Genetic Analysis of Agronomic and Quality Traits from Multi-Location white Yam Trials using Mixed Model with Genomic Relationship Matrix
Bibliographic record
Abstract
Traits that define the suitability of a crop for production and consumption are often assessed and predicted to identify superior genotypes for commercial deployment. This study assessed genetic parameter estimates and prediction for 25 agronomic and quality traits in 49 white yam clones. It employed best linear unbiased prediction (BLUP) in a mixed model analysis using genomic relationship matrix derived from 6337 Diversity Array Technology (DArT) molecular markers, multivariate technique of the principal component and canonical discriminant analysis with BLUP predicted values to select key traits for yam breeding. Findings revealed that additive genetic, non-additive genetic and non-genetic factors contributed substantially to phenotypic variation of the studied yam traits. The non-genetic effects accounted for higher variation than the total genetic effects for majority of the traits except yam mosaic virus (YMV), tuber number per plant, ash content, flour yield, peel loss, and protein content. The narrow sense heritability was generally low (<0.30) for all traits except yam anthracnose (0.31), ash content (0.30) and peel loss (0.89). Trait selection with multivariate analysis identified 15 from the 25 traits with fresh tuber yield, tuber dry matter content (DMC), YMV, root-knot and Scutellonema bradys nematode susceptibility as the most important traits for white yam variety testing. This paper presents the importance of complementing BLUP prediction that accounts for the relationship among the genotypes with multivariate analysis for genetic parameter estimation, prediction and selection in yam breeding trials to accelerate the genetic gains.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".