Abstract
With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley (Hordeum vulgare L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.
Highlights
In the last two decades, plant breeding programs have advanced from conventional approaches involving visual selection and trait screening over several generations to potentially faster methods by means of marker technologies (Crossa et al, 2017)
Prediction models that use realized relationships based on marker information lead to a substantial increase in the prediction accuracies of complex traits compared to those using relationships based on pedigree information (VanRaden et al, 2009), and this has been observed in several genomic selection studies (Crossa et al, 2010; Albrecht et al, 2011; Burgueño et al, 2012)
The lines used in this study came from active barley breeding programs run by two breeding companies
Summary
In the last two decades, plant breeding programs have advanced from conventional approaches involving visual selection and trait screening over several generations to potentially faster methods by means of marker technologies (Crossa et al, 2017). Empirical studies in barley (Zhong et al, 2009; Sallam et al, 2015; Nielsen et al, 2016; Thorwarth et al, 2017), wheat (Triticum aestivum; Crossa et al, 2010; Dong et al, 2018; Haile et al, 2018; Norman et al, 2018), maize (Zea mays; Zhao et al, 2012; Shikha et al, 2017; VélezTorres et al, 2018), rice (Oryza sativa; Spindel et al, 2015; Xu et al, 2018), and sorghum (Sorghum bicolor; De Oliveira et al, 2018; Hunt et al, 2018) have recently all proven that with current advances in the development of high-density array-based DNA marker technologies and reduced costs, genomic selection has become an important tool in cereal breeding. While pedigree-based breeding values of unphenotyped lines will only reflect midparent genetic contributions, genomic-estimated breeding values of unphenotyped full sibs will reflect genetic differences caused by Mendelian sampling (Velazco et al, 2019)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
