Skip to main content

Simultaneous improvement and genetic dissection of grain yield and its related traits in a backbone parent of hybrid rice (Oryza sativa L.) using selective introgression

Abstract

Three populations with a total of 125 BC2F3:4 introgression lines (ILs) selected for high yields from three BC2F2 populations were used for genetic dissection of rice yield and its related traits. The progeny testing in replicated phenotyping across two environments and genotyping with 140 polymorphic simple sequence repeat markers allowed the identification of 21 promising ILs that had significantly higher yields than the recurrent parent Shuhui527 (SH527). A total of 94 quantitative trait loci (QTL) were identified using the selective introgression method based on Chi-squared (χ 2) and multi-locus probability tests and the RSTEP-LRT method based on stepwise regression. These QTL were mostly mapped to 12 clusters on seven rice chromosomes. Several important properties of the QTL affecting grain yield (GY) and its related traits were revealed. The first one was the presence of strong and frequent non-random associations between or among QTL that affect low-heritability traits (GY and spikelet number per panicle, SN) in the ILs with high trait values. Second, beneficial alleles at 88.9 % GY and 75 % SN QTL for increased productivity were from the donors, suggesting that direct phenotypic selection for high yield in our introgression breeding program was a powerful way to transfer beneficial alleles at many loci from the donors into SH527. Third, most QTL were in clusters with large effects on multiple traits, which should be the focal points in further investigations and marker-assisted selection in rice. The majority of the QTL identified were expressed only in one of the environments, suggesting that differential expression of QTL in different environments is the primary genetic basis of genotype × environment interaction. Finally, a large variation in both the direction and magnitude of QTL effects was detected for different donor alleles at seven QTL in the same genetic background and environments. This finding suggests the possible presence of functional diversity among the donor alleles at these loci. The promising ILs and QTL identified provide valuable materials and genetic information for further improving the yield potential of SH527, which is a backbone restorer of hybrid rice in China.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Ali AJ, Xu JL, Ismail AM, Fu BY, Vijaykumar CHM, Gao YM, Domingo J, Maghirang R, Yu SB, Gregorio G, Yanaghihara S, Cohen M, Mackill D, Li ZK (2006) Hidden diversity for abiotic and biotic stress tolerances in the primary gene pool of rice revealed by a large backcross breeding program. Field Crops Res 97:66–76

    Article  Google Scholar 

  2. Ashikari M, Sakakibara H, Lin SY, Yamamoto T, Takashi T, Nishinura A, Angeles ER, Qian Q, Kitano H, Matsuoka M (2005) Cytokinin oxidase regulates rice grain production. Science 309:741–745

    PubMed  Article  CAS  Google Scholar 

  3. Brondani C, Rangel P, Brondani R, Ferreira M (2002) QTL mapping and introgression of yield-related traits from Oryza glumaepatula to cultivated rice (Oryza sativa) using microsatellite markers. Theor Appl Genet 104:1192–1203

    PubMed  Article  CAS  Google Scholar 

  4. Chen MY, Ali J, Fu BY, Xu JL, Zhao MF, Jiang YZ, Zhu LH, Yao DN, Gao YM, Li ZK (2011) Detection of drought-related loci in rice at reproductive stage using selected introgressed lines. Agric Sci China 10:1–8

    Article  CAS  Google Scholar 

  5. Fan CC, Xing YZ, Mao HL, Lu TT, Han B, Xu CG, Li XH, Zhang QF (2006) 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 112:1164–1171

    PubMed  Article  CAS  Google Scholar 

  6. Fu Q, Zhang PJ, Tan LB, Zhu ZF, Ma D, Fu YC, Zhan XC, Cai HW, Sun CQ (2010) Analysis of QTLs for yield-related traits in Yuanjiang common wild rice (Oryza rufipogon Griff.). J Genet Genomics 37:147–157

