Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding

Sebastian Michel,Ellen Sparry,Christian Ametz,Hermann Bürstmayr,Franziska Löschenberger,Bernadette Pachler

doi:10.1007/s00122-019-03312-5

Sebastian Michel, Ellen Sparry + Show 4 more

Open Access

https://doi.org/10.1007/s00122-019-03312-5

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Abstract

Key messageLarge genetic improvement can be achieved by simultaneous genomic selection for grain yield and protein content when combining different breeding strategies in the form of selection indices.Genomic selection has been implemented in many national and international breeding programmes in recent years. Numerous studies have shown the potential of this new breeding tool; few have, however, taken the simultaneous selection for multiple traits into account that is though common practice in breeding programmes. The simultaneous improvement in grain yield and protein content is thereby a major challenge in wheat breeding due to a severe negative trade-off. Accordingly, the potential and limits of multi-trait selection for this particular trait complex utilizing the vast phenotypic and genomic data collected in an applied wheat breeding programme were investigated in this study. Two breeding strategies based on various genomic-selection indices were compared, which (1) aimed to select high-protein genotypes with acceptable yield potential and (2) develop high-yielding varieties, while maintaining protein content. The prediction accuracy of preliminary yield trials could be strongly improved when combining phenotypic and genomic information in a genomics-assisted selection approach, which surpassed both genomics-based and classical phenotypic selection methods both for single trait predictions and in genomic index selection across years. The employed genomic selection indices mitigated furthermore the negative trade-off between grain yield and protein content leading to a substantial selection response for protein yield, i.e. total seed nitrogen content, which suggested that it is feasible to develop varieties that combine a superior yield potential with comparably high protein content, thus utilizing available nitrogen resources more efficiently.

Highlights

The implementation of genomic selection in many national and international plant breeding programmes in recent years (Guzmán et al 2016; Lado et al 2016; Michel et al 2016; Cericola et al 2017; Belamkar et al 2018; Juliana 2018) highlights the potential of this new breeding tool for variety development and accelerating the genetic improvement in crop plants
A large improvement could be achieved by including prior information from preliminary yield trials into the prediction models, which resulted in a genomicsassisted selection with an average prediction accuracy of r = 0.47 for grain yield that surpassed the prediction accuracy for phenotypic selection by 88%
According simulations suggested that the relative advantage of integrating genomic selection into a conventional breeding scheme in the form of a two-stage genomic-assisted selection in F 5 preliminary yield trials followed by F6 multi-environment trials was 54% for grain yield, 7% for protein content, and 32% for protein yield in response to selection when compared with two-stage phenotypic selection using data from the particular wheat breeding programme of this study

Summary

Introduction

The implementation of genomic selection in many national and international plant breeding programmes in recent years (Guzmán et al 2016; Lado et al 2016; Michel et al 2016; Cericola et al 2017; Belamkar et al 2018; Juliana 2018) highlights the potential of this new breeding tool for variety development and accelerating the genetic improvement in crop plants. The merit of employing genomic predictions has been frequently tested by cross-validation, and across families and years taking genomic relationship and genotype-by-environment interaction into account (Gezan et al 2017; Ben Hassen et al 2018; Kristensen et al 2018; Huang et al 2018; Pembleton et al 2018) These factors are highly relevant to enable an adequate comparison with phenotypic selection in conventional breeding schemes (Sallam and Smith 2016; Song et al 2017; Belamkar et al 2018)and optimizing resource allocations in hybrid and line variety breeding programmes Multi-trait prediction models have been recommended for cases in which prior information from a correlated trait is earlier available or easier to obtain than the main trait of interest (Jia and Jannink 2012; Fernandes et al 2017; Hayes et al 2017; Schulthess et al 2018)

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