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

Integrated Meta-QTL analysis and transcriptomic profiling reveal genomic regions for fruit quality, abiotic and biotic stress resilience in cucumber (Cucumis sativus L.).

Cucumber is a globally significant vegetable crop whose production and market value are affected by fruit quality and resilience to diverse environmental stressors. Despite the identification of numerous Quantitative Trait Loci (QTL) over the last two decades, their direct application in breeding has been hindered by inconsistent genomic positions and broad confidence intervals. In this study, we conducted a comprehensive Meta-QTL (mQTL) analysis by integrating 647 initial QTLs from 40 independent studies published between 2003 and 2024. Using a high-density consensus map containing 9,299 markers, we projected 531 QTLs, identifying 38 robust mQTLs associated with fruit quality, biotic and abiotic stress tolerance. The identified mQTLs exhibited a significant reduction in the average confidence interval (CI) by 5.3-fold, compared to the average CI of the original QTLs and phenotypic variance explained values reaching up to 49.81% (mQTL 6.8). Our results identifying specific genomic hotspots on chromosomes 1, 3, 5, and 6 that harbor high-confidence candidate genes responsible for stress tolerance and fruit quality. Comparative analysis with seven independent genome-wide association studies validated 16 mQTL regions, confirming their stability across diverse genetic backgrounds. Biotic stress resilience was linked to immune regulators such as LRK10L2 and MLO-like protein 12, while abiotic stress tolerance was anchored by genes like NCED5 (cold), ClpB1 (heat), and MYB44-like (waterlogging). Furthermore, we identified key drivers of fruit quality, including Expansin-A4 and CNR2 for dimensions, CsWOX9 for epidermal spine initiation, and Hd3a for flowering phenology. Transcriptomic profiling provided robust expression support for these prioritized candidate genes within the target mQTL intervals. The markers linked to these genes serve as robust tools for marker-assisted selection and fine mapping, offering precise targets for the development of climate-resilient, high-quality cucumber cultivars.

Genomic dissection of stable meta-quantitative trait loci and candidate genes enabling durable disease and insect resistance in maize to safeguard food production.

BACKGROUND Maize is a globally important cereal crop that supports food and nutritional security and sustains livelihoods through its use as food, feed, and industrial raw material. However, maize productivity is severely constrained by destructive diseases and insect pests. Breeding for durable resistance is challenging due to the quantitative, polygenic, and environment-sensitive nature of these traits. To refine the genomic basis of resistance and identify robust breeding targets, a comprehensive meta-quantitative trait loci (M-QTL) analysis was conducted by integrating 528 quantitative trait loci (QTLs), comprising 368 disease-resistant and 160 insect-resistance QTLs. RESULTS The collected QTLs were consolidated into 74 stable M-QTLs, including 31 disease-specific (DI-MQTLs), 23 insect-specific (IN-MQTLs), and 20 co-localized M-QTLs (PL-MQTLs) conferring combined resistance to both stresses. Confidence intervals (CIs) were reduced by an average of 70.6% for disease-related and 51.2% for insect-related loci, with the identified M-QTLs showing a mean phenotypic variance explained (PVE) of 14.6%. Several PL-MQTLs, including PL-MQTL4.1 (CI = 2.91 cM; PVE = 23.0%) and PL-MQTL4.2 (CI = 0.84 cM; PVE = 23.1%), emerged as highly stable resistance hotspots. A total of 1884 candidate genes were identified, including those encoding NBS-LRR receptors, receptor-like kinases, transcription factors (WRKY, MYB, NAC, and AP2/ERF), peroxidases, cytochrome P450s, and benzoxazinoid-pathway genes. Key components of the salicylic acid (SA) and jasmonic acid (JA) signaling pathways co-localized within PL-MQTL regions, suggesting a mechanistic basis for broad-spectrum resistance. CONCLUSION The identified stable M-QTLs and prioritized candidate genes provide robust genomic resources for marker-assisted breeding, genomic prediction, and genome-editing approaches, thereby accelerating the development of durable, broad-spectrum disease- and insect-resistant maize cultivars. © 2026 Society of Chemical Industry.

From QTL to candidate genes: a data-driven approach to unravel the genetic architecture of yellow rust resistance in central European wheat

Key message A data mining strategy capitalizing on high-resolution SNP information, sequence variant annotation and QTL information from three populations facilitated an efficient nomination of candidate genes for YR resistance, supported by Yr27 and functional annotations. Abstract Genome-wide association studies (GWAS) have become routine in many crops, but the prioritization of candidate genes remains challenging. Here, we developed a new approach to identify environment-specific quantitative trait loci (QTL) using GWAS and analyzed 5,840 wheat genotypes distributed over three experimental populations, including 5,243 single-cross hybrids and 597 elite lines. Resequencing the parental genotypes identified over 640,000 single-nucleotide polymorphisms (SNPs) after filtering. The hybrid panels were tested in 19 environments for susceptibility to Puccinia striiformis f. sp. tritici (Pst), a pathogen responsible for severe yellow rust (YR) epidemics especially after 2012. QTL patterns in diverse environments showed substantial differences, reflecting spatial and temporal dynamics. Combining high-resolution SNP information, sequence variant annotation and QTL information from different populations obtained via a novel GWAS approach enabled the efficient nomination of candidate genes for YR resistance loci of particular relevance for Central European wheat. The power of the developed strategy for mining data from different experimental populations was demonstrated as the validated resistance gene Yr27 was identified as sole candidate gene for one QTL region.

