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Open Access Research article

Genomic value prediction for quantitative traits under the epistatic model

Zhiqiu Hu1, Yongguang Li2, Xiaohui Song2, Yingpeng Han2, Xiaodong Cai3, Shizhong Xu1 and Wenbin Li2*

Author affiliations

1 Department of Botany and Plant Science, University of California, Riverside, California, 92521, USA

2 Soybean Research Institute (Chinese Education Ministry's Key Laboratory of Soybean Biology), Northeast Agricultural University, 150030 Harbin, PR China

3 Dept. of Electrical & Computer Engineering, University of Miami 1251 Memorial Drive, Coral Gables, FL 33146, USA

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Citation and License

BMC Genetics 2011, 12:15  doi:10.1186/1471-2156-12-15

Published: 26 January 2011

Abstract

Background

Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement.

Results

In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78.

Conclusions

This study provided an excellent example for the application of genome selection to plant breeding.