Preview

Vavilov Journal of Genetics and Breeding

Advanced search

Using genomic screening methods to create high-quality rice breeding material

https://doi.org/10.18699/vjgb-26-63

Abstract

Rice, as a key model in the study of agroecosystem genomics, is the focus of research meant to address the challenges of producing sufficient food for the growing global population. In breeding programs developing new varieties, improving the physicochemical properties of the grain is crucial. Based on the analysis of national and international research, this article presents information on new molecular genetic methodologies and advances in the development of new valuable rice genotypes using genome sequencing data. Continuous enrichment of rice germplasm at global breeding centers is achieved through the use of highly effective approaches employing postgenomic and cellular technologies in combination with traditional phenotyping methods. This review examines the achievements of molecular genetic research in rice, focusing on valuable grain quality traits such as vitreousness (chalkiness) and shape (size). GWAS analysis is widely used in marker-assisted and genomic rice breeding programs. More recently, GBS analysis has been used to identify relationships between phenotype and genotype based on the analysis of bi-parental mapping populations and varietal accessions. The post-genomic research period, focused on the search for candidate genes for valuable quality traits, had started after the genomic reference sequences were obtained. As a result, hundreds of QTLs for the chalkiness trait were discovered across 12 chromosomes, few were accurately mapped or sequenced. By 2018, several major QTLs affecting grain size were sequenced and characterized. For example, the presence of the recessive GS3 allele and the dominant GW7TFA allele increases the grain length-to-width ratio. In 2023, it was shown that overexpression of OsFIF3 inhibits the expression of FLO2 and SUT1, thereby increasing chalkiness and reducing grain size. This breeding breakthrough is attributed, for example, to the use of non-digital markers for length, width, thickness, and the grain length-to-width ratio, GS3RGS1 and RM505, as selection markers. All research is an ongoing process aimed at achieving the highest possible level of high-quality rice products.

About the Authors

N. G. Tumanyan
Federal Rice Research Centre of the Ministry of Agriculture of the Russian Federation
Russian Federation

Belozerny, Krasnodar



Zh. M. Mukhina
Federal Rice Research Centre of the Ministry of Agriculture of the Russian Federation
Russian Federation

Belozerny, Krasnodar



References

1. Addison M., Sarfo-Mensah P., Edusah S.E. Assessing Ghana’s initiative of increasing domestic rice production through the development of rice value chain. Glob Sci Res J. 2015;3(4):230-237

2. Alexandrov N., Tai Sh., Wang W., Mansueto L., Palis K., Fuentes R. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res. 2015;63(2):D1023-D1027. doi 10.1093/nar/gku1039

3. Ali N., Li D., Eltahawy M., Abdulmajid D., Bux L., Liu E., Dang X., Hong D. Mining of favorable alleles for seed reserve utilization efficiency in Oryza sativa by means of association mapping. BMC Genetics. 2020;21(1):4. doi 10.1186/s12863-020-0811-3

4. Alyoshin E.P., Alyoshin N.E. Rice. Krasnodar, 1997 (in Russian) Baxla B., Thiyagarajan K., Swaminathan M., Bellie A., Natarajan S., Govindan S.K. Advances in two-line hybrid rice breeding: leveraging thermosensitive genetic male sterility system in rice for improved global rice production. Euphytica. 2025;221:101. doi 10.1007/s10681-025-03550-3

5. Chen L., Chen W., Li J., Wei Y., Qing D., Huang J., Yang X., … Deng G., Dai G., Chen C., Liang T., Pan Y. Identifying heat adaptability QTLs and candidate genes for grain appearance quality at the flowering stage in rice. Rice. 2025;18(1):13. doi 10.1186/s12284-025-00770-y

6. Cuevas R., Pede V., McKinley J., Velarde O., Demont M. Rice grain quality and consumer preferences: a case study of two rural towns in the Philippines. PLoS One. 2016;11(3):e0150345. doi 10.1371/journal.pone.0150345

7. Dmitriev A.A., Pushkova E.N., Melnikova N.V. Plant genome sequencing: modern technologies and novel opportunities for breeding. Mol Biol. 2022;56(4):495-507. doi 10.1134/s0026893322040045

