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.

  • Article
  • Published:

LYMPHOMA

Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes

A Correction to this article was published on 04 September 2023

This article has been updated

Abstract

Lymphoma risk is elevated for relatives with common non-Hodgkin lymphoma (NHL) subtypes, suggesting shared genetic susceptibility across subtypes. To evaluate the extent of mutual heritability among NHL subtypes and discover novel loci shared among subtypes, we analyzed data from eight genome-wide association studies within the InterLymph Consortium, including 10,629 cases and 9505 controls. We utilized Association analysis based on SubSETs (ASSET) to discover loci for subsets of NHL subtypes and evaluated shared heritability across the genome using Genome-wide Complex Trait Analysis (GCTA) and polygenic risk scores. We discovered 17 genome-wide significant loci (P < 5 × 10−8) for subsets of NHL subtypes, including a novel locus at 10q23.33 (HHEX) (P = 3.27 × 10−9). Most subset associations were driven primarily by only one subtype. Genome-wide genetic correlations between pairs of subtypes varied broadly from 0.20 to 0.86, suggesting substantial heterogeneity in the extent of shared heritability among subtypes. Polygenic risk score analyses of established loci for different lymphoid malignancies identified strong associations with some NHL subtypes (P < 5 × 10−8), but weak or null associations with others. Although our analyses suggest partially shared heritability and biological pathways, they reveal substantial heterogeneity among NHL subtypes with each having its own distinct germline genetic architecture.

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

Access options

Buy this article

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

Fig. 1: Regional association plot of novel locus at chromosome 10q23.33 (rs11187157) for the NHL subset of CLL, FL, and MZL.
Fig. 2: Shared genetic correlations and pleiotropy among four NHL subtypes (CLL, DLBCL, FL, and MZL).

Similar content being viewed by others

Data availability

Genotype data from the NCI NHL GWAS is available on dbGaP (phs000801.v2.p1) for research purposes in accordance with dbGaP data access policies. Other data in this manuscript is available for shared research purposes through the InterLymph Consortium upon approval in accordance with institutional review boards and general data protection regulations.

Change history

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  PubMed  Google Scholar 

  2. Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127:2375–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Morton LM, Slager SL, Cerhan JR, Wang SS, Vajdic CM, Skibola CF, et al. Etiologic heterogeneity among non-Hodgkin lymphoma subtypes: the InterLymph Non-Hodgkin Lymphoma Subtypes Project. J Natl Cancer Inst Monogr. 2014;2014:130–44.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gibson TM, Morton LM, Shiels MS, Clarke CA, Engels EA. Risk of non-Hodgkin lymphoma subtypes in HIV-infected people during the HAART era: a population-based study. AIDS. 2014;28:2313–8.

    Article  CAS  PubMed  Google Scholar 

  5. Wang SS, Slager SL, Brennan P, Holly EA, De Sanjose S, Bernstein L, et al. Family history of hematopoietic malignancies and risk of non-Hodgkin lymphoma (NHL): a pooled analysis of 10 211 cases and 11 905 controls from the International Lymphoma Epidemiology Consortium (InterLymph). Blood. 2007;109:3479–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sud A, Chattopadhyay S, Thomsen H, Sundquist K, Sundquist J, Houlston RS, et al. Analysis of 153 115 patients with hematological malignancies refines the spectrum of familial risk. Blood. 2019;134:960–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cerhan JR, Berndt SI, Vijai J, Ghesquieres H, McKay J, Wang SS, et al. Genome-wide association study identifies multiple susceptibility loci for diffuse large B cell lymphoma. Nat Genet. 2014;46:1233–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Skibola CF, Berndt SI, Vijai J, Conde L, Wang Z, Yeager M, et al. Genome-wide association study identifies five susceptibility loci for follicular lymphoma outside the HLA region. Am J Hum Genet. 2014;95:462–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Vijai J, Wang Z, Berndt SI, Skibola CF, Slager SL, de SS, et al. A genome-wide association study of marginal zone lymphoma shows association to the HLA region. Nat Commun. 2015;6:5751.

