Two Forms of Sexual Dimorphism in Gene Expression in<i>Drosophila melanogaster</i>: Their Coincidence and Evolutionary Genetics
Bibliographic record
Abstract
Phenotypic sexual dimorphism can be mediated by sex differences in gene expression. We examine two forms of sexual dimorphism in gene expression in Drosophila melanogaster: 1) sex-biased gene expression (SBGE) in which the sexes differ in the amount a gene is expressed and 2) sexual dimorphism in isoform usage, that is, sex-specific splicing (SSS). In whole body (but not the head) expression, we find a negative association between SBGE and SSS, possibly suggesting that these are alternate routes to resolving sexual antagonistic selection. Next, we evaluate whether expression dimorphism contributes to the heterogeneity among genes in rmf, the intersexual genetic correlation in body expression that constrains the extent to which a gene's expression can evolve independently between the sexes. We find lower rmf values for genes with than without SSS. We find higher rmf values for male- than female-biased genes (except genes with extreme male bias), even though male-biased genes are known to have greater evolutionary divergence in expression. Finally, we examine population genetic patterns in relation to SBGE and SSS because genes with expression dimorphism have likely experienced a history of sex differences in selection. SSS is associated with reduced values of Tajima's D and elevated direction of selection (DoS) values, suggestive of higher rates of adaptive evolution. Though DoS is highly elevated for genes with extreme male bias, DoS otherwise tends to decline from female-biased to unbiased to male-biased genes. Collectively, the results indicate that SBGE and SSS are differentially distributed across the genome and are associated with different forms of 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".