8. Du Y., Long Ch., Deng X., Zhang Zh., Liu J., Xu Y., Xu Y., Liu D., Zeng Y. Physiological basis of high nighttime temperature-induced chalkiness formation during early grain-filling stage in rice (Oryza sativa L.). Agronomy. 2023;13(6):1475. doi 10.3390/agronomy13061475

9. Eltahawy M.S., Ali N., Zaid I.U., Li D., Abdulmajid D., Bux L., Wang H., Hong D. Association analysis between constructed SNPLDBs and GCA effects of 9 quality-related traits in parents of hybrid rice (Oryza sativa L.). BMC Genomics. 2020;21(1):30. doi 10.1186/s12864-019-6428-0

10. Furuta T., Ashikari M., Jena K.K., D o i K., Reuscher S. Adapting genotyping-by-sequencing for rice F2 populations. G3: Genes Genomes, Genetics. 2017;7(3):881-893. doi 10.1534/g3.116.038190

11. Gao F., Zeng L., Qiu L., Lu X., Ren J., Wu X., Su X., Gao Y., Ren G. QTL mapping of grain appearance quality traits and grain weight using а recombinant inbred populations in rice (Oryza sativa L.). J Integr Agric. 2016;15(8):1693-1702. doi 10.1016/S2095-3119(15)61259-X

12. Gao J., Gao L., Chen W., Huang J., Qing D., Pan Y., Ma Ch., Wu H., Zhou W., Li J., Yang X., Dai G., Deng G. Genetic effects of grain quality enhancement in Indica hybrid rice: insights for molecular design breeding. Rice. 2024;17:39. doi 10.1186/s12284-024-00719-7

13. Garkusha S.V., Tumanyan N.G., Mukhina Zh.M., Papulova E.Yu. Phenotyping of varieties of “Collections of genetic resources of rice, vegetable and melon crops” according to the technological characteristics of grain in connection with the development of breeding technology for the creation of rice varieties with high nutritional quality of grain. Trudy Kubanskogo Gosudarstvennogo Agrarnogo Universiteta = Proceedings of the Kuban State Agrarian University. 2022;56:59-63. doi 10.21515/1999-1703-98-59-63 (in Russian)

14. Gong D., Zhang X., He F., Chen Y., Li R., Yao J., Zhang M., Zheng W., Yu G. Genetic improvements in rice grain quality: a review of elite genes and their applications in molecular breeding. Agronomy. 2023; 13(5):1375. doi 10.3390/agronomy13051375

15. Goryunova S.V., Goryunov D.V., Chernova A.I., Martynova E.U., Dmitriev A.E., Boldyrev S.V., Ayupova A.F., … Gorlova L.A., Garkusha S.V., Mukhina Z.M., Savenko E.G., Demurin Y.N. Genetic and phenotypic diversity of the sunflower collection of the Pustovoit All-Russia Research Institute of Oil Crops (VNIIMK). Helia. 2019;42(70):45-60. doi 10.1515/helia-2018-0021

16. Guo T., Liu X., Wan X., Weng J., Liu S., Liu X., Chen M., … Guo X., Lei C., Wang J., Jiang L., Wan J. Identification of a stable quantitative trait locus for percentage grains with white chalkiness in rice (Oryza sativa). J Integr Plant Biol. 2011;53(8):598-607. doi 10.1111/j.1744-7909.2011.01041.x

17. Han Y., Xu M., Liu X., Yan Ch., Korban S.S., Chen X., Gu M. Genes coding for starch branching enzymes are major contributors to starch viscosity characteristics in waxy rice (Oryza sativa L.). Plant Sci. 2004;166(2):357-364. doi 10.1016/j.plantsci.2003.09.023

18. Heifetz E.M., Soller M. Targeted Recombinant Progeny: a design for ultra-high resolution mapping of Quantitative Trait Loci in crosses between inbred or pure lines. BMC Genetics. 2015;16:76. doi 10.1186/s12863-015-0206-z

19. Hua K., Zhang J., Botella J.R., Ma Ch., Kong F., Liu B., Zhu J.-K. Perspectives on the application of genome-editing technologies in crop breeding. Mol Plant. 2019;12(8):1047-1059. doi 10.1016/j.molp.2019.06.009

20. Jin S., Xu L., Yang Q., Zhang M., Wang Sh., Wang R., Tao T., Hong L., Guo Q., Song Sh., Leng Y., Cai X., Gao J. High-resolution quantitative trait locus mapping for rice grain quality traits using genotyping by sequencing. Front Plant Sci. 2023;13:860664. doi 10.3389/fpls.2022.860664

