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Plant Science Today (PST)

Research Articles

Vol. 13 No. 3 (2026)

Haplotype characterisation of the SMG11/D2 and GW7 genes reveals novel allelic variants for grain size improvement in rice

DOI
https://doi.org/10.14719/pst.13971
Submitted
3 February 2026
Published
17-06-2026 — Updated on 10-08-2026
Versions

Abstract

Rice is a staple food for more than half of the global population. Molecular breeders aim to enhance the grain yield to feed the ever-growing population. Grain size is a key determinant of rice yield and quality, influencing market value and consumer preferences. Grain size is a complex trait that is controlled by multiple genes. This study characterised allelic variation and haplotype diversity of two major grain size genes, SMG11/D2 and GW7, across 280 diverse rice germplasm accessions and evaluated their associations for grain length and width traits. Phenotypic analysis showed significant variation in grain length (5.48–11.1 mm) and grain width (1.80–3.72 mm), with both traits showing normal distributions. Haplotype diversity analysis identified four distinct SMG11/D2 haplotypes GGTAA(H1), GGTAG(H2), AGCGA(H3) and GTTAA(H4) with frequencies of 55.1 %, 21.3 %, 16.2 % and 7.0 %, respectively. In GW7, 2 haplotype groups, TG (H1) and CA (H2), with frequencies of 91.6 % and 8.2 %, respectively. Haplotype-phenotype association analysis revealed significant correlations among the haplotype groups. The one-way ANOVA showed haplotype AGCGA (H3) of SMG11/D2 had a significantly shorter grain length (7.87 mm) than GGTAA (H1) (8.64 mm), a difference of 0.77 mm. The CA(H2) haplotype of GW7 showed significantly increased grain width (3.13 mm) compared with TG(H1) (2.87 mm). These findings demonstrate that the superior haplotypes at the SMG11/D2 and GW7 loci have a significant impact on grain size. Identifying superior haplotype combinations provides valuable genetic resources for haplotype-based breeding strategies to develop rice varieties with desired grain-quality characteristics through marker-assisted selection and genome editing.

