The genetic relationship among plant-height traits found using multiple-trait QTL mapping of a dent corn and popcorn cross
Bibliographic record
Abstract
Plant height (PH) is one of the most important traits in maize breeding programs. In popcorn, inferior plant traits can be improved with the dent/flint corn germplasm. In the current study, a total of 259 F2:3 families, developed from a cross between a dent corn inbred and a popcorn inbred, were evaluated for 4 PH traits. Quantitative trait loci (QTLs) for each trait were detected using composite interval mapping methods. In addition, genetic interrelationships were investigated using multiple-trait joint analysis for PH with ear height (EH), and for PH with top height (TH). In total, 6, 5, 2, and 6 QTLs were identified for PH, EH, TH, and TH/PH in single-trait analysis, respectively. Joint-analysis data suggest a strong and complex genetic relationship between PH and EH, and between PH and EH, with no QTLs controlling any single trait independently. In addition, 4 kinds of QTLs detected were classified as closely linked QTLs, pleiotropic QTLs, QTLs with opposite effects, and additional QTLs. It was, consequently, difficult to improve lodge resistance through selection on any individual PH trait. The current study demonstrates that multiple-trait joint analysis not only identified additional QTLs, but also revealed the genetic relationship among different highly correlated traits at the molecular level.
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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".