Comparative population genomics reveals the domestication history of the peach, Prunus persica, and human influences on perennial fruit crops
Bibliographic record
Abstract
Abstract Background Recently, many studies utilizing next generation sequencing have investigated plant evolution and domestication in annual crops. Peach, Prunus persica, is a typical perennial fruit crop that has ornamental and edible varieties. Unlike other fruit crops, cultivated peach includes a large number of phenotypes but few polymorphisms. In this study, we explore the genetic basis of domestication in peach and the influence of humans on its evolution. Results We perform large-scale resequencing of 10 wild and 74 cultivated peach varieties, including 9 ornamental, 23 breeding, and 42 landrace lines. We identify 4.6 million SNPs, a large number of which could explain the phenotypic variation in cultivated peach. Population analysis shows a single domestication event, the speciation of P. persica from wild peach. Ornamental and edible peach both belong to P. persica, along with another geographically separated subgroup, Prunus ferganensis. We identify 147 and 262 genes under edible and ornamental selection, respectively. Some of these genes are associated with important biological features. We perform a population heterozygosity analysis in different plants that indicates that free recombination effects could affect domestication history. By applying artificial selection during the domestication of the peach and facilitating its asexual propagation, humans have caused a sharp decline of the heterozygote ratio of SNPs. Conclusions Our analyses enhance our knowledge of the domestication history of perennial fruit crops, and the dataset we generated could be useful for future research on comparative population genomics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".