Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

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.

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

“…These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.Continued crop improvement is paramount to feeding the continuously increasing human population, which is more critical considering climate change, meeting sustainability goals, and limited natural resources 1,2 . Wheat is consumed by two-thirds of the world's population, meeting 20% of dietary calories, and grown in a wide geographical distribution, 45° S in Argentina to 67° N in Scandinavia, including some high-altitude regions in the tropics and subtropics [3][4][5] . Owing to its wide distribution and climate variability, wheat is affected by various biotic (yellow, brown, and stem rusts, fusarium head blight, tan spot, and several other diseases, insects, and nematodes) and abiotic stresses (drought, heat, salinity, water-logging, pre-harvest sprouting, and mineral toxicity, among others) 6 .…”

“…Owing to its wide distribution and climate variability, wheat is affected by various biotic (yellow, brown, and stem rusts, fusarium head blight, tan spot, and several other diseases, insects, and nematodes) and abiotic stresses (drought, heat, salinity, water-logging, pre-harvest sprouting, and mineral toxicity, among others) 6 . Breeding climate-resilient wheat cultivars is the best approach to help wheat in surviving abiotic stresses, which could be facilitated by mapping the genomic regions involved, marker-assisted breeding, and other advanced approaches such as genome editing, genomic-assisted breeding (involving the use of highthroughput genotyping and phenotyping systems), and haplotype-based breeding 4,5,[7][8][9] .Drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) are the major abiotic stresses affecting wheat yield and production around the world 10,11 . Heat stress affects 58% of the wheat production area, while drought affects 42% 12 .…”

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea (Pisum sativum L.)

“…The availability of multi-environment dataset can improve estimate of genotypic values for quantitative traits. Since significant progress has been made in multi-trait multi-environment genomic prediction (Montesinos-López et al 2016, 2018, 2019; Gill et al 2021; Sandhu et al 2022), our findings suggest future research should focus on developing an optimal strategy for genomic prediction enabled sparse testing of multiple traits in multi-environment trials. This will likely further lower the cost of phenotyping and the time-consuming data collection process.…”

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

“…Continued crop improvement is paramount to feeding the continuously increasing human population, which is more critical regarding climate change, meeting sustainability goals, and limited natural resources 1,2 . Wheat is consumed by two-thirds of the world’s population, meeting 20% of dietary calories, and grown in a wide geographical distribution, 45 0 S in Argentina to 67 0 N in Scandinavia, including some high-altitude regions in the tropics and subtropics 3,4,5 . Owing to its wide distribution and climate variability, wheat is affected by various biotic (yellow, brown, and stem rusts, fusarium head blight, tan spot, and several other diseases, insects, and nematodes) and abiotic stresses (drought, heat, salinity, water-logging, pre-harvest sprouting, and mineral toxicity, among others) 6 .…”

“…Owing to its wide distribution and climate variability, wheat is affected by various biotic (yellow, brown, and stem rusts, fusarium head blight, tan spot, and several other diseases, insects, and nematodes) and abiotic stresses (drought, heat, salinity, water-logging, pre-harvest sprouting, and mineral toxicity, among others) 6 . Breeding climate-resilient wheat cultivars is the best approach to assisting wheat in surviving abiotic stresses, which could be facilitated by mapping the genomic regions involved, marker-assisted breeding, and other advanced approaches such as genome editing, genomic-assisted breeding (involving the use of high-throughput genotyping and phenotyping systems), and haplotype-based breeding 7,8,4,5,9 .…”