Genetic Differentiation is Constrained to Chromosomal Inversions and Putative Centromeres in Locally Adapted Populations With Higher Gene Flow
Bibliographic record
Abstract
The impact of genome structure on adaptation is a growing focus in evolutionary biology, revealing an important role for structural variation and recombination landscapes in shaping genetic diversity across genomes and among populations. This is particularly relevant when local adaptation occurs despite gene flow, where clustering of differentiated loci can maintain locally adapted variants by reducing recombination between them. However, the limited genomic resources for nonmodel species, including reference genomes and recombination maps, have constrained our understanding of these patterns. In this study, we leverage the Atlantic silverside-a nonmodel fish with extensive local adaptation across a steep latitudinal gradient-as an ideal system to explore how genome structure influences adaptation under varying levels of gene flow, using a newly available reference genome and multiple recombination maps. Analyzing 168 genomes from four populations, we found a continuum of genome-wide differentiation increasing from south to north, reflecting higher connectivity among southern populations and reduced gene flow at northern latitudes. With increasing gene flow, the number and clustering of FST outlier loci also increased, with differentiated loci found exclusively within large haploblocks harboring inversions and smaller peaks overlapping putative centromeric regions. Notably, sequence divergence was only evident in inversions, supporting their role in adaptive divergence with gene flow, whereas centromeric regions appeared differentiated because of low recombination and diversity, with no indication of elevated divergence. Our results support the hypothesis that clustered genomic architectures evolve with high gene flow and enhance our understanding of how inversions and centromeres are linked to different evolutionary processes.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".