21. Khlestkina E.K. Rice Genome editing using CRISPR/Cas system. Biotekhnologiya i Selektsiya Rastenij = Plant Biotechnology and Breeding. 2019;2(1):49-54. doi 10.30901/2658-6266-2019-1-49-54 (in Russian)

22. Kim W.J., Yang B., Kim D.G., Kim S.H., Lee Y.J., Kim J., Baek S.H., Kang S.Y., Ahn J.W., Choi Y.J., Bae C.H., Iwar K., Kim S.H., Ryu J. Genotyping-by-sequencing analysis reveals associations between agronomic and oil traits in gamma ray-derived mutant rape-seed (Brassica napus L.). Plants. 2024;13(11):1576. doi 10.3390/plants13111576

23. Kishor D., Lee H.-Y., Hemasundar A., Kim J.-G., Lee S.-Y., Kang B.-Ch., Song K., You C.-R. Identification of an allelic variant of the CsOr gene controlling fruit endocarp color in cucumber (Cucumis sativus L.) using genotyping-by-sequencing (GBS) and whole-genome sequencing. Front Plant Sci. 2021;12:676884. doi 10.3389/fpls.2021.676884

24. Koutroubas S., Mazzini F., Pons B., Ntanos D. Grain quality variation and relationships with morphophysiological traits in rice (Oryza sativa L.) genetic resources in Europe. Field Crops Res. 2004;86: 115-130. doi 10.1016/S0378-4290(03)00117-5

25. Kumar A., Thomas J., Gill N., Dwiningsih Yh., Ruiz Ch., Famoso A., Pereira A. Molecular mapping and characterization of QTLs for grain quality traits in a RIL population of US rice under high night-time temperature stress. Sci Rep. 2023;13(1):4880. doi 10.1038/s41598-023-31399-w

26. Kumari A., Sharma D., Sharma P., Sil S., Wang Ch., Verma V., Patil A., … Chandel G., Grover A., Jagadish S.V.K., Katiyar-Agarwal S., Agarwal M. Meta-QTL and haplo-pheno analysis reveal superior haplotype combinations associated with low grain chalkiness under high temperature in rice. Front Plant Sci. 2023;14:1133115. doi 10.3389/fpls.2023.1133115

27. Li J., Yang H., Xu G., Deng K., Yu J., Xiang S., Zhou K., Zhang Q., Li R., Li M., Ling Y., Yang Zh., He G., Zhao F. QTL analysis of Z414, a chromosome segment substitution line with short, wide grains, and substitution mapping of qGL11 in rice. Rice. 2022;15(1):25. doi 10.1186/s12284-022-00571-7

28. Li Y., Fan C., Xing Y., Yun P., Luo L., Yan B., Peng B., Xie W., Wang G., Li X., Xiao J., Xu C., He Y. Chalk5 encodes a vacuolar H(+)-translocating pyrophosphatase influencing grain chalkiness in rice. Nat Genet. 2014;46:398-404. doi 10.1038/ng.2923

29. Liu C., Wang L., Lu W., Zhong J., Du H., Liu P., Du Q., Du L., Qing J. Construction of SNP-based high-density genetic map using genotyping by sequencing (GBS) and QTL analysis of growth traits in Eucommia ulmoides oliver. Forests. 2022;13(9):1479. doi 10.3390/f13091479

30. Liu J., Zhang H., Wang Y., Liu E., Shi H., Gao G., Zhang Q., Zhang Q., Lou G., Jiang G., He Y. QTL analysis for rice quality-related traits and fine mapping of qWCR3. Int J Mol Sci. 2024;25(8):4389. doi 10.3390/ijms25084389

31. Liu Q., Han R., Wu K., Zhang J., Ye Y., Wang S., Chen J., Pan Y., Li Q., Xu X., Zhou J., Tao D., Wu Y., Fu X. G-protein βγ subunits determine grain size through interaction with MADS-domain transcription factors in rice. Nat Commun. 2018;9:852. doi 10.1038/s41467-018-03047-9

32. Lu Y., Zhu J.-K. Precise editing of a target base in the rice genome using a modified CRISPR/Cas9 system. Mol Plant. 2017;10(3):523-525. doi 10.1016/j.molp.2016.11.013

