A population-aware retrospective regression to detect genome-wide variants with sex difference in allele frequency
Bibliographic record
Abstract
Sex difference in allele frequency is an emerging topic that is crucial to our understanding of data quality and features, particularly when it comes to the largely overlooked X chromosome. To detect sex differences in allele frequency for both X chromosomal and autosomal variants, the existing method is conservative when applied to samples from multiple ancestral populations. Additionally, it remains unexplored whether the sex difference in allele frequency varies between populations, which is important for transancestral genetic studies. To answer these questions, we thus developed a novel, retrospective regression-based testing framework that led to interpretable and easy-to-implement solutions. We then applied the proposed methods to the high-coverage whole genome sequence data of the 1000 Genomes Project, robustly analyzing all samples available from the five super-populations. We had 97 novel findings by recognizing and modelling ancestral differences. Finally, we replicated the specific findings and overall conclusion using the gnomAD v3.1.2 data.
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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".