    PubMed  Article  CAS  Google Scholar 

  7. He GM, Luo XJ, Tian F, Li KG, Zhu ZF, Su W, Qian XY, Fu YC, Wang XK, Sun CQ, Yang JS (2006) Haplotype variation in structure and expression of a gene cluster associated with a quantitative trait locus for improved yield in rice. Genome Res 16:618–626

    PubMed  Article  CAS  Google Scholar 

  8. He YX, Zheng TQ, Hao XB, Wang LF, Gao YM, Hua ZT, Zhai HQ, Xu JL, Zhu LH, Li ZK (2010) Yield performances of japonica introgression lines selected for drought tolerance in a BC breeding programme. Plant Breed 129:167–175

    Article  Google Scholar 

  9. Hittalmani H, Huang N, Courtois B, Venuprasad R, Shashidhar HE, Zhuang JY, Zheng KL, Liu GF, Wang GC, Sidhu JS, Srivantaneeyakul S, Singh VP, Bagali PG, Prasanna HC, McLaren G, Khush GS (2003) Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia. Theor Appl Genet 107:679–690

    PubMed  Article  Google Scholar 

  10. Jiao YQ, Wang YH, Xue DW, Wang J, Yan MX, Liu GF, Dong GJ, Zeng DL, Lu ZF, Zhu XD, Qian Q, Li JY (2010) Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet 42:541–544

    PubMed  Article  CAS  Google Scholar 

  11. Khush GS (2005) What will it take to feed 5.0 billion rice consumers in 2030? Plant Mol Biol 59:1–6

    PubMed  Article  CAS  Google Scholar 

  12. Lafitte HR, Li ZK, Vijayakumar CHM, Gao YM, Shi Y, Xu JL, Fu BY, Yu SB, Ali AJ, Domingo J, Maghirang R, Torres R, Mackill D (2006) Improvement of rice drought tolerance through backcross breeding: evaluation of donors and selection in drought nurseries. Field Crops Res 97:77–86

    Article  Google Scholar 

  13. Lander ES, Green P, Abrahamson J, Barlow A, Daley MJ, Lincoln SE, Newberg LA, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181

    PubMed  Article  CAS  Google Scholar 

  14. Li ZK, Yu SB, Lafitte HR, Huang N, Courtois B, Hittalmani S, Vijayakumar CHM, Liu GF, Wang GC, Shashidhar HE, Zhuang JY, Zheng KL, Singh VP, Sidhu JS, Srivantaneeyakul S, Khush GS (2003) QTL × environment interactions in rice. I. Heading date and plant height. Theor Appl Genet 108:141–153

    PubMed  Article  CAS  Google Scholar 

  15. Li ZK, Fu BY, Gao YM, Xu JL, Ali AJ, Lafitte HR, Jiang YZ, Domingo Ray J, Vijayakumar CHM, Maghirang R, Zheng TQ, Zhu LH (2005) Genome-wide introgression lines and their use in genetic and molecular dissection of complex phenotypes in rice. Plant Mol Biol 59:33–52

    PubMed  Article  CAS  Google Scholar 

  16. Liu GF, Zhang ZM, Zhu HT, Zhao FM, Ding XH, Zeng RZ, Li WT, Zhang GQ (2008) Detection of QTLs with additive effects and additive-by-environment interaction effects on panicle number in rice (Oryza sativa L.) with single-segment substitution lines. Theor Appl Genet 116:923–931

    PubMed  Article  CAS  Google Scholar 

  17. Miura K, Ikeda M, Matsubara A, Song XJ, Ito M, Asano K, Matsuoka M, Kitano H, Ashikari M (2010) OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet 42:545–549

    PubMed  Article  CAS  Google Scholar 

  18. Moncada P, Martinez CP, Borrero J, Chatel M, Gauch H Jr, Guimaraes E, Tohme J, McCouch SR (2001) Quantitative trait loci for yield and yield components in an Oryza sativa-Oryza rufipogon BC2F2 population evaluated in an upland environment. Theor Appl Genet 102:41–52

    Article  CAS  Google Scholar 

  19. Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucl Acids Res 19:4321–4326