33. Mukhina Zh.M., Tumanyan N.G., Garkusha S.V., Papulova E.Yu., Chukhir N.P., Chukhir I.N., Gnenniy E.Yu., Esaulova L.V., Malyuchenko E.A., Vakhrusheva N.I. Improving coloured rice grain quality through accelerated breeding. SABRAO J Breed Genet. 2024; 56(1):89-100. doi 10.54910/sabrao2024.56.1.8

34. Naidu B.S.L., Reddy V.L.V., Naik B.J., Naik E.S. Allele mining and allelic diversity of genes governing grain size related traits in rice (Oryza sativa L.). Can J Biotechnol. 2017;1:200. doi 10.24870/cjb.2017-a186

35. Ngangkham U., Samantaray S., Yadav M., Kumar A., Chidambaranathan P., Katara J.L. Effect of multiple allelic combinations of genes on regulating grain size in rice. PLoS One. 2018;13(1):e0190684. doi 10.1371/journal.pone.0190684

36. Odoom D., Kugbe J., Dzomeku I., Berdjour A., Boateng D., Yaro R., Wireko P., Sam E., Ghanney Ph. Impact of production inputs and timing on crackness of rice in Northern Ghana. Int J Agronomy. 2021;4:1-14. doi 10.1155/2021/9982911

37. Pan Y., Chen L., Zhao Y., Guo H., Li J., Rashid M.A.R., Lu Ch., … Qing D., Gao L., Dai G., Li D., Deng G. Natural variation in OsMKK3 contributes to grain size and chalkiness in rice. Front Plant Sci. 2021;12:784037. doi 10.3389/fpls.2021.784037

38. Peng B., Wang L., Fan C., Jiang G., Luo L., Li Y., He Y. Comparative mapping of chalkiness components in rice using five populations across two environments. BMC Genet. 2014;15:49. doi 10.1186/1471-2156-15-49

39. Piao R.H., Chen M.-J., Meng F.-M., Qi C.-Y., Koh H.-J., Gao M.-M., Song A.-Q., Jin Y.-M., Yan Y.-F. Identification and characterization of the chalkiness endosperm gene CHALK-H in rice (Oryza sativa L.). J Integr Agric. 2023;22(10):2921-2933. doi 10.1016/j.jia.2023.04.020

40. Pronozin A.Y., Salina E.A., Afonnikov D.A. GBS-DP: a bioinformatics pipeline for processing data coming from genotyping by sequencing. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov J Genet Breed. 2023;27(7):737-745. doi 10.18699/VJGB-23-86

41. Qiu X., Pang Y., Yuan Z., Xing D., Xu J., Dingkuhn M., Li Z., Ye G. Genome-wide association study of grain appearance and milling quality in a worldwide collection of indica rice germplasm. PLoS One. 2015;10(12):e0145577. doi 10.1371/journal.pone.0145577

42. Quero G., Gutiérrez L., Monteverde E., Blanco P., Pérez de Vida F., Rosas J., Fernández S., Garaycochea S., McCouch S., Berberian N., Simondi S., Bonnecarrèr V. Genome-wide association study using historical breeding populations discovers genomic regions involved in high-qualit. Plant Genome. 2018;11(3):170076. doi 10.3835/plantgenome2017.08.0076

43. Rahmati R., Nemati Z., Naghavi M., Pfanzelt S. Phylogeography and genetic structure of Papaver bracteatum populations in Iran based on genotyping-by-sequencing (GBS). Sci Rep. 2024;14(1):16309. doi 10.1038/s41598-024-67190-8

44. Riangwong K., Saensuk Ch., Pitaloka M.K., Dumhai R., Ruanjaichon V., Toojinda Th., Wanchana S., Arikit S. Genetic diversity and population structure of a longan germplasm in Thailand revealed by genotyping-by-sequencing (GBS). Horticulturae. 2023;9(6):726. doi 10.3390/horticulturae9060726

45. Samsonova M.G., Nuzhdin S.V., Samsonova A.A., Kanapin A.A., Ayupova A.F., Khajtovich F.E., Boldyrev S.V. Markers for Marker Selection of Soya According to Utility Signs. Invention patent RU 2740798 C1. Publ. 21.01.2021 (in Russian)

