Assessing Single‐Trait and Multitrait Genomic Prediction Model Abilities Including Significant GWAS Markers for Fusarium Head Blight Disease Resistance in Wheat (<scp><i>Triticum aestivum</i></scp>)
Bibliographic record
Abstract
ABSTRACT Disease resistance traits are complex and quantitative in nature. Breeders regularly evaluate multiple important traits across diverse environments to employ them in genomics‐assisted breeding. In this study, we evaluated the prospects of genomic prediction models by incorporating genome‐wide association study (GWAS) results into single‐trait and multitrait genomic prediction scenarios, using two distinct panels: the NMBU panel and the GRAMINOR panel. A standard genomic prediction model ( Base ) and the Base model with the addition of significant GWAS markers as fixed covariates (Base + GWAS) were tested on both panels. The predictive ability of models was measured in terms of prediction ability by using Pearson's correlation method. An improvement of 0.05% to as high as a two‐fold improvement was observed in both the panels for single‐trait and multitrait scenarios. In general, multitrait models outperformed single‐trait models regardless of whether the GWAS markers were included. This study further concludes that multitrait‐based genomic predictions are superior to single trait–based ones when the associated traits are used and are well correlated.
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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".