Signatures of balancing selection are maintained at disease resistance loci following mating system evolution and a population bottleneck in the genus Capsella
Bibliographic record
Abstract
BACKGROUND: Population bottlenecks can lead to a loss of variation at disease resistance loci, which could have important consequences for the ability of populations to adapt to pathogen pressure. Alternatively, current or past balancing selection could maintain high diversity, creating a strong heterogeneity in the retention of polymorphism across the genome of bottlenecked populations. We sequenced part of the LRR region of 9 NBS-LRR disease resistance genes in the outcrossing Capsella grandiflora and the recently derived, bottlenecked selfing species Capsella rubella, and compared levels and patterns of nucleotide diversity and divergence with genome-wide reference loci. RESULTS: In strong contrast with reference loci, average diversity at resistance loci was comparable between C. rubella and C. grandiflora, primarily due to two loci with highly elevated diversity indicative of past or present balancing selection. Average between-species differentiation was also reduced at the set of R-genes compared with reference loci, which is consistent with the maintenance of ancestral polymorphism. CONCLUSIONS: Historical or ongoing balancing selection on plant disease resistance genes is a likely contributor to the retention of ancestral polymorphism in some regions of the bottlenecked Capella rubella genome.
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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 | 0.000 |
| 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".