Genetic overlap of QTL associated with low-temperature tolerance at germination and seedling stage using BILs in soybean
Bibliographic record
Abstract
Zhang, W.-B., Jiang, H.-W., Qiu, P.-C., Liu, C.-Y., Chen, F.-L., Xin, D.-W., Li, C.-D., Hu, G.-H. and Chen, Q.-S. 2012. Genetic overlap of QTL associated with low-temperature tolerance at germination and seedling stage using BILs in soybean. Can. J. Plant Sci. 92: 1381-1388. Low temperature is one of the critical environmental factors that limit agricultural production worldwide. In northeast China soybean frequently suffers low temperature stress, especially at germination stage and seedling stage. The most effective way to solve this problem is to breed cultivars with low-temperature tolerance. A set of advanced backcross introgression lines was constructed with Hongfeng 11 as recurrent parent, which was a local variety in Heilongjiang province, and Harosoy as donor parent, which was introduced from Canada. Their BC2F4 lines were screened in low-temperature condition at the two stages, and 41 transgressive lines were selected out at germination stage and 45 lines at seedling stage. Sixty-four and fifty-one pairs of simple sequence repeat primers with fine polymorphism were used for genotyping the selected population and random population at the two stages, respectively. Related quantitative trait loci (QTL) were obtained by chi-test and ANOVA analysis with genotypic and phenotypic data. Finally, 25 QTL at germination stage and 13 QTL at seedling stage were mapped. Among them, 10 QTL overlapped between two stages, which showed a partial genetic crossover on low-temperature tolerance stages in soybean. This would play an important role in marker-assisted selection for breeding elite variety with low-temperature tolerance at both stages.
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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 | 0.001 | 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".