Making the most of population genomic data to understand the importance of chromosomal inversions for adaptation and speciation
Bibliographic record
Abstract
Chromosomal inversions are increasingly found to differentiate locally adapted populations. This adaptive role is predictable because reduced recombination protects allelic combinations from gene flow. However, we are far from understanding how frequently inversions contribute to local adaptation and how widespread this phenomenon is across species. In a "From the Cover" article in this issue of Molecular Ecology, Huang, Andrew, Owens, Ostevik, and Rieseberg (2020) provide an important step towards this goal not only by finding adaptive inversions in a sunflower ecotype, but also by reversing the approach used to investigate the link between adaptation and inversions. Most studies compare two phenotypes and uncover divergence at a few regions, of which some can subsequently be identified as inversions. In contrast, Huang et al first catalogue putative inversions and then test genotype-environment associations, which allows them to ask systematically whether inversions may be adaptive and in which ecological contexts. They achieve that by revisiting a previous reduced-representation sequencing (RAD-sequencing) data set, demonstrating the suitability of this method to detect inversions in species with limited genomic resources. As such, Huang et al pave the way for a better understanding of the evolutionary role of structural genomic variation and highlight that accounting for inversions in population genomics is now possible, and much needed, in a wider range of organisms.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.022 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".