Significance Rice architecture is an important agronomic trait for determining yield; however, the complexity of this trait makes it difficult to elucidate the molecular mechanisms. This study applied a strategy… Click to show full abstract
Significance Rice architecture is an important agronomic trait for determining yield; however, the complexity of this trait makes it difficult to elucidate the molecular mechanisms. This study applied a strategy of using principal components (PCs) as dependent variables for a genome-wide association study (GWAS). SPINDLY was identified to regulate rice architecture by suppressing gibberellin (GA) signaling. Further study using GA-signaling mutants confirmed that levels of GA responsiveness regulate rice architecture, suggesting that the utilization of a favorable SPINDLY allele will improve crop productivity. The strategy presented in this study of performing GWAS using PC scores will provide valuable information for plant genetics and will improve our understanding of complex traits at the molecular level. Elucidation of the genetic control of rice architecture is crucial due to the global demand for high crop yields. Rice architecture is a complex trait affected by plant height, tillering, and panicle morphology. In this study, principal component analysis (PCA) on 8 typical traits related to plant architecture revealed that the first principal component (PC), PC1, provided the most information on traits that determine rice architecture. A genome-wide association study (GWAS) using PC1 as a dependent variable was used to isolate a gene encoding rice, SPINDLY (OsSPY), that activates the gibberellin (GA) signal suppression protein SLR1. The effect of GA signaling on the regulation of rice architecture was confirmed in 9 types of isogenic plant having different levels of GA responsiveness. Further population genetics analysis demonstrated that the functional allele of OsSPY associated with semidwarfism and small panicles was selected in the process of rice breeding. In summary, the use of PCA in GWAS will aid in uncovering genes involved in traits with complex characteristics.
               
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