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 mapping and haplotype analysis identify genetic loci and candidate genes associated with yield related traits in rice (Oryza sativa L.)

Rice ( Oryza sativa L.) is one of the most important staple crops worldwide, and improving grain yield remains a major objective of rice breeding programs. Yield is a complex quantitative trait influenced by multiple agronomic characteristics, including grain yield per plant (GY), seed setting rate (SSR), plant height (PH), and thousand grain weight (TGW). In this study, a natural population comprising 265 rice accessions was evaluated during two consecutive growing seasons (2022-2023) at one field location, and genome-wide association studies (GWAS) conducted using 4454137 high-quality single nucleotide polymorphisms (SNPs). Best linear unbiased prediction (BLUP) values estimated across the two year-environments were used for association analysis using a mixed linear model (MLM). A total of 102 significant SNPs were identified and consolidated into 20 quantitative trait loci (QTLs), including five associated with GY, four with SSR, ten with PH, and one with TGW. Among them, several loci were co-localized with previously reported genes associated with yield related traits, thereby supporting the reliability of the GWAS results. Candidate gene identification and haplotype analysis further revealed four prioritized genes: LOC_Os09g28230 (GID1L2) for GY, LOC_Os05g09500 for SSR, LOC_Os03g06930 for PH, and LOC_Os02g09170 for TGW. Significant phenotypic differences among haplotypes supported the potential involvement of these genes in regulating the natural trait variation. These findings provide insights into the genetic basis of rice yield related traits and offer genetic resources for future validation and breeding. However, because the present study included only two year-environments at one field location, additional multi-environment validation is required before the broader environmental reproducibility or breeding adaptation.

Genome wide association mapping of yield and agro-morphological traits under drought and heat stress in wheat (Triticum aestivum L.)

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.

Genetic Variability, Broad-Sense Heritability, and Trait Associations of Bread Wheat (Triticum aestivum L.) Across Multiple Environments in Kenya

Aims: This study evaluated phenotypic variation and genotypic differences, estimated broad-sense heritability for grain yield, and examined associations among agronomic traits in five bread wheat (Triticum aestivum L.) genotypes comprising two parental cultivars and three mutation-derived lines. Study Design: Randomised complete block design with three replications. Place and Duration of Study: The genotypes were evaluated at Kitale, Eldoret, and Njoro, Kenya, during the 2021 and 2022 long-rain seasons, representing six environments. Methodology: Seven agronomic traits were analysed using descriptive statistics, combined analysis of variance, restricted maximum likelihood estimation of variance components, entry-mean broad-sense heritability, and Pearson correlation analysis. Results: Grain yield ranged from 1.80 to 3.50 t ha⁻¹, with a mean of 2.70 t ha⁻¹. Genotype significantly affected all evaluated traits, demonstrating differentiation among the wheat materials, while environmental effects varied among traits. Grain yield was significantly influenced by genotype, location, and season, with a significant genotype × location interaction indicating differential genotypic responses across locations. For grain yield, genotypic variance (σ²G = 0.018) exceeded residual variance (σ²e = 0.012) and genotype × environment interaction variance (σ²GE = 0.006). Entry-mean broad-sense heritability was high (H² = 0.91), indicating strong repeatability of differences among genotype means under the multi-environment testing framework. Grain yield was moderately and positively associated with thousand-kernel weight (r = 0.42), while days to maturity was moderately and negatively associated with thousand-kernel weight (r = −0.53). Days to heading and days to maturity were moderately and positively correlated (r = 0.42). Conclusion: The relatively large genotypic variance and high entry-mean heritability indicate that the evaluated materials could be reliably differentiated for grain yield across the testing framework, although environmental and interaction effects remained relevant. Integrating replicated grain-yield performance with genetic parameters and complementary agronomic traits, particularly thousand-kernel weight, provides a sound basis for identifying promising parental and mutation-derived materials for advancement in bread wheat improvement.

Multi-Season Multi-Model GWAS Prioritizes Stable Genomic Loci and Candidate Genes for Six Agronomic Traits in Soybean Under Conditions in Southeastern Kazakhstan

Reliable identification of genomic regions controlling complex agronomic traits across variable growing seasons remains a major challenge in soybean genetics and breeding. Here, a diverse panel of 252 soybean accessions was evaluated over six consecutive growing seasons (2018–2023) for flowering time, maturity, plant height, number of seeds per plant, seed yield per plant, and thousand-seed weight. Whole-genome resequencing and variant filtering yielded 2,019,772 high-quality SNPs, and association signals were evaluated using Inclusive Integrative Input Multiple-locus Random-SNP-effect Mixed Linear Model (IIIVmrMLM), Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Multi-Locus Mixed Model (MLMM) together with linkage disequilibrium (LD)-based locus consolidation. Cross-model prioritization retained 21 high-confidence loci supported by all three GWAS models and distributed across 10 chromosomes. Among the identified loci, 18 overlapped or co-localized with previously reported SoyBase genes and QTLs, whereas three (q.VER2.13-1, q.YP.01-1, and q.TSW.15-1) showed no positional overlap with known genes and QTLs and were therefore considered presumably novel. These three loci were associated with flowering time, yield per plant, and thousand-seed weight, accounting for 1.60%, 2.13%, and 5.53% of phenotypic variation, respectively. Ten loci co-localized with genomic regions containing established soybean regulators, including E2, E3, GmDt2, and POWR1, support the biological plausibility of the association results. Integration of genomic position, functional annotation, and tissue-expression evidence prioritized 112 candidate genes across 18 loci. These findings provide a focused set of genomic loci and candidate genes for independent validation and further investigation of the genetic basis of soybean adaptation and yield formation under variable continental growing conditions.

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.