Identifying 31 novel breast cancer susceptibility loci using data from genome-wide association studies conducted in Asian and European women
Bibliographic record
Abstract
ABSTRACT Common genetic variants in 183 loci have been identified in relation to breast cancer risk in genome-wide association studies (GWAS). These risk variants combined explain only a relatively small proportion of breast cancer heritability, particularly in Asian populations. To search for additional genetic susceptibility loci for breast cancer, we performed a meta-analysis of data from GWAS conducted in Asians (24,206 cases and 24,775 controls). Variants showing an association with breast cancer risk at P < 0.01 were evaluated in GWAS conducted in European women including 122,977 cases and 105,974 controls. In the combined analysis of data from both Asian and European women, the lead variant in 28 loci not previously reported showed an association with breast cancer risk at P < 5 ×10 −8 . In the meta-analysis of all GWAS data from Asian and European descendants, we identified SNPs in three additional loci in association with breast cancer risk at P < 5 ×10 −8 . The associations for 10 of these loci were replicated in an independent sample of 16,787 cases and 16,680 controls of Asian women ( P < 0.05). Expression quantitative trait locus (eQTL) and gene-based analyses provided evidence for the possible involvement of the YBEY, MAN2C1, SNUPN, TBX1, SEMA4A, STC1, MUTYH, LOXL2 , and LINC00886 genes underlying the associations observed in eight of these 28 newly identified risk loci. In addition, we replicated the association for 78 of the 166 previously reported risk variants at P < 0.05 in women of Asian descent using GWAS data. These findings improve our understanding of breast cancer genetics and etiology and extend to Asian populations previous findings from studies of European women.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".