Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Research article

The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years

Yu Wang12, Michael Florian Mette1, Thomas Miedaner2, Marlen Gottwald3, Peer Wilde4, Jochen C Reif1* and Yusheng Zhao1

Author Affiliations

1 Leibniz Institute of Plant Breeding and Crop Plant Research (IPK), Gatersleben 06466, Germany

2 State Plant Breeding Institute, University of Hohenheim, Stuttgart 70599, Germany

3 Syngenta Agro GmbH, Am Technologiepark 1-5, Maintal 63477, Germany

4 KWS LOCHOW GMBH, Ferdinand-von-Lochow-Str. 5, 29303 Bergen, Germany

For all author emails, please log on.

BMC Genomics 2014, 15:556  doi:10.1186/1471-2164-15-556

Published: 4 July 2014

Abstract

Background

Marker-assisted selection (MAS) and genomic selection (GS) based on genome-wide marker data provide powerful tools to predict the genotypic value of selection material in plant breeding. However, case-to-case optimization of these approaches is required to achieve maximum accuracy of prediction with reasonable input.

Results

Based on extended field evaluation data for grain yield, plant height, starch content and total pentosan content of elite hybrid rye derived from testcrosses involving two bi-parental populations that were genotyped with 1048 molecular markers, we compared the accuracy of prediction of MAS and GS in a cross-validation approach. MAS delivered generally lower and in addition potentially over-estimated accuracies of prediction than GS by ridge regression best linear unbiased prediction (RR-BLUP). The grade of relatedness of the plant material included in the estimation and test sets clearly affected the accuracy of prediction of GS. Within each of the two bi-parental populations, accuracies differed depending on the relatedness of the respective parental lines. Across populations, accuracy increased when both populations contributed to estimation and test set. In contrast, accuracy of prediction based on an estimation set from one population to a test set from the other population was low despite that the two bi-parental segregating populations under scrutiny shared one parental line. Limiting the number of locations or years in field testing reduced the accuracy of prediction of GS equally, supporting the view that to establish robust GS calibration models a sufficient number of test locations is of similar importance as extended testing for more than one year.

Conclusions

In hybrid rye, genomic selection is superior to marker-assisted selection. However, it achieves high accuracies of prediction only for selection candidates closely related to the plant material evaluated in field trials, resulting in a rather pessimistic prognosis for distantly related material. Both, the numbers of evaluation locations and testing years in trials contribute equally to prediction accuracy.

Keywords:
Marker-assisted selection; Genomic selection; Cross-validation; Hybrid rye; Relatedness; Evaluation locations; Testing years