| Literature DB >> 24850820 |
Frank Technow1, Tobias A Schrag1, Wolfgang Schipprack1, Eva Bauer2, Henner Simianer3, Albrecht E Melchinger4.
Abstract
Maize (Zea mays L.) serves as model plant for heterosis research and is the crop where hybrid breeding was pioneered. We analyzed genomic and phenotypic data of 1254 hybrids of a typical maize hybrid breeding program based on the important Dent × Flint heterotic pattern. Our main objectives were to investigate genome properties of the parental lines (e.g., allele frequencies, linkage disequilibrium, and phases) and examine the prospects of genomic prediction of hybrid performance. We found high consistency of linkage phases and large differences in allele frequencies between the Dent and Flint heterotic groups in pericentromeric regions. These results can be explained by the Hill-Robertson effect and support the hypothesis of differential fixation of alleles due to pseudo-overdominance in these regions. In pericentromeric regions we also found indications for consistent marker-QTL linkage between heterotic groups. With prediction methods GBLUP and BayesB, the cross-validation prediction accuracy ranged from 0.75 to 0.92 for grain yield and from 0.59 to 0.95 for grain moisture. The prediction accuracy of untested hybrids was highest, if both parents were parents of other hybrids in the training set, and lowest, if none of them were involved in any training set hybrid. Optimizing the composition of the training set in terms of number of lines and hybrids per line could further increase prediction accuracy. We conclude that genomic prediction facilitates a paradigm shift in hybrid breeding by focusing on the performance of experimental hybrids rather than the performance of parental lines in test crosses.Entities:
Keywords: GenPred, shared data resources; genomic prediction; heterotic groups; hybrid breeding; linkage phases; training set design
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Year: 2014 PMID: 24850820 PMCID: PMC4125404 DOI: 10.1534/genetics.114.165860
Source DB: PubMed Journal: Genetics ISSN: 0016-6731 Impact factor: 4.562