Abstract 5314: DNA methylation quantitative trait loci and breast cancer risk: Data from nearly 230,000 women of European descent
Bibliographic record
Abstract
Abstract DNA methylation plays a critical role in the initiation and promotion of breast cancer. Previous studies have identified multiple promising methylation markers associated with breast cancer risk. However, these studies had very small sample sizes and potential confounding factors were not taken into consideration. In the present study, we used genetic variants as instruments to investigate DNA methylation in relation to breast cancer. We extracted beta coefficients and standard errors for methylation quantitative trait loci of 30,477 CpG sites and applied them into the genetic data of 122,977 breast cancer cases and 105,974 controls of European descent included in the Breast Cancer Association Consortium (BCAC). Associations of genetically predicted methylation levels with breast cancer risk were evaluated using an inverse-variance weighted method. For CpG sites showing a significant association with breast cancer, we estimated the correlation between methylation with gene expression using data from the Framingham Heart Study. For genes with expression levels correlated to methylation, statistical models were built to predict gene expression levels via genetic variants using data from the Genotype-Tissue Expression project. These models were then applied to the BCAC to assess the association of predicted gene expression and breast cancer risk. Of the 30,477 inspected CpG sites, significant associations with breast cancer risk were observed for 199 CpG sites at P < 1.64×10-6, a Bonferroni-corrected threshold. Among them, 22 CpG sites were located in genomic regions not yet reported as risk loci for breast cancer. Among the remaining 177 CpG sites located in breast cancer risk loci, significant associations were observed for 27 CpG sites after the breast cancer SNPs were adjusted. Among the 199 CpG sites, correlation was observed at 29 CpG sites with expression levels of 17 neighbor or enhancer target genes at P < 0.05. Among them, genetically predicted expression levels of 13 genes were significantly associated with breast cancer risk at a false discovery rate < 0.05, 11 of which showed consistent association directions with the associations between methylation and gene expression, and between gene expression and breast cancer. For example, the higher methylation level at cg00577578 was associated with an increased breast cancer risk (P = 1.31×10-7) and a decreased expression level of GBAP1 (P = 5.07×10-15), and meanwhile, a lower expression level of GBAP1 was associated with increased breast cancer risk (P = 2.22×10-9). Using genetic variants as instruments, we identified 199 CpG sites significantly associated with breast cancer risk. Our study demonstrates the potential of integrating genomics, DNA methylation and gene expression in identifying new biomarkers for breast cancer, and provides new insights into the etiology of this malignancy. Citation Format: Yaohua Yang, Qiuyin Cai, Xiang Shu, Lang Wu, Bingshan Li, Xingyi Guo, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang, Joe Dennis, Jacques Simard, Douglas F. Easton, Wei Zheng, Jirong Long, Breast Cancer Association Consortium (BCAC). DNA methylation quantitative trait loci and breast cancer risk: Data from nearly 230,000 women of European descent [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5314.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".