Genomic architecture of artificially and sexually selected traits in a wild cervid
Bibliographic record
Abstract
Abstract Characterization of the genomic architecture of fitness-related traits such as body size and male ornamentation in mammals provides tools for conservation and management: as both indicators of quality and health, these traits are often subject to sexual and artificial selective pressures. Here we performed high-depth whole genome re-sequencing on pools of individuals representing the phenotypic extremes in our study system for antler and body size in white-tailed deer ( Odocoileus virginianus ). Samples were selected from a tissue repository containing phenotypic data for 4,466 male white-tailed deer from Anticosti Island, Quebec, with four pools representing the extreme phenotypes for antler and body size in the population, after controlling for age. Our results revealed a largely panmictic population, but detected highly diverged windows between pools for both traits with high shifts in allele frequency (mean allele frequency difference of 14% for and 13% for antler and body SNPs in outlier windows). These regions often contained putative genes of small-to-moderate effect consistent with a polygenic model of quantitative traits. Genes in outlier antler windows had known direct or indirect effects on growth and pathogen defence, while body genes, overall GO terms, and transposable element analyses were more varied and nuanced. Through qPCR analysis we validated both a body and antler gene. Overall, this study revealed the polygenic nature of both antler morphology and body size in free-ranging white-tailed deer and identified target loci for additional analyses.
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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".