Genomic prediction has emerged as a powerful tool for achieving greater rates of genetic gain in quantitative traits, and its integration into breeding programs requires careful consideration. In rice (Oryza sativa), there is strong interest in genomic prediction, with numerous studies addressing various aspects of its use. This chapter reviews the literature on rice and provides important considerations for integrating genomic prediction into breeding programs. The authors use the irrigated breeding program at the International Rice Research Institute as a concrete example to highlight practical challenges associated with using predictions. Data and R scripts are provided to reproduce the analysis and emphasize that applying genomic prediction is not always the best approach nor the first step to increasing the rate of genetic gain in every context. Accurate phenotyping is one critical aspect of successful implementation of genomic prediction. While genomics can reduce some phenotyping steps, accurate field phenotyping for important traits such as grain yield is crucial to efficiently train the prediction model and capture genotype-by-environment interactions. High-throughput phenotyping methods have emerged as a potential solution to this bottleneck, with several tools and platforms available for field and laboratory phenotyping. When integrated into a genomic prediction model, high-throughput phenotypic data can substantially increase accuracy; however, there are still constraints to routine use of these approaches including acquisition of multi-environment field data, availability of data management systems that can handle large time-series datasets, and initial costs associated with equipment. As technologies and regulations mature dedicated companies offering high-throughput phenotyping services are expected to emerge. In addition to high-throughput phenotyping better characterization of environmental factors affecting crop performance is necessary for successful implementation of genomic prediction. The authors caution against overreliance on any single technology or approach without considering their limitations and benefits within specific contexts. By avoiding common psychological pitfalls associated with new technologies' integration into established disciplines like breeding programs researchers can harness the full potential of genomic prediction to achieve greater rates of genetic gain in rice and other crops.
Genomic prediction is a tool that helps improve the traits of plants. People are interested in using it for rice. To use it successfully, we need to accurately measure important traits like how much rice a plant can produce. There are new ways to measure these traits, but we still need more information about the environment and better ways to manage data. Scientists should be careful not to rely too much on one method without thinking about its limits and benefits in different situations.
Definitions
- Genomic prediction: a tool that uses genetic information to predict traits
- Quantitative traits: measurable characteristics of an organism, such as height or weight
- Phenotyping: measuring an organism's physical or biochemical characteristics
- High-throughput phenotyping methods: fast and automated ways of measuring many plants at once
- Environmental factors: things like temperature, water availability, and soil quality that affect how well plants grow
Genomic Prediction for Rice Breeding Programs
Genomic prediction is a powerful tool that has the potential to increase genetic gain in quantitative traits. In rice (Oryza sativa), there is strong interest in using genomic prediction, and numerous studies have addressed various aspects of its use. This chapter reviews the literature on rice and provides important considerations for integrating genomic prediction into breeding programs. The authors use the irrigated breeding program at the International Rice Research Institute as an example to highlight practical challenges associated with using predictions. Data and R scripts are provided to reproduce the analysis and emphasize that applying genomic prediction is not always the best approach nor the first step to increasing rates of genetic gain in every context.
Accurate Phenotyping
Accurate phenotyping is one critical aspect of successful implementation of genomic prediction. While genomics can reduce some phenotyping steps, accurate field phenotyping for important traits such as grain yield is crucial to efficiently train the prediction model and capture genotype-by-environment interactions. High-throughput phenotyping methods have emerged as a potential solution to this bottleneck, with several tools and platforms available for field and laboratory phenotyping. When integrated into a genomic prediction model, high-throughput phenotypic data can substantially increase accuracy; however, there are still constraints to routine use of these approaches including acquisition of multi-environment field data, availability of data management systems that can handle large time-series datasets, and initial costs associated with equipment. As technologies and regulations mature dedicated companies offering high-throughput phenotyping services are expected to emerge.
Better Characterization Of Environmental Factors
In addition to high-throughput phenotyping better characterization of environmental factors affecting crop performance is necessary for successful implementation of genomic prediction models. The authors caution against overreliance on any single technology or approach without considering their limitations and benefits within specific contexts. By avoiding common psychological pitfalls associated with new technologies' integration into established disciplines like breeding programs researchers can harness the full potential of genomic prediction to achieve greater rates of genetic gain in rice and other crops.
Conclusion
This chapter provides an overview on how genomic predictions can be used in rice breeding programs along with important considerations when implementing them successfully such as accurate phenotypes collection through high throughput methods or better characterization environmental factors affecting crop performance . By understanding these concepts breeders will be able to maximize their rate genetic gains while avoiding common psychological pitfalls associated with new technologies’ integration into established disciplines like breeding programs
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage: "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