References

  1. 1. Birla DS, Malik K, Sainger M, Chaudhary D, Jaiwal R, Jaiwal PK. Progress and challenges in improving the nutritional quality of rice (Oryza sativa L.). Crit Rev Food Sci Nutr. 2017;57(11):2455–81. https://doi.org/10.1080/10408398.2015.1084992
  2. 2. FAO. Food and agriculture organization of the United Nations. Rome: FAO; 2021. http://faostat.fao.org
  3. 3. Chamara RM, Senevirathne SM, Samarasinghe SA, Premasiri MW, Sandaruwani KH, Dissanayake DM, et al. Role of artificial intelligence in achieving global food security: a promising technology for future. Sri Lanka J Food Agric. 2020;6(2). https://doi.org/10.4038/sljfa.v6i2.88
  4. 4. Calingacion M, Laborte A, Nelson A, Resurreccion A, Concepcion JC, Daygon VD, et al. Diversity of global rice markets and the science required for consumer-targeted rice breeding. PLoS One. 2014;9(1):e85106. https://doi.org/10.1371/journal.pone.0085106
  5. 5. Fitzgerald MA, McCouch SR, Hall RD. Not just a grain of rice: The quest for quality. Trends Plant Sci. 2009;14(3):133–39. https://doi.org/10.1016/j.tplants.2008.12.004
  6. 6. Rosegrant MW, Cline SA. Global food security: Challenges and policies. Science. 2003;302(5652):1917–19. https://doi.org/10.1126/science.1092958
  7. 7. Li N, Xu R, Li Y. Molecular networks of seed size control in plants. Annu Rev Plant Biol. 2019;70:435–63. https://doi.org/10.1146/annurev-arplant-050718-095851
  8. 8. Wang S, Li S, Liu Q, Wu K, Zhang J, Wang S, et al. The OsSPL16-GW7 regulatory module determines grain shape and simultaneously improves rice yield and grain quality. Nat Genet. 2015;47(8):949–54. https://doi.org/10.1038/ng.3352
  9. 9. Hong Z, Ueguchi-Tanaka M, Umemura K, Uozu S, Fujioka S, Takatsuto S, et al. A rice brassinosteroid-deficient mutant, ebisu dwarf (d2), is caused by a loss of function of a new member of cytochrome P450. Plant Cell. 2003;15(12):2900–10. https://doi.org/10.1105/tpc.014712
  10. 10. Fang N, Xu R, Huang L, Zhang B, Duan P, Li N, et al. SMALL GRAIN 11 controls grain size, grain number and grain yield in rice. Rice. 2016;9(1):64. https://doi.org/10.1186/s12284-016-0136-z
  11. 11. Purugganan MD. Evolutionary insights into the nature of plant domestication. Curr Biol. 2019;29(14):R705–14. https://doi.org/10.1016/j.cub.2019.05.053
  12. 12. Tian F, Li DJ, Fu Q, Zhu ZF, Fu YC, Wang XK, et al. Construction of introgression lines carrying wild rice (Oryza rufipogon Griff.) segments in cultivated rice (Oryza sativa L.) background and characterization of introgressed segments associated with yield-related traits. Theor Appl Genet. 2006;112(3):570–80. https://doi.org/10.1007/s00122-005-0165-2
  13. 13. Varshney RK, Shi C, Thudi M, Mariac C, Wallace J, Qi P, et al. Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments. Nat Biotechnol. 2017;35(10):969–76. https://doi.org/10.1038/nbt.3943
  14. 14. Huang X, Yang S, Gong J, Zhao Q, Feng Q, Zhan Q, et al. Genomic architecture of heterosis for yield traits in rice. Nature. 2016;537(7622):629–33. https://doi.org/10.1038/nature19760
  15. 15. Tong H, Chu C. Functional specificities of brassinosteroid and potential utilization for crop improvement. Trends Plant Sci. 2018;23(12):1016–28. https://doi.org/10.1016/j.tplants.2018.08.007
  16. 16. Xiong M, Feng G, Gao Q, Zhang C, Li Q, Li Y, et al. Brassinosteroid regulation in rice seed biology. Seed Biol. 2022;1:2. https://doi.org/10.48130/SeedBio-2022-0002
  17. 17. Li H, Jiang L, Youn JH, Sun W, Cheng Z, Jin T, et al. A comprehensive genetic study reveals a crucial role of CYP90D2/D2 in regulating plant architecture in rice (Oryza sativa). New Phytol. 2013;200(4):1076–88. https://doi.org/10.1111/nph.12427
  18. 18. Wang Y, Xiong G, Hu J, Jiang L, Yu H, Xu J, et al. Copy number variation at the GL7 locus contributes to grain size diversity in rice. Nat Genet. 2015;47(8):944–48. https://doi.org/10.1038/ng.3346
