Multi-Trait Meta-QTL Analysis Reveals Genomic Hotspot Classes for Strategic Maize Improvement

BackgroundDecades of maize (Zea mays L.) QTL mapping have produced fragmented results across hundreds of independent studies, characterized by broad confidence intervals, population-specific effects, and a predominantly single-trait analytical scope. Comprehensive multi-trait integration remains limited, yet it could substantially improve our understanding of trait relationships for strategic breeding. We integrated 2,701 QTLs published over 30 years across five functionally distinct trait categories (grain yield and components; plant development and architecture; plant physiology and stress adaptation; grain quality and nutritional composition; and disease and pest resistance) in order to identify functionally classified genomic hotspots and prioritize candidate genes for multi-trait breeding applications. ResultsBioMercator V4.2 consolidated 2,518 projectable QTLs into 187 high-confidence meta-QTLs (MQTLs), achieving an average 59% reduction in confidence interval width; 128 of 187 MQTLs (68.4%) achieved dual-platform support through GWAS co-localization. Twenty-three genomic hotspots harbored 132 of 187 MQTLs (70.6%) and were classified into three functional categories: twelve multi-trait hubs that may enable simultaneous improvement of multiple traits through pleiotropic or tightly linked genes; seven single-trait clusters with pathway-specific effects, exemplified by the chromosome 9 starch biosynthesis cluster; and four major-effect loci with reported individual effects exceeding 20% PVE, including vgt1 (54% PVE) and opaque2 (34.2% PVE). Descriptive environmental classification distinguished MQTLs predominantly supported by optimal-condition QTLs (42%) from those predominantly supported by stress-condition QTLs (28%), the latter showing approximately 3.5-fold greater mean contributing-QTL phenotypic variance, directionally consistent with conditional genetic effect amplification under stress. Network-based candidate gene prioritization combined with cross-cereal ortholog analysis showed that 67% of the top candidates possess orthologs in rice, sorghum, wheat, or barley, and 53% are conserved across all four species, identifying priority targets for functional genomics investment. ConclusionsThis functionally classified and environmentally characterized meta-QTL framework provides breeders with a structured resource for multi-trait hotspot selection, environment-appropriate allele deployment, and functional genomics prioritization, with broader applicability as a transferable analytical template for other crop species confronting analogous challenges of fragmented QTL literature and complex multi-trait breeding objectives.