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

Genome-wide association study for sucrose content and cane yield-related traits in sugarcane (Saccharum spp.) using globally diverse germplasm

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.

Assessment of Heritability and Correlation Analysis of Yield and Oil-Related Traits in Different Safflower (Carthamus tinctorius L.) Genotypes

Genetic variability and reliable heritability estimates are prerequisites for effective selection in plant breeding. This study assessed heritability and trait correlations for yield and yield-related characters in ten safflower (Carthamus tinctorius L.) genotypes. The experiment was carried out during the Rabi season of 2025–2026 at the Experimental Field, Botanical Garden, Department of Plant Breeding and Genetics, Sindh Agriculture University, Tandojam, Sindh, Pakistan. A Randomized Complete Block Design (RCBD) with three replications was used. Nine yield and yield-related traits were recorded and analyzed. Analysis of variance revealed highly significant (P ≤ 0.01) differences among the genotypes for all studied traits, indicating the presence of substantial genetic variability and considerable potential for genetic improvement through selection. Based on mean performance, genotypes PI-304093 and PI-306956 exhibited superior performance for most of the evaluated traits, suggesting their potential as valuable genetic resources for developing high-yielding safflower cultivars. Correlation analysis showed that most yield-related traits were positively and significantly associated with one another, indicating the possibility of simultaneous improvement through selection. High broad-sense heritability estimates were observed for days to 90% maturity (96.23%), plant height (97.99%), days to 75% flowering (95.09%), biomass per plant (79.97%), seed yield per plant (78.84%), oil content (67.69%), harvest index (62.25%), and capsules per plant (61.93%). These results indicate that direct phenotypic selection can be effective for most traits, and that harvest index may serve as a useful indirect selection criterion for improving seed yield. Genotypes PI-304093 and PI-306956 are recommended as promising parental material for safflower breeding, while multi-location, multi-season trials are suggested to confirm their stability before use in cultivar development.

Genetic Diversity of Agronomic Traits and Identification of Genes Regulating Thousand‐Grain Weight in Foxtail Millet ( Setaria italica ) Germplasm Resources

Thousand‐grain weight (TGW) is a critical determinant of grain yield in foxtail millet. To elucidate the molecular regulatory network governing this trait, we systematically evaluated the main agronomic traits and genetic diversity of 40 core germplasm accessions, aiming to identify elite genetic resources. Subsequently, transcriptomic differential expression analysis, weighted gene co‐expression network analysis (WGCNA), pathway enrichment analysis, and inter‐pathway interaction network analysis were performed on materials with contrasting TGW (high vs. low), with the goal of pinpointing key candidate genes involved in TGW regulation. The phenotypic analysis results indicated that the Shannon‐Wiener diversity index (H′) of eight agronomic traits ranged from 1.82 to 2.04, and the coefficients of variation (CV) ranged from 21.83% to 68.14%. Genetic parameter analysis indicated that TGW exhibited the highest broad‐sense heritability (87.10%). The GE variance components were significant for all traits ( p  < 0.05). Meanwhile, for TGW, plant height (PH), and panicle length (PL), these components were significantly smaller than the genotypic variances, except for SW and PD. Cluster analysis classified the 40 germplasm accessions into four groups. Group III exhibited superior overall performance, particularly for TGW, highlighting its application potential in high‐yield breeding. Principal component analysis (PCA) extracted four principal components, explaining 84.97% of the total variation. Correlation analysis revealed a highly significant positive association between TGW and both GWMP and panicle weight per main stem (PWMS). Transcriptomic data revealed that pathways related to carbohydrate metabolism, sugar transport, starch biosynthesis, and cell wall formation were significantly enhanced in high‐TGW materials, collectively constituting the core regulatory network for TGW formation. Through integrative analysis of multiple datasets, the cell wall invertase gene CIN1 was identified as a key candidate gene, whose expression level showed a significant positive correlation with TGW. Furthermore, the transcription factor WRKY50 was predicted to regulate CIN1 , potentially contributing to TGW determination in foxtail millet. These findings provide candidate genes and a theoretical basis for molecular breeding and the discovery of yield‐related genes in foxtail millet.

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.

Genetic Variability, Character Association and Path Coefficient Analysis for Fruit Yield Improvement in Okra (Abelmoschus esculentus (L.) Moench)

Genetic variability and trait associations are essential for improving fruit yield in okra (Abelmoschus esculentus (L.) Moench). The present study was conducted during Kharif 2025 at the Research Farm, Department of Genetics and Plant Breeding, AKS University, Satna, Madhya Pradesh, to evaluate 17 diverse okra genotypes using a randomised block design with three replications. Thirteen quantitative traits were recorded and analysed for genetic variability, heritability, genetic advance, correlation, and path coefficient analysis. Significant differences among genotypes for most traits indicated considerable genetic variability. The phenotypic coefficient of variation was higher than the genotypic coefficient of variation for all traits, reflecting environmental influences on trait expression. High heritability coupled with high genetic advance was observed for days to 50% flowering, days to first flowering, days to first picking, and plant height, suggesting the predominance of additive gene action. Fruit yield per plant showed significant positive associations with the number of fruits per plant, fruit width, fruit weight, days to first picking, and days to 50% flowering. Path coefficient analysis revealed that days to first flowering had the highest positive direct effect on fruit yield, followed by plant height and fruit width. These traits may therefore serve as useful selection criteria for improving fruit yield and developing high-yielding okra cultivars.