What can patterns of differentiation across plant genomes tell us about adaptation and speciation?
Bibliographic record
Abstract
Genome scans have become a common approach to identify genomic signatures of natural selection and reproductive isolation, as well as the genomic bases of ecologically relevant phenotypes, based on patterns of polymorphism and differentiation among populations or species. Here, we review the results of studies taking genome scan approaches in plants, consider the patterns of genomic differentiation documented and their possible causes, discuss the results in light of recent models of genomic differentiation during divergent adaptation and speciation, and consider assumptions and caveats in their interpretation. We find that genomic regions of high divergence generally appear quite small in comparisons of both closely and more distantly related populations, and for the most part, these differentiated regions are spread throughout the genome rather than strongly clustered. Thus, the genome scan approach appears well-suited for identifying genomic regions or even candidate genes that underlie adaptive divergence and/or reproductive barriers. We consider other methodologies that may be used in conjunction with genome scan approaches, and suggest further developments that would be valuable. These include broader use of sequence-based markers of known genomic location, greater attention to sampling strategies to make use of parallel environmental or phenotypic transitions, more integration with approaches such as quantitative trait loci mapping and measures of gene flow across the genome, and additional theoretical and simulation work on processes related to divergent adaptation and speciation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".