Integrating Genomic Selection and CRISPR/Cas Editing for Predictive and Precision Wheat Breeding

Abstract

Wheat (Triticum aestivum L.) is an important staple food and feed crop that makes a major contribution to global food and nutritional security, yet continued improvement is increasingly constrained by climate change, evolving pathogens, and the slow pace of conventional breeding.Recent evidence indicates that, in some regions, climatic warming may outpace breeding-driven gains in wheat productivity, highlighting the need for faster and more efficient breeding strategies.Genomic technologies are therefore playing an increasingly important role in wheat improvement.Genomic selection (GS) uses genome-wide marker information to predict breeding values, enabling earlier selection decisions and potentially shortening breeding cycles, whereas CRISPR/Cas genome editing enables targeted modification of genes associated with agronomically important traits such as disease resistance, drought response, grain quality, and yield-related characteristics.Their integration could contribute to genetic gain by combining genome-wide predictive selection with targeted allele modification.In this context, GS can identify superior genotypes and favorable breeding backgrounds, while functional genomic evidence can be used to prioritize biologically supported targets for CRISPR/Cas editing.This narrative review synthesizes recent peer-reviewed literature on GS, CRISPR/Cas genome editing, quantitative genetic gain, and their potential integration in wheat breeding, with emphasis on empirical evidence, breeding relevance, and current limitations.We propose a conceptual predict-edit-validate-retrain framework that links genomic prediction, target prioritization, genome editing, phenotypic validation, and model updating within an iterative breeding cycle.This framework is not yet a routinely validated breeding pipeline but provides a structured basis for evaluating future integration of predictive and precision breeding approaches.Important constraints include the polygenic architecture of complex traits, genotypedependent transformation and regeneration, the need for multi-environment validation, regulatory differences among jurisdictions, and the economic and infrastructural requirements of advanced breeding platforms.Despite these challenges, integrating GS and CRISPR/Cas provides a promising pathway toward more predictive, biologically informed, and efficient wheat improvement.