Empowering global rice breeding programs using genomic selection. [W291]

Prakash Parthiban Thathapalli, Arbelaez Velez Juan David, Verdeprado Holden, Lopena Vitaliano, Imee Zhella Rose, Bartholome Jérôme, Rutkoski Jessica, Murori Rosemary, Ndayiragije Alexis, Rafiqul Mohammad, Kumar Katiyar Sanjay, Cobb Joshua. 2020. Empowering global rice breeding programs using genomic selection. [W291]. In : Abstracts workshops of the PAG XXVIII. San Diego : PAG, Résumé, 129-130. Plant and Animal Genome. 28, San Diego, États-Unis, 11 Janvier 2020/15 Janvier 2020.

Résumé : Rice is the staple food for half of the global population and irrigated rice contributes 70% of total rice produced. Given the strategic importance of this market segment to global food security, the irrigated rice breeding program at IRRI is mandated to breed varietal rice and empower rice breeding programs in South Asia and Eastern and Southern Africa. As public sector breeding budgets are insufficient to adequately test all new lines across such a varied environmental landscape, advanced molecular tools such as genomic selection (GS) are critical to achieving high levels of selection intensity and selection accuracy within each region. For the past two years, the irrigated lowland breeding program at IRRI has distributed carefully selected 'estimation sets' containing a few hundred breeding lines to global partners in Africa and South Asia with the intention of using genomic prediction to evaluate several thousand selection candidates that have only been genotyped. Combining different software tools such as B4R (phenotypic data management), GOBii (genotypic data management) and ASReml-R (modeling) into an analytical workflow, we have generated initial prediction accuracies for grain yield, plant height, and flowering time which are routinely used in the program to make optimized breeding decisions for each of these disparate global environments. An overview of the results will be presented as well as a discussion about the future integration of sparse testing designs, multi-environmental models, use of weather data through machine learning, and phenotyping with unmanned aerial vehicles (UAV).

Auteurs et affiliations

• Prakash Parthiban Thathapalli, IRRI [International Rice Research Institute] (PHL)
• Arbelaez Velez Juan David, University of Illinois (USA)
• Verdeprado Holden, IRRI [International Rice Research Institute] (PHL)
• Lopena Vitaliano, IRRI [International Rice Research Institute] (PHL)
• Imee Zhella Rose, IRRI [International Rice Research Institute] (PHL)
• Bartholome Jérôme, CIRAD-BIOS-UMR AGAP (PHL) ORCID: 0000-0002-0855-3828
• Rutkoski Jessica, University of Illinois (USA)
• Murori Rosemary, IRRI [International Rice Research Institute] (PHL)
• Ndayiragije Alexis, IRRI [International Rice Research Institute] (MOZ)
• Rafiqul Mohammad, IRRI [International Rice Research Institute] (IDN)
• Kumar Katiyar Sanjay, IRRI [International Rice Research Institute] (PHL)
• Cobb Joshua, Cornell University (USA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/594832/)

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