Genotype by Environment Interaction of Quantitative Traits: A Case Study in Barley
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Bibliographic record
Abstract
Genotype by environment interaction is a phenomenon that a better genotype in one environment may perform poorly in another environment. When the genotype refers to a quantitative trait locus (QTL), this phenomenon is called QTL by environment interaction, denoted by Q×E. Using a recently developed new Bayesian method and genome-wide marker information, we estimated and tested QTL main effects and Q×E interactions for a well-known barley dataset produced by the North American Barley Genome Mapping Project. This dataset contained seven quantitative traits collected from 145 doubled-haploid (DH) lines evaluated in multiple environments, which derived from a cross between two Canadian two-row barley lines, Harrington and TR306. Numerous main effects and Q×E interaction effects have been detected for all seven quantitative traits. However, main effects seem to be more important than the Q×E interaction effects for all seven traits examined. The number of main effects detected varied from 26 for the maturity trait to 75 for the heading trait, with an average of 61.86. The heading trait has the most detected effects, with a total of 98 (75 main, 29 Q×E). Among the 98 effects, 6 loci had both the main and Q×E effects. Among the total number of detected loci, on average, 78.5% of the loci show the main effects whereas 34.9% of the loci show Q×E interactions. Overall, we detected many loci with either the main or the Q×E effects, and the main effects appear to be more important than the Q×E interaction effects for all the seven traits. This means that most detected loci have a constant effect across environments. Another discovery from this analysis is that Q×E interaction occurs independently, regardless whether the locus has main effects.
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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 it