    Article  Google Scholar 

  20. Paterson AH, Damon S, Hewitt JD, Zamir D, Rabinowitch HD, Lincoln SE, Lander ES, Tanksley SD (1991) Mendelian factors underlying quantitative traits in tomato: comparison across species, generations and environments. Genetics 127:181–197

    PubMed  CAS  Google Scholar 

  21. Ragot M, Sisco PH, Hoisington DA, Stuber CW (1995) Molecular-marker-mediated characterization of favorable exotic alleles at quantitative trait loci in maize. Crop Sci 35:1306–1315

    Article  CAS  Google Scholar 

  22. Septiningsih EM, Prasetiyono J, Lubis E, Tai TH, Tjubaryat T, Moeljopawiro S, McCouch SR (2003) Identification of quantitative trait loci for yield and yield components in an advanced backcross population derived from the Oryza sativa variety IR64 and the wild relative O. rufipogon. Theor Appl Genet 107:1419–1432

    PubMed  Article  CAS  Google Scholar 

  23. Song XJ, Huang W, Shi M, Zhu MZ, Lin XH (2007) A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet 39:623–630

    PubMed  Article  CAS  Google Scholar 

  24. Tan LB, Liu FX, Xue W, Wang GJ, Ye S, Zhu ZF, Fu YC, Wang XK, Sun CQ (2007) Development of Oryza rufipogon and O. sativa introgression lines and assessment for yield-related quantitative trait loci. J Integr Plant Biol 49:871–884

    Article  CAS  Google Scholar 

  25. Tan LB, Li XR, Liu FX, Sun XY, Li CG, Zhu ZF, Fu YC, Cai HW, Wang XK, Xie DX, Sun CQ (2008a) Control of a key transition from prostrate to erect growth in rice domestication. Nat Genet 40:1360–1364

    PubMed  Article  CAS  Google Scholar 

  26. Tan LB, Zhang PJ, Liu FX, Wang GJ, Ye S, Zhu ZF, Fu YC, Cai HW, Sun CQ (2008b) Quantitative trait loci underlying domestication and yield-related traits in Oryza rufipogon × Oryza sativa advanced backcross population. Genome 51:692–704

    PubMed  Article  CAS  Google Scholar 

  27. Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277:1063–1066

    PubMed  Article  CAS  Google Scholar 

  28. Tanksley SD, Nelson JC (1996) Advanced backcross QTL analysis: a method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theor Appl Genet 92:191–203

    Article  Google Scholar 

  29. Temnykh S, Park SW, Ayres N, Cartinhour S, Hauck N, Lipovich L, Cho YG, Ishii T, McCouch SR (2000) Mapping and genome organization of microsatellite sequences in rice (Oryza sativa L.). Theor Appl Genet 100:697–712

    Article  CAS  Google Scholar 

  30. Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga ME, Lobos KB, Yu Y, Martinez CP, McCouch SR (2003) Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor Appl Genet 107:479–493

    PubMed  Article  CAS  Google Scholar 

  31. Wang DL, Zhu J, Li ZK, Paterson AH (1999) Mapping QTLs with epistatic effects and QTL × environment interactions by mixed linear model approaches. Theor Appl Genet 99:1255–1264

    Article  Google Scholar 

  32. Wang JK, Wan XY, Crossa J, Crouch J, Weng JF, Zhai HQ, Wang JM (2006) QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines. Genet Res 88:93–104

    PubMed  Article  CAS  Google Scholar 

  33. Wang CR, Chen S, Yu SB (2011) Functional markers developed from multiple loci in GS3 for fine marker-assisted selection of grain length in rice. Theor Appl Genet 122:905–913

    PubMed  Article  Google Scholar 

  34. Weng JF, Gu SH, Wan XY, Gao H, Guo T, Su N, Lei CL, Zhang X, Cheng Zj, Guo XP, Wang JL, Jiang L, Zhai HQ, Wang JM (2008) Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Res 18:1199–1209

