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


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.


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.



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).


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

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

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