

  7. Sanson, M. et al. Chromosome 7p11.2 (EGFR) variation influences glioma risk. Hum. Mol. Genet. 20, 2897–2904 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shete, S. et al. Genome-wide association study identifies five susceptibility loci for glioma. Nat. Genet. 41, 899–904 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wrensch, M. et al. Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat. Genet. 41, 905–908 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kinnersley, B. et al. Genome-wide association study identifies multiple susceptibility loci for glioma. Nat. Commun. 6, 8559 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Walsh, K.M. et al. Variants near TERT and TERC influencing telomere length are associated with high-grade glioma risk. Nat. Genet. 46, 731–735 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jenkins, R.B. et al. A low-frequency variant at 8q24.21 is strongly associated with risk of oligodendroglial tumors and astrocytomas with IDH1 or IDH2 mutation. Nat. Genet. 44, 1122–1125 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rajaraman, P. et al. Genome-wide association study of glioma and meta-analysis. Hum. Genet. 131, 1877–1888 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Stacey, S.N. et al. A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nat. Genet. 43, 1098–1103 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kinnersley, B. et al. Quantifying the heritability of glioma using genome-wide complex trait analysis. Sci. Rep. 5, 17267 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lonsdale, J. et al. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

  17. Westra, H.J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex-trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Ruark, E. et al. Identification of nine new susceptibility loci for testicular cancer, including variants near DAZL and PRDM14. Nat. Genet. 45, 686–689 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Wu, C. et al. Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene–environment interactions. Nat. Genet. 44, 1090–1097 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. Riemenschneider, M.J. et al. Amplification and overexpression of the MDM4 (MDMX) gene from 1q32 in a subset of malignant gliomas without TP53 mutation or MDM2 amplification. Cancer Res. 59, 6091–6096 (1999).

    CAS  PubMed  Google Scholar 

  24. Boland, E. et al. Mapping of deletion and translocation breakpoints in 1q44 implicates the serine–threonine kinase AKT3 in postnatal microcephaly and agenesis of the corpus callosum. Am. J. Hum. Genet. 81, 292–303 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Turner, K.M. et al. Genomically amplified Akt3 activates DNA repair pathway and promotes glioma progression. Proc. Natl. Acad. Sci. USA 112, 3421–3426 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gur, G. et al. LRIG1 restricts growth factor signaling by enhancing receptor ubiquitylation and degradation. EMBO J. 23, 3270–3281 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yang, J.A. et al. LRIG1 enhances the radio-sensitivity of radio-resistant human glioblastoma U251 cells via attenuation of the EGFR–AKT signaling pathway. Int. J. Clin. Exp. Pathol. 8, 3580–3590 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Wei, J. et al. miR-20a mediates temozolomide resistance in glioblastoma cells via negatively regulating LRIG1 expression. Biomed. Pharmacother. 71, 112–118 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Watanabe, T., Nobusawa, S., Kleihues, P. & Ohgaki, H. IDH1 mutations are early events in the development of astrocytomas and oligodendrogliomas. Am. J. Pathol. 174, 1149–1153 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yan, H. et al. IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 360, 765–773 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Noushmehr, H. et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17, 510–522 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Christensen, B.C. et al. DNA methylation, isocitrate dehydrogenase mutation and survival in glioma. J. Natl. Cancer Inst. 103, 143–153 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sanson, M. et al. Isocitrate dehydrogenase 1 codon 132 mutation is an important prognostic biomarker in gliomas. J. Clin. Oncol. 27, 4150–4154 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. Eckel-Passow, J.E. et al. Glioma groups based on 1p/19q, IDH and TERT promoter mutations in tumors. N. Engl. J. Med. 372, 2499–2508 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Walsh, K.M. et al. Telomere maintenance and the etiology of adult glioma. Neuro-oncol. 17, 1445–1452 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Miyake, Y. et al. RPA-like mammalian Ctc1–Stn1–Ten1 complex binds to single-stranded DNA and protects telomeres independently of the Pot1 pathway. Mol. Cell 36, 193–206 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Chen, L.Y., Redon, S. & Lingner, J. The human CST complex is a terminator of telomerase activity. Nature 488, 540–544 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Bainbridge, M.N. et al. Germline mutations in shelterin complex genes are associated with familial glioma. J. Natl. Cancer Inst. 107, 384 (2014).