  19. 19. Zhou Y, Miao J, Gu H, Peng X, Leburu M, Yuan F, et al. Natural variations in SLG7 regulate grain shape in rice. Genetics. 2015;201(4):1591–99. https://doi.org/10.1534/genetics.115.181115
  20. 20. Fan C, Xing Y, Mao H, Lu T, Han B, Xu C, et al. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet. 2006;112(6):1164–71. https://doi.org/10.1007/s00122-006-0218-1
  21. 21. Li Y, Liu X, Li Y, Chen Z, Zhao Y, Li X, et al. Genome-wide association study reveals the advantaged genes regulating japonica rice grain shape traits in northern China. PeerJ. 2024;12:e18746. https://doi.org/10.7717/peerj.18746
  22. 22. Wang J, He Y, Yuan Y, Dong J, Yang W, Zhang S, et al. Candidate gene analysis of rice grain shape based on genome-wide association study. Theor Appl Genet. 2024;137:244. https://doi.org/10.1007/s00122-024-04724-8
  23. 23. Abbai R, Singh VK, Nachimuthu VV, Sinha P, Selvaraj R, Vipparla AK, et al. Haplotype analysis of key genes governing grain yield and quality traits across 3K RG panel reveals scope for the development of tailor-made rice with enhanced genetic gains. Plant Biotechnol J. 2019;17(8):1612–22. https://doi.org/10.1111/pbi.13087
  24. 24. Xia D, Xu P, Zheng S, Xue L, Yin Y, Yao Z, et al. Haplotype analysis of the GL7/GW7/SLG7 gene and its application in improving the grain quality of three-line hybrid rice. Rice. 2025;18(1):38. https://doi.org/10.1186/s12284-025-00786-4
  25. 25. Li N, Li Y. Signaling pathways of seed size control in plants. Curr Opin Plant Biol. 2016;33:23–32. https://doi.org/10.1016/j.pbi.2016.05.008
  26. 26. Zou T, Zhang K, Zhang J, Liu S, Liang J, Liu J, et al. DWARF AND LOW-TILLERING 2 functions in brassinosteroid signaling and controls plant architecture and grain size in rice. Plant J. 2023;116(6):1766–83. https://doi.org/10.1111/tpj.16464
  27. 27. Miura K, Matsuoka M. Improving rice quality: the use of a natural allele for GW7. Nat Genet. 2015;47(8):858–59.
  28. 28. Zuo J, Li J. Molecular genetic dissection of quantitative trait loci regulating rice grain size. Annu Rev Genet. 2014;48:99–118. https://doi.org/10.1146/annurev-genet-120213-092138
  29. 29. Bevan MW, Uauy C, Wulff BBH, Zhou J, Krasileva K, Clark MD. Genomic innovation for crop improvement. Nature. 2017;543(7645):346–54. https://doi.org/10.1038/nature22011
  30. 30. Kumar A, Sandhu N, Dixit S, Yadav S, Swamy BPM, Shamsudin NAA. Marker-assisted selection strategy to pyramid two or more QTLs for quantitative trait—grain yield under drought. Rice. 2018;11(1):35. https://doi.org/10.1186/s12284-018-0227-0
  31. 31. Yang T, Gu H, Yang W, Liu B, Liang S, Zhao J. Artificially selected grain shape gene combinations in Guangdong simiao varieties of rice (Oryza sativa L.). Rice. 2023;16(1):3. https://doi.org/10.1186/s12284-023-00620-9
  32. 32. Yang X, Pan Y, Xia X, Qing D, Chen W, Nong B, et al. Molecular basis of genetic improvement for key rice quality traits in southern China. Genomics. 2023;115(6):110745. https://doi.org/10.1016/j.ygeno.2023.110745
  33. 33. Singh N, Choudhury DR, Tiwari G, Singh AK, Kumar S, Srinivasan K, et al. Marker-assisted breeding accelerates the development of multiple-stress-tolerant rice genotypes adapted to wider environments. Front Plant Sci. 2024;15:1402368. https://doi.org/10.3389/fpls.2024.1402368
  34. 34. Song XJ, Huang W, Shi M, Zhu MZ, Lin HX. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet. 2007;39(5):623–30. https://doi.org/10.1038/ng2014
  35. 35. Selvaraj R, Singh VK, Nachimuthu VV, Abbai R, Vipparla AK, Sinha P, et al. Superior haplotypes towards development of low glycemic index rice with preferred grain and cooking quality. Sci Rep. 2021;11:10082. https://doi.org/10.1038/s41598-021-87964-8
  36. 36. Ishimaru K, Hirotsu N, Madoka Y, Murakami N, Hara N, Onodera H, et al. Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet. 2013;45(6):707–11. https://doi.org/10.1038/ng.2612

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