QTL Analysis of Low Temperature Induced Browning in Soybean Seed Coats
Bibliographic record
Abstract
Exposure of soybean [Glycine max (L.) Merr.] to chilling temperatures at flowering stage induces browning around the hilum of the seed coats. The brown pigmentation spoils the external appearance of soybean seeds and reduces their commercial value. Our previous studies revealed that pigmentation was controlled by a few major genes, and one of the genes is closely associated with a maturity gene. This study was conducted to further investigate inheritance of pigmentation using DNA markers. Fifty-eight F(2) plants derived from a cross between a tolerant cv. Koganejiro and a sensitive cv. Kitakomachi were exposed to 15 degrees C for 2 weeks beginning 8 days after anthesis. Genotypes of 522 genetic markers were determined using the F(2) plants. Composite interval mapping revealed 5 quantitative trait loci (QTLs) for pigmentation, pig1 to pig5 (pig1 in molecular linkage group A2 [MLG A2], pig2 in MLG B1, pig3 in MLG C2, pig4 in molecular linkage group (MLG), and pig5 in MLG N) and 4 QTLs for flowering date, fd1 to fd4 (fd1 in MLG C1, fd2 in MLG C2, fd3 in MLG J, and fd4 in MLG L). Based on the relative location with markers, fd2 and fd4 probably correspond to E1 and E3, respectively. pig3 and fd2 were found at a similar position, and logarithm of odds (LOD) score plots for pigmentation and flowering date almost overlapped around this region. Considering the fact that pig3 had the most intense effects on pigmentation, E1 is presumed to be the maturity gene that profoundly affects pigmentation. Further, E3 has a small effect on pigmentation in accordance with the previous reports. These results support the idea that soybean maturity genes control low temperature-induced pigmentation with various intensities specific to each maturity gene. QTLs for seed coat pigmentation with small or no impact on maturity identified in this study may be useful in breeding for chilling tolerance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".