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.

Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk

Nature Genetics volume 44, pages 58–61 (2012)Cite this article

Subjects

Abstract

To identify risk variants for multiple myeloma, we conducted a genome-wide association study of 1,675 individuals with multiple myeloma and 5,903 control subjects. We identified risk loci for multiple myeloma at 3p22.1 (rs1052501 in ULK4; odds ratio (OR) = 1.32; P = 7.47 × 10−9) and 7p15.3 (rs4487645, OR = 1.38; P = 3.33 × 10−15). In addition, we observed a promising association at 2p23.3 (rs6746082, OR = 1.29; P = 1.22 × 10−7). Our study identifies new genomic regions associated with multiple myeloma risk that may lead to new etiological insights.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Regional plots of association results and recombination rates for the 2p23.3, 3p22.1 and 7p15.3 susceptibility loci.

References

  1. Kyle, R.A. & Rajkumar, S.V. Multiple myeloma. N. Engl. J. Med. 351, 1860–1873 (2004).

    Article  CAS  Google Scholar 

  2. Palumbo, A. & Anderson, K. Multiple myeloma. N. Engl. J. Med. 364, 1046–1060 (2011).

    Article  CAS  Google Scholar 

  3. Kyle, R.A. et al. A long-term study of prognosis in monoclonal gammopathy of undetermined significance. N. Engl. J. Med. 346, 564–569 (2002).

    Article  Google Scholar 

  4. Landgren, O. et al. Risk of plasma cell and lymphoproliferative disorders among 14621 first-degree relatives of 4458 patients with monoclonal gammopathy of undetermined significance in Sweden. Blood 114, 791–795 (2009).

    Article  CAS  Google Scholar 

  5. Altieri, A., Chen, B., Bermejo, J.L., Castro, F. & Hemminki, K. Familial risks and temporal incidence trends of multiple myeloma. Eur. J. Cancer 42, 1661–1670 (2006).

    Article  Google Scholar 

  6. Morgan, G.J. et al. First-line treatment with zoledronic acid as compared with clodronic acid in multiple myeloma (MRC Myeloma IX): a randomised controlled trial. Lancet 376, 1989–1999 (2010).

    Article  CAS  Google Scholar 

  7. Power, C. & Elliott, J. Cohort profile: 1958 British birth cohort (National Child Development Study). Int. J. Epidemiol. 35, 34–41 (2006).

    Article  Google Scholar 

  8. 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  Google Scholar 

  9. Clayton, D.G. et al. Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat. Genet. 37, 1243–1246 (2005).

    Article  CAS  Google Scholar 

  10. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  11. Pettiti, D. Meta-analysis Decision Analysis and Cost-Effectiveness Analysis (Oxford University Press, New York, USA, 1994).

  12. Tomlinson, I.P. et al. A genome-wide association study identifies colorectal cancer susceptibility loci on chromosomes 10p14 and 8q23.3. Nat. Genet. 40, 623–630 (2008).

    Article  CAS  Google Scholar 

  13. Jung, C.H., Ro, S.H., Cao, J., Otto, N.M. & Kim, D.H. mTOR regulation of autophagy. FEBS Lett. 584, 1287–1295 (2010).

    Article  CAS  Google Scholar 

  14. Liang, C. & Jung, J.U. Autophagy genes as tumor suppressors. Curr. Opin. Cell Biol. 22, 226–233 (2010).

    Article  CAS  Google Scholar 

  15. Ocio, E.M., Mateos, M.V., Maiso, P., Pandiella, A. & San-Miguel, J.F. New drugs in multiple myeloma: mechanisms of action and phase I/II clinical findings. Lancet Oncol. 9, 1157–1165 (2008).

    Article  CAS  Google Scholar 

  16. Taddesse-Heath, L., Meloni-Ehrig, A., Scheerle, J., Kelly, J.C. & Jaffe, E.S. Plasmablastic lymphoma with MYC translocation: evidence for a common pathway in the generation of plasmablastic features. Mod. Pathol. 23, 991–999 (2010).

    Article  Google Scholar 

  17. Dib, A., Gabrea, A., Glebov, O.K., Bergsagel, P.L. & Kuehl, W.M. Characterization of MYC translocations in multiple myeloma cell lines. J. Natl. Cancer Inst. Monogr. 39, 25–31 (2008).

    Article  CAS  Google Scholar 

  18. Huang, A. et al. Identification of a novel c-Myc protein interactor, JPO2, with transforming activity in medulloblastoma cells. Cancer Res. 65, 5607–5619 (2005).

    Article  CAS  Google Scholar 

  19. Maertens, G.N., Cherepanov, P. & Engelman, A. Transcriptional co-activator p75 binds and tethers the Myc-interacting protein JPO2 to chromatin. J. Cell Sci. 119, 2563–2571 (2006).

