Genome-based prediction of testcross values in maize

Theresa Albrecht, Valentin Wimmer, Hans Jürgen Auinger, Malena Erbe, Carsten Knaak, Milena Ouzunova, Henner Simianer, Chris Carolin Schön

Research output: Contribution to journalArticlepeer-review

224 Scopus citations

Abstract

Abstract This is the first large-scale experimental study on genome-based prediction of testcross values in an advanced cycle breeding population of maize. The study comprised testcross progenies of 1,380 doubled haploid lines of maize derived from 36 crosses and phenotyped for grain yield and grain dry matter content in seven locations. The lines were genotyped with 1,152 single nucleotide polymorphism markers. Pedigree data were available for three generations. We used best linear unbiased prediction and stratified cross-validation to evaluate the performance of prediction models differing in the modeling of related-ness between inbred lines and in the calculation of genome-based coefficients of similarity. The choice of similarity coefficient did not affect prediction accuracies. Models including genomic information yielded significantly higher prediction accuracies than the model based on pedigree information alone. Average prediction accuracies based on genomic data were high even for a complex trait like grain yield (0.72-0.74) when the cross-validation scheme allowed for a high degree of relatedness between the estimation and the test set. When predictions were performed across distantly related families, prediction accuracies decreased significantly (0.47-0.48). Prediction accuracies decreased with decreasing sample size but were still high when the population size was halved (0.67-0.69). The results from this study are encouraging with respect to genome-based prediction of the genetic value of untested lines in advanced cycle breeding populations and the implementation of genomic selection in the breeding process.

Original languageEnglish
Pages (from-to)339-350
Number of pages12
JournalTheoretical and Applied Genetics
Volume123
Issue number2
DOIs
StatePublished - Jul 2011

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