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Theoretical and Applied Genetics

, Volume 110, Issue 7, pp 1268–1274 | Cite as

Methods for predicting superior genotypes under multiple environments based on QTL effects

  • Jian Yang
  • Jun ZhuEmail author
Original Paper

Abstract

Methods were developed for predicting two kinds of superior genotypes (superior line and superior hybrid) based on quantative trait locus (QTL) effects including epistatic and QTL × environment interaction effects. Formulae were derived for predicting the total genetic effect of any individual with known QTLs genotype derived from the mapping population in a specific environment. Two algorithms, enumeration algorithm and stepwise tuning algorithm, were used to select the best multi-locus combination of all the putative QTLs. Grain weight per plant (GW) in rice was analyzed as a working example to demonstrate the proposed methods. Results showed that the predicted superior lines and superior hybrids had great superiorities over the F1 hybrid, indicating large breeding potential remained for further improvement on GW. Results also showed that epistatic effects and their interaction with environments largely contributed to the superiorities of the predicted superior lines and superior hybrids. User-friendly software, QTLNetwork, version 1.0, was developed based on the methods in the present paper.

Keywords

Composite Interval Mapping Epistatic Effect Grain Weight Enumeration Algorithm Superior Genotype 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We greatly thank two anonymous reviewers for useful comments and suggestions on the earlier version of the manuscript. This research was partially supported by the National Natural Science Foundation of China and by the National High Technology Research and Development Program of China (863 Program).

References

  1. Allard RW (1996) Genetic basis of the evolution of adaptedness in plants. Euphytica 92:1–11Google Scholar
  2. Fijneman RJA, Devries SS, Jansen RC, Demant P (1996) Complex interactions of new quantitative trait loci, sluc1, sluc2, sluc3 and sluc4, that influence the susceptibility to lung-cancer in the mouse. Nat Genet 14:465–467Google Scholar
  3. Hua JP, Xing YZ, Xu CG, Sun XL, Yu SB, Zhang QF (2002) Genetic dissection of an elite rice hybrid revealed that heterozygotes are not always advantageous for performance. Genetics 162:1885–1995PubMedGoogle Scholar
  4. Jannink JL Jansen RC (2001) Mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 157:445–454Google Scholar
  5. Jansen RC (1993) Interval mapping of multiple quantitative trait loci. Genetics 135:205–211PubMedGoogle Scholar
  6. Jansen RC (1994) Controlling the type I and type II errors in mapping quantitative trait loci. Genetics 138:871–881Google Scholar
  7. Jansen RC, van Ooijen JW, Stam P, Lister C, Dean C (1995) Genotype by environment interaction in genetic mapping of multiple quantitative trait loci. Theor Appl Genet 91:33–37Google Scholar
  8. Jiang CJ, Zeng ZB (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140:1111–1127PubMedGoogle Scholar
  9. Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152:1203–1216PubMedGoogle Scholar
  10. Lander ES, Botstein S (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedGoogle Scholar
  11. Li ZK, Luo LJ, Mei HW, Wang DL, Shu QY, Tabien R, Zhong DB, Ying CS, Stansel JW, Khush GS, Paterson AH (2001) Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I Biomass and grain yield. Genetics 158:1737–1753Google Scholar
  12. Lukens LN, Doebley JF (1999) Epistatic and environmental interactions for quantitative trait loci involved in maize evolution. Genet Res 74:291–302CrossRefGoogle Scholar
  13. Piepho HP (2000) A mixed model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics 156:2043–2050Google Scholar
  14. Wang DL, J Zhu, ZK Li, AH Paterson (1999) Mapping QTLs with epistatic effects and QTL environment interactions by mixed linear model approaches. Theor Appl Genet 99:1255–1264CrossRefGoogle Scholar
  15. Zeng ZB (1993) Theoretical basis of separation of multiple linked gene effects on mapping quantitative trait loci. Proc Natl Acad Sci USA 90:10972–10976Google Scholar
  16. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedGoogle Scholar
  17. Zeng ZB, Kao CH, Basten CJ (1999) Estimating the genetic architecture of quantitative traits. Genet Res 74:279–289Google Scholar
  18. Zhu J (1999) Mixed model approaches of mapping genes for complex quantitative traits. J Zhejiang Univ (Nat Sci) 33(3):327–335Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Institute of BioinformaticsZhejiang UniversityHangzhou, ZhejiangPeople’s Republic of China

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