Whole-genome meta-analysis coupled with haplotype analysis reveal new genes and functional haplotypes conferring pre-harvest sprouting in rice
Bibliographic record
Abstract
Pre-harvest sprouting (PHS), which adversely impacts grain yield and quality, is controlled by seed dormancy genes. However, only a few dormancy-related genes have been characterized, and the effects of allelic variation in genes and the genetic basis of seed dormancy in rice remain largely unknown. Here, we performed a whole-genome meta-quantitative trait loci study to elucidate the genetic basis of seed dormancy in rice. One hundred and sixty-seven QTL were identified for PHS from which 134 were successfully projected onto the reference map yielding 20 consensus regions, meta-QTL (mQTL). The mean confidence interval of the mQTL was narrower (9.56-fold reduction) than that of the initial QTL. Six of the 20 identified mQTL were designated as breeders’ mQTL based on their small confidence intervals, large phenotypic variance explained, and the involvement of high number of QTL. Further, we retrieved 559 high-confidence genes from breeders’ mQTL regions conferring resistance to PHS. Comparative analysis of genes found in breeders’ mQTL loci and an RNA-seq-based transcriptomic dataset discovered 34 common genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed a significant enrichment of the common genes in amino sugar and nucleotide sugar metabolism, carbon metabolism, and carbon fixation in photosynthetic organs. Combined in silico expression profiling and qRT-PCR validation showed that LOC_Os10g18364, LOC_Os10g21940, LOC_Os10g22590, and LOC_Os10g25140 exhibited high fold-change expression in PHS resistant cultivar (23xS-261) than PHS susceptible cultivar (23xS-262). Association analysis of these genes with germination rate index demonstrated that LOC_Os10g18364Hap1, LOC_Os10g21940Hap1, LOC_Os10g22590Hap1, and LOC_Os10g25140Hap1/Hap3 exhibited low germination rate (GR) in cultivars carrying these haplotypes. In summary, this study delineates the genetic basis of PHS and provides a new set of target genes for improving PHS resistance. The natural variants identified in these genes and markers associated with breeders’ mQTL serve as potential resources for incorporating PHS resistance in rice.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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".