Searching for intra-locus sexual conflicts in the three-spined stickleback ( <i>Gasterosteus aculeatus</i> ) genome
Bibliographic record
Abstract
Abstract Differences between sexes in trait fitness optima can generate intra-locus sexual conflicts that have the potential to maintain genetic diversity through balancing selection. However, these differences are unlikely to be associated with string selective coefficients and as a result are challenging to detect. Additionally, recent studies have highlighted that duplication on sexual chromosomes can create artefactual signals of inter-sex differentiation and increased genetic diversity. Thus, testing the relationship between intra-locus sexual conflicts and balancing selection requires a high-quality reference genome, stringent filtering of potentially duplicated regions, and dedicated methods to detect loci with low levels of inter-sex differentiation. In this study, we investigate intra-locus sexual conflicts in the three-spined stickleback using whole genome sequencing (mean coverage = 12X) of 50 females and 49 males from an anadromous population in the St. Lawrence River, Québec, Canada. After stringent filtering of duplications from the sex chromosomes, we compared three methods to detect intra-locus sexual conflicts. This allowed us to detect various levels of inter-sex differentiation, from stronger single locus effects to small cumulative or multivariate signals. Overall, we found only four genomic regions associated with confidence to intra-locus sexual conflict that displayed associations with long-term balancing selection maintaining genetic diversity. Altogether, this suggests that most intra-locus sexual conflicts do not drive long-term balancing selection and are most likely transient. However, they might still play a role in maintaining genetic diversity over shorter time scales by locally reducing the effects of purifying selection and the rates of genetic diversity erosion.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".