Genome-wide association identifies and validates genomic region controlling grain yield and agronomic traits in extra-early orange maize inbred lines under drought.

In order to meet the expected maize yield by 2050, breeders must work to improve breeding program efficiency by intensifying the implementation of new and improved technologies such as marker-assisted selection (MAS). Dissecting the genomic regions associated with drought tolerance is the first step forward in MAS program deployment for maize improvement under drought stress. Genome-wide association studies (GWAS) were used to investigate and identify quantitative trait loci (QTLs) associated with six traits under drought stress. One hundred and eighty-seven extra-early orange maize inbred lines were evaluated under managed drought stress at Ikenne, in Nigeria, during the 2022 and 2023 dry seasons. The materials were also genotyped using 9355 DArTseq SNP markers and analyzed using the enriched compressed mixed linear model (ECMLM). Enriched compressed mixed linear model was used for association-trait analysis. The ECMLM-based GWAS identified 45 candidate genomic loci associated with the six traits, including five for grain yield, with R2 ranging from 8.79 to 25.3%. Independent validation using the multi-locus 3VmrMLM approach confirmed seven high-confidence genomic loci consistently detected by both methods across grain yield, anthesis-silking interval, ear aspect, and ears per plant, providing additional statistical support for these genomic regions. Candidate gene annotation identified biologically relevant genes underlying the validated loci, including Zm00001eb238250 (protein-serine/threonine phosphatase), Zm00001eb040940 (trehalose-phosphatase), Zm00001eb117820 (homeobox protein knotted-1-like 4), Zm00001eb145560 (zinc ion-binding protein), and Zm00001eb294180 (WRKY DNA-binding domain protein), suggesting their potential roles in drought adaptation and grain productivity. These findings improve our understanding of the genetic architecture of drought tolerance in extra-early orange maize and provide valuable genomic resources for accelerating drought-resilient maize breeding.

Genetic mapping and genomic prediction for agronomic, grain compositional, and sensing-enabled traits in a cowpea MAGIC population along an environmental gradient

Cowpea (Vigna unguiculata [L.] Walp.) is a resilient grain legume and an important global source of dietary protein, yet the genetic and environmental basis of phenological and canopy development, as well as grain composition, remains incompletely characterized across production environments. In this study, we evaluated a cowpea multi-parent advanced generation intercross (MAGIC) population along an environmental gradient in California (with contrasting daylengths, temperatures, and soil types) using agronomic, grain compositional, and uncrewed aerial vehicle (UAV) and rover-enabled phenotyping. Near-infrared spectroscopy (NIRS) enabled assessment of grain compositional traits, while sensing-enabled time-series imaging captured canopy and reproductive dynamics. Quantitative trait locus (QTL) mapping identified 267 QTL, and genome-wide association studies (GWAS) detected 1,973 marker-trait associations. Integrating QTL mapping and GWAS results identified two major genomic hotspots affecting multiple traits. A chromosome 9 hotspot (5.8–6.0 Mb) was associated with flowering time and co-localized with sensing-enabled measures of flower and pod counts, plant height, and vegetation fraction, indicating broad effects on phenological and canopy development. A chromosome 8 hotspot (37.3–37.9 Mb) contained co-localized signals for seed weight, protein, starch, phytate, and moisture. A total of 22 prioritized candidate genes were identified within these and other loci with multi-environment QTL and GWAS support. Genomic predictive abilities were moderate to high for most traits and scenarios, with multi-trait MegaLMM outperforming RR-BLUP. Together, these results define major genomic regions controlling cowpea phenology, canopy development, and grain composition, and provide targets and strategies for breeding cowpea cultivars with favorable and environmentally resilient productivity and grain composition. Significance Statement To dissect the genetic basis of cowpea productivity, adaptation, and grain composition, and how performance for these traits varies and can be predicted across environments, we combined multi-environment phenotyping, including sensing of canopy and reproductive traits, with quantitative genetic analyses in a multi-parental population. We identified genomic hotspots for seed size/composition and reproductive phenology and an across-environment predictive advantage for multi-trait vs. single-trait genomic prediction. Overall, these findings support the comprehensive improvement of cowpea.

Genetic diversity, GWAS, and candidate genes identification for plant architecture traits in maize (Zea mays L.)