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bioRxiv · 10.64898/2026.06.06.730627

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

Parthasarathy, S.·Rocheford, T.·Koehler, K.
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Abstract

BackgroundDecades of maize (Zea mays L.) quantitative trait locus (QTL) mapping have produced fragmented results across hundreds of independent studies, characterized by broad confidence intervals, population-specific effects, and a predominantly single-trait scope. Comprehensive multi-trait integration remains limited in the public domain, yet it could improve our understanding of trait relationships for strategic maize breeding. We integrated 2,701 QTLs published over 30 years across five trait categories (grain yield and components (GYC); plant development and architecture (PDA); plant physiology and stress adaptation (PHS); grain quality and nutritional composition (GQN); and disease and pest resistance (DPR)) to identify functionally classified genomic hotspots and prioritize candidate genes for multi-trait breeding. ResultsBioMercator V4.2.3 consolidated 2,518 projectable QTLs into 187 high-confidence meta-QTLs (MQTLs), achieving a 4.0-fold (75%) reduction in mean genetic-map confidence interval width (47.6 to 11.8 cM); 128 of 187 MQTLs (68.4%) showed genome-wide association study (GWAS) co-localization, and 119 (63.6%) met the strict dual-platform criterion. Twenty-three genomic hotspots harbored 131 of 187 MQTLs (70.1%) and were classified by an ordered, data-driven rule into three categories: ten multi-trait hubs enabling simultaneous improvement through pleiotropic or tightly linked genes; seven pathway-focused clusters dominated by a single trait pathway, exemplified by the chromosome 8 kernel-yield cluster; and six major-effect loci, each carrying a member meta-QTL of large effect (phenotypic variance explained (PVE) of at least 28%), including the Rp1 common-rust locus (35% member-MQTL PVE) and the chromosome 9 Wx1/THP9 starch-and-protein locus (up to 52% member-MQTL PVE). Environmental classification distinguished MQTLs predominantly supported by optimal-condition QTLs (42%) from those supported by stress-condition QTLs (28%), the latter showing [~]1.7-fold greater mean contributing-QTL phenotypic variance (12.8% vs. 7.4%), consistent with conditional effect amplification under stress; within-population comparisons under contrasting water regimes showed a larger, [~]3.5-fold amplification. Network-based candidate gene prioritization with cross-cereal ortholog analysis identified priority candidates with confirmed orthologs in one or more comparison cereals (rice, sorghum, wheat, or barley), with conservation highest for developmental and metabolic genes and lowest for pathogen-resistance genes, 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 template for other crops confronting fragmented QTL literature and complex multi-trait breeding objectives.

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Parthasarathy, S., Rocheford, T., Koehler, K.. 2026-06-10. Multi-Trait Meta-QTL Analysis Reveals Genomic Hotspot Classes for Strategic Maize Improvement. https://doi.org/10.64898/2026.06.06.730627

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