New Developments for Embracing Genomic Selection in Breeding Applications

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University of Florida

International Rice Research Institute (IRRI)

Institute of Crop Science (NARO)

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The advancement of new technologies in different plant science-related fields and disciplines, such as environtyping, soil science, high-throughput phenotyping and sequencing among others, allow the characterization of the phenotype equation’s model components (P = E + G + G × E + e) with a high degree of detail. The integration of these sources of information (multi-omics and multi-layer) in the prediction context, using different methods, could potentially help to better understand the role of genetics and its interaction with environmental factors. Examples of the aforementioned methods being crop growth models, machine learning techniques, elaborated statistical models, etc.

Understanding how the interaction of these layers shape genotypes can enhance our ability for performing predictions under complex scenarios. The aim of this Research Topic is thus to present the recent advancements and developments in leveraging the availability of novel sources of information together with the new prediction tools in plant and animal breeding applications.

Topics of interest include:
1) Multi-omics data integration, multiple layer integration may help to improve the predictive ability of phenotypes through exploring the potential uses from an animal and plant breeder's perspective;
2) Integration of multi-layered data, crop growth models, machine learning and elaborated statistical models for Genomic Selection applications. The integration of these novel methods could improve our ability to predict crop performance in complex scenarios.

The advancement of new technologies in different plant science-related fields and disciplines, such as environtyping, soil science, high-throughput phenotyping and sequencing among others, allow the characterization of the phenotype equation’s model components (P = E + G + G × E + e) with a high degree of detail. The integration of these sources of information (multi-omics and multi-layer) in the prediction context, using different methods, could potentially help to better understand the role of genetics and its interaction with environmental factors. Examples of the aforementioned methods being crop growth models, machine learning techniques, elaborated statistical models, etc.

Understanding how the interaction of these layers shape genotypes can enhance our ability for performing predictions under complex scenarios. The aim of this Research Topic is thus to present the recent advancements and developments in leveraging the availability of novel sources of information together with the new prediction tools in plant and animal breeding applications.

Topics of interest include:
1) Multi-omics data integration, multiple layer integration may help to improve the predictive ability of phenotypes through exploring the potential uses from an animal and plant breeder's perspective;
2) Integration of multi-layered data, crop growth models, machine learning and elaborated statistical models for Genomic Selection applications. The integration of these novel methods could improve our ability to predict crop performance in complex scenarios.

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Editors

University of Florida

International Rice Research Institute (IRRI)

Institute of Crop Science (NARO)

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Frontiers in Genetics

Statistical Genetics and Methodology

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