The genetic basis of variation in sexual aggression: Evolution versus social plasticity
Bibliographic record
Abstract
Male sexual aggression towards females is a form of sexual conflict that can result in increased fitness for males through forced copulations (FCs) or coercive matings at the cost of female lifetime fitness. We used male fruit flies (Drosophila melanogaster) as a model system to uncover the genomic contributions to variation in FC, both due to standing variation in a wild population, and due to plastic changes associated with variation in social experience. We used RNAseq to analyse whole-transcriptome differential expression (DE) in male head tissue associated with evolved changes in FC from lineages previously selected for high and low FC rate and in male flies with varying FC rates due to social experience. We identified hundreds of genes associated with evolved and plastic variation in FC, however only a small proportion (27 genes) showed consistent DE due to both modes of variation. We confirmed this trend of low concordance in gene expression effects across broader sets of genes significant in either the evolved or plastic analyses using multivariate approaches. The gene ontology terms neuropeptide hormone activity and serotonin receptor activity were significantly enriched in the set of significant genes. Of seven genes chosen for RNAi knockdown validation tests, knockdown of four genes showed the expected effect on FC behaviours. Taken together, our results provide important information about the apparently independent genetic architectures that underlie natural variation in sexual aggression due to evolution and plasticity.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".