Theoretical and Applied Genetics

, Volume 110, Issue 7, pp 1334–1346 | Cite as

Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments

  • X. Y. Wan
  • J. M. Wan
  • J. F. Weng
  • L. Jiang
  • J. C. Bi
  • C. M. Wang
  • H. Q. Zhai
Original Paper

Abstract

Rice appearance quality, including traits specifying grain dimension and endosperm chalkiness, represents a major problem in many rice-producing areas of the world. In this study, the genetic basis of six appearance quality traits of milled rice was dissected into quantitative trait loci (QTL) main effects, and the stability of these QTLs was assessed in a population of 66 chromosome segment substitution lines (CSSLs) across eight environments. The CSSLs showed transgressive segregation for many of the traits, and significant correlations were detected among most of the traits. Twenty-two QTLs were identified on eight chromosomes, and numerous QTLs affecting related traits were mapped in the same regions, probably reflecting pleiotropic effects. Nine QTLs, namely qGL-1,qGL-3, qGW-5,qLWR-3, qLWR-5,qPGWC-8, qPGWC-9, qACE-8, and qDEC-8, were consistently detected across the eight environments. The additive main effect and multiplicative interaction (AMMI) analysis showed that genotype (G) × environment (E) interaction was significant for all six traits, with the first three iPCA terms accounting for over 80% of the G × E variance. Both D I values and the iPCA1-iPCA2 biplots showed that the CSSLs harboring the nine QTL alleles were more stable than those carrying any of the additional 13 QTL alleles, thereby confirming their environmental stability and pointing to their appropriateness as targets for marker-assisted selection for high-quality rice varieties.

Notes

Acknowledgements

We are extremely grateful to Prof. A. Yoshimura, Kyushu University, Japan, for kindly providing the CSSL population and genotype data. We thank http://www.smartenglish.co.uk for linguistic correction. This research is supported by the grants from the National High Technology Research and Development Program of China (No. 2003AA222131; 2003AA207020), the National Natural Science Foundation of China (No.30270811).

