Article CC BY 4.0
refereed
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Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials

Affiliation
Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
Terraillon, Jérôme;
GND
173310966
Affiliation
Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
Frisch, Matthias;
GND
133392988
Affiliation
Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
Falke, K. Christin;
Affiliation
Saatzucht Josef Breun GmbH Co. KG, Herzogenaurach, Germany
Jaiser, Heidi;
GND
140060545
Affiliation
KWS Lochow GmbH, Northeim, Germany
Spiller, Monika;
Affiliation
W. von Borries-Eckendorf GmbH Co. KG, Leopoldshöhe, Germany
Cselényi, László;
GND
139342192
Affiliation
Limagrain GmbH, Peine-Rosenthal, Germany
Krumnacker, Kerstin;
Affiliation
Ackermann Saatzucht GmbH Co. KG, Irlbach, Germany
Boxberger, Susanna;
GND
1059141299
Affiliation
Julius Kühn-Institute (JKI), Institute for Resistance Research and Stress Tolerance, Germany
Habekuß, Antje;
GND
1059141396
Affiliation
Julius Kühn-Institute (JKI), Institute for Resistance Research and Stress Tolerance, Germany
Kopahnke, Doris;
GND
137068751
Affiliation
Julius Kühn-Institute (JKI), Institute for Resistance Research and Stress Tolerance, Germany
Serfling, Albrecht;
GND
172295300
Affiliation
Julius Kühn-Institute (JKI), Institute for Resistance Research and Stress Tolerance, Germany
Ordon, Frank;
GND
1136350179
Affiliation
Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
Zenke-Philippi, Carola

Genomic prediction has been established in breeding programs to predict the genotypic values of selection candidates without phenotypic data. First results in wheat showed that genomic predictions can also prove useful to select among material for which phenotypic data are available. In such a scenario, the selection candidates are evaluated with low intensity in the field. Genome-wide effects are estimated from the field data and are then used to predict the genotypic values of the selection candidates. The objectives of our simulation study were to investigate the correlations r(y, g) between genomic predictions y and genotypic values g and to compare these with the correlations r(p, g) between phenotypic values p and genotypic values g. We used data from a yield trial of 250 barley lines to estimate variance components and genome-wide effects. These parameters were used as basis for simulations. The simulations included multiple crossing schemes, population sizes, and varying sizes of the components of the masking variance. The genotypic values g of the selection candidates were obtained by genetic simulations, the phenotypic values p by simulating evaluation in the field, and the genomic predictions y by RR-BLUP effect estimation from the phenotypic values. The correlations r(y, g) were greater than the correlations r(p, g) for all investigated scenarios. We conclude that using genomic predictions for selection among candidates tested with low intensity in the field can proof useful for increasing the efficiency of barley breeding programs.

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