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J Natl Cancer Inst. 2019 May 29. pii: djz109. doi: 10.1093/jnci/djz109. [Epub ahead of print]

Genetically predicted levels of DNA methylation biomarkers and breast cancer risk: data from 228,951 women of European descent.

Author information

1
Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
2
Department of Molecular Physiology & Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.
3
Division of Cancer Biostatistics, Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
4
Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
5
Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada.
6
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
7
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
9
Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.
10
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
11
Department of Biology, University of Pisa, Pisa, Italy.
12
Oncology and Genetics Unit, Instituto de Investigacion Biomedica (IBI) Orense-Pontevedra-Vigo, Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain.
13
Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago De Compostela, Spain.
14
Moores Cancer Center, University of California San Diego, La Jolla, California, USA.
15
Gynaecology Research Unit, Hannover Medical School, Hannover, Germany.
16
Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
17
Division of Health Sciences, Warwick Medical School, Warwick University, Coventry, UK.
18
Institute of Population Health, University of Manchester, Manchester, UK.
19
Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California, USA.
20
Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden.
21
Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, North Carolina, USA.
22
Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, Québec, Canada.
23
Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
24
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.

Abstract

BACKGROUND:

DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Utilizing a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk.

METHODS:

Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (N=1,595). The prediction models were validated using data from the Women's Health Initiative (N=883). We applied these models to genome-wide association study (GWAS) data of 122,977 breast cancer cases and 105,974 controls to evaluate if the genetically predicted DNA methylation levels at CpGs are associated with breast cancer risk. All statistical tests were two-sided.

RESULTS:

Of the 62,938 CpG sites (CpGs) investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P<7.94 × 10-7, including 45 CpGs residing in 18 genomic regions which have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes.

CONCLUSION:

Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.

PMID:
31143935
DOI:
10.1093/jnci/djz109

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