Genomic prediction: progress and perspectives for rice improvement

TL;DR

This paper reviews the progress and challenges of implementing genomic prediction in rice breeding, emphasizing practical considerations, benefits, and limitations for effective integration into breeding strategies.

Contribution

It provides a comprehensive review of genomic prediction in rice, including real data, R scripts, and practical insights to guide breeders in its application.

Findings

Genomic prediction can significantly enhance rice breeding efficiency.

Practical challenges include data quality and model integration.

Genomic prediction is not always the optimal first step in breeding programs.

Abstract

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.