Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Early Access

Haplotype-phenotype association in key grain size-controlling genes in rice (Oryza sativa L.)

DOI
https://doi.org/10.14719/pst.13842
Submitted
26 January 2026
Published
02-07-2026
Versions

Abstract

Grain size is the key determinant of yield potential and consumer preference in rice, making it a primary target for genetic improvement. However, the allelic architecture underlying the traits needs to be resolved for precision breeding. Here, we conducted a haplotype-based dissection of four major grain size regulating candidate genes (OsGS3.1, OsGW2, OsGW7 and OsGW8) using high-quality single nucleotide polymorphism (SNP) data from 197 indica accessions of the 3K rice genome panel. Neutrality and diversity analyses revealed heterogeneous evolutionary histories. OsGS3.1 exhibited reduced haplotype diversity consistent with directional selection, whereas OsGW7 and OsGW8 showed strong negative Tajimas’ D (< -1.2) and Fus’ Fs (< -2.5), indicating an excess of rare alleles under purifying or expansion-driven selection. The LD block structure varied markedly across loci, suggesting selective retention of favourable alleles in OsGW2. Marker-trait associations identified exonic SNPs within OsGW2 (chr02_8115620) and OsGW7 (chr07_24666398), explaining up to 5.3 % of the phenotypic variance. Significant haplotype effects were observed in OsGS3.1, OsGW2, OsGW7 and OsGW8, with specific allelic combinations jointly modulating grain length (GL) and grain breadth (GB). These results establish a validated haplotype map and causal SNPs suitable for haplotype-based breeding, marker-assisted selection and genomic prediction models targeting grain size ideotype development in rice.

