Data from: Whole-genome resequencing reveals polygenic signatures of directional and balancing selection on alternative migratory life histories
Bibliographic record
Abstract
Migration in animals and associated adaptations to contrasting environments are underpinned by complex genetic architecture. Here, we explore the genomic basis of facultative anadromy in brown trout (Salmo trutta), wherein some individuals migrate to sea while others remain resident in natal rivers, to better understand how alternative migratory tactics (AMTs) are maintained evolutionarily. To identify genomic variants associated with AMTs, we sequenced whole genomes for 194 individual trout from five anadromous–resident population pairs, situated above and below waterfalls, in five different Irish rivers. These waterfalls act as natural barriers to upstream migration and hence we predicted that loci underpinning AMTs should be under similar divergent selection across these replicate pairs. A sliding windows based analysis revealed a highly polygenic adaptive divergence between anadromous and resident populations, encompassing 329 differentiated genomic regions. These regions were associated with 292 genes involved in various processes crucial for AMTs, including energy homeostasis, reproduction, osmoregulation, immunity, circadian rhythm and neural function. Furthermore, examining patterns of diversity we were able to link specific genes and biological processes to putative AMT trait classes: migratory-propensity, migratory-lifestyle and residency. Importantly, AMT outlier regions possessed higher genetic diversity than the background genome, particularly in the anadromous group, suggesting balancing selection may play a role in maintaining genetic variation. Overall, the results from this study provide important insights into the genetic architecture of migration and the evolutionary mechanisms shaping genomic diversity within and across populations.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".