%0 Journal Article
%T Genomic value prediction for quantitative traits under the epistatic model
%A Zhiqiu Hu
%A Yongguang Li
%A Xiaohui Song
%A Yingpeng Han
%A Xiaodong Cai
%A Shizhong Xu
%A Wenbin Li
%J BMC Genetics
%D 2011
%I BioMed Central
%R 10.1186/1471-2156-12-15
%X 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.This study provided an excellent example for the application of genome selection to plant breeding.Genome selection refers to a method for genomic value prediction using markers of the entire genome [1,2]. It is effective for genetic improvement of quantitative traits that are controlled by multiple quantitative trait loci (QTL). Some traits may be controlled by only a few QTL and marker assisted selection using only the few detected QTL can be effective. However, most quantitative traits are determined by multiple QTL and their interactions. Marker assisted selection using only a few detected loci may not be efficient for these traits. Using all QTL collectively to predict the breeding values of individual plants can outperform the traditional marker assisted selection [3,4]. However, there might be some trade off between the numbers of QTL included in the model and the efficiency of prediction. Cross validation can help us determine the optimal number of QTL included in the model to maximize the efficiency of genome selection.The importance of epistasis in genetic determination may vary across different species. In agricultural crops, most quantitative traits in barley do not have a strong basis of epistatic effects [5]. However, epistasis has been shown to be important in QTL studies in rice [6-8]. Dudley and Johnson [9] found that epistatic effects are more important than additive effects in determination of oil,
%U http://www.biomedcentral.com/1471-2156/12/15