The Use of Ancestral Haplotypes in the Molecular Diagnosis of Familial Breast Cancer
Bibliographic record
Abstract
Mutations in BRCA1 and BRCA2 account for about 40% of families with an inherited susceptibility to breast and/or ovarian cancer. Mutational analysis of these two genes has become the standard of care for families with a strong suggestion of inherited susceptibility. Methodologies for screening vary, but one of the more popular techniques is dHPLC, due to its combination of high sensitivity and low cost. The presence of a large number of polymorphisms in the two BRCA genes complicates dHPLC analysis, often leading to complex elution profiles. There are concerns that a pattern produced by a sample heterozygous for a polymorphism may be very similar to that produced by a sample heterozygous for a unique mutation within the same amplicon. Further molecular analysis is often required to resolve whether any given shift is due to a polymorphism or a disease-causing mutation. The use of ancestral haplotypes was explored as a means to minimize the need for further analysis. Groups of 86 patients were genotyped for 12 BRCA1 polymorphisms or 20 BRCA2 polymorphisms. For BRCA1, eight distinct haplotypes were identified, which are largely derivatives of two main lineages. For BRCA2, 17 distinct haplotypes were identified, leading to a much more complex polymorphic pattern. With this knowledge, we have defined a system to determine which patients, if any, require further investigations. This method could be used to supplement any comprehensive screening methodology for other large genes that lie within strong regions of linkage disequilibrium such as NF1, CFTR, MLH1, or MSH2.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".