    Article  CAS  PubMed  Google Scholar 

  10. Berndt SI, Skibola CF, Joseph V, Camp NJ, Nieters A, Wang Z, et al. Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia. Nat Genet. 2013;45:868–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Berndt SI, Camp NJ, Skibola CF, Vijai J, Wang Z, Gu J, et al. Meta-analysis of genome-wide association studies discovers multiple loci for chronic lymphocytic leukemia. Nat Commun. 2016;7:10933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Law PJ, Berndt SI, Speedy HE, Camp NJ, Sava GP, Skibola CF, et al. Genome-wide association analysis implicates dysregulation of immunity genes in chronic lymphocytic leukaemia. Nat Commun. 2017;8:14175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Conde L, Halperin E, Akers NK, Brown KM, Smedby KE, Rothman N, et al. Genome-wide association study of follicular lymphoma identifies a risk locus at 6p21.32. Nat Genet. 2010;42:661–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Crowther-Swanepoel D, Broderick P, Di Bernardo MC, Dobbins SE, Torres M, Mansouri M, et al. Common variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 influence chronic lymphocytic leukemia risk. Nat Genet. 2010;42:132–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Di Bernardo MC, Crowther-Swanepoel D, Broderick P, Webb E, Sellick G, Wild R, et al. A genome-wide association study identifies six susceptibility loci for chronic lymphocytic leukemia. Nat Genet. 2008;40:1204–10.

    Article  PubMed  Google Scholar 

  16. Slager SL, Skibola CF, Di Bernardo MC, Conde L, Broderick P, McDonnell SK, et al. Common variation at 6p21.31 (BAK1) influences the risk of chronic lymphocytic leukemia. Blood. 2012;120:843–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Speedy HE, Di Bernardo MC, Sava GP, Dyer MJ, Holroyd A, Wang Y, et al. A genome-wide association study identifies multiple susceptibility loci for chronic lymphocytic leukemia. Nat Genet. 2014;46:56–60.

    Article  CAS  PubMed  Google Scholar 

  18. Slager SL, Rabe KG, Achenbach SJ, Vachon CM, Goldin LR, Strom SS, et al. Genome-wide association study identifies a novel susceptibility locus at 6p21.3 among familial CLL. Blood. 2011;117:1911–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Skibola CF, Bracci PM, Halperin E, Conde L, Craig DW, Agana L, et al. Genetic variants at 6p21.33 are associated with susceptibility to follicular lymphoma. Nat Genet. 2009;41:873–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chubb D, Weinhold N, Broderick P, Chen B, Johnson DC, Forsti A, et al. Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk. Nat Genet. 2013;45:1221–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Enciso-Mora V, Broderick P, Ma Y, Jarrett RF, Hjalgrim H, Hemminki K, 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. 2010;42:1126–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Urayama KY, Jarrett RF, Hjalgrim H, Diepstra A, Kamatani Y, Chabrier A, et al. Genome-wide association study of classical Hodgkin lymphoma and Epstein-Barr virus status-defined subgroups. J Natl Cancer Inst. 2012;104:240–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hanscombe KB, Morris DL, Noble JA, Dilthey AT, Tombleson P, Kaufman KM, et al. Genetic fine mapping of systemic lupus erythematosus MHC associations in Europeans and African Americans. Hum Mol Genet. 2018;27:3813–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Miller FW, Chen W, O’Hanlon TP, Cooper RG, Vencovsky J, Rider LG, et al. Genome-wide association study identifies HLA 8.1 ancestral haplotype alleles as major genetic risk factors for myositis phenotypes. Genes Immun. 2015;16:470–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mitchell JS, Li N, Weinhold N, Forsti A, Ali M, van Duin M, et al. Genome-wide association study identifies multiple susceptibility loci for multiple myeloma. Nat Commun. 2016;7:12050.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sherborne AL, Hosking FJ, Prasad RB, Kumar R, Koehler R, Vijayakrishnan J, et al. Variation in CDKN2A at 9p21.3 influences childhood acute lymphoblastic leukemia risk. Nat Genet. 2010;42:492–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xu H, Zhang H, Yang W, Yadav R, Morrison AC, Qian M, et al. Inherited coding variants at the CDKN2A locus influence susceptibility to acute lymphoblastic leukaemia in children. Nat Commun. 2015;6:7553.