    PubMed  Google Scholar 

  39. Zhang, C. et al. Genetic determinants of telomere length and risk of common cancers: a mendelian randomization study. Hum. Mol. Genet. 24, 5356–5366 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Walsh, K.M. et al. Longer genotypically estimated leukocyte telomere length is associated with increased adult glioma risk. Oncotarget 6, 42468–42477 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Xipell, E. et al. Endoplasmic-reticulum-stress-inducing drugs sensitize glioma cells to temozolomide through downregulation of MGMT, MPG and Rad51. Neuro-oncol. 18, 1109–1119 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nicolas, C.S. et al. The role of JAK–STAT signaling within the CNS. JAK-STAT 2, e22925 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481–2498 (2015).

  45. Hepworth, S.J. et al. Mobile phone use and risk of glioma in adults: case–control study. BMJ 332, 883–887 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Amirian, E.S. et al. The Glioma International Case–Control Study: a report from the Genetic Epidemiology of Glioma International Consortium. Am. J. Epidemiol. 183, 85–91 (2016).

    PubMed  Google Scholar 

  47. Li, Y. et al. FastPop: a rapid principal component–derived method to infer intercontinental ancestry using genetic data. BMC Bioinformatics 17, 122 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Cardis, E. et al. The INTERPHONE study: design, epidemiological methods and description of the study population. Eur. J. Epidemiol. 22, 647–664 (2007).

    Article  PubMed  Google Scholar 

  49. Hercberg, S. et al. The SU.VI.MAX study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals. Arch. Intern. Med. 164, 2335–2342 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Wichmann, H.E., Gieger, C., Illig, T. & MONICA–KORA Study Group. KORA-gen—resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67 (Suppl. 1), S26–S30 (2005).

    Article  PubMed  Google Scholar 

  51. Krawczak, M. et al. PopGen: population-based recruitment of patients and controls for the analysis of complex genotype–phenotype relationships. Community Genet. 9, 55–61 (2006).

    PubMed  Google Scholar 

  52. Schmermund, A. et al. Assessment of clinically silent atherosclerotic disease, and established and novel risk factors for predicting myocardial infarction and cardiac death in healthy middle-aged subjects: rationale and design of the Heinz Nixdorf RECALL study. Risk factors, evaluation of coronary calcium and lifestyle. Am. Heart J. 144, 212–218 (2002).

    Article  PubMed  Google Scholar 

  53. Hunter, D.J. et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat. Genet. 39, 870–874 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Yeager, M. et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. 39, 645–649 (2007).

    Article  CAS  PubMed  Google Scholar 

  55. Wiemels, J.L. et al. History of allergies among adults with glioma and controls. Int. J. Cancer 98, 609–615 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Felini, M.J. et al. Reproductive factors and hormone use, and risk of adult gliomas. Cancer Causes Control 20, 87–96 (2009).

    Article  PubMed  Google Scholar 

  57. 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  PubMed  PubMed Central  CAS  Google Scholar 

  58. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  59. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Patterson, N., Price, A.L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Jenkins, R.B. et al. Distinct germline polymorphisms underlie glioma morphologic heterogeneity. Cancer Genet. 204, 13–18 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Pritchard, J.K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Liu, J.Z. et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat. Genet. 42, 436–440 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Higgins, J.P., Thompson, S.G., Deeks, J.J. & Altman, D.G. Measuring inconsistency in meta-analyses. Br. Med. J. 327, 557–560 (2003).

    Article  Google Scholar 

  66. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex-trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ng, S.B. et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  72. Pharoah, P.D., Antoniou, A.C., Easton, D.F. & Ponder, B.A. Polygenes, risk prediction and targeted prevention of breast cancer. N. Engl. J. Med. 358, 2796–2803 (2008).

