Biology subjects

Rocheford, T.

Publications and source records attributed to Rocheford, T..

3 recordsLinked to original sources

bioRxiv2026

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

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.

plant biology↗

bioRxiv2020

Eleven biosynthetic genes explain the majority of natural variation for carotenoid levels in maize grain

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize is a food staple. Extensive natural variation exists for carotenoids in maize grain; to understand its genetic basis, we conducted a joint linkage and genome-wide association study in the U.S. maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percent phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide the most comprehensive genome-level understanding of the genetic and molecular control of carotenoids in any plant system, and a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

genetics↗

bioRxiv2019

Genome-Wide Association Study and Pathway-Level Analysis of Kernel Color in Maize

Rapid development and adoption of biofortified, provitamin A-dense orange maize (Zea mays L.) varieties could be facilitated by a greater understanding of the natural variation underlying kernel color, including as relates to carotenoid biosynthesis and retention in maize grain. Greater abundance of carotenoids in maize kernels is generally accompanied by deeper orange color, useful for distinguishing provitamin A-dense varieties to consumers. While kernel color can be scored and selected with high-throughput, low-cost phenotypic methods within breeding selection programs, it remains to be well established as to what would be the logical genetic loci to target for selection for kernel color. We conducted a genome-wide association study of maize kernel color, as determined by colorimetry, in 1,651 yellow and orange inbreds from the Ames maize inbred panel. Associations were found with y1, encoding the first committed step in carotenoid biosynthesis, and with dxs2, which encodes the enzyme responsible for the first committed step in the biosynthesis of the isoprenoid precursors of carotenoids. These genes logically could contribute to overall carotenoid abundance and thus kernel color. The lcyE and zep1 genes, which can affect carotenoid composition, were also found to be associated with colorimeter values. A pathway-level analysis, focused on genes with a priori evidence of involvement in carotenoid biosynthesis and retention, revealed associations for dxs3 and dmes1, involved in isoprenoid biosynthesis; ps1 and vp5, within the core carotenoid pathway; and vp14, involved in cleavage of carotenoids. Collectively, these identified genes appear relevant to the accumulation of kernel color.

plant biology↗