Nature of the Genetic Variation in an Elite Maize Breeding Cross
Bibliographic record
Abstract
Maize (Zea mays L.) breeders through selection have had profound impacts on the maize genome. In this study we examine one aspect of this intense selection pressure, the extent and nature of genetic variation present in an elite maize breeding cross. Specifically genetic variation is examined with regards to genotype × environment interactions (G × E), magnitude of the genetic variance (Vg) estimates, and the underlying grain yield quantitative trait loci (QTL). Using two elite Iodent sister‐lines that are 64% identical by descent, 128 recombinant inbred lines (RILs) were generated and testcrossed to a Stiff Stalk inbred line (CG102). Hybrid RILs were grown in 24 trials encompassing 4 yr, three locations, and three planting densities. Additive main effects and multiplicative interaction analysis resolved the trials into eight unique patterns of G × E. Smaller Vg estimates were associated with the more frequently observed patterns of G × E. Nine single‐effect QTL and four epistatic interactions were detected across seven of the G × E patterns; however, the single‐effect QTL and epistatic interactions were, in general, specific to a G × E pattern. In summary, we found extensive linkage disequilibrium (LD), reduced Vg in the more commonly occurring G × E patterns, and genetic variation due to larger effect epistatic interactions and smaller single effect QTL specific to the G × E pattern. Consequences of the genetic variation are discussed in relation to modern maize breeding programs.
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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 | 0.001 | 0.000 |
| 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".