Testcross vs. randomly paired single‐cross progeny tests for genomic prediction of new inbreds and hybrids derived from multiparent maize populations
Bibliographic record
Abstract
Abstract New maize ( Zea mays L.) inbred lines are commonly evaluated based on the performance of their progeny when crossed to a single tester line from a different heterotic group (testcross), making tester choice critical. An alternative progeny test is pairing new lines between heterotic groups such that every parent is observed in one hybrid combination. This approach requires half the number of hybrids to test but the general combining ability (GCA) of each parent cannot be estimated. Genomic information can be used to partition parental GCA, while potentially enabling the prediction of dominance deviations. We evaluated doubled haploid lines extracted from two six‐parent synthetic populations from the Iodent (IO) and Stiff Stalk (SS) heterotic groups using either single crosses from random pairs of inbreds or testcrosses. Hybrids were evaluated for yield and agronomic traits in 2014 and 2015 in south‐central Wisconsin. The experiment was conducted using a randomized complete block design with each population type grown in two replicates within two environments. Coincidence of selection of the 25 top performing lines based on testcross yield and single‐cross hybrid yield was 48% for the SS and 11% for the IO doubled haploid lines, increasing to 56% for the SS and 56% for the IO with genomic prediction based on an additive genomic model. Partitioning dominance deviations with the model increased coincidence to 62% for the SS population. Genomic estimates of grain yield GCA in the single‐cross hybrids were significantly more correlated than phenotypic GCA to testcross values for the IO lines, but not SS. Natural and orthogonal interaction estimates of dominance variation were insignificant for all traits, indicating that the inability to partition dominance based on testcross performance should not be a major limitation to identification of high‐performing hybrids. Genomic information improves the prospects of using single‐cross hybrids made from random pairs of new inbreds as an efficient progeny test by allowing the recovery of parental GCA information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".