Skip to main content

Advertisement

SpringerLink
Log in

DNA methylation and breast cancer-associated variants

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Background

A breast cancer polygenic risk score (PRS) comprising 313 common variants reliably predicts disease risk. We examined possible relationships between genetic variation, regulation, and expression to clarify the molecular alterations associated with these variants.

Methods

Genome-wide methylomic variation was quantified (MethylationEPIC) in Asian breast cancer patients (1152 buffy coats from peripheral whole blood). DNA methylation (DNAm) quantitative trait loci (mQTL) mapping was performed for 235 of the 313 variants with minor allele frequencies > 5%. Stability of identified mQTLs (p < 5e-8) across lifetime was examined using a public mQTL database. Identified mQTLs were also mapped to expression quantitative trait loci (eQTLs) in the Genotype-Tissue Expression Project and the eQTLGen Consortium.

Results

Breast cancer PRS was not associated with DNAm. A higher proportion of significant cis-mQTLs were observed. Of 822 significant cis-mQTLs (179 unique variants) identified in our dataset, 141 (59 unique variants) were significant (p < 5e-8) in a public mQTL database. Eighty-six percent (121/141) of the matched mQTLs were consistent at multiple time points (birth, childhood, adolescence, pregnancy, middle age, post-diagnosis, or treatment). Ninety-three variants associated with DNAm were also cis-eQTLs (35 variants not genome-wide significant). Multiple loci in the breast cancer PRS are associated with DNAm, contributing to the polygenic nature of the disease. These mQTLs are mostly stable over time.

Conclusions

Consistent results from DNAm and expression data may reveal new candidate genes not previously associated with breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The data that support the findings of this study are available on request from Mikael Hartman (ephbamh@nus.edu.sg). The data are not publicly available due to privacy or ethical restrictions.

References

  1. Frayling TM (2014) Genome-wide association studies: the good, the bad and the ugly. Clin Med (Lond) 14(4):428–431

    Article  CAS  Google Scholar 

  2. Maurano MT et al (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337(6099):1190–1195

    Article  CAS  Google Scholar 

  3. Ernst J et al (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473(7345):43–49

    Article  CAS  Google Scholar 

  4. Schaub MA et al (2012) Linking disease associations with regulatory information in the human genome. Genome Res 22(9):1748–1759

    Article  CAS  Google Scholar 

  5. Biernacka JM, Cordell HJ (2007) Exploring causality via identification of SNPs or haplotypes responsible for a linkage signal. Genet Epidemiol 31(7):727–740

    Article  Google Scholar 

  6. Husquin LT et al (2018) Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation. Genome Biol 19(1):222

    Article  CAS  Google Scholar 

  7. Michailidou K et al (2017) Association analysis identifies 65 new breast cancer risk loci. Nature 551(7678):92–94

    Article  Google Scholar 

  8. Milne RL et al (2017) Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 49(12):1767–1778

    Article  CAS  Google Scholar 

  9. Bahcall O (2013) Functional annotation of susceptibility loci identified by COGS. Nature Genetics

  10. Mavaddat N et al (2019) Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet 104(1):21–34

    Article  CAS  Google Scholar 

  11. Ho WK et al (2020) European polygenic risk score for prediction of breast cancer shows similar performance in Asian women. Nat Commun 11(1):3833

    Article  Google Scholar 

  12. Amos CI et al (2017) The oncoarray consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomarkers Prev 26(1):126–135

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Delaneau O, Zagury JF, Marchini J (2013) Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods 10(1):5–6

    Article  CAS  Google Scholar 

  15. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6):e1000529

    Article  Google Scholar 

  16. Delaneau O et al (2014) Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nat Commun 5:3934

    Article  CAS  Google Scholar 

  17. Muller, F., et al., RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome Biol, 2019. 20(1): p. 55.

  18. Pidsley R et al (2016) Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol 17(1):208

    Article  Google Scholar 

  19. Triche TJ Jr et al (2013) Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res 41(7):e90

    Article  CAS  Google Scholar 

  20. Teschendorff AE et al (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29(2):189–196

    Article  CAS  Google Scholar 

  21. Assenov Y et al (2014) Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods 11(11):1138–1140

    Article  CAS  Google Scholar 

  22. Aran D, Sirota M, Butte AJ (2015) Systematic pan-cancer analysis of tumour purity. Nat Commun 6:8971

    Article  CAS  Google Scholar 

  23. Mansell G et al (2019) Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics 20(1):366

    Article  Google Scholar 

  24. Gaunt TR et al (2016) Systematic identification of genetic influences on methylation across the human life course. Genome Biol 17:61

    Article  Google Scholar 

  25. Watanabe K et al (2017) Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8(1):1826

    Article  Google Scholar 

  26. Võsa U et al (2018) Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv:447367.

  27. Kuleshov MV et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44(W1):W90–W97

    Article  CAS  Google Scholar 

  28. Chen EY et al (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:128

    Article  Google Scholar 

  29. Rivandi M, Martens JWM, Hollestelle A (2018) Elucidating the underlying functional mechanisms of breast cancer susceptibility through post-GWAS analyses. Front Genet 9:280

    Article  Google Scholar 

  30. Fachal L et al (2020) Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nat Genet 52(1):56–73

    Article  CAS  Google Scholar 

  31. Feng H et al (2020) Transcriptome-wide association study of breast cancer risk by estrogen-receptor status. Genet Epidemiol 44(5):442–468

    Article  Google Scholar 

  32. Liu Y et al (2019) An analysis about heterogeneity among cancers based on the DNA methylation patterns. BMC Cancer 19(1):1259

