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

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

This study conducted a comprehensive Meta-QTL analysis by integrating 647 initial QTLs from 40 independent studies published between 2003 and 2024, identifying 38 robust mQTLs associated with fruit quality, biotic and abiotic stress tolerance and key drivers of fruit quality.

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.

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

Dissection of genetic architecture for tiller and yield - related traits in sugarcane (Saccharum spontaneum L.) by genome - wide association study.

Sugarcane (Saccharum spp.) is an important crop for food and energy security. Identifying SNPs and genes associated with sugarcane yield and related traits is crucial for developing high - yielding sugarcane cultivars through molecular breeding. Here, we measured nine phenotypic traits across 160 sugarcane genotypes and employed multiple statistical models (namely MLM, CMLM, MLMM, FarmCPU and SUPER) in GWAS to identify stable and pleiotropic loci. A total of 200 SNPs corresponding to 137 QTLs were detected to be significantly associated with nine traits using multiple statistical models, among which 18 QTLs were consistently identified by two or more models. Notably, the SNP S9A_47793177 on chromosome 9A showed the strongest association with phenotypic variation in aboveground biomass, with a phenotypic explanation rate of 70.54%. Additionally, several QTLs significantly associated with tillering - related traits were identified, suggesting that these QTLs may play crucial roles in the regulation of tillering. The QTLs and SNPs identified in this study provide a significant foundation for molecular marker - assisted breeding in sugarcane. This advancement can significantly enhance the efficiency of genetic improvement for sugarcane yield and tillering - related traits.

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.