Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

Abstract

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

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

“…Multienvironment prediction represents a perfect scenario to reduce the number of locations or plots needed in subsequent selection trials (Tolhurst et al, 2019;de Oliveira et al, 2020). Multi-trait GS models showed improved prediction accuracy in previous studies when traits are correlated and have low heritability; these models provide an opportunity to predict traits simultaneously by borrowing information from each other (Gill et al, 2021;Larkin et al, 2021). 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.…”

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.…”

Forecasting the genetic and economic impacts of genomic selection in perennial ryegrass

“…GS allows a subsequent cycle to be completed within a year or less (Jighly et al, 2019) as GEBVs can be estimated when the selection candidates are seedlings. However, for this to be effective in accelerating a multi-trait breeding programme, a GS model needs to be effective for all essential traits required under selection in the breeding programme, as has recently been demonstrated in cereal breeding (Gill et al, 2021). The longstanding issues of negatively correlated traits, genotype x environment interactions, and the challenge whereby increasing the number of traits generally slows the rate of gain for the selection index are not directly overcome by GS.…”

“…The longstanding issues of negatively correlated traits, genotype x environment interactions, and the challenge whereby increasing the number of traits generally slows the rate of gain for the selection index are not directly overcome by GS. However, there are examples where secondary traits can be used to improve the accuracy of a GS model, which warrant further evaluation (Arojju et al, 2020;Gill et al, 2021).…”