Genetic subdivision and candidate genes under selection in North American grey wolves
Bibliographic record
Abstract
Previous genetic studies of the highly mobile grey wolf (Canis lupus) found population structure that coincides with habitat and phenotype differences. We hypothesized that these ecologically distinct populations (ecotypes) should exhibit signatures of selection in genes related to morphology, coat colour and metabolism. To test these predictions, we quantified population structure related to habitat using a genotyping array to assess variation in 42 036 single-nucleotide polymorphisms (SNPs) in 111 North American grey wolves. Using these SNP data and individual-level measurements of 12 environmental variables, we identified six ecotypes: West Forest, Boreal Forest, Arctic, High Arctic, British Columbia and Atlantic Forest. Next, we explored signals of selection across these wolf ecotypes through the use of three complementary methods to detect selection: FST /haplotype homozygosity bivariate percentilae, bayescan, and environmentally correlated directional selection with bayenv. Across all methods, we found consistent signals of selection on genes related to morphology, coat coloration, metabolism, as predicted, as well as vision and hearing. In several high-ranking candidate genes, including LEPR, TYR and SLC14A2, we found variation in allele frequencies that follow environmental changes in temperature and precipitation, a result that is consistent with local adaptation rather than genetic drift. Our findings show that local adaptation can occur despite gene flow in a highly mobile species and can be detected through a moderately dense genomic scan. These patterns of local adaptation revealed by SNP genotyping likely reflect high fidelity to natal habitats of dispersing wolves, strong ecological divergence among habitats, and moderate levels of linkage in the wolf genome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".