Genome-Wide Association Study of Brown Rice Weight Identifies an RNA-Binding Protein Antagonistically Regulating Grain Weight and Panicle Number
Rice grain yield is primarily determined by three key agronomic traits: panicle number, grain number per panicle, and grain weight (GW). However, the inherent tradeoffs among these yield components remain a persistent challenge in rice breeding programs. Notably, compared with GW, brown rice weight...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Rice Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1672630825000733 |
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| Summary: | Rice grain yield is primarily determined by three key agronomic traits: panicle number, grain number per panicle, and grain weight (GW). However, the inherent tradeoffs among these yield components remain a persistent challenge in rice breeding programs. Notably, compared with GW, brown rice weight (BRW) provides a more direct metric associated with actual grain yield potential. In this study, we conducted a two-year replicated genome-wide association study to elucidate the genetic architecture of BRW and identify new loci regulating GW. Among seven consistently detected loci across experimental replicates, four were not co-localized with previously reported genes associated with BRW or GW traits. BRW1.1, one of the four newly identified loci, was found to encode a novel RNA-binding protein. Functional characterization revealed that BRW1.1 acts as a negative regulator of BRW, potentially through modulating mRNA translation processes. Intriguingly, through integrated analysis of mutant phenotypes and haplotype variations, we demonstrated that BRW1.1 mediates the physiological tradeoff between GW and panicle number. This study not only delineates the genetic determinants of BRW but also identifies BRW1.1 as a promising molecular target for breaking the yield component tradeoff in precision rice breeding. |
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| ISSN: | 1672-6308 |