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.
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http://dx.doi.org/10.1073/pnas.1514547112 | DOI Listing |
Front Plant Sci
September 2024
Maize Research Institute/State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, Sichuan, China.
When genomic prediction is implemented in breeding maize ( L.), it can accelerate the breeding process and reduce cost to a large extent. In this study, 11 yield-related traits of maize were used to evaluate four genomic prediction methods including rrBLUP, HEBLP|A, RF, and LightGBM.
View Article and Find Full Text PDFMol Breed
July 2024
Department of Agronomy, Purdue University, West Lafayette, IN 47907 USA.
BMC Plant Biol
April 2024
Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, 650205, China.
Sci Rep
March 2024
Department of Botany and Microbiology, College of Science, King Saud University, 11451, Riyadh, Saudi Arabia.
Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping.
View Article and Find Full Text PDFBMC Genom Data
September 2023
International Institute of Tropical Agriculture (IITA), PMB 5320, Ibadan, 200001, Nigeria.
Background: The establishment of heterotic groups of inbred lines is crucial for hybrid maize breeding programs. Currently, there is no information on the heterotic patterns of the Provitamin A (PVA) inbred lines developed in the maize improvement program of the International Institute of Tropical Agriculture (IITA) to form productive PVA enriched hybrids for areas affected by vitamin A deficiency. This study assessed the feasibility of classifying PVA-enriched inbred lines into heterotic groups based on PVA content without compromising grain yield in hybrids.
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