Inheritance characteristics and potential of genomic prediction for pungency levels in F<sub>1</sub> progeny of chili pepper (<i>Capsicum annuum</i>)
Bibliographic record
Abstract
Pungency levels (capsaicinoid content) are critical traits influencing the quality and commercial value of chili peppers (Capsicum annuum). However, their complex inheritance patterns make controlling them challenging when crossing different progeny in current breeding programs. As a potential solution, we explored genomic prediction (GP) for crossing different progeny based solely on parental data. In this initial study, we assessed the feasibility of GP in 156 F1 accessions derived from 20 parents within 132 inbred C. annuum accessions. Capsaicinoid content (capsaicin, dihydrocapsaicin, and their total) was quantified using high-performance liquid chromatography. Inheritance analysis revealed that nearly half of the F1 accessions exhibited high-parent heterosis (F1 > higher parent), particularly in crosses between lower-pungency parents. We then performed GP for F1 accessions using 3,149 single nucleotide polymorphisms from inbred accessions. Among 11 models tested, GBLUP-GAUSS tended to show high accuracy, with predicted values showing a significant positive correlation (r = 0.770, P < 0.01) with observed capsaicinoid content (μg·gDW–1), although the involvement of heterosis in reducing accuracy was observed. These findings suggest that GP can effectively rank pungency levels among F1 progeny based solely on parental information, providing valuable insights for developing GP-based breeding strategies in chili pepper.
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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 | 0.000 | 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) | 0.000 | 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".