    Article  CAS  PubMed  Google Scholar 

  28. McMaster ML, Berndt SI, Zhang J, Slager SL, Li SA, Vajdic CM, et al. Two high-risk susceptibility loci at 6p25.3 and 14q32.13 for Waldenstrom macroglobulinemia. Nat Commun. 2018;9:4182.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Smedby KE, Foo JN, Skibola CF, Darabi H, Conde L, Hjalgrim H, et al. GWAS of follicular lymphoma reveals allelic heterogeneity at 6p21.32 and suggests shared genetic susceptibility with diffuse large B-cell lymphoma. PLoS Genet. 2011;7:e1001378.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Schumacher FR, Berndt SI, Siddiq A, Jacobs KB, Wang Z, Lindstrom S, et al. Genome-wide association study identifies new prostate cancer susceptibility loci. Hum Mol Genet. 2011;20:3867–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Siddiq A, Couch FJ, Chen GK, Lindstrom S, Eccles D, Millikan RC, et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet. 2012;21:5373–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. De Vivo I, Prescott J, Setiawan VW, Olson SH, Wentzensen N, Australian National Endometrial Cancer Study G. et al. Genome-wide association study of endometrial cancer in E2C2. Hum Genet. 2014;133:211–24.

    Article  PubMed  Google Scholar 

  33. Turner JJ, Morton LM, Linet MS, Clarke CA, Kadin ME, Vajdic CM, et al. InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions. Blood. 2010;116:e90–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Bhattacharjee S, Rajaraman P, Jacobs KB, Wheeler WA, Melin BS, Hartge P, et al. A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am J Hum Genet. 2012;90:821–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rand KA, Song C, Dean E, Serie DJ, Curtin K, Sheng X, et al. A Meta-analysis of Multiple Myeloma Risk Regions in African and European Ancestry Populations Identifies Putatively Functional Loci. Cancer Epidemiol Biomarkers Prev. 2016;25:1609–18.

  36. Cozen W, Timofeeva MN, Li D, Diepstra A, Hazelett D, Delahaye-Sourdeix M, et al. A meta-analysis of Hodgkin lymphoma reveals 19p13.3 TCF3 as a novel susceptibility locus. Nat Commun. 2014;5:3856.

    Article  CAS  PubMed  Google Scholar 

  37. Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 2012;28:2540–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sampson JN, Wheeler WA, Yeager M, Panagiotou O, Wang Z, Berndt SI, et al. Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for Thirteen Cancer Types. J Natl Cancer Inst. 2015;107:djv279.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Pers TH, Karjalainen JM, Chan Y, Westra HJ, Wood AR, Yang J, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890.

    Article  CAS  PubMed  Google Scholar 

  41. Breeze CE, Haugen E, Reynolds A, Teschendorff A, van Dongen J, Lan Q, et al. Integrative analysis of 3604 GWAS reveals multiple novel cell type-specific regulatory associations. Genome Biol. 2022;23:13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Paz H, Lynch MR, Bogue CW, Gasson JC. The homeobox gene Hhex regulates the earliest stages of definitive hematopoiesis. Blood. 2010;116:1254–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jackson JT, Nasa C, Shi W, Huntington ND, Bogue CW, Alexander WS, et al. A crucial role for the homeodomain transcription factor Hhex in lymphopoiesis. Blood. 2015;125:803–14.

    Article  CAS  PubMed  Google Scholar 

  44. Nagel S, MacLeod RAF, Meyer C, Kaufmann M, Drexler HG. NKL homeobox gene activities in B-cell development and lymphomas. PLoS One. 2018;13:e0205537.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Song JH, Kim HJ, Lee CH, Kim SJ, Hwang SY, Kim TS. Identification of gene expression signatures for molecular classification in human leukemia cells. Int J Oncol. 2006;29:57–64.

    CAS  PubMed  Google Scholar 

  46. Jackson JT, Ng AP, Shields BJ, Haupt S, Haupt Y, McCormack MP. Hhex induces promyelocyte self-renewal and cooperates with growth factor independence to cause myeloid leukemia in mice. Blood Adv. 2018;2:347–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lappalainen T, Sammeth M, Friedlander MR, T Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Vosa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53:1300–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Broderick P, Cunningham D, Vijayakrishnan J, Cooke R, Ashworth A, Swerdlow A, et al. IRF4 polymorphism rs872071 and risk of Hodgkin lymphoma. Br J Haematol. 2010;148:413–5.

    Article  CAS  PubMed  Google Scholar 

  50. Tessoulin B, Papin A, Gomez-Bougie P, Bellanger C, Amiot M, Pellat-Deceunynck C, et al. BCL2-Family Dysregulation in B-Cell Malignancies: From Gene Expression Regulation to a Targeted Therapy Biomarker. Front Oncol. 2018;8:645.

    Article  PubMed  Google Scholar 

  51. Kleinstern G, Yan H, Hildebrandt MAT, Vijai J, Berndt SI, Ghesquieres H, et al. Inherited variants at 3q13.33 and 3p24.1 are associated with risk of diffuse large B-cell lymphoma and implicate immune pathways. Hum Mol Genet. 2020;29:70–9.