46. Shen L., Hua Y., Fu Y., Li J., Liu Q., Jiao X., Xin G., Wang J., Wang X., Yan C., Wang K. Rapid generation of genetic diversity by multiplex CRISPR/Cas9 genome editing in rice. Sc China Life Sci. 2017;60: 506-515. doi 10.1007/s11427-017-9008-8

47. Shi H., Zhu Y., Yun P., Lou G., Wang L., Wang Y., Gao G., Zhang Q., Li X., He Y. Fine mapping of qWCR4, a rice chalkiness QTL affecting yield and quality. Agronomy. 2022;12(3):706. doi 10.3390/agronomy12030706

48. Song Sh., Tian D., Li C., Tang B., Dong L., Xiao J., Bao Y., Zhao W., He H., Zhang Zh. Genome Variation Map: a data repository of genome variations in BIG Data Center. Nucleic Acids Res. 2018; 4(46)(D1):944-949. doi 10.1093/nar/gkx986

49. Sreenivasulu N., Zhang C., Tiozon R.N. Jr., Liu Q. Post-genomics revolution in the design of premium quality rice in a high-yielding background to meet consumer demands in the 21st century. Plant Commun. 2022;3(3):100271. doi 10.1016/j.xplc.2021.100271

50. Sun C., Hu Z., Zheng T., Lu K., Zhao Y., Wang W., Shi J., Wang C., Lu J., Zhang D., Li Z., Wei C. RPAN: rice pan-genome browser for ~3000 rice genomes. Nucleic Acids Res. 2017;45(2):597-605. doi 10.1093/nar/gkw958

51. Susiyanti S., Rusmana R., Maryani Y., Sjaifuddin S., Krisdianto N., Syabana M. The physicochemical properties of several Indonesian rice varieties. Biotropia. 2020;27(1):41050. doi 10.11598/btb.2020.27.1.1030

52. Tang X., Zhong W., Wang K., Gong X., Xia Y., Nong J., Xiao L., Xia S. Regulation of grain chalkiness and starch metabolism by FLO2 interaction factor 3, a bHLH transcription factor in Oryza sativa. Int J Mol Sci. 2023;24(16):12778. doi 10.3390/ijms241612778

53. Tyrka M., Mokrzycka M., Bakera B., Tyrka D., Szeliga M., Stojałowski S., Matysik P., Rokicki M., Rakoczy-Trojanowska M., Krajewski P. Evaluation of genetic structure in European wheat cultivars and advanced breeding lines using high-density genotyping-by-sequencing approach. BMC Genomics. 2021;22(1):81. doi 10.1186/s12864-020-07351-x

54. Wang H., Zhang J., Farkhanda N., Li J., Sun S., He G., Zhang T., Ling Y., Zhao F. Identification of rice QTL for important agronomic traits with long-kernel CSSL-Z741 and three SSSLs. Rice Sci. 2020; 27(5):414-423. doi 10.1016/j.rsci.2020.04.008

55. Wang N., Chen H., Qian Y., Liang Z., Zheng G., Xiang J., Feng T., Li M., Zeng W., Bao Y., Liu E., Zhang C., Xu J., Shi Y. Genome-wide association study of rice grain shape and chalkiness in a worldwide collection of Xian accessions. Plants. 2023;12(3):419. doi 10.3390/plants12030419

56. Wang Y., Wang J., Zhai L., Liang Ch., Chen K., Xu J. Identify QTLs and candidate genes underlying source-, sink-, and grain yield-related traits in rice by integrated analysis of bi-parental and natural populations. PloS One. 2020;15:e0237774. doi 10.1371/journal.pone.0237774

57. Wing R., Purugganan M., Zhang Q. The rice genome revolution: from an ancient grain to Green Super Rice. Nat Rev Genet. 2018;19:505-517. doi 10.1038/s41576-018-0024-z

58. Wu M., Cai M., Zhai R., Ye J., Zhu G., Yu F., Ye Sh., Zhang X. A mitochondrion-associated PPR protein, WBG1, regulates grain chalkiness in rice. Front Plant Sci. 2023;14:1136849. doi 10.3389/fpls.2023.1136849

59. Xu Q., Jiang J., Jing C., Hu C., Zhang M., Li X., Shen J., Hai M., Hai M., Zhang Y., Zhang Y., Wang D., Dang X. Genome-wide association mapping of quantitative trait loci for chalkiness-related traits in rice (Oryza sativa L.). Front Genet. 2024;15:1423648. doi 10.3389/fgene.2024.1423648

