A high-resolution genotype–phenotype map identifies the TaSPL17 controlling grain number and size in wheat
Bibliographic record
Abstract
BACKGROUND: Large-scale genotype-phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments. RESULTS: We perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection. CONCLUSION: Our study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.
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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".