Integrative Population and Physiological Genomics Reveals Mechanisms of Adaptation in Killifish
Bibliographic record
Abstract
Adaptive divergence between marine and freshwater (FW) environments is important in generating phyletic diversity within fishes, but the genetic basis of this process remains poorly understood. Genome selection scans can identify adaptive loci, but incomplete knowledge of genotype-phenotype connections makes interpreting their significance difficult. In contrast, association mapping (genome-wide association mapping [GWAS], random forest [RF] analyses) links genotype to phenotype, but offer limited insight into the evolutionary forces shaping variation. Here, we combined GWAS, RF, and selection scans to identify loci important in adaptation to FW environments. We utilized FW-native and brackish water (BW)-native populations of Atlantic killifish (Fundulus heteroclitus) as well as a naturally admixed population between the two. We measured morphology and multiple physiological traits that differ between populations and may contribute to osmotic adaptation (salinity tolerance, hypoxia tolerance, metabolic rate, body shape) and used a reduced representation approach for genome-wide genotyping. Our results show patterns of population divergence in physiological capabilities that are consistent with local adaptation. Population genomic scans between BW-native and FW-native populations identified genomic regions evolving by natural selection, whereas association mapping revealed loci that contribute to variation for each trait. There was substantial overlap in the genomic regions putatively under selection and loci associated with phenotypic traits, particularly for salinity tolerance, suggesting that these regions and genes are important for adaptive divergence between BW and FW environments. Together, these data provide insight into the mechanisms that enable diversification of fishes across osmotic boundaries.
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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".