    Article  CAS  Google Scholar 

  20. Blake, D.J. et al. β-dystrobrevin, a member of the dystophin-related protein family. Proc. Natl. Acad. Sci. USA 95, 241–246 (1998).

    Article  CAS  Google Scholar 

  21. Matys, V. et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108–D110 (2006).

    Article  CAS  Google Scholar 

  22. Ferretti, V. et al. PReMod: a database of genome-wide mammalian cis-regulatory module predictions. Nucleic Acids Res. 35, D122–D126 (2007).

    Article  CAS  Google Scholar 

  23. Walker, B.A. et al. Integration of global SNP-based mapping and expression arrays reveals key regions, mechanisms, and genes important in the pathogenesis of multiple myeloma. Blood 108, 1733–1743 (2006).

    Article  CAS  Google Scholar 

  24. Stranger, B.E. et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 1, e78 (2005).

    Article  Google Scholar 

  25. Stranger, B.E. et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007).

    Article  CAS  Google Scholar 

  26. Fonseca, R. et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 23, 2210–2221 (2009).

    Article  CAS  Google Scholar 

  27. Dewald, G.W., Kyle, R.A., Hicks, G.A. & Greipp, P.R. The clinical significance of cytogenetic studies in 100 patients with multiple myeloma, plasma cell leukemia, or amyloidosis. Blood 66, 380–390 (1985).

    CAS  PubMed  Google Scholar 

  28. Debes-Marun, C.S. et al. Chromosome abnormalities clustering and its implications for pathogenesis and prognosis in myeloma. Leukemia 17, 427–436 (2003).

    Article  CAS  Google Scholar 

  29. Walker, B.A. et al. A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value. Blood 116, e56–e65 (2010).

    Article  CAS  Google Scholar 

  30. Landgren, O. & Weiss, B.M. Patterns of monoclonal gammopathy of undetermined significance and multiple myeloma in various ethnic/racial groups: support for genetic factors in pathogenesis. Leukemia 23, 1691–1697 (2009).

    Article  CAS  Google Scholar 

  31. Enciso-Mora, V. et al. A genome-wide association study of Hodgkin's lymphoma identifies new susceptibility loci at 2p16.1 (REL), 8q24.21 and 10p14 (GATA3). Nat. Genet. 42, 1126–1130 (2010).

    Article  CAS  Google Scholar 

  32. Crowther-Swanepoel, D. et al. Common variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 influence chronic lymphocytic leukemia risk. Nat. Genet. 42, 132–136 (2010).

    Article  CAS  Google Scholar 

  33. Chiecchio, L. et al. Deletion of chromosome 13 detected by conventional cytogenetics is a critical prognostic factor in myeloma. Leukemia 20, 1610–1617 (2006).

    Article  CAS  Google Scholar 

  34. Neben, K. et al. Combining information regarding chromosomal aberrations t(4;14) and del(17p13) with the International Staging System classification allows stratification of myeloma patients undergoing autologous stem cell transplantation. Haematologica 95, 1150–1157 (2010).

    Article  CAS  Google Scholar 

  35. Wuilleme, S. et al. Ploidy, as detected by fluorescence in situ hybridization, defines different subgroups in multiple myeloma. Leukemia 19, 275–278 (2005).

    Article  CAS  Google Scholar 

  36. 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  Google Scholar 

  37. Higgins, J.P. & Thompson, S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).

    Article  Google Scholar 

  38. Ioannidis, J.P., Ntzani, E.E. & Trikalinos, T.A. 'Racial' differences in genetic effects for complex diseases. Nat. Genet. 36, 1312–1318 (2004).

    Article  CAS  Google Scholar 

  39. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  Google Scholar 

  40. Houlston, R.S. & Ford, D. Genetics of coeliac disease. QJM 89, 737–743 (1996).

    Article  CAS  Google Scholar 

  41. Myers, S., Bottolo, L., Freeman, C., McVean, G. & Donnelly, P. A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321–324 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  43. Cuzick, J. A Wilcoxon-type test for trend. Stat. Med. 4, 87–90 (1985).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to all study participants and investigators at the individual centers for taking part. We are extremely grateful to all investigators who contributed to the generation of this data set. We also thank the staff of the Clinical Trials Research Unit (CTRU), University of Leeds, and the National Cancer Research Institute (NCRI) Haematology Clinical Studies Group. We are grateful to all investigators who contributed to the Colorectal Tumour Gene Identification (CORGI) consortium, from which controls in the replication analysis were drawn. The current study made use of genotyping data on the 1958 Birth Cohort. Genotyping data for control subjects was generated by the Wellcome Trust Sanger Institute. A full list of the investigators who contributed to the generation of the data is available from the WTCCC website (see URLs). The GWAS made use of genotyping data from the population-based HNR study. The HNR study was supported by the Heinz-Nixdorf Foundation. The genotyping of the HNR subjects on Illumina HumanOmni1-Quad BeadChips was financed by the German Centre for Neurodegenerative Disorders (DZNE), Bonn. Myeloma UK provided the main funding for the study. Additional funding was provided by Cancer Research UK (C1298/A8362 supported by the Bobby Moore Fund), the Leukaemia Lymphoma Research Fund and the National Health Services (NHS) through the Biological Research Centre of the National Institute for Health Research at the Royal Marsden Hospital NHS Trust. In Germany, funding was provided to Dietmar-Hopp-Stiftung in Walldorf, the University Hospital Heidelberg and a grant from APO-STS (European Union Health-F4-2007-200767). Additionally, the study was funded by the German Ministry of Education and Science and the German Research Council (DFG; Projects SI 236/8-1, SI236/9-1 and ER 155/6-1).

