Abstract
Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.
Highlights
Resistance to the disease Fusarium head blight (FHB) is important in wheat (Triticum aestivum L.) production, in the Southeastern US
This study showed that both naïve GS (NGS) and multitrait GS (MTGS) could be successfully implemented into a soft red winter wheat (SRWW) breeding program, while using other agronomic and disease traits as covariates with reasonable accuracy compared to phenotypic selection and again asserted its value as a tool for plant breeders
We found that MTGS models performed significantly better than NGS models in terms of both cross-validation within training population (TP) as well as forward prediction of untested genotypes for economically important traits, such as FHB resistance traits
Summary
Resistance to the disease Fusarium head blight (FHB) is important in wheat (Triticum aestivum L.) production, in the Southeastern US. Alternatives to phenotypic selection include marker assisted selection (MAS) and genomic selection (GS). MAS is less effective for complex quantitative traits controlled by many small-effect QTL (Bernardo and Yu, 2007; Heffner et al, 2009). Genomic selection is an effective alternative to both phenotypic selection and MAS, in that it incorporates allelic effects across the entire genome, making it ideal for quantitative traits. Genomic selection can reduce the time within a breeding cycle, as two rounds of GS can be performed compared to one cycle of phenotypic selection allowing for greater genetic gain over time (Bernardo and Yu, 2007; Heffner et al, 2009; Asoro et al, 2013; Rutkoski et al, 2015)
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