    Article  CAS  PubMed  Google Scholar 

  73. Scheurer, M.E. et al. Familial aggregation of glioma: a pooled analysis. Am. J. Epidemiol. 172, 1099–1107 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Scales, M., Jager, R., Migliorini, G., Houlston, R.S. & Henrion, M.Y. visPIG—a web tool for producing multiregion, multitrack, multiscale plots of genetic data. PLoS One 9, e107497 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Gabriel, S.B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful to all of the patients and individuals for their participation, and we would also like to thank the clinicians and other hospital staff members, cancer registries and the study staff members in the respective centers who contributed to the blood sample and data collection.

The GICC was supported by grants from the US National Institutes of Health (NIH) (R01CA139020 (M.L.B. and B.S.M.), R01CA52689 (M.R.W.), R01CA52689 (M.L.B.) and P30CA125123 (M. Scheurer). Additional support was provided by the McNair Medical Institute (M. Scheurer) and the Population Sciences Biorepository at Baylor College of Medicine (M. Scheurer).

In Sweden, work was additionally supported by Acta Oncologica through the Royal Swedish Academy of Science (B.S.M.'s salary) and by the Swedish Research Council (B.S.M.) and the Swedish Cancer Foundation (B.S.M.). We are grateful to the National Clinical Brain Tumor Group and to all of the clinicians and research nurses throughout Sweden who identified all of the cases.

In the UK, funding was provided by Cancer Research UK (C1298/A8362 supported by the Bobby Moore Fund (R.S.H., B.K. and P.B.), the Wellcome Trust (R.S.H., B.K. and P.B.) and the DJ Fielding Medical Research Trust (R.S.H., B.K. and P.B.). The National Brain Tumor Study is supported by the National Cancer Research Network, and we acknowledge all clinicians and healthcare professionals who contributed to this initiative. The UK INTERPHONE study was supported by the European Union Fifth Framework Program 'Quality of Life and Management of Living Resources' (QLK4-CT-1999-01563) (A.S., M.J.S. and S.J.F.) and the International Union against Cancer (UICC) (A.S., M.J.S. and S.J.F.). The UICC received funds from the Mobile Manufacturers' Forum and the GSM Association. Provision of funds via the UICC was governed by agreements that guaranteed INTERPHONE's scientific independence (http://www.iarc.fr/ENG/Units/RCAd.html), and the views expressed in the paper are not necessarily those of the funders. The UK centers were also supported by the Mobile Telecommunications and Health Research (MTHR) Programme, and the Northern UK Centre (A.S., M.J.S. and S.J.F.) was supported by the Health and Safety Executive, Department of Health and Safety Executive and the UK Network Operators.

In France, funding was provided by the Ligue Nationale Contre le Cancer (J.-Y.D.), the Fondation ARC (M. Sanson), the Institut National du Cancer (INCa; PL046; (M. Sanson)), the French Ministry of Higher Education and Research and the program “Investissements d'avenir” ANR-10-IAIHU-06 (M. Sanson, J.-Y.D., M. Labussière, A.-L.D.S., P.G., K.M., A.I., K.H.-X. and K.L.). This study was additionally supported by a grant from Génome Québec, le Ministère de l'Enseignement supérieur, de la Recherche, de la Science et de la Technologie (MESRST) Québec (M. Lathrop) and McGill University (M. Lathrop).

In Germany, funding was provided to M. Simon and J. Schramm by the Deutsche Forschungsgemeinschaft (Si552, Schr285), the Deutsche Krebshilfe (70-2385-Wi2, 70-3163-Wi3, 10-6262) and BONFOR. Funding for the WTCCC was provided by the Wellcome Trust (076113 and 085475; M. Simon and J. Schramm). The KORA Ausburg studies are supported by grants from the German Federal Ministry of Education and Research (BMBF) and were mainly financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg. This work was financed by the German National Genome Research Network (NGFN) (S. Schreiber and H.E.-W.) and supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ (S. Schreiber and H.E.-W.). Generation of the German control data was partially supported by a grant of the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders), under the auspices of the e:Med research and funding concept (01ZX1314A) (M.M.N., S. Herms and S. Heilmann). M.M.N. is a member of the DFG-funded Excellence Cluster ImmunoSensation and received support from the Alfried Krupp von Bohlen und Halbach-Stiftung.

