Effects of Genetics and Environment on the Metabolome of Commercial Maize Hybrids: A Multisite Study
Bibliographic record
Abstract
This study was designed to elucidate the biological variation in expression of many metabolites due to environment, genotype, or both, and to investigate the potential utility of metabolomics to supplement compositional analysis for substantial equivalence assessments of genetically modified (GM) crops. A total of 654 grain and 695 forage samples from 50 genetically diverse non-GM DuPont Pioneer maize hybrids grown at six locations in the U.S. and Canada were analyzed by coupled gas chromatography time-of-flight-mass spectrometry (GC/TOF-MS). A total of 156 and 185 metabolites were measured in grain and forage samples, respectively. Univariate and multivariate statistical analyses were employed extensively to compare and correlate the metabolite profiles. We show that the environment had far more impact on the forage metabolome compared to the grain metabolome, and the environment affected up to 50% of the metabolites compared to less than 2% by the genetic background. The findings from this study demonstrate that the combination of GC/TOF-MS metabolomics and comprehensive multivariate statistical analysis is a powerful approach to identify the sources of natural variation contributed by the environment and genotype.
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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 | 0.000 | 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 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".