    PubMed  Article  CAS  Google Scholar 

  35. Xiao J, Li J, Yuan L, Tanksley SD (1996) Identification of QTLs affecting traits of agronomic importance in a recombinant inbred population derived from a subspecific rice cross. Theor Appl Genet 92:230–244

    Article  CAS  Google Scholar 

  36. Xiao J, Li J, Grandillo S, Ahn SN, Yuan LP, Tanksley SD, McCouch SR (1998) Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 150:899–909

    PubMed  CAS  Google Scholar 

  37. Xing YZ, Zhang QF (2010) Genetic and molecular bases of rice yield. Annu Rev Plant Biol 61:421–442

    PubMed  Article  CAS  Google Scholar 

  38. Xing Y, Tan F, Hua P, Sun L, Xu G, Zhang Q (2002) Characterization of the main effects, epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice. Theor Appl Genet 105:248–257

    PubMed  Article  CAS  Google Scholar 

  39. Xing Y, Tang W, Xue W, Xu C, Zhang Q (2008) Fine mapping of a major quantitative trait loci, qSSP7, controlling number of spikelets per panicle as a single Mendelian factor in rice. Theor Appl Genet 116:789–796

    PubMed  Article  CAS  Google Scholar 

  40. Xue WY, Xing YZ, Weng XY, Zhao Y, Tang WJ, Wang L, Zhou HJ, Yu SB, Xu CG, Li XH, Zhang QF (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet 40:761–767

    PubMed  Article  CAS  Google Scholar 

  41. Yoon DB, Kang KH, Kim HJ, Ju HG, Kwon SJ, Suh JP, Jeong OY, Ahn SN (2006) Mapping quantitative trait loci for yield components and morphological traits in an advanced backcross population between Oryza grandiglumis and the O. sativa japonica cultivar Hwaseongbyeo. Theor Appl Genet 112:1052–1062

    PubMed  Article  CAS  Google Scholar 

  42. Yu SB, Li JX, Xu CG, Tan YF, Li XH, Zhang QF (2002) Identification of quantitative trait loci and epistatic interactions for plant height and heading date in rice. Theor Appl Genet 104:619–625

    PubMed  Article  CAS  Google Scholar 

  43. Zhang YS, Luo LJ, Liu TM, Xu CG, Xing YZ (2009) Four rice QTL controlling number of spikelets per panicle expressed the characteristics of single Mendelian gene in near isogenic backgrounds. Theor Appl Genet 118:1035–1044

    PubMed  Article  CAS  Google Scholar 

  44. Zhang F, Zhai HQ, Paterson AH, Xu JL, Gao YM, Zheng TQ, Wu RL, Fu BY, Ali JH, Li ZK (2011) Dissecting genetic networks underlying complex phenotypes: the theoretical framework. PLoS ONE 6:e14541

    PubMed  Article  CAS  Google Scholar 

  45. Zhuang JY, Lin HX, Lu J, Qian HR, Hittalmani S, Huang N, Zheng KL (1997) Analysis of QTL × environment interaction for yield components and plant height in rice. Theor Appl Genet 95:799–808

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was funded by grants from the “973” Program (2011CB100102) of the Ministry of Science and Technology of China, the China National Natural Science Foundation (30771378), the “863” program (2010AA101806), the Bill & Melinda Gates Foundation (OPP51587), and the “948” program from the China Ministry of Agriculture (2006–G51, 2011–G2B).

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Yongming Gao or Zhikang Li.

Additional information

Hongjun Zhang and Hui Wang contributed equally to this work.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zhang, H., Wang, H., Qian, Y. et al. Simultaneous improvement and genetic dissection of grain yield and its related traits in a backbone parent of hybrid rice (Oryza sativa L.) using selective introgression. Mol Breeding 31, 181–194 (2013). https://doi.org/10.1007/s11032-012-9782-z

Download citation

Keywords

  • Complex traits
  • Quantitative trait loci (QTL)
  • Association groups
  • Allelic diversity
  • QTL by environment interaction