For the UK GWAS, we acknowledge the funders, organizations and individuals who contributed to the blood sample and data collection as listed in Hepworth et al.45. MD Anderson acknowledges the work of P. Adatto, F. Morice, H. Zhang, V. Levin, A.W.K. Yung, M. Gilbert, R. Sawaya, V. Puduvalli, C. Conrad, F. Lang and J. Weinberg from the Brain and Spine Center for the MDA GWAS. For the French study, we are indebted to A. Rahimian (Onconeurotek), A.M. Lekieffre and M. Brandel for help in collecting data and to Y. Marie for database support. For the German study, we are indebted to B. Harzheim (Bonn), S. Ott and A. Müller-Erkwoh (Bonn) for help with the acquisition of clinical data and to R. Mahlberg (Bonn), who provided technical support. The UK study made use of control genotyping data generated by the Wellcome Trust Case–Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. The MDA GWAS made use of control genotypes from the CGEMS prostate and breast cancer studies. A full list of the investigators who contributed to the generation of the data is available from http://cgems.cancer.gov/. French controls were taken from the SU.VI.MAX study. The German GWAS made use of genotyping data from three population control sources: KORA-gen39, the Heinz-Nixdorf RECALL study and POPGEN. The HNR cohort was established with the support of the Heinz-Nixdorf Foundation. F.D. received support from the BONFOR Programme of the University of Bonn, Germany.

The UCSF Adult Glioma Study was supported by the NIH (grant numbers R01CA52689 (M.R.W. and J.K.W.), P50CA097257 (M.R.W. and J.K.W.), R01CA126831 (J.K.W.) and R01CA139020 (M.R.W.)), the Loglio Collective (M.R.W. and J.K.W.), the National Brain Tumor Foundation (M.R.W.), the Stanley D. Lewis and Virginia S. Lewis Endowed Chair in Brain Tumor Research (M.R.W.), the Robert Magnin Newman Endowed Chair in Neuro-oncology (J.K.W.) and by donations from the families and friends of J. Berardi, H. Glaser, E. Olsen, R.E. Cooper and W. Martinusen. This project also was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, NIH, through UCSF–CTSI grant UL1 RR024131 (UCSF CTSI). The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code section 103885, the National Cancer Institute's Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C (awarded to the Cancer Prevention Institute of California), contract HHSN261201000035C (awarded to the University of Southern California) and contract HHSN261201000034C (awarded to the Public Health Institute), and the Centers for Disease Control and Prevention's National Program of Cancer Registries under agreement # U58DP003862-01 (awarded to the California Department of Public Health). The ideas and opinions expressed herein are those of the author(s), and endorsement by the State of California Department of Public Health, the National Cancer Institute and the Centers for Disease Control and Prevention, or their contractors and subcontractors, is not intended nor should be inferred. Other significant contributors for the UCSF Adult Glioma Study include M. Berger, P. Bracci, S. Chang, J. Clarke, A. Molinaro, A. Perry, M. Pezmecki, M. Prados, I. Smirnov, T. Tihan, K. Walsh, J. Wiemels and S. Zheng.

At Mayo, the authors wish to acknowledge the study participants and the clinicians and research staff at the participating medical centers, the Mayo Clinic Biobank and Biospecimens Accessioning and Processing Shared Resource (in particular its manager, M. Cicek). Work at the Mayo Clinic beyond the GICC was also supported by the NIH (grants P50CA108961 (B. O'Neill) and P30CA15083 (R. Diasio)), the National Institute of Neurological Disorders and Stroke (grant RC1NS068222Z (R.B.J.)), the Bernie and Edith Waterman Foundation (R.B.J.) and the Ting Tsung and Wei Fong Chao Family Foundation (R.B.J.).

