Genome-wide association studies (GWASs) based on common single nucleotide polymorphisms (SNPs) have identified several loci associated with the risk of monoclonal gammopathy of unknown significance (MGUS), a precursor condition for multiple myeloma (MM). We hypothesized that analyzing haplotypes might be more useful than analyzing individual SNPs, as it could identify functional chromosomal units that collectively contribute to MGUS risk. To test this hypothesis, we used data from our previous GWAS on 992 MGUS cases and 2910 controls from three European populations. We identified 23 haplotypes that were associated with the risk of MGUS at the genome-wide significance level (p < 5 × 10-8) and showed consistent results among all three populations. In 10 genomic regions, strong promoter, enhancer and regulatory element-related histone marks and their connections to target genes as well as genome segmentation data supported the importance of these regions in MGUS susceptibility. Several associated haplotypes affected pathways important for MM cell survival such as ubiquitin-proteasome system (RNF186, OTUD3), PI3K/AKT/mTOR (HINT3), innate immunity (SEC14L1, ZBP1), cell death regulation (BID) and NOTCH signaling (RBPJ). These pathways are important current therapeutic targets for MM, which may highlight the advantage of the haplotype approach homing to functional units.

Clinical Pharmacology Department of Pharmacology and Clinical Neuroscience Umea University Umea Sweden

Department of Biomedicine University of Basel Basel Switzerland

Department of Cancer Epidemiology German Cancer Research Center Heidelberg Germany

Department of Diagnostics and Intervention Cancer Center Hematology Umeå University Umeå Sweden

Department of Hematooncology University Hospital Ostrava and Faculty of Medicine University of Ostrava Ostrava Czech Republic

Department of Internal Medicine 5 University of Heidelberg Heidelberg Germany

Department of Public Health and Clinical Medicine Umea University Umea Sweden

Division of Clinical Genetics Department of Laboratory Medicine Lund University Lund Sweden

Division of Pediatric Neurooncology German Cancer Research Center Heidelberg Germany

Faculty of Medicine and Biomedical Center in Pilsen Charles University Prague Pilsen Czech Republic

Hopp Children's Cancer Center Heidelberg Germany

Institute for Medical Informatics Biometry and Epidemiology University Hospital Essen University of Duisburg Essen Essen Germany

Institute of Biology and Medical Genetics 1st Medical Faculty Charles University Prague Czech Republic

Institute of Experimental Medicine Academy of Sciences of the Czech Republic Prague Czech Republic

Institute of Human Genetics University of Bonn Bonn Germany

MSB Medical School Berlin Hochschule für Gesundheit und Medizin Berlin Germany

National Centre of Tumor Diseases Heidelberg Germany

See more in PubMed

Rahman N. Realizing the promise of cancer predisposition genes. Nature. 2014;505:302–8. 10.1038/nature12981 PubMed DOI PMC

Hauser E, Cremer N, Hein R, Deshmukh H. Haplotype-based analysis: a summary of GAW16 Group 4 analysis. Genet Epidemiol. 2009;33:S24–8. 10.1002/gepi.20468 PubMed DOI PMC

Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526:68–74. 10.1038/nature15393 PubMed DOI PMC

International HapMap Consortium. A haplotype map of the human genome. Nature. 2005;437:1299–320. PubMed PMC

Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer. 2017;17:692–704. 10.1038/nrc.2017.82 PubMed DOI

Zhong C, Cozen W, Bolanos R, Song J, Wang SS. The role of HLA variation in lymphoma aetiology and survival. J Intern Med. 2019;286:154–80. 10.1111/joim.12911 PubMed DOI

Bergman A, Einbeigi Z, Olofsson U, Taib Z, Wallgren A, Karlsson P, et al. The western Swedish BRCA1 founder mutation 3171ins5; a 3.7 cM conserved haplotype of today is a reminiscence of a 1500-year-old mutation. Eur J Hum Genet. 2001;9:787–93. 10.1038/sj.ejhg.5200704 PubMed DOI

Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS. Genomic selection in dairy cattle: The USDA experience. Annu Rev Anim Biosci. 2017;5:309–27. 10.1146/annurev-animal-021815-111422 PubMed DOI

