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Prediction of Means and Variances of Crosses With Genome-Wide Marker Effects in Barley

Osthushenrich, Tanja ; Frisch, Matthias ; Zenke-Philippi, Carola ; Jaiser, Heidi ; Spiller, Monika ; Cselenyi, Laszlo ; Krumnacker, Kerstin ; Boxberger, Susanna ; Kopahnke, Doris ; Habekuß, Antje ; Ordon, Frank ; Herzog, Eva


Originalveröffentlichung: (2018) Frontiers in Plant Science 9:1899 doi: 10.3389/fpls.2018.01899
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URN: urn:nbn:de:hebis:26-opus-157336
URL: http://geb.uni-giessen.de/geb/volltexte/2020/15733/


Freie Schlagwörter (Englisch): cross prediction , genomic prediction , variance prediction , segregation variance , genetic variance
Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-UniversitĂ€t Gießen
Institut: Institute of Agronomy and Plant Breeding II
Fachgebiet: Agrarwissenschaften und Umweltmanagement
DDC-Sachgruppe: Landwirtschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2018
Publikationsdatum: 01.12.2020
Kurzfassung auf Englisch: Background: The expected genetic variance is an important criterion for the selection of crossing partners which will produce superior combinations of genotypes in their progeny. The advent of molecular markers has opened up new vistas for obtaining precise predictors for the genetic variance of a cross, but fast prediction methods that allow plant breeders to select crossing partners based on already available data from their breeding programs without complicated calculations or simulation of breeding populations are still lacking. The main objective of the present study was to demonstrate the practical applicability of an analytical approach for the selection of superior cross combinations with experimental data from a barley breeding program. We used genome-wide marker effects to predict the yield means and genetic variances of 14 DH families resulting from crosses of four donor lines with five registered elite varieties with the genotypic information of the parental lines. For the validation of the predicted parameters, the analytical approach was extended by the masking variance as a major component of phenotypic variance. The predicted parameters were used to fit normal distribution curves of the phenotypic values and to conduct an Anderson-Darling goodness-of-fit test for the observed phenotypic data of the 14 DH families from the field trial.
Results: There was no evidence that the observed phenotypic values deviated from the predicted phenotypic normal distributions in 13 out of 14 crosses. The correlations between the observed and the predicted means and the observed and predicted variances were r = 0.95 and r = 0.34, respectively. After removing two crosses with downward outliers in the phenotypic data, the correlation between the observed and predicted variances increased to r = 0.76. A ranking of the 14 crosses based on the sum of predicted mean and genetic variance identified the 50% best crosses from the field trial correctly.
Conclusions: We conclude that the prediction accuracy of the presented approach is sufficiently high to identify superior crosses even with limited phenotypic data. We therefore expect that the analytical approach based on genome-wide marker effects is applicable in a wide range of breeding programs.
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