Localization of breast cancer susceptibility loci by genome‐wide SNP linkage disequilibrium mapping
Why this work is in the frame
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Bibliographic record
Abstract
We studied the feasibility of a novel approach to localize breast cancer susceptibility genes, using a low-density genome-wide panel of single-nucleotide polymorphisms and taking advantage of large regions of linkage disequilibrium (LD) flanking Jewish disease genes in high-risk cases. With Affymetrix GeneChip arrays, we genotyped 8,576 polymorphisms in three sets of Ashkenazi Jewish breast cancer cases: a "validation" set of 27 breast cancer cases, all of whom carried the BRCA2*6174delT founder mutation; a "field" set of 19 breast cancer cases from male breast cancer kindreds, which simulated conditions for finding new genes; and a "test" set of 57 probands from breast cancer kindreds (4 or more cases/kindred), in which mutations in BRCA1 and BRCA2 had been excluded. To identify associations, we compared the frequency of genotypes and haplotypes in cases vs. controls by the Fisher's exact test and a maximum likelihood ratio test. In the "validation" set, we demonstrated the presence of a region of linkage disequilibrium on BRCA2*6174delT chromosomes that spanned over 5 million bases. In the "field" set, we showed that this large region of linkage disequilibrium flanking BRCA2 was detectable despite the presence of heterogeneity in the sample set. Finally, in the "test" set, at least three regions of interest emerged that could contain novel breast cancer genes, one of which had been identified previously by linkage analysis. While these results demonstrate the feasibility of genome-wide association strategies, further application of this approach will critically depend on optimizing the density and distribution of SNPs and the size and type of study design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it