
Genome-Wide Association Study of Brown Rice Weight Identifies an RNA-Binding Protein Antagonistically Regulating Grain Weight and Panicle Number
#These authors contributed equally to this work
Received date: 2025-02-11
Accepted date: 2025-06-10
Online published: 2025-08-06
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.
Zhou Lin, Jiang Hong, Huang Long, Li Ziang, Yao Zhonghao, Li Linhan, Ji Kangwei, Li Yijie, Tang Haijuan, Cheng Jinping, Bao Yongmei, Huang Ji, Zhang Hongsheng, Chen Sunlu . Genome-Wide Association Study of Brown Rice Weight Identifies an RNA-Binding Protein Antagonistically Regulating Grain Weight and Panicle Number[J]. Rice Science, 2025 , 32(4) : 525 -536 . DOI: 10.1016/j.rsci.2025.06.002
| [1] | Alexander D H, Novembre J, Lange K. 2009. Fast model-based estimation of ancestry in unrelated individuals. Genome Res, 19(9): 1655-1664. |
| [2] | Bai X F, Huang Y, Hu Y, et al. 2017. Duplication of an upstream silencer of FZP increases grain yield in rice. Nat Plants, 3(11): 885-893. |
| [3] | Bianchini G, Sánchez-Baracaldo P. 2024. TreeViewer: Flexible, modular software to visualise and manipulate phylogenetic trees. Ecol Evol, 14(2): e10873. |
| [4] | Browning K S, Bailey-Serres J. 2015. Mechanism of cytoplasmic mRNA translation. Arabidopsis Book, 13: e0176. |
| [5] | Chang C C, Chow C C, Tellier L C, et al. 2015. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience, 4: 7. |
| [6] | Chen R J, Xiao N, Lu Y, et al. 2023. A de novo evolved gene contributes to rice grain shape difference between indica and japonica. Nat Commun, 14(1): 5906. |
| [7] | Chiou W Y, Kawamoto T, Himi E, et al. 2019. LARGE GRAIN encodes a putative RNA-binding protein that regulates spikelet hull length in rice. Plant Cell Physiol, 60(3): 503-515. |
| [8] | Cho H, Cho H S, Hwang I. 2019. Emerging roles of RNA-binding proteins in plant development. Curr Opin Plant Biol, 51: 51-57. |
| [9] | Cooper M, van Eeuwijk F A, Hammer G L, et al. 2009. Modeling QTL for complex traits: Detection and context for plant breeding. Curr Opin Plant Biol, 12(2): 231-240. |
| [10] | Corley M, Burns M C, Yeo G W. 2020. How RNA-binding proteins interact with RNA: Molecules and mechanisms. Mol Cell, 78(1): 9-29. |
| [11] | Davalos V, Blanco S, Esteller M. 2018. SnapShot: Messenger RNA modifications. Cell, 174(2): 498. |
| [12] | Dong S S, He W M, Ji J J, et al. 2021. LDBlockShow: A fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief Bioinform, 22(4): bbaa227. |
| [13] | Du Z X, Huang Z, Li J B, et al. 2021. qTGW12a, a naturally varying QTL, regulates grain weight in rice. Theor Appl Genet, 134(9): 2767-2776. |
| [14] | Duan P G, Xu J S, Zeng D L, et al. 2017. Natural variation in the promoter of GSE5 contributes to grain size diversity in rice. Mol Plant, 10(5): 685-694. |
| [15] | Gao C X. 2021. Genome engineering for crop improvement and future agriculture. Cell, 184(6): 1621-1635. |
| [16] | Gilbert C, Svejstrup J Q. 2006. RNA immunoprecipitation for determining RNA-protein associations in vivo. Curr Protoc Mol Biol, 75(1): 27.4.1-27.4.11. |
| [17] | Guo H M, Cui Y C, Huang L J, et al. 2022. The RNA binding protein OsLa influences grain and anther development in rice. Plant J, 110(5): 1397-1414. |
| [18] | Guo T, Chen K, Dong N Q, et al. 2018. GRAIN SIZE AND NUMBER1 negatively regulates the OsMKKK10-OsMKK4-OsMPK6 cascade to coordinate the trade-off between grain number per panicle and grain size in rice. Plant Cell, 30(4): 871-888. |
| [19] | Hu Z J, Lu S J, Wang M J, et al. 2018. A novel QTL qTGW3 encodes the GSK3/SHAGGY-like kinase OsGSK5/OsSK41 that interacts with OsARF4 to negatively regulate grain size and weight in rice. Mol Plant, 11(5): 736-749. |
| [20] | Huang X H, Han B. 2014. Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol, 65: 531-551. |
| [21] | Ishimaru K, Hirotsu N, Madoka Y, et al. 2013. Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet, 45(6): 707-711. |
| [22] | Jiao Y Q, Wang Y H, Xue D W, et al. 2010. Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet, 42(6): 541-544. |
