Rapid evolution of the inter-sexual genetic correlation for fitness in <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Abstract Sexual antagonism (SA) arises when male and female phenotypes are under opposing selection, yet genetically correlated. Until resolved, antagonism limits evolution towards optimal sex-specific phenotypes. Despite its importance for sex-specific adaptation and existing theory, the dynamics of SA resolution are not well understood empirically. Here, we present data from Drosophila melanogaster , compatible with a resolution of SA. We compared two independent replicates of the ‘LH M ’ population in which SA had previously been described. Both had been maintained under identical, controlled conditions, and separated for <250 generations. Although heritabilities of male and female fitness were similar, the inter-sexual genetic correlation differed significantly, being negative in one replicate (indicating SA) but close to zero in the other. Using population sequencing, we show that phenotypic differences were associated with population divergence in allele frequencies at non-random loci across the genome. Large frequency changes were more prevalent in the population without SA and were enriched at loci mapping to genes previously shown to have a sexually antagonistic relationships between expression and fitness. Our data suggest that rapid evolution towards SA resolution has occurred in one of the populations and open avenues towards studying the genetics of SA and its resolution.
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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 | 0.000 |
| 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".