Screening for epistatic selection signatures: A simulation study
Bibliographic record
Abstract
Abstract Detecting combinations of alleles that diverged between subpopulations via selection signature statistics can contribute to decipher the phenomenon of epistasis. This research focused on the simulation of genomic data from subpopulations under divergent epistatic selection (ES). We used D’IS2 and FST statistics in pairs of loci to scan the whole-genome. The results showed the ability to identify loci under additive-by-additive ES (ESaa) by reporting large statistical departures between subpopulations with a high level of divergence, while it did not show the same advantage in the other types of ES. Despite this, limitations such as the difficulty to distinguish between the quasi-complete fixation of one locus by ESaa from other events were observed. However, D’IS2 can detect loci under ESaa by defining a minimum boundary for the minor allele frequency on a multiple subpopulation analysis where ES only takes place in one subset. Even so, the major limitation was distinguishing between ES and single-locus selection (SS); therefore, we can conclude that divergent locus can be also a result of ES. The test conditions with D-statistics of both Ohta (1982a, 1982b) and Black and Krafsur (1985) did not provide evidence to differentiate ES in our simulation framework of isolated subpopulations.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".