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Record W3171996942 · doi:10.1002/csc2.20545

Testcross vs. randomly paired single‐cross progeny tests for genomic prediction of new inbreds and hybrids derived from multiparent maize populations

2021· article· en· W3171996942 on OpenAlexaff
Brett Burdo, Natalia de León, Shawn M. Kaeppler

Bibliographic record

VenueCrop Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyHybridDoubled haploidyPopulationGeneticsZea maysInbred strainDominance (genetics)Grain yieldPloidyAgronomyGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.271
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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