Author response: Long-term balancing selection drives evolution of immunity genes in Capsella
Bibliographic record
Abstract
Capsella rubella is a small plant that is found in southern and western Europe. This plant is young in evolutionary terms: it is thought to have emerged less than 200,000 years ago from a small group of plants belonging to an older species known as Capsella grandiflora. Individuals of the same species may carry alternative versions of the same genes – known as alleles – and the total number of alleles present in a population is referred to as genetic diversity. When a few individuals form a new species, the gene pool and the genetic diversity in the new species is initially much lower than in the ancestral species, which may make the new species less robust to fluctuations in the environment. For example, alternative versions of a gene might be preferable in hot or cold climates, and loss of one of these versions would limit the species’ ability to survive in both climates. A mechanism known as balancing selection can maintain various alleles in a species, even if the population is very small. However, it was not clear how common long-lasting balancing selection was after a species had split. To address this question, Koenig et al. assembled collections of wild C. rubella and C. grandiflora plants and sequenced their genomes in search of alleles that were shared between individuals of the two species. The analysis found not just a few, but thousands of examples where the same genetic differences had been maintained in both C. rubella and C. grandiflora. Some of these allele pairs were also shared with individuals of a third species of Capsella that had split from C. rubella and C. grandiflora over a million years ago. The shared alleles did not occur randomly in the genome; genes involved in immune responses were far more likely to be targets of balancing selection than other types of genes. These findings indicate that there is strong balancing selection to maintain different alleles of immunity genes in wild populations of plants, and that some of this diversity can be maintained over hundreds of thousands, if not millions of years. The strategy developed by Koenig et al. may help to identify new versions of immunity genes from wild relatives of crop plants that could be used to combat crop diseases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.160 | 0.055 |
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".