Correlations between soybean seed quality traits using a genome-wide association study panel grown in Canadian and Ukrainian mega-environments
Bibliographic record
Abstract
Improvement of soybean [ Glycine max (L.) Merr.] seed quality traits in addition to agronomic traits requires a detailed understanding of correlations between these traits. The objective of this study was to determine the correlations between seed compositions in soybeans grown in Canadian and Ukrainian mega-environments (MEs). The correlations between seed quality traits and agronomic traits were also studied. A genome-wide association study panel consisting of 184 soybean accessions was used for the study. The panel was grown in three Ontario field locations and two Ukrainian locations for 2 years, from 2018 to 2019. A total of 18 traits were measured and analyzed. The Pearson's correlation coefficients ( r) were calculated, and the genotype-by-trait biplots were generated to analyze the linear correlations between the traits. The well-documented negative correlations between protein and oil, as well as oil and the amino acids Lys, Cys, Met, and Thr, were confirmed. In addition, a positive correlation was observed between stearic acid and palmitic acid, while linolenic acid and oleic acid concentrations were negatively correlated. Sucrose was positively correlated with linolenic acid and raffinose and negatively with protein and the four amino acids. Most of the agronomic traits had positive correlations with each other, while there was no strong linear association detected between agronomic traits and the seed quality traits in either ME. The results of this study suggest that improvement of yield and other agronomic traits through breeding may be possible in both Canada and Ukraine without affecting the important seed quality traits.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".