Bello SF, Lawal RA, Adeola AC, Nie Q. The study of selection signature and its applications on identification of candidate genes using whole genome sequencing data in chicken-a review. Poult Sci. 2023;102:102657. 10.1016/j.psj.2023.102657 PubMed DOI PMC

Barnekow E, Hasslow J, Liu W, Bryant P, Thutkawkorapin J, Wendt C, et al. A swedish familial genome-wide haplotype analysis identified five novel breast cancer susceptibility loci on 9p24.3, 11q22.3, 15q11.2, 16q24.1 and Xq21.31. Int J Mol Sci. 2023;24:4468. 10.3390/ijms24054468 PubMed DOI PMC

Barnekow E, Liu W, Helgadottir HT, Michailidou K, Dennis J, Bryant P, et al. A swedish genome-wide haplotype association analysis identifies a novel breast cancer susceptibility locus in 8p21.2 and characterizes three loci on chromosomes 10, 11 and 16. Cancers. 2022;14:1206. 10.3390/cancers14051206 PubMed DOI PMC

Chattopadhyay S, Thomsen H, da Silva Filho MI, Weinhold N, Hoffmann P, Nothen MM, et al. Enrichment of B cell receptor signaling and epidermal growth factor receptor pathways in monoclonal gammopathy of undetermined significance: a genome-wide genetic interaction study. Mol Med. 2018;24:30. PubMed PMC

Chattopadhyay S, Thomsen H, Weinhold N, Meziane I, Huhn S, da Silva Filho MI, et al. Eight novel loci implicate shared genetic etiology in multiple myeloma, AL amyloidosis, and monoclonal gammopathy of unknown significance. Leukemia. 2020;34:1187–91. 10.1038/s41375-019-0619-1 PubMed DOI

Clay-Gilmour A, Chattopadhyay S, Hildebrandt MAT, Thomsen H, Weinhold N, Vodicka P, et al. Genome-wide meta-analysis of monoclonal gammopathy of undetermined significance (MGUS) identifies risk loci impacting IRF-6. Blood Cancer J. 2022;12:60. 10.1038/s41408-022-00658-w PubMed DOI PMC

Thomsen H, Chattopadhyay S, Weinhold N, Vodicka P, Vodickova L, Hoffmann P, et al. Genome-wide association study of monoclonal gammopathy of unknown significance (MGUS): comparison with multiple myeloma. Leukemia. 2019;33:1817–21. 10.1038/s41375-019-0396-x PubMed DOI

Broderick P, Chubb D, Johnson DC, Weinhold N, Forsti A, Lloyd A, et al. Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk. Nat Genet. 2012;44:58–61.10.1038/ng.993 PubMed DOI PMC

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. 10.1038/ng.2733 PubMed DOI PMC

Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511. 10.1038/nrg2796 PubMed DOI

Delaneau O, Coulonges C, Zagury JF. Shape-IT: new rapid and accurate algorithm for haplotype inference. BMC Bioinformatics. 2008;9:540. PubMed PMC

Utsunomiya YT, Milanesi M, Utsunomiya AT, Ajmone-Marsan P, Garcia JF. GHap: an R package for genome-wide haplotyping. Bioinformatics. 2016;32:2861–2. 10.1093/bioinformatics/btw356 PubMed DOI

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75. 10.1086/519795 PubMed DOI PMC

Curtis D, Amos W. The human genome harbours widespread exclusive yin yang haplotypes. Eur J Hum Genet. 2024;32:691–6. 10.1038/s41431-023-01399-5 PubMed DOI PMC

Ribas G, Milne RL, Gonzalez-Neira A, Benítez J. Haplotype patterns in cancer-related genes with long-range linkage disequilibrium: no evidence of association with breast cancer or positive selection. Eur J Hum Genet. 2008;16:252–60. 10.1038/sj.ejhg.5201953 PubMed DOI

Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12:996–1006. 10.1101/gr.229102 PubMed DOI PMC

Raney BJ, Barber GP, Benet-Pagès A, Casper J, Clawson H, Cline MS, et al. The UCSC genome browser database: 2024 update. Nucleic Acids Res. 2024;52:D1082–8. PubMed PMC

