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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(More)
High density genotyping data are indispensable for genomic analyses of complex traits in animal and crop species. Maize is one of the most important crop plants worldwide, however a high density SNP genotyping array for analysis of its large and highly dynamic genome was not available so far. We developed a high density maize SNP array composed of 616,201(More)
The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units. One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With(More)
Maize (Zea mays L.) is particularly sensitive to chilling in the early growth stages. The objective of this study was to determine quantitative trait loci (QTL) for early plant vigour of maize grown under cool and moderately warm conditions in Central Europe. A population of 720 doubled haploid (DH) lines was derived from a cross between two dent inbred(More)
Partial restoration of male fertility limits the use of C-type cytoplasmic male sterility (C-CMS) for the production of hybrid seeds in maize. Nevertheless, the genetic basis of the trait is still unknown. Therefore, the aim to this study was to identify genomic regions that govern partial restoration by means of a QTL analysis carried out in an F(2)(More)
In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive(More)
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