60. Yadav S., Sandhu N., Singh V., Catolos M., Kumar A. Genotyping-by-sequencing based QTL mapping for rice grain yield under reproductive stage drought stress tolerance. Sci Rep. 2019;9(1):14326. doi 10.1038/s41598-019-50880-z

61. Yang W., Liang J., Hao Q., Luan X., Tan Q., Lin S., Zhu H., Liu G., Liu Z., Bu S., Wang S., Zhang G. Fine mapping of two grain chalkiness QTL sensitive to high temperature in rice. Rice. 2021a;14:33. doi 10.1186/s12284-021-00476-x

62. Yang W., Xiong L., Liang J., Hao Q., Luan X., Tan Q., Lin S., Zhu H., Liu G., Liu Z., Bu S., Wang S., Zhang G. Substitution mapping of two closely linked QTLs on chromosome 8 controlling grain chalkiness in rice. Rice. 2021b;14:85. doi 10.1186/s12284-021-00526-4

63. Yang W., Chen S., Hao Q., Zhu H., Tan Q., Lin S., Chen G., Li Z., Bu S., Liu Z., Liu G., Wang S., Zhang G. Pyramiding of low chalkiness QTLs is an effective way to reduce rice chalkiness. Rice. 2024; 17(1):4. doi 10.1038/s41598-019-50880-z

64. Yang X., Lu J., Shi W., Chen Y., Yu J., Chen S., Zhao D., Huang L., Fan X., Zhang C., Zhang L., Liu Q., Li Q. RGA1 regulates grain size, rice quality and seed germination in the small and round grain mutant srg5. BMC Plant Biol. 2024;24(1):167. doi 10.1186/s12870-024-04864-5

65. Yao X., Wu K., Yao Y., Bai Y., Ye J., Chi D. Construction of a high-density genetic map: genotyping by sequencing (GBS) to map purple seed coat color (Psc) in hulless barley. Hereditas. 2018;155:37. doi 10.1186/s41065-018-0072-6

66. Yuyu C., Aike Z., Pao X., Xiaoxia W., Yongrun C., Beifang W., Yue Z., Liaqat S., Shihua C., Liyong C., Yingxin Zh. Effects of GS3 and GL3.1 for grain size editing by CRISPR/Cas9 in rice. Rice Sci. 2020; 27(5):405-413. doi 10.1016/j.rsci.2019.12.010

67. Zhao D., Li Q., Zhang C., Zhang C., Yang Q., Pan L., Ren X., Lu J., Gu M., Liu Q. GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality. Nat Commun. 2018;9(1):1240. doi 10.1038/s41467-018-03616-y

68. Zhao D., Zhang C., Li Q., Liu Q. Genetic control of grain appearance quality in rice. Biotechnol Adv. 2022;60:108014. doi 10.1016/j.biotechadv.2022.108014

69. Zhao H., Yao W., Ouyang Y., Yang W., Wang G., Lian X., Xing Y., Chen L., Xie W. RiceVarMap: a comprehensive database of rice genomic variations. Nucleic Acids Res. 2015;43(D1):D1018-D1022. doi 10.1093/nar/gku894

70. Zhao K., Wright M., Kimball J., Eizenga G., McClung A., Kovach M., Tyagi W., Ali M.L., Tung C.W., Reynolds A., Bustamante C.D., McCouch S.R. Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS One. 2010;5(5):e107780. doi 10.1371/journal.pone.0010780

71. Zhou L., Chen L., Jiang L., Zhang W., Liu L., Liu X., Zhao Z., Liu S., Zhang L., Wang J., Wan J. Fine mapping of the grain chalkiness QTL qPGWC-7 in rice (Oryza sativa L.). Theor Appl Genet. 2009; 118(3):581-590. doi 10.1007/s00122-008-0922-0

72. Zhu A., Zhang Y., Zhang Z., Wang B., Xue P., Cao Y., Chen Y., Li Z., Liu Q., Cheng S., Cao L. Genetic dissection of qPCG1 for a quantitative trait locus for percentage of chalky grain in rice (Oryza sativa L.). Front Plant Sci. 2018;9:1173. doi 10.3389/fpls.2018.01173


Review

Views: 25

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2500-3259 (Online)