Rosenbloom KR, Sloan CA, Malladi VS, Dreszer TR, Learned K, Kirkup VM, et al. ENCODE data in the UCSC Genome Browser: year 5 update. Nucleic Acids Res. 2013;41:D56–63. 10.1093/nar/gks1172 PubMed DOI PMC

Fishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford). 2017;2017. PubMed PMC

Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30. 10.1038/nature14248 PubMed DOI PMC

Clay-Gilmour AI, Hildebrandt MAT, Brown EE, Hofmann JN, Spinelli JJ, Giles GG, et al. Coinherited genetics of multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance. Blood Adv. 2020;4:2789–97. 10.1182/bloodadvances.2020001435 PubMed DOI PMC

Wirth M, Schick M, Keller U, Krönke J. Ubiquitination and ubiquitin-like modifications in multiple myeloma: biology and therapy. Cancers. 2020;12:3764. PubMed PMC

Bobin A, Liuu E, Moya N, Gruchet C, Sabirou F, Lévy A, et al. Multiple myeloma: an overview of the current and novel therapeutic approaches in 2020. Cancers. 2020;12:2885. 10.3390/cancers12102885 PubMed DOI PMC

Ramakrishnan V, Kumar S. PI3K/AKT/mTOR pathway in multiple myeloma: from basic biology to clinical promise. Leuk Lymphoma. 2018;59:2524–34. 10.1080/10428194.2017.1421760 PubMed DOI

Catalano C, Paramasivam N, Blocka J, Giangiobbe S, Huhn S, Schlesner M, et al. Characterization of rare germline variants in familial multiple myeloma. Blood Cancer J. 2021;11:33. 10.1038/s41408-021-00422-6 PubMed DOI PMC

Chen W, Gullett JM, Tweedell RE, Kanneganti TD. Innate immune inflammatory cell death: PANoptosis and PANoptosomes in host defense and disease. Eur J Immunol. 2023;53:e2250235. 10.1002/eji.202250235 PubMed DOI PMC

Ponnusamy K, Tzioni MM, Begum M, Robinson ME, Caputo VS, Katsarou A, et al. The innate sensor ZBP1-IRF3 axis regulates cell proliferation in multiple myeloma. Haematologica. 2022;107:721–32. 10.3324/haematol.2020.274480 PubMed DOI PMC

Tominaga K, Minato H, Murayama T, Sasahara A, Nishimura T, Kiyokawa E, et al. Semaphorin signaling via MICAL3 induces symmetric cell division to expand breast cancer stem-like cells. Proc Natl Acad Sci USA. 2019;116:625–30. 10.1073/pnas.1806851116 PubMed DOI PMC

Kaloni D, Diepstraten ST, Strasser A, Kelly GL. BCL-2 protein family: attractive targets for cancer therapy. Apoptosis. 2023;28:20–38. 10.1007/s10495-022-01780-7 PubMed DOI PMC

Raab MS. Venetoclax in myeloma: to B, or not to B. Blood. 2024;143:4–5. 10.1182/blood.2023022535 PubMed DOI

Khan WJ, Ali M, Hashim S, Nawaz H, Hashim SN, Safi D, et al. Use of venetoclax in t(11;14) positive relapsed/refractory multiple myeloma: A systematic review. J Oncol Pharm Pract. 2023:10781552231218999. PubMed

Weinhold N, Johnson DC, Chubb D, Chen B, Försti A, Hosking FJ, et al. The CCND1 G870A polymorphism is a risk factor for t(11;14)(q13;q32) multiple myeloma. Nat Genet. 2013;45:522–5. 10.1038/ng.2583 PubMed DOI PMC

Yan W, Menjivar RE, Bonilla ME, Steele NG, Kemp SB, Du W, et al. Notch Signaling Regulates Immunosuppressive Tumor-Associated Macrophage Function in Pancreatic Cancer. Cancer Immunol Res. 2024;12:91–106. 10.1158/2326-6066.CIR-23-0037 PubMed DOI PMC

Sabol HM, Delgado-Calle J. The multifunctional role of Notch signaling in multiple myeloma. J Cancer Metastasis Treat. 2021;7:20. PubMed PMC

Cook JP, Mahajan A, Morris AP. Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes. Eur J Hum Genet. 2017;25:240–5. 10.1038/ejhg.2016.150 PubMed DOI PMC