Integrative frameworks for next-generation peanut (Arachis hypogaea L.) improvement: from pan-genome-informed MAGIC populations to systems genetics and precision breeding

Abstract

Peanut (Arachis hypogaea L.) is a globally important legume crop, the yield and quality traits are governed by complex polygenic interactions that are difficult to resolve using conventional biparental populations or natural association panels, particularly given the crop’s narrow cultivated gene pool. Multi-parent Advanced Generation Inter-Cross (MAGIC) populations have emerged as a powerful alternative, combining structured recombination with high allelic diversity to enable high-resolution dissection of complex traits. This review synthesizes recent progress and emerging directions in peanut MAGIC research, highlighting five key frontiers: pan-genome-informed founder selection; multi-environment MAGIC for genotype-by-environment (G × E) dissection; systems genetics integration of multi-omics data; introgression MAGIC to incorporate wild Arachis diversity; and AI-guided simulation for optimized population design. By integrating genomics, multi-omics phenotyping, and artificial intelligence, MAGIC populations provide a high-resolution platform for precision breeding and the development of climate-resilient, high-yielding, and nutritionally enhanced peanut cultivars. Beyond trait discovery, this integration enables a shift from descriptive genetic mapping toward predictive and systems-level breeding, in which allelic effects can be evaluated across environments, genetic backgrounds, and management scenarios. Future progress will therefore depend on translating MAGIC-derived insights into subgenome-aware selection strategies, environment-informed genomic prediction models, and shared breeding resources that support sustainable and coordinated peanut improvement under increasing climatic and production constraints.