    Article  CAS  PubMed  Google Scholar 

  52. Suvas S, Singh V, Sahdev S, Vohra H, Agrewala JN. Distinct role of CD80 and CD86 in the regulation of the activation of B cell and B cell lymphoma. J Biol Chem. 2002;277:7766–75.

    Article  CAS  PubMed  Google Scholar 

  53. Law PJ, Sud A, Mitchell JS, Henrion M, Orlando G, Lenive O, et al. Genome-wide association analysis of chronic lymphocytic leukaemia, Hodgkin lymphoma and multiple myeloma identifies pleiotropic risk loci. Sci Rep. 2017;7:41071.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Went M, Sud A, Speedy H, Sunter NJ, Forsti A, Law PJ, et al. Genetic correlation between multiple myeloma and chronic lymphocytic leukaemia provides evidence for shared aetiology. Blood Cancer J. 2018;9:1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Tan DE, Foo JN, Bei JX, Chang J, Peng R, Zheng X, et al. Genome-wide association study of B cell non-Hodgkin lymphoma identifies 3q27 as a susceptibility locus in the Chinese population. Nat Genet. 2013;45:804–7.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH. The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication. The authors thank Mr. William Wheeler (Information Management Services, Inc.) for his analytic support. A complete list of funding sources and acknowledgements for individual studies is listed in the Supplementary Material.

Disclaimers

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: SIB, JV, YB, NJC, AN, ZW, KES, GK, HH, CB, CFS, LMM, ARB, LRT, KO, WC, XW, JRC, SJC, SLS, NR. Collected/contributed study data or samples: SIB, JV, YB, NJC, AN, ZW, KES, GK, HH, CB, CFS, LMM, ARB, LRT, HOA, DA, KCA, SMA, DWS, BB, NB, PB, BMB, PB, PMB, PB, EEB, LB, LAC, ETC, BCHC, CCC, JC, PC, GC, LC, DVC, DGC, KC, DC, IDV, AD, WRD, AD, CKE, LF, JFF, AG, HG, GGG, SG, MG, BG, JG, TMH, CAH, CH, JNH, TRH, EAH, AH, AI, RDJ, RFJ, RK, EK, LNK, YK, PK, AK, AL, QL, CL, DL, ML, BKL, CM, MM, JM, MM, LM, RLM, TJM, AM, RM, KEN, AJN, KO, MPP, KAR, ER, JR, ER, GS, RKS, TDS, MTS, AS, KWS, MCS, JJS, AS, HJS, KT, CAT, HT, LFT, RCT, DS, JT, CMV, AVDB, DJVDB, RCHV, PV, SSW, EW, GJW, SW, NWD, YY, MY, AZ, YZ, TZ, EZ. Formal analysis: SIB, ZW, GK, LS, KY, CB, JA, JS, NC. Writing – original draft: SIB, JV, YB, NJC, AN, ZW, JRC, SJC, SLS, NR. Writing-review & editing: All authors.

Corresponding author

Correspondence to Sonja I. Berndt.

Ethics declarations

Competing interests

TS received research support to his institution from Genetech, Pharacyclics, AbbVie, Cephalon, Hospira, GlaxoSmithKline, Polyphenon E International, Merck, and Celgene and holds a patent (US14/292,075) on green tea extract epigallocatechin gallate in combination with chemotherapy for chronic lymphocytic leukemia. KS received research funding from Janssen Pharmaceuticals AB for research unrelated to this project. CH received honoraria from Novartis, Amgen, Servier/Pfizer, and Gilead Sciences, acted as a consultant or advisor to Roche, Celgene, Janssen-Cilag, Gilead Sciences, Takeda, Miltenyi Biotec, Abbvie, and ADC Therapeutics, and received travel, accommodations and/or expenses from Roche, Celgene, and Amgen. KO is currently a full-time employee at Sema4. TH is on the data monitoring boards for Seagen and Tessa Therapeutics, scientific advisory boards for Eli Lilly, Morpohsys, Incyte, Biegene, and Loxo Oncology, and received research support from Genentech and Sorrento Therapeutics. The other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: The list of authors has been revised. The author Arjan Diepstra was added.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berndt, S.I., Vijai, J., Benavente, Y. et al. Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes. Leukemia 36, 2835–2844 (2022). https://doi.org/10.1038/s41375-022-01711-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-022-01711-0

This article is cited by

Search

Quick links