References

  1. 1. Mohidem NA, Hashim N, Shamsudin R, Che Man H. Rice for food security: Revisiting its production, diversity, rice milling process and nutrient content. Agriculture. 2022;12(6):741. https://doi.org/10.3390/agriculture12060741
  2. 2. Lu Y, Chuan M, Wang H, Chen R, Tao T, Zhou Y, et al. Genetic and molecular factors in determining grain number per panicle of rice. Front Plant Sci. 2022;13:964246. https://doi.org/10.3389/fpls.2022.964246
  3. 3. Park HS, Lee CM, Baek MK, Jeong OY, Kim SM. Application of a novel quantitative trait locus combination to improve grain shape without yield loss in rice (Oryza sativa L. spp. japonica). Plants. 2023;12(7):1513. https://doi.org/10.3390/plants12071513
  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. Liu Q, Han R, Wu K, Zhang J, Ye Y, Wang S, et al. G-protein βγ subunits determine grain size through interaction with MADS-domain transcription factors in rice. Nat Commun. 2018;9(1):852. https://doi.org/10.1038/s41467-018-03047-9
  6. 6. Zhao DS, Li QF, Zhang CQ, Zhang C, Yang QQ, Pan LX, et al. GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality. Nat Commun. 2018;9(1):1240. https://doi.org/10.1038/s41467-018-03616-y
  7. 7. Satrio RD, Fendiyanto MH, Nurrahmah N, Anofri N, Ikhsan M, Nugroho S, et al. Rice QTL hotspots related with seed grain size, shape, weight and color based on genome wide association study and linkage mapping. Sci Rep. 2025;15(1):21470. https://doi.org/10.1038/s41598-025-05814-3
  8. 8. 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
  9. 9. 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
  10. 10. Yu X, Xia S, Xu Q, Cui Y, Gong M, Zeng D, et al. ABNORMAL FLOWER AND GRAIN 1 encodes OsMADS6 and determines palea identity and affects rice grain yield and quality. Sci China Life Sci. 2020;63(2):228–38. https://doi.org/10.1007/s11427-019-1593-0
  11. 11. Hu J, Wang Y, Fang Y, Zeng L, Xu J, Yu H, et al. A rare allele of GS2 enhances grain size and grain yield in rice. Mol Plant. 2015;8(10):1455–65. https://doi.org/10.1016/j.molp.2015.07.002
  12. 12. Bai F, Ma H, Cai Y, Shahid MQ, Zheng Y, Lang C, et al. Natural allelic variation in GRAIN SIZE AND WEIGHT 3 of wild rice regulates the grain size and weight. Plant Physiol. 2023;193(1):502–18. https://doi.org/10.1093/plphys/kiad320
  13. 13. Qi P, Lin YS, Song XJ, Shen JB, Huang W, Shan JX, et al. The novel quantitative trait locus GL3.1 controls rice grain size and yield by regulating Cyclin-T1;3. Cell Res. 2012;22(12):1666–80. https://doi.org/10.1038/cr.2012.151
  14. 14. Zhang YM, Yu HX, Ye WW, Shan JX, Dong NQ, Guo T, et al. A rice QTL GS3.1 regulates grain size through metabolic-flux distribution between flavonoid and lignin metabolons without affecting stress tolerance. Commun Biol. 2021;4(1):1–14. https://doi.org/10.1038/s42003-021-02686-x
  15. 15. Dong G, Xiong H, Zeng W, Li J, Du D. Ectopic expression of the rice grain-size-affecting gene GS5 in maize affects kernel size by regulating endosperm starch synthesis. Genes (Basel). 2022;13(9):1542. https://doi.org/10.3390/genes13091542
  16. 16. Achary VMM, Reddy MK. CRISPR-Cas9 mediated mutation in GRAIN WIDTH and WEIGHT2 (GW2) locus improves aleurone layer and grain nutritional quality in rice. Sci Rep. 2021;11(1):21941. https://doi.org/10.1038/s41598-021-00828-z
  17. 17. Ayyenar B, V R, Premnath A, D S. Allelic diversity of OsGW5.1 regulating grain width in rice. Madras Agric J. 2022;109:92–8. https://doi.org/10.29321/maj.10.000582
  18. 18. Zhang T, Wang Z, Liu Q, Zhao D. Genetic improvement of rice grain size using the CRISPR/Cas9 system. Rice. 2025;18(1):3. https://doi.org/10.1186/s12284-025-00758-8
  19. 19. Huang J, Chen W, Gao L, Qing D, Pan Y, Zhou W, et al. Rapid improvement of grain appearance in three-line hybrid rice via CRISPR/Cas9 editing of grain size genes. Theor Appl Genet. 2024;137(7):173. https://doi.org/10.1007/s00122-024-04627-8
  20. 20. Li Y, Xiao J, Chen L, Huang X, Cheng Z, Han B, et al. Rice functional genomics research: Past decade and future. Mol Plant. 2018;11(3):259–80. https://doi.org/10.1016/j.molp.2018.01.007
  21. 21. Dash GK, Sabarinathan S, Donde R, Gouda G, Gupta MK, Behera L, et al. Status and prospectives of genome-wide association studies in plants. In: Gupta, M K, Behera L, editors Bioinformatic analysis of rice respones to abiotic stresses. Singapore: Springer Nature Singapore; 2021. p. 413–57. https://doi.org/10.1007/978-981-16-3993-7_19
