Whole-genome analysis of recombinant inbred rice lines reveals a quantitative trait locus on chromosome 3 with genotype-by-environment interaction effects
Bibliographic record
Abstract
Elucidating genotype-by-environment interactions is fundamental for understanding the interplay between genetic and environmental factors that shape complex traits in crops. Genotype-by-environment interactions are of practical importance, as they determine the performance of cultivars grown in different environments, prompting the need for an efficient approach for evaluating genotype-by-environment interactions. Here, we describe a method for genotype-by-environment detection that involves comparing linear mixed models. This method successfully detected genotype-by-environment interactions in rice (Oryza sativa) recombinant inbred lines grown at 3 locations. We identified a quantitative trait locus (QTL) on chromosome 3 that was associated with heading date, grain number, and leaf length. The effect of this QTL on plant growth-related traits varied with environmental conditions, indicating the presence of genotype-by-environment interactions. Therefore, our method enables a powerful genotype-by-environment detection pipeline that should facilitate the production of high-yielding crops in a given environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".