Omics advances and integrative approaches for the simultaneous improvement of seed oil and protein content in soybean (<i>Glycine max</i>L.)
Bibliographic record
Abstract
Genetic improvement of soybean, one of the major crops providing edible oil and protein-rich food, is important to ensure balanced nutrition for the growing world population. To make soybean cultivation more rewarding, an increase in seed oil and protein content is most desirable. Here, a critical review of the efforts employed over a half-century to accomplish the improvement of soybean oil and protein content has been presented. Many studies have used diverse parental lines to map and characterize quantitative trait loci (QTL)/genes regulating these two essential traits. Here, we highlighted such genomic loci that were consistently identified with different mapping approaches, like QTL mapping, genome-wide association studies (GWAS), and meta-QTL analysis. In addition, the information generated through efforts utilizing omics approaches, such as genomics, transcriptomics, and proteomics has also been compiled to anticipate the molecular mechanism. Several innovative approaches like multi-parental mapping, induced mutagenesis, genomic selection, transgenics, and genome-editing have been discussed in terms of effective utilization of technological advances to improve the oil and protein content in soybean. Information provided here will be helpful for better understanding and designing an effective strategy for simultaneous improvement in seed oil and protein content 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".