  22. 22. Gupta MK, Gouda G, Donde R, Sabarinathan S, Dash GK, Rajesh N, et al. 3000 genome project: A brief insight. In: Gupta, M K, Behera L, editors. Bioinformatic analysis of rice respones to abiotic stresses. Singapore: Springer Nature Singapore; 2021. p. 89–100. https://doi.org/10.1007/978-981-16-3993-7_5
  23. 23. Guru A, Sahoo SK, Dash GK, Jena J, Dwivedi P. Recent advances in multi-omics and breeding approaches towards drought tolerance in crops. In: Gupta MK, Behera L, editors. Applied bioinformatics, statistics and economics in rice research. Singapore: Springer Nature Singapore; 2021. p. 333–59. https://doi.org/10.1007/978-981-16-3997-5_16
  24. 24. Qian L, Hickey LT, Stahl A, Werner CR, Hayes B, Snowdon RJ, et al. Exploring and harnessing haplotype diversity to improve yield stability in crops. Front Plant Sci. 2017;8:1534. https://doi.org/10.3389/fpls.2017.01534
  25. 25. Bhat JA, Yu D, Bohra A, Ganie SA, Varshney RK. Features and applications of haplotypes in crop breeding. Commun Biol. 2021;4(1):1266. https://doi.org/10.1038/s42003-021-02782-y
  26. 26. Dixit N, Dokku P, Amitha Mithra SV, Parida SK, Singh AK, Singh NK, et al. Haplotype structure in grain weight gene GW2 and its association with grain characteristics in rice. Euphytica. 2013;192(1):55–61. https://doi.org/10.1007/s10681-012-0852-4
  27. 27. Singh N, Singh B, Rai V, Sidhu S, Singh AK, Singh NK. Evolutionary insights based on SNP haplotypes of red pericarp, grain size and starch synthase genes in wild and cultivated rice. Front Plant Sci. 2017;8:972. https://doi.org/10.3389/fpls.2017.0972
  28. 28. Hao J, Wang D, Wu Y, Huang K, Duan P, Li N, et al. The GW2-WG1-OsbZIP47 pathway controls grain size and weight in rice. Mol Plant. 2021;14(8):1266–80. https://doi.org/10.1016/j.molp.2021.04.011
  29. 29. 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
  30. 30. Zhu X, Gou Y, Heng Y, Ding W, Li Y, Zhou D, et al. Targeted manipulation of grain shape genes effectively improves outcrossing rate and hybrid seed production in rice. Plant Biotechnol J. 2023;21(2):381–90. https://doi.org/10.1111/pbi.13959
  31. 31. Mansueto L, Fuentes RR, Borja FN, Detras J, Abriol-Santos JM, Chebotarov D, et al. Rice SNP-seek database update: New SNPs, indels and queries. Nucleic Acids Res. 2017;45(D1):1075–81. https://doi.org/10.1093/nar/gkw1135
  32. 32. Zhang R, Jia G, Diao X. geneHapR: An R package for gene haplotypic statistics and visualization. BMC Bioinformatics. 2023;24(1):199. https://doi.org/10.1186/s12859-023-05318-9
  33. 33. Tamura K, Stecher G, Kumar S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol Biol Evol. 2021;38(7):3022–7. https://doi.org/10.1093/molbev/msab120
  34. 34. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34(12):3299–302. https://doi.org/10.1093/molbev/msx248
  35. 35. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–35. https://doi.org/10.1093/bioinformatics/btm308
  36. 36. Wang J, Zhang Z. GAPIT version 3: Boosting power and accuracy for genomic association and prediction. Genomics Proteomics Bioinformatics. 2021;19(4):629–40. https://doi.org/10.1016/j.gpb.2021.08.005
  37. 37. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. https://doi.org/10.1038/s41586-021-03819-2
  38. 38. Zhou Y, Pan Q, Pires DEV, Rodrigues CHM, Ascher DB. DDMut: Predicting effects of mutations on protein stability using deep learning. Nucleic Acids Res. 2023;51(W1):W122–28. https://doi.org/10.1093/nar/gkad472
  39. 39. Tanaka T, Hayakawa T, Teshima KM. Power of neutrality tests for detecting natural selection. G3 (Bethesda). 2023;13(10):jkad161. https://doi.org/10.1093/g3journal/jkad161
  40. 40. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010;42(11):961–7. https://doi.org/10.1038/ng.695
  41. 41. Selvaraj S, Chidambaranathan P, Dash GK, Sanghamitra P, Jeughale KP, Balasubramaniasai C, et al. Long-range admixture linkage disequilibrium and allelic responses of Sub1 and TPP7 under consecutive stress in rice validated through mendelian randomization. Rice Sci. 2025;32(5):704–16. https://doi.org/10.1016/j.rsci.2025.06.009
  42. 42. Muduli BC, Selvaraj S, Sahu S, Dhall S, Swain N, Chidambaranathan P, et al. Haplotype characterization of phosphorus homeostasis gene, SPX-MFS3 under combination of low nitrogen and phosphorus conditions in indica rice at seedling stage. 3 Biotech. 2025;15(6):188. https://doi.org/10.1007/s13205-025-04350-1
