Abstract

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

Highlights

  • Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using random cross validation

  • Our intention is to demonstrate and explain why genomic prediction accuracy can only be interpreted in consideration of the prediction scenario, which requires a clear prediction objective, and to show how population and family structure can have an effect on genomic prediction accuracies obtained from different cross validation scenarios

  • Genomic prediction accuracies obtained from cross validation can be strongly inflated due to population or family structure and do not necessarily represent accuracies to be expected in a plant breeding program

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Summary

Introduction

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using random cross validation. Genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many experimental plant populations and breeding populations. Genomic selection is a form of marker-assisted selection which utilizes associations between the phenotype and a large number of molecular markers across the whole genome (Goddard and Hayes, 2007) By capturing these associations, genomic selection can enable to predict genotype performance and make selections based on markers even before a seed has been planted. The training population is used to establish a genomic prediction model that can be used to predict the genetic values of unphenotyped genotypes or to obtain improved evaluations in case phenotypic information is strongly limited, e.g., in early selection stages. If applied appropriately, breeding strategies using genomic selection can benefit from optimized resource allocation, accurate evaluation of unphenotyped germplasm, and shortened breeding cycle time (Heffner et al, 2010; Heslot et al, 2015)

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