Admixture Aberration Analysis: Application to Mapping in Admixed Population Using Pooled DNA
Bibliographic record
Abstract
Abstract Admixture mapping is a gene mapping approach used for the identification of genomic regions harboring disease susceptibility genes in the case of recently admixed populations such as African Americans. We present a novel method for admixture mapping, called admixture aberration analysis (AAA) that uses a DNA pool of affected admixed individuals. We demonstrate through simulations that AAA is a powerful and economical mapping method under a range of scenarios, capturing complex human diseases such as hypertension and end-stage kidney disease. The method has a low false-positive rate and is robust to deviation from model assumptions. Finally, we apply AAA on 600 prostate cancer-affected African Americans, replicating a known risk locus. Simulation results indicate that the method can yield over 96% reduction in genotyping. Our method is implemented as a Java program called AAAmap and is freely available at http://bioinfo.cs.technion.ac.il/AAAmap.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".