  43. 43. Selvaraj S, Nayak S, Chidambaranathan P, Sanghamitra P, Mohanty S, Balasubramaniasai C, et al. Natural variation in OsTPP7 affects the root traits in combined germination under submergence and nutrient deficiency in indica rice. Trop Plant Biol. 2026;19(1):5. https://doi.org/10.1007/s12042-026-09462-3
  44. 44. Lu Y, Xu Y, Li N. Early domestication history of Asian rice revealed by mutations and genome-wide analysis of gene genealogies. Rice (N Y). 2022;15(1):15. https://doi.org/10.1186/s12284-022-00556-6
  45. 45. Olsen KM, Caicedo AL, Polato N, McClung A, McCouch S, Purugganan MD. Selection under domestication: Evidence for a sweep in the rice waxy genomic region. Genetics. 2006;173(2):975–83. https://doi.org/10.1534/genetics.106.056473
  46. 46. Kiran K, Selvaraj S, Parameswaran C, Balasubramaniasai C, Katara JL, Devanna BN, et al. Genome-wide association analysis and candidate genes identification for pericarp color in rice (Oryza sativa L.). Trop Plant Biol. 2024;18(1):8. https://doi.org/10.1007/s12042-024-09371-3
  47. 47. Nayak G, Parameswaran C, Vaidya N, Parida M, Sabarinathan S, Chaudhari P, et al. Genome-wide association analysis in identification of superior haplotypes for vegetative stage drought stress tolerance in rice. Physiol Mol Biol Plants. 2025;31(3):435–52. https://doi.org/10.1007/s12298-025-01573-7
  48. 48. Nayak G, C P, Selvaraj S, Nayak I, Balasubramaniasai C, Katara J, et al. Pleiotropic effects of violaxanthin de-epoxidase (OsVDE) haplotypes in regulation of spikelet fertility and drought response in rice. Plant Physiol Biochem. 2026;230:110894. https://doi.org/10.1016/j.plaphy.2025.110894
  49. 49. Zhao Y, Li T, Liu D, Yin H, Wang L, Lu S, et al. The origin and evolution of cultivated rice and genomic signatures of heterosis for yield traits in super-hybrid rice. BMC Biol. 2025;23(1):1. https://doi.org/10.1186/s12915-025-02255-2
  50. 50. Chidambaranathan P, Nayak G, Jeughale KP, Selvaraj S, Balasubramania Sai C, Awaji S, et al. OsNCED2, OsNAM and OsDEC genes association with vegetative stage drought stress in rice validated through mendelian randomization. Nucleus. 2025. https://doi.org/10.1007/s13237-025-00607-5
  51. 51. Mao H, Sun S, Yao J, Wang C, Yu S, Xu C, et al. Linking differential domain functions of the GS3 protein to natural variation of grain size in rice. Proc Natl Acad Sci U S A. 2010;107(45):19579–84. https://doi.org/10.1073/pnas.1014419107
  52. 52. Sun S, Wang L, Mao H, Shao L, Li X, Xiao J, et al. A G-protein pathway determines grain size in rice. Nat Commun. 2018;9(1):851. https://doi.org/10.1038/s41467-018-03141-y
  53. 53. Gupta MK, Gouda G, Sabarinathan S, Donde R, Dash GK, Ponnana M, et al. Brief insight into the evolutionary history and domestication of wild rice relatives. In: Gupta, M K, Behera L, editors. Bioinformatic analysis of rice respones to abiotic stresses. Singapore: Springer Nature Singapore; 2021. p. 71–88. https://doi.org/10.1007/978-981-16-3993-7_4
  54. 54. Liu L, Zhou Y, Mao F, Gu Y, Tang Z, Xin Y, et al. Fine-tuning of the grain size by alternative splicing of GS3 in rice. Rice (N Y). 2022;15(1):4. https://doi.org/10.1186/s12284-022-00549-5
  55. 55. 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
  56. 56. Verma A, Prakash G, Ranjan R, Tyagi AK, Agarwal P. Silencing of an ubiquitin ligase increases grain width and weight in indica rice. Front Genet. 2021;11:600378. https://doi.org/10.3389/fgene.2020.600378
  57. 57. Kis A, Polgári D, Dalmadi Á, Ahmad I, Rakszegi M, Sági L, et al. Targeted mutations in the GW2.1 gene modulate grain traits and induce yield loss in barley. Plant Sci. 2024;340:111968. https://doi.org/10.1016/j.plantsci.2023.111968
  58. 58. Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, et al. Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet. 2012;44(8):950–4. https://doi.org/10.1038/ng.2327
  59. 59. Cheng Y, Li G, Yin M, Adegoke TV, Wang Y, Tong X, et al. Verification and dissection of one quantitative trait locus for grain size and weight on chromosome 1 in rice. Sci Rep. 2021;11(1):18252. https://doi.org/10.1038/s41598-021-97622-8
  60. 60. Feng Y, Yuan X, Wang Y, Yang Y, Zhang M, Yu H, et al. Validation of a QTL for grain size and weight using an introgression line from a cross between Oryza sativa and Oryza minuta. Rice (N Y). 2021;14(1):43. https://doi.org/10.1186/s12284-021-00486-9

Downloads

Download data is not yet available.