<i>Cis-</i> regulatory variation in relation to sex and sexual dimorphism in <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Abstract Much of sexual dimorphism is likely due to sex-biased gene expression, which results from differential regulation of a genome that is largely shared between males and females. Here we use allele-specific expression to explore cis -regulatory variation in Drosophila melanogaster in relation to sex. We develop a Bayesian framework to infer the transcriptome-wide joint distribution of cis -regulatory effects across the sexes. We use this approach to quantify transcriptome-wide sex differences in cis -regulatory effects as well as examine patterns of cis -regulatory variation with respect to two other levels of variation in sexual dimorphism: (i) across genes varying in their degree of sex-biased expression, and (ii) among tissues that vary in their degree of dimorphism (e.g., relatively low dimorphism in heads vs high dimorphism in gonads). We uncover evidence of widespread cis -regulatory variation in all tissues examined, with female-biased genes being especially enriched for this variation. A sizeable proportion of cis -regulatory variation is inferred to have sex-specific effects, with sex-dependent cis effects being much more frequent in gonads than in heads. Finally, we detect some genes with reversed allelic imbalance between the sexes. Such variants could provide a mechanism for sex-specific dominance reversals, a phenomenon important for sexually antagonistic balancing selection.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".