Calibration and validation of predicted genomic breeding values in an advanced cycle maize population

Hans-Jürgen Auinger,Carsten Knaak,Christina Lehermeier,Milena Ouzunova,Chris-Carolin Schön,Manfred Mayer,Sofia Da Silva,Daniel Gianola,Albrecht E Melchinger

doi:10.1007/s00122-021-03880-5

Abstract

Key messageModel training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets.The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.

Highlights

The prediction of breeding values from molecular data has become a key component of many plant breeding programmes
Trait heritabilities (h2) on a progenymean basis were high for both traits in most data sets with the exception of GDY in S4 and S6
Within data sets S1, S3 and S4, family substructures were visible in the heatmap of pairwise realised kinship coefficients between doubled haploid (DH) lines

Summary

Introduction

The prediction of breeding values from molecular data has become a key component of many plant breeding programmes. In breeding hybrid crops such as maize, genomic prediction can be applied at different stages of the breeding scheme. For each of these prediction steps, a statistical model must be trained on experimental calibration data comprising highquality phenotypes and genotypes. Deterministic formulas forecasting prediction accuracy suggest a strong influence of the sample size, the heritability, the genetic architecture of the target trait and the genome structure of the species under study (Daetwyler et al 2010; Schopp et al 2017). Simulation studies have shown that the mating design and family structure of the calibration set have a strong influence on prediction accuracy (Hickey et al 2014). Results from experimental studies corroborate these findings irrespective of whether the studied populations were designed for research purposes (Lehermeier et al 2014) or originated from commercial breeding programmes (Albrecht et al 2014; Krchov et al 2015; Auinger et al 2016)

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Discussion

Conclusion