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

Harsimardeep S. Gill,Cody Hall,Sunish K. Sehgal,Jyotirmoy Halder,Navreet K. Brar,Paul St Amand,Shaukat Ali,Amy Bernardo,Teerath S. Rai,Jinfeng Zhang,Eric Olson,Guihua Bai,Brent Turnipseed

doi:10.3389/fpls.2021.709545

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.

Highlights

Global wheat production needs to be increased by 60% to meet the demand of a projected population of 9 billion by 2050 (Tester and Langridge, 2010; Fischer et al, 2014)
Among the five traits evaluated in the study, test weight (TW), HT, and HDs had moderate to high heritability values in most of the environments and over both years
Genomic prediction has been intensively evaluated in wheat breeding programs to select and advance lines for several traits of interest (Rutkoski et al, 2014, 2016; Haile et al, 2020; Juliana et al, 2020)

Summary

Introduction

Global wheat production needs to be increased by 60% to meet the demand of a projected population of 9 billion by 2050 (Tester and Langridge, 2010; Fischer et al, 2014). In the past few decades, wheat breeding successfully achieved a significant increase in grain yield owing to significantly improved genetic resources, implementation of modern agronomic practices, accurate experimental designs, and other improved technology packages (Tadesse et al, 2019), which translates into an annual increase of 1% in terms of genetic gain in grain yield. This increase is still far from the expected yearly growth of 1.7% to meet the future wheat demand (Oury et al, 2012; Tadesse et al, 2019). MAS has shown a good potential in wheat breeding for the deployment of qualitative trait loci (QTLs) with large effects, its application has been limited to improve complex traits governed by many QTLs with small effects (Heffner et al, 2009)

Objectives

Results

Conclusion