A Systematic Assessment of Common Genetic Variation in <i>CYP11A</i> and Risk of Breast Cancer
Bibliographic record
Abstract
CYP11A catalyzes the rate-limiting step in the biosynthesis of sex-steroid hormones. In this study, we employed a systematic approach that involved gene resequencing and a haplotype-based analysis to investigate the relationship between common variation in CYP11A and breast cancer risk among African-Americans, Latinas, Japanese-Americans, Native Hawaiians, and Whites in the Multiethnic Cohort Study. Resequencing in a multiethnic panel of 95 advanced breast cancer cases revealed no common missense variant (> or =5% frequency). Common haplotype patterns were assessed by genotyping 36 densely spaced single nucleotide polymorphisms (SNPs) spanning 67 kb of the CYP11A locus in a multiethnic panel of subjects (n = 349; 1 SNP/1.86 kb on average). We identified one to two regions of strong linkage disequilibrium in these populations. Twelve tagging SNPs were selected to predict the common haplotypes (> or =5% frequency) in these regions with high probability (average R(h)(2) = 0.94) and were examined in a breast cancer case-control study in the Multiethnic Cohort Study (1,615 cases and 1,962 controls). A global test for differences in risk according to common haplotypes over the locus was statistically significant (P = 0.006), as were associations with haplotypes in each block (block 1 global test, P = 0.008; haplotype 1D, effect per haplotype copy, odds ratios, 1.23; 95% confidence interval, 1.03-1.48) and block 2 (global test, P = 0.016; haplotype 2F odds ratios, 1.52; 95% confidence interval, 1.15-2.01). These haplotypes were most common in Japanese-Americans and Native Hawaiians, followed by Whites then Latinas, and were rare in African-Americans (<5% frequency); the haplotype effects on risk across each group were homogeneous. Based on these findings, CYP11A deserves further consideration as a candidate breast cancer susceptibility gene.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".