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

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.

Figure 1.

Study design flowchart. BCAC = Breast Cancer Association Consortium; CpGs = CpG sites; ER = estrogen receptor; FHS = Framingham Heart Study; GWAS = genomewide association study; GTEx = genotype-tissue expression; meQTL = DNA methylation quantitative trait loci; SNP = single nucleotide polymorphism; WHI = Women’s Health Initiative.

Figure 2.

Performances of DNA methylation prediction models in the prediction dataset and the validation dataset. A total of 81 361 models had a prediction performance in the FHS (${\mathrm{R}}_{\text{FHS}}^2$) greater than or equal to 0.01. This figure shows the performance of these models in the prediction dataset, FHS, and in the validation dataset, WHI. The x-axis represents the ${\mathrm{R}}_{\text{FHS}}^2$ (squared correlation coefficients of predicted and measured DNA methylation levels). We then apply these models to the genetic data in WHI to predict the DNA methylation levels of these 81 361 CpGs. The y-axis represents the performance of these models in the WHI (${\mathrm{R}}_{\text{WHI}}^2$, squared correlation coefficients of predicted and measured DNA methylation levels). The black line represents the identity line (y = x). CpGs = CpG sites; FHS = Framingham Heart Study; WHI = Women’s Health Initiative.

Figure 3.

The performance of DNA methylation prediction using the prediction model approach and using the single-meQTL SNP approach. For a total of 24 845 CpGs, prediction models were built in the present study and meQTLs were identified in a previous study. This figure presents the prediction performances of models and meQTLs for these CpGs. The x-axis represents the performance (R²) of DNA methylation prediction using the single-meQTL SNP approach (ie, squared correlation coefficients of predicted and measured DNA methylation levels in the meQTL data). The y-axis represents performance (R²) of DNA methylation prediction using the prediction model approach (ie, squared correlation coefficients of predicted and measured DNA methylation levels in the FHS data). The black line represents the identity line (y = x). CpGs = CpG sites; FHS = Framingham Heart Study; meQTL = DNA methylation quantitative trait loci; SNP = single-nucleotide polymorphism.