Machine Learning-Assisted Genome-Wide Association Study (GWAS) in Plants

Genome-wide association studies (GWAS) have become a pivotal tool for dissecting the genetic basis of complex traits in plants. However, traditional statistical methods often face limitations in addressing the intricate genetic architectures and environmental interactions that govern many agronomically important phenotypes. The integration of machine learning (ML) algorithms into GWAS, termed ML–GWAS, offers a transformative approach to overcome these challenges. This chapter explores the revolutionary impact of ML on plant genomics, highlighting its capacity to enhance predictive accuracy for complex traits, uncover novel genetic associations including epistatic interactions and small-effect loci, and improve the efficiency of breeding programs. We discuss how ML techniques, such as deep learning and ensemble methods, can model non-linear relationships, perform feature selection, and ultimately provide a more comprehensive understanding of the genotype–phenotype map in plants. Furthermore, we touch upon the crucial advancements in the interpretability of ML models in the context of GWAS. By leveraging the power of ML, researchers are gaining unprecedented insights into the genetic control of complex traits, paving the way for the development of superior crop varieties with enhanced yield, nutritional quality, and resilience to environmental stresses. This chapter provides an overview of the current state and future perspectives of ML-assisted GWAS in plant genomics, emphasizing its potential to accelerate crop improvement for global food security.