Advances in predictive breeding for wheat: concepts, methods, and applications

Abstract

Wheat is one of the world’s main crops. Its improvement is pivotal given the threat of climate change and the growing population. However, enhancing breeding efficiency and improving wheat are challenging due to strong genotype-by-environment (G×E) interactions and the biological complexity underlying the wheat genome and key agronomic traits. In this context, predictive frameworks and data-driven approaches can offer new strategies to address these challenges. This article provides a comprehensive review of the latest developments in wheat breeding, highlighting emerging predictive frameworks and their contributions to modern breeding pipelines. First, we report on genomic selection (GS) applications, emphasizing GS’s ability to improve complex traits by shortening the breeding cycle and increasing selection accuracy. We then describe the applications of phenomics in wheat breeding, including both ground- and unmanned aerial vehicle (UAVs)-based systems. We also discuss the potential for implementing multi-omics strategies to improve complex wheat traits. We debate how predictive breeding frameworks can assist in identifying the best parents and crosses in wheat breeding. Finally, we presented the latest panorama of software for predictive breeding and its integration with other technologies. This review reports recent advances demonstrating how predictive frameworks are reshaping wheat breeding methods, highlighting current progress and outlining future opportunities to accelerate genetic gain in wheat improvement.