Calibration and validation of predicted genomic breeding values in an advanced cycle maize population
| Autor: | Daniel Gianola, Sofia da Silva, Milena Ouzunova, Manfred Mayer, Carsten Knaak, Christina Lehermeier, Albrecht E. Melchinger, Chris-Carolin Schön, Hans-Jürgen Auinger |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Calibration (statistics) Quantitative Trait Loci Population Biology Quantitative trait locus Polymorphism Single Nucleotide Zea mays 01 natural sciences Chromosomes Plant 03 medical and health sciences Statistics Genetics education Selection (genetic algorithm) Reliability (statistics) 030304 developmental biology Linkage (software) 0303 health sciences education.field_of_study Chromosome Mapping General Medicine ddc Data set Plant Breeding Phenotype Sample size determination Original Article Agronomy and Crop Science Genome Plant 010606 plant biology & botany Biotechnology |
| Zdroj: | TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik |
| Popis: | Key message Model 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. Abstract 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|