Author information

Author notes

  1. Peter Broderick, Daniel Chubb, David C Johnson and Niels Weinhold: These authors contributed equally to this work.

  2. Kari Hemminki, Gareth J Morgan and Richard S Houlston: These authors jointly directed this work.

Authors and Affiliations

  1. Division of Genetics and Epidemiology, Molecular and Population Genetics, Institute of Cancer Research, Surrey, UK

    Peter Broderick, Daniel Chubb, Amy Lloyd, Bianca Olver, Yussanne P Ma, Sara E Dobbins & Richard S Houlston

  2. Division of Molecular Pathology, Haemato-Oncology Research Unit, Institute of Cancer Research, Surrey, UK

    David C Johnson, Brian A Walker, Faith E Davies, Gareth J Morgan & Richard S Houlston

  3. Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany

    Niels Weinhold, Kai Neben & Hartmut Goldschmidt

  4. German Cancer Research Center, Heidelberg, Germany

    Asta Försti & Kari Hemminki

  5. Clinical Trials Research Unit, University of Leeds, Leeds, UK

    Walter A Gregory & J Anthony Child

  6. Cytogenetics Group, Wessex Regional Cytogenetic Laboratory, Salisbury, UK

    Fiona M Ross

  7. Royal Victoria Infirmary, Newcastle upon Tyne, UK

    Graham H Jackson

  8. Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany

    Anna Jauch

  9. Institute of Human Genetics, University of Bonn, Bonn, Germany

    Per Hoffmann, Thomas W Mühleisen & Markus M Nöthen

  10. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Markus M Nöthen

  11. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg–Essen, Essen, Germany

    Susanne Moebus

  12. Molecular and Population Genetics, Wellcome Trust Centre for Human Genetics, Oxford, UK

    Ian P Tomlinson

  13. National Centre of Tumour Diseases, Heidelberg, Germany

    Hartmut Goldschmidt

  14. Center for Primary Health Care Research, Lund University, Malmo, Sweden

    Kari Hemminki

Authors
  1. Peter Broderick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Chubb
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David C Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Niels Weinhold
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Asta Försti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amy Lloyd
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bianca Olver
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yussanne P Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sara E Dobbins
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Brian A Walker
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Faith E Davies
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Walter A Gregory
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. J Anthony Child
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Fiona M Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Graham H Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kai Neben
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Anna Jauch
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Per Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Thomas W Mühleisen
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Markus M Nöthen
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Susanne Moebus
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Ian P Tomlinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Hartmut Goldschmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Kari Hemminki
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Gareth J Morgan
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Richard S Houlston
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.S.H. designed the study. R.S.H. and G.J.M. obtained financial support in the UK, and K.H. and H.G. obtained funding in Germany. D.C. performed the main statistical and bioinformatic analyses, and Y.P.M. and S.E.D. performed additional related analyses. P.B. coordinated laboratory studies. A.L. and B.O. performed genotyping of the UK samples. P.H., T.W.M. and M.M.N. performed and coordinated genotyping of the German controls; K.H. and A.F. coordinated genotyping of the German cases. D.C.J. managed and prepared the Myeloma-VII and Myeloma-IX case study DNA samples. H.G., K.N. and N.W. coordinated and managed the German DNA samples. G.J.M., F.E.D., W.A.G., G.H.J. and J.A.C. ascertained and collected case study samples from the UK Myeloma-VII and Myeloma-IX studies. S.M. obtained and managed the HNR samples. I.P.T. acquired colorectal cancer control samples. B.A.W. performed expression analyses on the UK samples. F.M.R. performed FISH analyses on the UK samples, and A.J. performed these analyses on the German samples. R.S.H. drafted the manuscript, and all authors contributed to the final version.

Corresponding author

Correspondence to Richard S Houlston.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–3 and Supplementary Figures 1–4. (PDF 5579 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Broderick, P., Chubb, D., Johnson, D. et al. Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk. Nat Genet 44, 58–61 (2012). https://doi.org/10.1038/ng.993

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.993

This article is cited by

Search

Advanced 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