Genomics of adaptive divergence with chromosome‐scale heterogeneity in crossover rate
Bibliographic record
Abstract
Genetic differentiation between divergent populations is often greater in chromosome centres than peripheries. Commonly overlooked, this broadscale differentiation pattern is sometimes ascribed to heterogeneity in crossover rate and hence linked selection within chromosomes, but the underlying mechanisms remain incompletely understood. A literature survey across 46 organisms reveals that most eukaryotes indeed exhibit a reduced crossover rate in chromosome centres relative to the peripheries. Using simulations of populations diverging into ecologically different habitats through sorting of standing genetic variation, we demonstrate that such chromosome-scale heterogeneity in crossover rate, combined with polygenic divergent selection, causes stronger hitchhiking and especially barriers to gene flow across chromosome centres. Without requiring selection on new mutations, this rapidly leads to elevated population differentiation in the low-crossover centres relative to the high-crossover peripheries of chromosomes ("Chromosome Centre-Biased Differentiation", CCBD). Using simulated and empirical data, we then show that strong CCBD between populations can provide evidence of polygenic adaptive divergence with a phase of gene flow. We further demonstrate that chromosome-scale heterogeneity in crossover rate impacts analyses beyond that of population differentiation, including the inference of phylogenies and parallel adaptive evolution among populations, the detection of genetic loci under selection, and the interpretation of the strength of selection on genomic regions. Overall, our results call for a greater appreciation of chromosome-scale heterogeneity in crossover rate in evolutionary genomics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".