The effects and interaction of soybean maturity gene alleles controlling flowering time, maturity, and adaptation in tropical environments
Bibliographic record
Abstract
BACKGROUND: Soybean is native to the temperate zones of East Asia. Poor yields of soybean in West African countries may be partially attributed to inadequate adaptation of soybean to tropical environments. Adaptation will require knowledge of the effects of allelic combinations of major maturity genes (E1, E2, and E3) and stem architecture. The long juvenile trait (J) influences soybean flowering time in short, ~ 12 h days, which characterize tropical latitudes. Soybean plant architecture includes determinate or indeterminate stem phenotypes controlled by the Dt1 gene. Understanding the influence of these genetic components on plant development and adaptation is key to optimize phenology and improve soybean yield potential in tropical environments. RESULTS: Soybean lines from five recombinant inbred populations were developed that varied in their combinations of targeted genes. The soybean lines were field tested in multiple environments and characterized for days to flowering (DTF), days to maturity (DTM), and plant height in locations throughout northern Ghana, and allelic combinations were determined for each line for associating genotype with phenotype. The results revealed significant differences based on genotype for DTF and DTM and allowed the comparison of different variant alleles of those genes. The mutant alleles of J and E1 had significant impact on DTF and DTM, and alleles of those genes interacted with each other for DTF but not DTM. The Dt1 gene significantly influenced plant height but not DTF or DTM. CONCLUSIONS: This research identified major and minor effect alleles of soybean genes that can be combined to control DTF, DTM, and plant height in short day tropical environments in Ghana. These phenotypes contribute to adaptation to a low latitude environment that can be optimized in a soybean breeding program with targeted selection of desired allele combinations. The knowledge of the genetic control of these traits will enhance molecular breeding to produce optimally adapted soybean varieties targeted to tropical environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".