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Record W4367186579 · doi:10.1093/evolut/qpad075

Searching for intralocus sexual conflicts in the three-spined stickleback ( <i>Gasterosteus aculeatus</i> ) genome

2023· article· en· W4367186579 on OpenAlexaffabout
Florent Sylvestre, Claire Mérot, Éric Normandeau, Louis Bernatchez

Bibliographic record

VenueEvolution · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGasterosteusBiologySticklebackThree-spined sticklebackEvolutionary biologyZoologyGenomeGeneticsFish <Actinopterygii>GeneFishery

Abstract

fetched live from OpenAlex

Differences between sexes in trait fitness optima can generate intralocus sexual conflicts that have the potential to maintain genetic diversity through balancing selection. However, these differences are unlikely to be associated with strong selective coefficients and are challenging to detect. Additionally, recent studies have highlighted that duplications on sexual chromosomes can create artifactual signals of intralocus sexual conflicts. Thus, testing the relationship between intralocus sexual conflicts and balancing selection requires stringent filtering of duplicated regions, and dedicated methods to detect loci with low levels of intersex differentiation. In this study, we investigated intralocus sexual conflicts in the three-spined stickleback using whole-genome sequencing (mean coverage = 12×) 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 intralocus sexual conflicts. We found only two genomic regions under potential intralocus sexual conflict that also showed signals of balancing selection. Overall, our results suggest that most intralocus sexual conflicts do not drive long-term balancing selection and are most likely transient.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.269
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2023
Admission routes2
Has abstractyes

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