Genome-based prediction of testcross values in maize |
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Authors: | Theresa Albrecht Valentin Wimmer Hans-Jürgen Auinger Malena Erbe Carsten Knaak Milena Ouzunova Henner Simianer Chris-Carolin Schön |
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Institution: | 1.Plant Breeding,Technische Universit?t München,Freising,Germany;2.Department of Animal Sciences,Georg-August-Universit?t G?ttingen,G?ttingen,Germany;3.KWS SAAT AG,Einbeck,Germany |
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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 relatedness
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. |
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