Integrating Abiotic Stress Adaptation and Disease Resistance in Climate-Resilient Crops: Pangenomics, Genomic Selection, and AI-Driven Breeding

Abstract

Climate change is increasing the frequency and intensity of abiotic stresses, including drought, high temperature, salinity, and waterlogging, which not only directly constrain crop growth and productivity but also reshape plant–pathogen interactions by altering host physiology and immune responses. These climate-driven environmental pressures can accelerate pathogen spread and generational turnover while compromising crop disease resistance through complex interactions between abiotic stress responses and plant immunity. Concurrently, modern cultivars have lost a substantial portion of their genetic diversity through prolonged productivity-focused selection, limiting their capacity to respond to rapidly changing environmental and disease pressures. Consequently, resistance strategies relying primarily on a few major resistance genes and Marker-Assisted Selection (MAS) are becoming increasingly insufficient for maintaining stable crop performance under climate change. This review discusses next-generation strategies for developing climate-resilient disease-resistant crops through three complementary approaches. First, we examine quantitative disease resistance (QDR), which is typically governed by multiple loci with small to moderate effects and can contribute to durable resistance, although its effectiveness depends on the host genotype, pathogen isolate, and environment. We also evaluate the application of genomic selection (GS) to predict quantitative resistance and select individuals with favorable genomic estimated breeding values across major crops. Third, we highlight AI-driven precision breeding that integrates genomic, transcriptomic, metabolomic, phenomic, and environmental information to decipher complex genotype-by-environment (G × E) interactions and improve the prediction of stress-associated disease resistance. Collectively, integrating genetic diversity, genome-wide prediction, multi-omics, and AI provides a framework for breeding toward durable disease resistance and stable productivity under increasingly complex abiotic stress environments, with resistance performance requiring validation across relevant pathogen isolates and environments.