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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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