    Article  CAS  Google Scholar 

  33. Lin D et al (2018) Characterization of cross-tissue genetic-epigenetic effects and their patterns in schizophrenia. Genome Med 10(1):13

    Article  Google Scholar 

  34. Smith AK et al (2014) Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics 15:145

    Article  Google Scholar 

  35. Hannon E et al (2018) Leveraging DNA-methylation quantitative-trait loci to characterize the relationship between methylomic variation, gene expression, and complex traits. Am J Hum Genet 103(5):654–665

    Article  CAS  Google Scholar 

  36. Zhu Z et al (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48(5):481–487

    Article  CAS  Google Scholar 

  37. Wu Y et al (2018) Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9(1):918

    Article  Google Scholar 

  38. Parton M, Dowsett M, Smith I (2001) Studies of apoptosis in breast cancer. BMJ 322(7301):1528–1532

    Article  CAS  Google Scholar 

  39. Liu H, Ye H (2017) Screening of the prognostic targets for breast cancer based co-expression modules analysis. Mol Med Rep 16(4):4038–4044

    Article  CAS  Google Scholar 

  40. Wu D et al (2016) Molecular mechanisms associated with breast cancer based on integrated gene expression profiling by bioinformatics analysis. J Obstet Gynaecol 36(5):615–621

    Article  CAS  Google Scholar 

  41. Holbrook J et al (2019) Tumour necrosis factor signalling in health and disease. F1000Res 8.

Download references

Acknowledgements

SGBCC thanks the participants and all research coordinators for their excellent help with recruitment, data, and sample collection. The breast cancer genome-wide association analyses were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710) and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Michailidou et al. (2017).

Funding

This study was supported by the National Research Foundation Singapore (NRF-NRFF2017-02), NUS start-up Grant, National University Cancer Institute Singapore (NCIS) Centre Grant [NMRC/CG/NCIS/2010, NMRC/CG/012/2013, CGAug16M005], Saw Swee Hock School of Public Health Research Programme of Research Seed Funding (Breast Cancer Prevention Program), Asian Breast Cancer Research Fund, and the NMRC Clinician Scientist Award (SI Category) [NMRC/CSA-SI/0015/2017]. The breast cancer genome-wide association analyses were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710) and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Michailidou et al. (2017).

Author information

Author notes

  1. These authors Mikael Hartman and Jingmei Li have share senior authorship

Authors and Affiliations

  1. Genome Institute of Singapore, Human Genetics, Singapore, Singapore

    Peh Joo Ho, Rajkumar Dorajoo, Seeu Si Ong, Alexis Jiaying Khng & Jingmei Li

  2. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

    Peh Joo Ho & Mikael Hartman

  3. Health Systems and Services Research, Duke-NUS Medical School Singapore, Singapore, Singapore

    Rajkumar Dorajoo

  4. Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland

    Ivna Ivanković

  5. Biomedical Informatics, University Hospital of Zurich, Zurich, Switzerland

    Ivna Ivanković

  6. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore

    Seeu Si Ong, Mikael Hartman & Jingmei Li

  7. Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore

    Benita Kiat-Tee Tan, Veronique Kiak Mien Tan & Yirong Sim

  8. Division of Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore

    Benita Kiat-Tee Tan, Veronique Kiak Mien Tan & Yirong Sim

  9. KK Breast Department, KK Women’s and Children’s Hospital, Singapore, 229899, Singapore

    Swee Ho Lim, Qing Ting Tan & Zhiyan Yan

  10. Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433, Singapore

    Ern Yu Tan, Patrick Chan & Juliana Chen Jia Chuan

  11. Division of Breast Surgery, Changi General Hospital, Singapore, Singapore

    Su-Ming Tan

  12. Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, Singapore

    Joanne Ngeow

  13. Cancer Genetics Service, National Cancer Centre Singapore, Singapore, Singapore

    Joanne Ngeow

  14. Oncology Academic Clinical Program, Duke NUS, Singapore, Singapore

    Joanne Ngeow

  15. Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore

    Ching Wan Chan, Siau Wei Tang & Mikael Hartman

Authors
  1. Peh Joo Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajkumar Dorajoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivna Ivanković
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seeu Si Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexis Jiaying Khng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benita Kiat-Tee Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Veronique Kiak Mien Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Swee Ho Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ern Yu Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Su-Ming Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Qing Ting Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhiyan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Joanne Ngeow
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yirong Sim
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Patrick Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Juliana Chen Jia Chuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ching Wan Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Siau Wei Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Mikael Hartman
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jingmei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: JLi, Data curation: MH, Formal Analysis: PJH, Funding acquisition: JLi, MH, Methodology: RD, JLi, PJH, Project administration: PJH, Resources: MH, Writing – original draft: JLi, PJH, Writing – review & editing: All co-authors.

Corresponding author

Correspondence to Jingmei Li.

Ethics declarations

Conflict of interest

The authors declare no potential conflicts of interest.

Ethical approval

This study is approved by the Agency of Science, Technology and Research, Human Biomedical Research Office (Reference: 2020-005). Recruitment for the Singapore Breast Cancer Cohort is approved by the National Healthcare Group Domain Specific Review Board (Reference: 2009/000501) and the SingHealth Centralised Institutional Review Board (Reference: 2019/2246).

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

(XLSX 8118 kb)

(DOCX 8118 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ho, P.J., Dorajoo, R., Ivanković, I. et al. DNA methylation and breast cancer-associated variants. Breast Cancer Res Treat 188, 713–727 (2021). https://doi.org/10.1007/s10549-021-06185-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-021-06185-9

Keywords

Search

Navigation