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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".