Mutation Load in Sunflower Inversions Is Negatively Correlated with Inversion Heterozygosity
Bibliographic record
Abstract
Recombination is critical both for accelerating adaptation and purging deleterious mutations. Chromosomal inversions can act as recombination modifiers that suppress local recombination in heterozygotes and thus, under some conditions, are predicted to accumulate such mutations. In this study, we investigated patterns of recombination, transposable element abundance, and coding sequence evolution across the genomes of 1,445 individuals from three sunflower species, as well as within nine inversions segregating within species. We also analyzed the effects of inversion genotypes on 87 phenotypic traits to test for overdominance. We found significant negative correlations of long terminal repeat retrotransposon abundance and deleterious mutations with recombination rates across the genome in all three species. However, we failed to detect an increase in these features in the inversions, except for a modest increase in the proportion of stop codon mutations in several very large or rare inversions. Consistent with this finding, there was little evidence of overdominance of inversions in phenotypes that may relate to fitness. On the other hand, significantly greater load was observed for inversions in populations polymorphic for a given inversion compared to populations monomorphic for one of the arrangements, suggesting that the local state of inversion polymorphism affects deleterious load. These seemingly contradictory results can be explained by the low frequency of inversion heterozygotes in wild sunflower populations, apparently due to divergent selection and associated geographic structure. Inversions contributing to local adaptation represent ideal recombination modifiers, acting to facilitate adaptive divergence with gene flow, while largely escaping the accumulation of deleterious mutations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".