References

  1. Campbell BT, Baenzigar PS, Gill KS, Eskridge KM, Budak H, Erayman M, Dweikat I, Yen Y (2003) Identification of QTLs and environmental interactions associated with agronomic traits on chromosome 3A of wheat. Crop Sci 43:1493–1505Google Scholar
  2. Causse MA, Fulton TM, Cho YG, Ahn SN, Wu KS, Xiao JH, Yu ZH, Ronald PC, Harrington SE, Second G, McCouch SR, Tanksley SD (1994) Saturated molecular map of the rice genome based on an interspecific backcross population. Genetics 138:1251–1274PubMedGoogle Scholar
  3. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedGoogle Scholar
  4. Crossa J, Gauch HG, Zobel RW (1990) Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci 30:493–500Google Scholar
  5. Del Rosario AR, Briones VP, Vidal AJ, Juliano BO (1968) Composition and endosperm structure of developing and mature rice kernel. Cereal Chem 45:225–235Google Scholar
  6. Falconer DS (1981) Introduction to quantitative genetics, 2nd edn. London New YorkGoogle Scholar
  7. Frary An, Nesbitt TC, Frary Am, Grandillo S, Knaap EVD, Cong B, Liu JP, Meller J, Elber R, Alpert KB, Tanksley SD (2000) fw2.2: A quantitative trait locus key to the evolution of Tomato fruit size. Science 289:85–88CrossRefPubMedGoogle Scholar
  8. Fujita N, Kubo A, Francisco PB, Nakakita M, Harada K, Minaka N, Nakamura Y (1999) Purification, characterization, and cDNA structure of isoamylase from developing endosperm of rice. Planta 208:283–293Google Scholar
  9. Gauch HG, Zobel RW (1988) Predictive and postdictive success of statistical analysis of yield trails. Theor Appl Genet 76:1–10Google Scholar
  10. He P, Li SG, Qian Q, Ma YQ, Li JZ, Wang WM, Chen Y, Zhu LH (1999) Genetic analysis of rice grain quality. Theor Appl Genet 98:502–508CrossRefGoogle Scholar
  11. Hittalmani S, Huang N, Courtois B, Venuprasad R, Shashidhar HE, Zhuang JY, Zheng KL, Liu GF, Wang GC, Sidhu JS, Srivantaneeyahul S, Singh VP, Bagali PG, Prasanna HC, Mclaren G, Khush GS (2003) Identification of QTL for growth-yield and grain yield-related traits in rice across nine locations of Asia. Theor Appl Genet 107:679–690Google Scholar
  12. Howell PM, Marshall DF, Lydiate DJ (1996) Towards developing intervarietal substitution lines in Brassica napus using marker-assisted selection. Genome 39:348–358Google Scholar
  13. Huang N, Parco A, Mew T, Magpantay G, McCouch S, Guiderdoni E, Xu JC, Subudhi P, Angeles ER, Khush GS (1997) RFLP mapping of isozymes, RAPD and QTLs for grain shape, brown planthopper resistance in a doubled haploid rice population. Mol Breed 3:105–113Google Scholar
  14. Jiang HW (2002) The cloning and characterization of the gene for starch synthesis in rice endosperms and studies on the molecular physiological effects of high temperature on rice grain quality forming. PhD thesis, Zhejiang University, Hangzhou ChinaGoogle Scholar
  15. Kubo T, Nakamura K, Yoshimura A (1999) Development of a series of Indica chromosome segment substitution lines in Japonica background of rice. Rice Genet Newsl 16:104–106Google Scholar
  16. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L (1987) mapmaker: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181PubMedGoogle Scholar
  17. Li ZK (2001) QTL mapping in rice: a few critical considerations. Rice genetics . In: Khush GS, Brar DS, Hrady B (eds) Proc 4th Int Rice Genet Symp. IRRI, Los Banos, Philippines, pp 153–171Google Scholar
  18. Li ZK, Yu SB, Lafitte HR, Huang L, 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 108:141–153CrossRefGoogle Scholar
  19. Lin HX, Yamamoto T, Sasaki T, Yano M (2000) Characterization and detection of epistatic interactions of 3 QTLs, Hd-1,Hd-2 and Hd-3, controlling heading date of rice using nearly isogenic lines. Theor Appl Genet 101:1021–1028CrossRefGoogle Scholar
  20. Lin JR, Wu MG, Shi CH (2001) Analysis on genetic effects of appearance quality traits in japonica hybrid rice. Chin J Rice Sci 15:93–96Google Scholar
  21. McCouch SR, Teytelman L, Xu YB (2002) Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA Res 9:199–207PubMedGoogle Scholar
  22. Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res 8:4321–4325PubMedGoogle Scholar
  23. Nagato K, Ebata M (1959) Studies on white –core rice kernel II. On the physical properties of the kernel. Proc Crop Sci Soc Jpn 28:46–50Google Scholar
  24. Nakamura Y (2002) Towards a better understanding of the metabolic system for amylopectin biosynthesis in plants: rice endosperm as a model tissue. Plant Cell Physiol 43:718–725CrossRefPubMedGoogle Scholar
  25. NSPRC (National Standard of People Republic of China) (1999) High quality paddy, GB/T17891-1999, Standards Press of ChinaGoogle Scholar
  26. 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 127:181–197PubMedGoogle Scholar
  27. Redoña ED, Mackill DJ (1998) Quantitative trait locus analysis for rice panicle and grain characteristics. Theor Appl Genet 96:957–963Google Scholar
  28. SAS Institute (1994) JMP statistics and graphics guide: version 3: SAS Institute, Cary, N.C.Google Scholar
  29. Smith AM, Denyer K, Martin C (1997) The synthesis of the starch granule. Annu Rev Plant Physiol. Plant Mol Biol 48:67–87Google Scholar
  30. Tan YF, Xing YZ, Li JX, Yu SB, Xu CG, Zhang QF (2000) Genetic bases of appearance quality of rice grains in Shanyou 63, an elite rice hybrid. Theor Appl Genet 101:823–829Google Scholar
  31. Tanksley SD (1993) Mapping ploygenes. Annu Rev Genet 27:205–233CrossRefPubMedGoogle Scholar
  32. Teulat B, Zoumarou-Wallis N, Rotter B, Salem MB, Bahri H, This D (2003) QTL for relative water content in field-grown barley and their stability across Mediterranean environments. Theor Appl Genet 108:181–188Google Scholar
  33. Tsunematsu H, Yoshimura A, Harushima Y, Nagamura Y, Kurata N, Yano M, Sasaki T, Iwata N (1996) RFLP framework map using recombinant inbred lines in rice. Breed Sci 46:279–284Google Scholar
  34. Vargas MH, Crossa J (2000) The AMMI analysis and graphing the biplot. Universidad Autónoma Chapingo, Biometrics and Statistics Unit, CIMMYT, Mexico. http://www.cimmyt.cgiar.org/biometrics
  35. Vargas M, Crossa J, van Eeuwijk FA, Ramírez ME, Sayre K (1999) Using partial least squares regression, factorial regression, and AMMI models for interpreting genotype × environment interaction. Crop Sci 39:955–967Google Scholar
  36. Wan XY, Wan JM, Su CC, Wang CM, Shen WB, Li JM, Wang HL, Jiang L, Liu SJ, Chen LM, Yasui H, Yoshimura A (2004) QTL detection for eating quality of cooked rice in a population of chromosome segment substitution lines. Theor Appl Genet 110:71–79Google Scholar
  37. Xing YZ, Tan YF, Xu CG, Hua JP, Sun XL (2001) Mapping quantitative trait loci for grain appearance traits of rice using a recombinant inbred line population. Acta Bot Sin 43:840–845Google Scholar
  38. Yamamoto R, Horisue N, Ikeda R (1995) Rice breeding manual. Miscellaneous Publication of the National Agriculture Research Centre in Japan. No. 30, OctoberGoogle Scholar
  39. Yano M, Katayose Y, Ashikari M, Yamanouchi U, Monna Lisa, Fuse T, Baba T, Yamamoto K, Umehara Y, Nagamura Y, Sasaki T (2000) Hd-1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. Plant Cell 12:2473–2483CrossRefPubMedGoogle Scholar
  40. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedGoogle Scholar
  41. Zhang Z, Lu C, Xiang ZH (1998) Analysis of variety stability based on the AMMI model in silkworm. Sci Agric Sin 31:62–68Google Scholar
  42. Zhuang JY, Lin HX, Lu J, Qian HR, Hittamani S, Huang N, Zheng KL (1997) Analysis of QTL × environment interaction for yield components and plant height in rice. Theor Appl Genet 95:799–808Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • X. Y. Wan
    • 1
  • J. M. Wan
    • 1
    • 2
  • J. F. Weng
    • 1
  • L. Jiang
    • 1
  • J. C. Bi
    • 1
  • C. M. Wang
    • 1
  • H. Q. Zhai
    • 2
  1. 1.National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research CenterNanjing Agricultural UniversityNanjingChina
  2. 2.Institute of Crop ScienceChinese Academy of Agriculture SciencesBeijingChina

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