| Literature DB >> 26663911 |
Yusheng Zhao1, Zuo Li1, Guozheng Liu1, Yong Jiang1, Hans Peter Maurer2, Tobias Würschum2, Hans-Peter Mock3, Andrea Matros3, Erhard Ebmeyer4, Ralf Schachschneider5, Ebrahim Kazman6, Johannes Schacht7, Manje Gowda2, C Friedrich H Longin2, Jochen C Reif8.
Abstract
Hybrid breeding promises to boost yield and stability. The single most important element in implementing hybrid breeding is the recognition of a high-yielding heterotic pattern. We have developed a three-step strategy for identifying heterotic patterns for hybrid breeding comprising the following elements. First, the full hybrid performance matrix is compiled using genomic prediction. Second, a high-yielding heterotic pattern is searched based on a developed simulated annealing algorithm. Third, the long-term success of the identified heterotic pattern is assessed by estimating the usefulness, selection limit, and representativeness of the heterotic pattern with respect to a defined base population. This three-step approach was successfully implemented and evaluated using a phenotypic and genomic wheat dataset comprising 1,604 hybrids and their 135 parents. Integration of metabolomic-based prediction was not as powerful as genomic prediction. We show that hybrid wheat breeding based on the identified heterotic pattern can boost grain yield through the exploitation of heterosis and enhance recurrent selection gain. Our strategy represents a key step forward in hybrid breeding and is relevant for self-pollinating crops, which are currently shifting from pure-line to high-yielding and resilient hybrid varieties.Entities:
Keywords: genomic prediction; heterotic pattern; hybrid breeding
Mesh:
Year: 2015 PMID: 26663911 PMCID: PMC4697414 DOI: 10.1073/pnas.1514547112
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205