New Mutations in a <i>Delta‐9‐Stearoyl‐Acyl Carrier Protein Desaturase</i> Gene Associated with Enhanced Stearic Acid Levels in Soybean Seed
Bibliographic record
Abstract
ABSTRACT Soybean [Glycine max (L.) Merr.] oil from conventional cultivars typically contains ~30 g kg−1 stearic acid of the total seed oil. Increased stearic acid concentration in the seed oil of soybeans is desirable for certain food and industrial uses. To date a small number of mutants have been developed with increased stearic acid levels three to six times that of normal. At least two such lines were found to possess separate mutations in the delta‐9‐stearoyl‐acyl carrier protein desaturase C gene (SACPD‐C) that dramatically increased seed stearic acid concentration. We now report additional independent mutations in this gene that increase seed stearic acid levels of the soybean germplasm line RG7 to ~116 g kg−1. An F5 recombinant inbred line (RIL) population was developed to determine the relationship between the RG7 SACPD‐C mutation and stearic acid concentration. Transgressive segregation in the progeny of the cross between mutant lines RG7 and RG2 (low palmitic acid) further increases seed stearic acid to almost 180 g kg−1, suggesting the presence of yet another gene having a significant effect on stearic acid accumulation. We also discerned an independent, presumably allelic, mutation within the SACPD‐C gene in line RG8, which has about 106 g kg−1 stearic acid. Molecular markers diagnostic for the RG7 and RG8 SACPD‐C mutations were developed, enabling rapid selection for these mutations in the development of cultivars with increased seed stearic acid content in the seed oil.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".