MAGIC populations: a next-generation framework for dissecting complex quantitative traits and accelerating molecular breeding in crops.

Dissecting complex quantitative traits is constrained by limited genetic diversity in biparental populations and population structure confounding in genome-wide association studies. Multi-parent Advanced Generation Inter-Cross (MAGIC) populations address these limitations by intercrossing multiple diverse founders followed by selfing to generate immortalized recombinant inbred lines exhibiting extensive recombination and balanced allele frequencies. MAGIC populations synergistically combine high mapping resolution with broad genetic diversity, enabling detection of small-effect QTLs, epistatic interactions, and genotype-by-environment effects. Despite their immense potential and successful deployment across diverse crops, several critical challenges remain regarding founder selection strategies, computational efficiency of haplotype reconstruction, and seamless integration into existing breeding pipeline. In this review, we synthesize current knowledge of MAGIC construction principles, crossing designs, and inbreeding strategies, and critically evaluate genotyping technologies and statistical frameworks including hidden Markov models, identity-by-descent mapping, and multi-locus mixed models. Furthermore, we explored how integrating with high-throughput phenotyping enhances multi-environment trait characterization, with applications across diverse crops revealing common bottlenecks and successful strategies. We also outlined transformative opportunities through joint linkage-association analysis for causal variant identification, integrating MAGIC Populations with AI-driven genomic selection for accelerated genetic gain, and multi-omics approaches for mechanistic trait dissection. This synthesis provides actionable frameworks for optimizing MAGIC population development and exploitation, advancing precision crop improvement in the face of climate change and resource constraints.