Whole-genome meta-analysis coupled with haplotype analysis reveal new genes and functional haplotypes conferring pre-harvest sprouting in rice
Abstract
Background: 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.
Result: 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,…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.