Genomic Signatures of Selection along a Climatic Gradient in the Northern Range Margin of the White-Footed Mouse (Peromyscus leucopus)
Bibliographic record
Abstract
Identifying genetic variation involved in thermal adaptation is likely to yield insights into how species adapt to different climates. Physiological and behavioral responses associated with overwintering (e.g., torpor) are thought to serve important functions in climate adaptation. In this study, we use 2 isolated Peromyscus leucopus lineages on the northern margin of the species range to identify single nucleotide polymorphisms (SNPs) showing a strong environmental association and test for evidence of parallel evolution. We found signatures of clinal selection in each lineage, but evidence of parallelism was limited, with only 2 SNPs showing parallel allele frequencies across transects. These parallel SNPs map to a gene involved in protection against iron-dependent oxidative stress (Fxn) and to a gene with unknown function but containing a forkhead-associated domain (Fhad1). Furthermore, within transects, we find significant clinal patterns in genes enriched for functions associated with glycogen homeostasis, synaptic function, intracellular Ca2+ balance, H3 histone modification, as well as the G2/M transition of cell division. Our results are consistent with recent literature on the cellular and molecular basis of climate adaptation in small mammals and provide candidate genomic regions for further study.
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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".