Evaluating the potential penalty of sclerotinia stem rot resistance on agronomic and seed quality traits in a Canadian soybean germplasm panel
Bibliographic record
Abstract
Abstract Genome‐wide association studies (GWAS) have become a pivotal tool in identifying marker‐trait associations (MTAs), thus ultimately facilitating the improvement of desirable traits like disease resistance in plants. However, the introduction of new alleles poses challenges due to the potential co‐integration of undesirable traits. This study builds upon the findings of MTAs for sclerotinia stem rot (SSR) resistance in soybeans ( Glycine Willd.) that we reported previously. By employing the same soybean genetic diversity GWAS panel used in the previous study, we performed a set of genomic analyses to examine any potential linkage drag. This was performed through GWAS that aimed to explore the co‐localization of MTAs associated with SSR resistance so as to assess the effects of resistance alleles on both agronomic and seed quality traits. Of the 29 MTAs identified in this study, only seven protein‐related MTAs shared a chromosome with the previously identified SSR resistance MTA. In addition, there seems to be no yield penalty for the partially resistant soybean genotypes. To the contrary, in certain instances, an advantage was associated with carrying SSR resistance alleles concerning the agronomic and seed quality traits. While these findings are promising, they should be considered preliminary and warrant further investigation. We anticipate that these results will provide a solid foundation for studying the potential effects of SSR resistance alleles on other desirable traits in soybean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".