Details

Autor(en) / Beteiligte

• Yang, Yaohua
• Wu, Lang
• Shu, Xiao-Ou
• Cai, Qiuyin
• Shu, Xiang
• Li, Bingshan
• Guo, Xingyi
• Ye, Fei
• Michailidou, Kyriaki
• Bolla, Manjeet K
• Wang, Qin
• Dennis, Joe
• Andrulis, Irene L
• Brenner, Hermann
• Chenevix-Trench, Georgia
• Campa, Daniele
• Castelao, Jose E
• Gago-Dominguez, Manuela
• Dörk, Thilo
• Hollestelle, Antoinette
• Lophatananon, Artitaya
• Muir, Kenneth
• Neuhausen, Susan L
• Olsson, Håkan
• Sandler, Dale P
• Simard, Jacques
• Kraft, Peter
• Pharoah, Paul D P
• Easton, Douglas F
• Zheng, Wei
• Long, Jirong

Titel

Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk: Data From 228 951 Women of European Descent

Ist Teil von

• JNCI : Journal of the National Cancer Institute, 2020-03, Vol.112 (3), p.295-304

Ort / Verlag

United States: Oxford University Press

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• 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. Using 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 = 1595). The prediction models were validated using data from the Women’s Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (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 less than 7.94 × 10–7, including 45 CpGs residing in 18 genomic regions, that 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.