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.

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基因预测的DNA甲基化生物标记物水平与乳腺癌风险：来自228951名欧洲人后裔的数据。

背景技术DNA甲基化在乳腺癌的发展中起着至关重要的作用。先前的研究已经确定白细胞中的DNA甲基化标记是有希望的乳腺癌生物标记。但是，这些研究受到较低的统计能力和潜在偏见的限制。利用一种新的方法，我们调查了DNA甲基化标记与乳腺癌风险的关系。方法建立统计模型，使用来自Framingham心脏研究（N = 1595）的HumanMethylation450 BeadChip的遗传数据和DNA甲基化数据预测DNA甲基化标记的水平。使用来自妇女健康倡议（N = 883）的数据对预测模型进行了验证。我们将这些模型应用于122977个乳腺癌病例和105个乳腺癌病例的全基因组关联研究（GWAS）数据，974对照评估CpGs的遗传预测DNA甲基化水平是否与乳腺癌风险相关。所有统计检验都是两面的。结果在62938个CpG位点（CpGs）中，在Bonferroni校正的P <7.94×10-7阈值下，发现450个CpGs与乳腺癌风险有统计学意义的关联，包括位于18个基因组区域的45 CpGs与患乳腺癌的风险有关。在由70个GWAS鉴定的乳腺癌风险变异体的500千碱基剥落区域中的其余405 CpG中，与11个CpG的关联独立于GWAS鉴定的变异体。对遗传，DNA甲基化和基因表达数据的综合分析发现，38个CpGs可能通过调节21个基因的表达来影响乳腺癌的风险。