Genomics, environment and balancing selection in behaviourally bimodal populations: The caribou case
Bibliographic record
Abstract
Selection forces that favour different phenotypes in different environments can change frequencies of genes between populations along environmental clines. Clines are also compatible with balancing forces, such as negative frequency-dependent selection (NFDS), which maintains phenotypic polymorphisms within populations. For example, NFDS is hypothesized to maintain partial migration, a dimorphic behavioural trait prominent in species where only a fraction of the population seasonally migrates. Overall, NFDS is believed to be a common phenomenon in nature, yet a scarcity of studies were published linking naturally occurring allelic variation with bimodal or multimodal phenotypes and balancing selection. We applied a Pool-seq approach and detected selection on alleles associated with environmental variables along a North-South gradient in western North American caribou, a species displaying partially migratory behaviour. On 51 loci, we found a signature of balancing selection, which could be related to NFDS and ultimately the maintenance of the phenotypic polymorphisms known within these populations. Yet, remarkably, we detected directional selection on a locus when our sample was divided into two behaviourally distinctive groups regardless of geographic provenance (a subset of GPS-collared migratory or sedentary individuals), indicating that, within populations, phenotypically homogeneous groups were genetically distinctive. Loci under selection were linked to functional genes involved in oxidative stress response, body development and taste perception. Overall, results indicated genetic differentiation along an environmental gradient of caribou populations, which we found characterized by genes potentially undergoing balancing selection. We suggest that the underlining balancing force, NFDS, plays a strong role within populations harbouring multiple haplotypes and phenotypes, as it is the norm in animals, plants and humans too.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".