Abstract
Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait–based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait– and multi-trait–based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait–based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.
Highlights
Wheat (Triticum aestivum L.) is one of the most important staple crops worldwide, providing 18% of the caloric intake (Awika, 2011; Saini et al, 2022)
Genomic selection models: We evaluated the performances of four uni-trait and multi-trait genomic selection (GS) models for predicting seven enduse quality traits, and prediction accuracy was compared under different validation scenarios to mimic the breeding program
This study explored the potential of using multi-trait–based GS models to predict seven end-use quality traits in soft white wheat population planted at two locations in Washington, United States, from 2015 to 2019
Summary
Wheat (Triticum aestivum L.) is one of the most important staple crops worldwide, providing 18% of the caloric intake (Awika, 2011; Saini et al, 2022). Soft white wheat (SWW) is a predominant class in eastern Washington. SWW is one of the wheat classes with high demands from overseas markets in countries like the Philippines, Korea, Japan, and Indonesia, due to its high end-use quality. Soft wheat is mainly used for making cakes, cookies, pastries, Asian-style noodles, crackers, and pretzels (Morris et al, 2008). In addition to having high grain yield, disease and insect resistance, wide adaptability, and cold tolerance, the released wheat cultivar needs to maintain high end-use quality attributes required by millers, bakers, and grain markets (Morris et al, 2009; Carter et al, 2012; Guzman et al, 2016; Sandhu et al, 2021d)
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.
