Genome-wide association study of pre-harvest sprouting resistance and grain color in common wheat (Triticum aestivum L.)
Bibliographic record
Abstract
BACKGROUND: Pre-harvest sprouting (PHS) is a serious problem in wheat production globally. Grain color (GC) has a notable impact on PHS resistance, red grains typically show higher resistance compared to white grains. To understand the genetic factors influencing PHS and GC, a genome-wide association study (GWAS) was conducted on a natural population of 235 wheat cultivars using a 90 K single nucleotide polymorphism (SNP) arrays. RESULTS: A strong correlation between PHS and GC was observed, with the highest correlation coefficient of 0.85 (P < 0.0001). Association mapping was performed using four different models (BLINK, FarmCPU, MMLM and MLM) in the GAPIT along with MLM model in the Tassel. The study identified twelve stable quantitative trait loci (QTLs) related to PHS resistance and another twelve stable QTLs associated with GC. Notably, six QTLs for PHS resistance were newly discovered, explaining 5.8-10.0% of the phenotypic variation. Additionally, four common QTLs were identified that are linked to both PHS resistance and GC. Among these, Qphs.hbaas-3B.2/Qgc.hbaas-3B.2 and Qphs.hbaas-3D/Qgc.hbaas-3D were recognized as major loci significantly affecting both traits, likely associated with the genes Tamyb-B1 and Tamyb-D1, respectively. The other two new QTLs on chromosome 2B explained 7.0-10.0% of phenotypic variation in PHS resistance and 4.7-7.4% of phenotypic variation in GC. Furthermore, six candidate genes associated with PHS resistance were predicted, warranting further investigation. Three KASP markers IACX5850, Tdurum_contig11028_236 and wsnp_Ex_c269_518324 linked to three QTLs (Qphs.hbaas-2B.2, Qphs.hbaas-2B.4, and Qphs.hbaas-7B.2) are applicable for marker-assisted selection in wheat breeding to enhance PHS resistance. CONCLUSIONS: This study provides valuable genetic loci and KASP markers that can enhance PHS resistance in wheat breeding programs and offers insights for discovering PHS resistance genes.
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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".