Enhanced disease resistance in chickpea and canola via MAGPIE: Major‐Effect Allele and Genomic Polygenic Integration for Enhancement

The global agricultural industry suffers major economic losses due to fungal diseases. Though selective breeding and introgression of resistance genes with major effects have helped to reduce the impact of these diseases, resistance breakdown occurs within a few generations. Quantitative resistance that relies on multiple small effect genes has proven to be more durable than relying on major effect genes. In this study, we present MAGPIE (Major-Effect Allele and Genomic Polygenic Integration for Enhancement), a computational framework that integrates genomic data, predictive breeding approaches, and cross selection to accumulate favorable major-effect alleles and polygenic resistance. MAGPIE was implemented to improve fungal resistance in chickpea and canola. Seven cycles of selection resulted in 76% genetic gain for resistance to Ascochyta blight in chickpea, and 15% genetic gain for blackleg resistance in canola. For both diseases, more than 90% of genetic gain could be attributed to polygenic resistance rather than major effect genes. Progenies from both breeding programs were field validated, with stem breakage being reduced by 9.7% in chickpea, and survival percentage improved by 10% compared to the founder parents in canola. These results highlight the power of integrating genomic data and predictive breeding approaches to deliver durable, polygenic resistance. This work demonstrates a scalable framework for accelerating resistance improvement in crops vulnerable to rapidly evolving fungal pathogens.