Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Sebastian Michel,Christian Ametz,Doru Epure,Heinrich Grausgruber,Batuhan Akgöl,Franziska Löschenberger,Hermann Buerstmayr,Huseyin Gungor

doi:10.1007/s00122-016-2818-8

Abstract

Key messageEarly generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials.The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

Highlights

Selection and development of new varieties of autogamous crops relies on a number of different breeding schemes including the pedigree and bulk methods as well as breeding acceleration using doubled haploids or single seed descent with off-season generations
The mean accuracies for both sampling methods were significant different according to a Wilcoxon rank sum test (p < 0.01), we chose to design training populations consisting of 60 lines from each year with 30 coming from either tail of the distribution to provide a high prediction accuracy with sized training populations for all folds in the comparison between conventional phenotypic and genomic selection
This study showed the strong advantage of genomic selection over conventional phenotypic selection in line breeding schemes on the example of bread wheat

Summary

Introduction

Selection and development of new varieties of autogamous crops relies on a number of different breeding schemes including the pedigree and bulk methods as well as breeding acceleration using doubled haploids or single seed descent with off-season generations. Notwithstanding, they all share a step of conventional phenotypic selection based on preliminary yield trials in their methodology. The phenotypic data obtained in this way allow only preliminary predictions of their final values they strongly influence the selection of lines that enter the following more resourcedemanding multi-environment trials, a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. Genomic selection could support the accumulation of many small effect alleles to provide higher and more durable quantitative disease resistance (Lorenz et al 2012; Ornella et al 2012; Daetwyler et al 2014; Arruda et al 2015; Rutkoski et al 2015b), which could be subsequently combined with labor-intensive and costly to assess quality traits (Heffner et al 2011b; Schmidt et al 2015)

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