The GliomaScan Consortium comprised (apart from authors listed in the author list): L.E.B. Freeman (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA), S. Koutros (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA), D. Albanes (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA), K. Visvanathan (Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA and Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA), V.L. Stevens (Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA), R. Henriksson (Department of Radiation Sciences, Oncology, Umea University, Umea, Sweden), D.S. Michaud (Department of Public Health and Community Medicine, Tufts University Medical School, Boston, Massachusetts, USA), M. Feychting (Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden), A. Ahlbom (Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden), G.G. Giles (Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Victoria, Australia and Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Melbourne, Victoria, Australia), R. Milne (Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Victoria, Australia and Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Melbourne, Victoria, Australia), R. McKean-Cowdin (Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA), L. Le Marchand (Cancer Research Center, University of Hawaii, Honolulu, Hawaii, USA), M. Stampfer (Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA and Departments of Epidemiology and Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA), A.M. Ruder (National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA), T. Carreon (National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA), G. Hallmans (Department of Public Health and Clinical Medicine/Nutritional Research, Umea University, Umea, Sweden), A. Zeleniuch-Jacquotte (Division of Epidemiology, Department of Environmental Medicine, New York University School of Medicine, New York, New York, USA), J.M. Gaziano (Massachusetts Veteran's Epidemiology, Research and Information Center, Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, Massachusetts, USA), H.D. Sesso (Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA), M.P. Purdue (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA), E. White (Fred Hutchinson Cancer Research Center, Seattle, Washington, USA and Department of Epidemiology, University of Washington, Seattle, Washington, USA) and J. Buring (Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA).

UK10K data generation and access was organized by the UK10K consortium and funded by the Wellcome Trust.

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M.L.B., B.S.M., R.S.H. and J.S.B.-S. managed the project; R.S.H., M.L.B., B.S.M., J.S.B.-S., R.B.J., Q.T.O., B.K. and M.R.W. drafted the manuscript; Q.T.O., K.L., B.K., J.E.E.-P. and P.A.D. performed statistical analyses; Y.C., K.L., Y.L. and B.K. performed bioinformatics analyses; B.S.M., J.S.B.-S., M.R.W., J.K.W., C.J., D.I., R.K.L., G.A., P.A.D., U.A., T.R., H.H., L.M., M.L.K., H.S., J.L.B., F.D., D.L, C.I.A, C.L., R.T.M., J. Shildkraut, F.A.-O., S. Sadetski, M. Scheurer, S. Shete, E.B.C., S.H.O., R.B.J., R.S.H. and M.L.B. developed the GICC protocol and performed sample acquisition; and P.R., S.C., M. Linet, Z.W. and M.Y. provided the National Cancer Institute (NCI) data. In the UK, P.B., A.S., M.J.S., S.J.F. and R.S.H. developed patient recruitment, performed sample acquisition and performed sample collection of cases; P.B. oversaw DNA isolation and storage, and performed case and control ascertainment, and supervision of DNA extractions. In Germany, M. Simon, M.M.N., H.-E.W., S. Schreiber and J. Schramm developed patient recruitment, and oversaw performed blood sample collection; M. Simon oversaw DNA isolation and storage and performed case and control ascertainment, and supervision of DNA extractions; and S. Herms, S. Heilmann and K.G. performed experimental work. In France, M. Sanson and J.-Y.D. developed patient recruitment; M. Labussière, A.-L.D.S., P.G., K.M., A.I. and K.H.-X. performed patient ascertainment. M. Lathrop performed laboratory management and oversaw genotyping of the French samples. All authors contributed to the final manuscript.

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Correspondence to Beatrice S Melin, Richard S Houlston or Melissa L Bondy.

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The authors declare no competing financial interests.

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A full list of members and affiliations appears in the Acknowledgments.

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Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Tables 1–7 (PDF 11658 kb)

Supplementary Data 1

Association results for known and newly identified glioma risk loci (XLSX 29 kb)

Supplementary Data 2

Haploreg enhancer enrichment analysis (XLSX 20 kb)

Supplementary Data 3

SMR analysis at new glioma risk loci (XLSX 117 kb)

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Melin, B., Barnholtz-Sloan, J., Wrensch, M. et al. Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors. Nat Genet 49, 789–794 (2017). https://doi.org/10.1038/ng.3823

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