Putative Quantitative Trait Loci Associated with Calcium Content in Soybean Seed
Bibliographic record
Abstract
Seed calcium content is an important quality attribute of specialty soybean [Glycine max (L.) Merr.] for soyfoods. However, analyzing seed for calcium content is time consuming and labor intensive. Knowing quantitative trait loci (QTL) for seed calcium will facilitate the development of elite cultivars with proper calcium content through marker-assisted selection (MAS). The objective of this study was to identify major QTL associated with calcium content in soybean seed. Calcium content was tested in 178 F(2:3) and 157 F(2:4) lines derived from the cross of SS-516 (low calcium) x Camp (high calcium). The F(2:3) lines were genotyped with 148 simple sequence repeat markers in a previous study on seed hardness, and the genotypic data were used in the QTL analysis of the current study. Four QTL designated as Ca1, Ca2, Ca3, and Ca4 on linkage groups (LGs) A2, I, and M were identified by both single-marker analysis and composite-interval mapping, and the QTL accounted for 10.7%, 16.3%, 14.9%, and 9.7% of calcium content variation, respectively. In addition, multiple-interval mapping analysis revealed a significant dominant-by-dominant interaction effect between Ca1 and Ca3, which accounted for 4.3% calcium content variation. These QTL will facilitate the implementation of MAS for calcium content in soybean-breeding programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".