| [23] | Khan N, Zhang Y F, Wang J Y, et al. 2022. TaGSNE, a WRKY transcription factor, overcomes the trade-off between grain size and grain number in common wheat and is associated with root development. J Exp Bot, 73(19): 6678-6696. |
| [24] | Kumar S, Stecher G, Tamura K. 2016. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol, 33(7): 1870-1874. |
| [25] | Lee K, Kang H. 2016. Emerging roles of RNA-binding proteins in plant growth, development, and stress responses. Mol Cells, 39(3): 179-185. |
| [26] | Li J Y, Li H Y, Chen J L, et al. 2020. Toward precision genome editing in crop plants. Mol Plant, 13(6): 811-813. |
| [27] | Li L F, Li J J, Liu K K, et al. 2024. DGW1, encoding an hnRNP-like RNA binding protein, positively regulates grain size and weight by interacting with GW6 mRNA. Plant Biotechnol J, 22(2): 512-526. |
| [28] | Li W J, Yan J J, Zhang Y, et al. 2023. Serine protease NAL1 exerts pleiotropic functions through degradation of TOPLESS-related corepressor in rice. Nat Plants, 9(7): 1130-1142. |
| [29] | Liu H J, Yan J B. 2019. Crop genome-wide association study: A harvest of biological relevance. Plant J, 97(1): 8-18. |
| [30] | Liu J F, Chen J, Zheng X M, et al. 2017. GW5 acts in the brassinosteroid signalling pathway to regulate grain width and weight in rice. Nat Plants, 3: 17043. |
| [31] | Liu J X, Wu M W, Liu C M. 2022. Cereal endosperms: Development and storage product accumulation. Annu Rev Plant Biol, 73: 255-291. |
| [32] | Lorković Z J. 2009. Role of plant RNA-binding proteins in development, stress response and genome organization. Trends Plant Sci, 14(4): 229-236. |
| [33] | Lyu J, Wang D K, Duan P G, et al. 2020. Control of grain size and weight by the GSK2-LARGE1/OML4 pathway in rice. Plant Cell, 32(6): 1905-1918. |
| [34] | Mackay T F C, Stone E A, Ayroles J F. 2009. The genetics of quantitative traits: Challenges and prospects. Nat Rev Genet, 10(8): 565-577. |
| [35] | Mason J M, Arndt K M. 2004. Coiled coil domains: Stability, specificity, and biological implications. ChemBioChem, 5(2): 170-176. |
| [36] | Minkenberg B, Zhang J W, Xie K B, et al. 2019. CRISPR-PLANT v2: An online resource for highly specific guide RNA spacers based on improved off-target analysis. Plant Biotechnol J, 17(1): 5-8. |
| [37] | Miura K, Ikeda M, Matsubara A, et al. 2010. OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet, 42(6): 545-549. |
| [38] | Ouyang X, Zhong X Y, Chang S Q, et al. 2022. Partially functional NARROW LEAF1 balances leaf photosynthesis and plant architecture for greater rice yield. Plant Physiol, 189(2): 772-789. |
| [39] | Price M N, Dehal P S, Arkin A P. 2009. FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol, 26(7): 1641-1650. |
| [40] | Raudvere U, Kolberg L, Kuzmin I, et al. 2019. g: Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res, 47: W191-W198. |
| [41] | Ren D, Liu H, Sun X J, et al. 2024. Post-transcriptional regulation of grain weight and shape by the RBP-A-J-K complex in rice. J Integr Plant Biol, 66(1): 66-85. |
| [42] | Ren D Y, Rao Y C, Huang L C, et al. 2016. Fine mapping identifies a new QTL for brown rice rate in rice (Oryza sativa L.). Rice, 9(1): 4. |
| [43] | Robinson M R, Wray N R, Visscher P M. 2014. Explaining additional genetic variation in complex traits. Trends Genet, 30(4): 124-132. |
| [44] | Ruan B P, Shang L G, Zhang B, et al. 2020. Natural variation in the promoter of TGW2 determines grain width and weight in rice. New Phytol, 227(2): 629-640. |
| [45] | Salvi S, Tuberosa R. 2015. The crop QTLome comes of age. Curr Opin Biotechnol, 32: 179-185. |
| [46] | Sato Y, Namiki N, Takehisa H, et al. 2013a. RiceFREND: A platform for retrieving coexpressed gene networks in rice. Nucleic Acids Res, 41: D1214-D1221. |
| [47] | Sato Y, Takehisa H, Kamatsuki K, et al. 2013b. RiceXPro Version 3.0: Expanding the informatics resource for rice transcriptome. Nucleic Acids Res, 41: D1206-D1213. |
| [48] | Shi Y G. 2017. Mechanistic insights into precursor messenger RNA splicing by the spliceosome. Nat Rev Mol Cell Biol, 18(11): 655-670. |
| [49] | Si L Z, Chen J Y, Huang X H, et al. 2016. OsSPL13 controls grain size in cultivated rice. Nat Genet, 48(4): 447-456. |
