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

Multi-Environment Multi-Locus Association Mapping Reveals Genomic Regions for Drought-Related Traits in Tropical Maize.

Maize grain yield is frequently constrained by water scarcity, particularly in tropical regions characterized by irregular rainfall patterns. Dissecting the genetic basis of drought-related traits remains challenging because their expression is strongly influenced by environmental conditions. In this study, we applied a multi-environment multi-locus genome-wide association study (MEML-GWAS) to identify genomic regions associated with drought-related traits in tropical maize. The association panel comprised 190 inbred lines from the Embrapa breeding program, which were genotyped with 500,108 GBS-derived SNPs, and crossed with two tester lines. Phenotypic data corresponded to the performance of the testcross hybrids, divided in Dent and Flint heterotic groups, evaluated across two years at two locations in Brazil under well-watered and water-stressed conditions. Traits analyzed included grain yield, anthesis-silking interval, female and male flowering time, and plant and ear height. Drought stress reduced grain yield by approximately 50% and increased the anthesis-silking interval by about two days. A total of 179 significant SNP-trait associations were detected, of which 166 showed significant SNP-by-environment interaction effects, while 13 displayed stable effects across environments. Several associations were detected specifically under water-stressed conditions, highlighting genomic regions potentially involved in drought adaptation. Functional annotation revealed candidate genes previously implicated in abiotic stress responses, including ZmTIP1, which encodes an S-acyltransferase regulating root hair development and drought tolerance. Among the novel candidate genes, GRMZM2G159125, encoding a phospholipase D, emerged as a particularly promising candidate due to its strong association with grain yield and its role in membrane lipid signaling pathways related to stress responses. Although a few associations overlapped genomic regions previously reported for drought tolerance in maize, most loci represent potentially novel genetic factors that may contribute to improving drought resilience in tropical maize breeding programs.

Genome-wide association study reveals marker-trait associations for key agronomic and grain quality traits in durum wheat under rainfed and irrigated conditions in Turkish environments

Genome-Wide Association Studies of Agronomic and Yield Traits in Sweet Corn (Zea mays L. var. saccharata)

Sweet corn is a globally important dual-purpose crop for both food and fresh vegetables. The plant architecture and ear-related traits directly determine its yield potential and field ecological adaptability. To elucidate the genetic architecture of these traits and identify superior alleles for breeding, we conducted a genome-wide association study (GWAS) on 11 agronomic traits using 30,597 high-quality SNP markers in a panel of 101 elite sweet corn inbred lines. Population genetic structure was analyzed using sparse non-negative matrix factorization (sNMF) and discriminant analysis of principal components (DAPC) algorithms, revealing three main clusters and six subpopulations. The clustering pattern was highly consistent with germplasm origin. Association mapping with the fixed and random Circulating Probability Unification (FarmCPU) model identified 16 significant marker–trait associations (MTAs), distributed across seven target agronomic traits. The phenotypic variance explained (PVE) by individual loci ranged from 8.0% to 16.0%. Among these, five stable MTAs across environments, a novel ERN locus (SNP25518) specific to sweet corn, and most association intervals overlapped with previously reported quantitative trait loci (QTLs). Within the ±0.15 Mb (defined by LD decay) flanking windows around the significant SNP loci, a total of 236 candidate genes were annotated, which are primarily involved in hormone signaling, carbon and nitrogen metabolism, cell division, and plant growth and development. In summary, this study dissected the genetic basis of key agronomic traits in sweet corn and provides a foundation for marker-assisted selection and functional validation.

Integrated Genome-Wide Association and QTL Mapping Elucidate the Genetic Basis of Fruit Yield and Quality Traits in Pepper under Water Stress.

Water deficit is a major constraint on pepper (Capsicum annuum) yield, yet the genetic architecture of reproductive-stage drought tolerance remains poorly resolved. We phenotyped a Balkan C. annuum diversity panel (n = 133) and an interspecific backcross inbred line (BIL) population (n = 76) under well-watered (WW) and water-stress (WS) conditions. WS was applied from anthesis of the second truss as a stepwise reduction in irrigation volume relative to WW (30% for 7 days, then 60% thereafter), maintained for 90 days across the reproductive period. We assessed yield components, soluble solids, and stress-tolerance (STI) and stress-susceptibility (SSI) indices. Genome-wide association study (GWAS) identified 104 SNP-trait associations (P < 1×10-5), and QTL mapping detected 38 significant QTLs (1,000 permutations, α = 0.01), with the QTL intervals defined at LOD ≥ 8. Integrating GWAS and QTL mapping under WS revealed overlapping loci on chromosomes 5 and 6, harboring two consensus intergenic SNPs associated with yield components and soluble solids. Haplotype analysis linked chromosome 5 alleles to higher fruit number and soluble solids. At chromosome 6, the G allele at SNP 6_28348737 was enriched in tolerant lines for fruit number. These regions harbor candidate genes for reproductive development and stress response, including GREEN RIPE-LIKE1 (GRL1), CYP77A19, Endoglucanase-like, and FLOWERING PROMOTING FACTOR 1 (FPF1), possibly through cis-regulatory variation. Together, these results advance understanding of the genetic basis of pepper yield under drought and identify candidate breeding markers.

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

A new approach to identify environment-specific quantitative trait loci (QTL) using GWAS and the validated resistance gene Yr27 was identified as sole candidate gene for one QTL region of particular relevance for Central European wheat.

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.