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

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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.

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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).

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Authors and Affiliations

  1. Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang, 310029, People’s Republic of China

    Jian Yang & Jun Zhu

Authors
  1. Jian Yang
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  2. Jun Zhu
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Corresponding author

Correspondence to Jun Zhu.

Additional information

Communicated by H.C. Becker

Appendix

Appendix

Denote n as the total number of QTLs. The stepwise tuning algorithm can be described by the following procedure:

  1. 1.

    Initialize the genotype of an individual as the same genotype of P1 and set i=1.

  2. 2.

    To predict SLs, GSLs, SHs or GSHs, go to Procedure A, Procedure B, Procedure C, or Procedure D, respectively.

  3. 3.

    Set i=i+1. If i≤n repeat step 2, otherwise, stop.

Procedure A

  1. (1)

    If i=1, calculate the total genetic effect of the individual in the hth environment \(\left( {\hat G'_h } \right)\) using all the QTL effects.

  2. (2)

    Change the genotype of the i-th QTL from Q i Q i to qi qi and calculate its total genetic effect \(\left( {\hat G'} \right)\) again. If \(\hat G'_h > \hat G_h \;({\text{or}}\;\hat G'_h < \hat G_h ),\) keep the change and set \(\hat G_h = \hat G'_h \) otherwise, set the genotype of the ith QTL back to Q i Q i .

Procedure B

  1. (1)

    If i=1, calculate the general genetic effect of the individual \(\left( {\hat G_G } \right)\) using only the estimated QTL main effects.

  2. (2)

    Change the genotype of the ith QTL from Q i Q i to q i q i and calculate its general genetic effect \(\left( {\hat G'_G } \right)\) again. If \(\hat G'_G > \hat G_G \;({\text{or}}\;\hat G'_G < \hat G_G ),\) keep the change and set \(\hat G_G = \hat G'_G \) otherwise, set the genotype of the ith QTL back to Q i Q i .

Procedure C

  1. (1)

    If i=1, calculate the total genetic effect of the individual in the hth environment \(\left( {\hat G_h } \right)\) using all the QTL effects.

  2. (2)

    Change the genotype of the ith QTL from QiQi to qiqi and calculate its total genetic effect of superior line \(\left( {\hat G'_h } \right)\) again. If \(\hat G'_h > \hat G_h \;({\text{or}}\;\hat G'_h < \hat G_h ),\) keep the change and set \(\hat G_h = \hat G'_h ,\) otherwise, set the genotype of the ith QTL back to Q i Q i .

  3. (3)

    Change the genotype of the ith QTL from QiQi to Qi qi and calculate its total genetic effect \(\left( {\hat G'_h } \right)\) again. If \(\hat G'_h > \hat G_h \;(or\;\hat G'_h < \hat G_h ),\) keep the change and set \(\hat G_h = \hat G'_h ,\) otherwise, set the genotype of the ith QTL back to Q i Q i .

Procedure D

  1. (1)

    If i=1, calculate the general genetic effect of the individual \(\left( {\hat G_G } \right)\) using onlythe estimated QTL main effects.

  2. (2)

    Change the genotype of the ith QTL from to q i q i and calculate its general genetic effect \(\left( {\hat G'_G } \right)\) again. If \(\hat G'_G > \hat G_G \;({\text{or}}\;\hat G'_G < \hat G_G ),\) keep the Q i Q i change and set \(\hat G_G = \hat G'_G ,\) otherwise, set the genotype of the ith QTL back to Q i Q i .

  3. (3)

    Change the genotype of the ith QTL from Q i Q i to Q i q i and calculate its general genetic effect \(\left( {\hat G'_G } \right)\) again. If \(\hat G'_G > \hat G_G \;({\text{or}}\;\hat G'_G < \hat G_G ),\) keep the change and set \(\hat G_G = \hat G'_G ,\) otherwise, set the genotype of the ith QTL back to Q i Q i ;

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Yang, J., Zhu, J. Methods for predicting superior genotypes under multiple environments based on QTL effects. Theor Appl Genet 110, 1268–1274 (2005). https://doi.org/10.1007/s00122-005-1963-2

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  • DOI: https://doi.org/10.1007/s00122-005-1963-2

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