| [50] | Song X G, Meng X B, Guo H Y, et al. 2022. Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat Biotechnol, 40(9): 1403-1411. |
| [51] | Song X J, Kuroha T, Ayano M, et al. 2015. Rare allele of a previously unidentified histone H4 acetyltransferase enhances grain weight, yield, and plant biomass in rice. Proc Natl Acad Sci USA, 112(1): 76-81. |
| [52] | Tilman D, Balzer C, Hill J, et al. 2011. Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci USA, 108: 20260-20264. |
| [53] | Ule J, Jensen K B, Ruggiu M, et al. 2003. CLIP identifies Nova-regulated RNA networks in the brain. Science, 302: 1212-1215. |
| [54] | Wang C C, Yu H, Huang J, et al. 2020. Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0. Plant Biotechnol J, 18(1): 14-16. |
| [55] | Wang J W, Qi M F, Liu J, et al. 2015. CARMO: A comprehensive annotation platform for functional exploration of rice multi-omics data. Plant J, 83(2): 359-374. |
| [56] | Wang J Y, Chitsaz F, Derbyshire M K, et al. 2023. The conserved domain database in 2023. Nucleic Acids Res, 51: D384-D388. |
| [57] | Wang S K, Wu K, Yuan Q B, et al. 2012. Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet, 44(8): 950-954. |
| [58] | Wang W S, Mauleon R, Hu Z Q, et al. 2018. Genomic variation in 3, 010 diverse accessions of Asian cultivated rice. Nature, 557: 43-49. |
| [59] | Wei S B, Li X, Lu Z F, et al. 2022. A transcriptional regulator that boosts grain yields and shortens the growth duration of rice. Science, 377: eabi8455. |
| [60] | Wei X, Qiu J, Yong K C, et al. 2021. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat Genet, 53(2): 243-253. |
| [61] | Wei X, Chen M J, Zhang Q, et al. 2024. Genomic investigation of 18, 421 lines reveals the genetic architecture of rice. Science, 385: eadm8762. |
| [62] | Wu Y, Wang Y, Mi X F, et al. 2016. The QTL GNP1 encodes GA20ox1, which increases grain number and yield by increasing cytokinin activity in rice panicle meristems. PLoS Genet, 12(10): e1006386. |
| [63] | Würschum T. 2012. Mapping QTL for agronomic traits in breeding populations. Theor Appl Genet, 125(2): 201-210. |
| [64] | Xie Q, Sparkes D L. 2021. Dissecting the trade-off of grain number and size in wheat. Planta, 254(1): 3. |
| [65] | Xing H L, Dong L, Wang Z P, et al. 2014. A CRISPR/Cas9 toolkit for multiplex genome editing in plants. BMC Plant Biol, 14: 327. |
| [66] | Yan L, Jiao B Y, Duan P G, et al. 2024. Control of grain size and weight by the RNA-binding protein EOG1 in rice and wheat. Cell Rep, 43(11): 114856. |
| [67] | Yano K, Morinaka Y, Wang F M, et al. 2019. GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture. Proc Natl Acad Sci USA, 116: 21262-21267. |
| [68] | Ying J Z, Ma M, Bai C, et al. 2018. TGW3, a major QTL that negatively modulates grain length and weight in rice. Mol Plant, 11(5): 750-753. |
| [69] | Yu J P, Xiong H Y, Zhu X Y, et al. 2017. OsLG3 contributing to rice grain length and yield was mined by Ho-LAMap. BMC Biol, 15(1): 28. |
| [70] | Yue Z C, Wang Z P, Yao Y L, et al. 2024. Variation in WIDTH OF LEAF AND GRAIN contributes to grain and leaf size by controlling LARGE2 stability in rice. Plant Cell, 36(9): 3201-3218. |
| [71] | Zargar S M, Raatz B, Sonah H, et al. 2015. Recent advances in molecular marker techniques: Insight into QTL mapping, GWAS and genomic selection in plants. J Crop Sci Biotechnol, 18(5): 293-308. |
| [72] | Zhai L Y, Wang F, Yan A, et al. 2020. Pleiotropic effect of GNP1 underlying grain number per panicle on sink, source and flow in rice. Front Plant Sci, 11: 933. |
| [73] | Zhang L, Yu H, Ma B, et al. 2017. A natural tandem array alleviates epigenetic repression of IPA1 and leads to superior yielding rice. Nat Commun, 8: 14789. |
| [74] | Zhao H, Li J C, Yang L, et al. 2021. An inferred functional impact map of genetic variants in rice. Mol Plant, 14(9): 1584-1599. |
| [75] | Zhou X, Stephens M. 2014. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods, 11(4): 407-409. |
| [76] | Zhou X Y, Huang X H. 2019. Genome-wide association studies in rice: How to solve the low power problems? Mol Plant, 12(1): 10-12. |
/
| 〈 |
|
〉 |