Assessing population structure and morpho-molecular characterization of sunflower (Helianthus annuus L.) for elite germplasm identification

Introduction

Sunflower (Helianthus annuus L.) stands out as a significant oilseed crop worldwide, alongside groundnut, mustard and soybean. The genus is native to southwestern North America, and the cultivated sunflower was domesticated in central North America, it belongs to the Asteraceae family (Ahmadpour et al., 2022; Reddy et al., 2024). It is a highly cross-pollinated crop with 2n = 34 chromosomes, sunflower demonstrates exceptional yield potential and adaptability to various environmental conditions (Reddy et al., 2024). Moreover, sunflower ranks as the world’s fourth most crucial source of edible vegetable oil, contributing up to 12% of global edible oil production (Dimitrijević et al., 2017; Rauf et al., 2017).

Multivariate analysis encompasses statistical techniques used to analyze data arising from multiple variables. Among these techniques are principal component analysis (PCA) and cluster analysis. Cluster analysis is utilized to designate genetic diversity by examining resemblances or dissimilarities among genotypes. Meanwhile, PCA aims to eliminate redundancy in datasets and reveal patterns of distribution (Dudhe et al., 2020).

Evaluating genetic diversity through agro-morphological traits can be arduous and time-consuming, particularly without prior knowledge of variability and often proves unreliable due to significant environmental influences (Ikram et al., 2020; Ibrar et al., 2022). Yield, a complex quantitative trait, faces further complications due to its dependence on other variables, especially in crops like sunflower characterized by high cross-pollination and heterozygosity (Arshad, Ilyas & Khan, 2007; Benchasri, Simla & Harakotr, 2020; Ahmad et al., 2021; Ibrar et al., 2022).

A diverse germplasm collection is vital for crop improvement, facilitating efficient allele accumulation and reducing screening efforts (Darvishzadeh et al., 2010). In sunflower, genetic characterization is essential for diversity assessment, germplasm conservation, and marker-assisted selection. While morphological traits often fail to distinguish closely related cultivars, DNA markers provide reliable insights into polymorphism and genetic diversity, unaffected by environmental factors or epistasis (Zeinalzadeh-Tabrizi et al., 2018). Estimating genetic diversity using molecular markers is crucial in sunflower breeding, where accurate identification of parental lines is key (Darvishzadeh et al., 2010; Zeinalzadeh-Tabrizi et al., 2018; Ahmed et al., 2022).

Morphological characterization, biochemical and molecular marker techniques are used to assess genetic diversity, but the former two are less reliable due to environmental effects (Rani et al., 2023). DNA markers, unaffected by environmental factors, are essential for understanding genetic diversity (Kumar et al., 2021; Rani et al., 2023). Molecular markers like random amplified polymorphic DNA (RAPD), simple sequence repeat (SSR), Expressed Sequence Tag-Simple Sequence Repeat (EST-SSR), amplified fragment length polymorphism (AFLP), genotyping-by-sequencing (GBS) and single nucleotide polymorphism (SNP) have been employed to assess the genetic diversity in sunflower germplasm. SSR markers due to their genome-wide distribution, high polymorphic information content, and reproducibility proved to be valuable for analyzing genetic diversity, and linkage mapping (Guan et al., 2010). Single nucleotide polymorphisms (SNPs) are stable and ideal for complex trait analysis but are less preferred for genetic diversity studies due to their limited information content, biallelic nature, and higher cost (Alemu et al., 2020). Population structure (PS) assessment often relies on SSR markers due to their established utility and higher information content compared to biallelic markers (Dreisigacker et al., 2005; Filippi et al., 2015). Several authors have assessed diversity and population structure in sunflower using SSR markers (Filippi et al., 2015; Ramanaiah & Kadirvel, 2021; Ranathunge, Chimahusky & Welch, 2022; Ahmed et al., 2022; Dudhe et al., 2024). SSRs, being highly polymorphic and codominant, are widely used in genome mapping, gene tagging, diversity estimation, variety identification, and marker-assisted selection (Ozkan et al., 2022). This study was aimed to assess SSR markers’ performance in revealing the genetic diversity and population structure of sunflower genotypes, identifying suitable germplasms. It also explores using genetic divergence from morphological and molecular data to select elite germplasms, providing valuable information for sunflower breeding programs.

Material and Methods

Results

Analysis of SSR markers and molecular clustering patterns

The initial assessment screened 130 SSR primers chosen from literature review. Out of these, 103 primers demonstrated efficient traits like robust amplification, reproducible patterns, and multiple polymorphic fragments per assay, while the rest were monomorphic. Detailed outcomes including polymorphism percentages, polymorphism information content (PIC), resolving power (Rp), and marker index (MI) are provided in the accompanying Table S2. The screened primers produced well-defined amplified bands, with allele counts ranging from 2 to 6 per individual across all loci. A total of 267 alleles were identified, averaging 2.6 alleles per locus, with ORS621 showing the highest count at 6 alleles. All alleles exhibited polymorphism, resulting in a 100% polymorphism percentage, a notable finding in this study. PCR amplification of SSR markers for 48 sunflower genotypes are shown in Figs. S1–S6 and Figs. 1–2. The average PIC value among all primers ranged from 0.08 to 0.49 in this survey. Primer ORS853 exhibited the minimum average PIC value, whereas primer ORS613 displayed the maximum. Primer Index values fluctuated from 0.16 to 1.94, with ORS725 having the highest and ORS853 the lowest values. Resolving Power (Rp) values varied from 0.17 to 5.67, with the highest observed in ORS887 and the lowest in ORS1120. The mean Rp values ranged from 0.08 to 1.77, with ORS565 having the highest and ORS1120 the lowest. The average PIC was calculated to be 0.30, while the Multiplex Ratio (MR), Effective Multiplex Ratio (EMR), and Marker Index (MI) were determined to be 2.59, 2.59, and 0.78, respectively.

Cluster analysis through neighbor joining method with jaccard’s pairwise distance matrix, based on SSR marker data from 48 sunflower germplasm accessions revealed five distinct clusters. Cluster I comprised 12 genotypes, cluster II had 7 genotypes, cluster III contained 14 genotypes, cluster IV included 5 genotypes and cluster V had 10 genotypes (Fig. 3 and Table S3). The jaccard’s pairwise coefficient indicated a maximum genetic dissimilarity of 0.65 among accessionRHA 272-1 and COSF 13B; RHA95-C-10 and COSF 13B. Conversely, there was a minimum genetic dissimilarity of 0.33 concerning HOCL 6R and CMS 107B (Table S4).

Analysis of Molecular Variance (AMOVA)

Molecular variance within sunflower populations was evaluated through Analysis of Molecular Variance (AMOVA). The results revealed a notable predominance of variation within populations, accounting for 89%, whereas variation among populations constituted a significantly lower proportion of 11% (Table 2 and Fig. 5).

Table 1:

Percentage of variation explained by the first 3 axes of Principal Coordinate Analysis (PCoA).

S. no	Axis	1	2	3
1	Percent of variation	10.50	9.69	7.04
2	Cumulative percent of variation	10.50	20.20	27.24

DOI: 10.7717/peerj.18205/table-1

Model based population structure

The structure analysis based on SSR marker data grouped the 48 sunflower germplasm into five distinct subpopulations. Utilizing Bayesian methods in structure software facilitated efficient population structure analysis, assigning individuals to appropriate subpopulations. Admixture model-based simulations determined the optimal number of subpopulations (K = 5), confirming the subdivision (Fig. 6). Genetic relatedness among populations was assessed using structure, with membership coefficients indicating subpopulation affiliation. Accessions scoring above 0.80 were considered genetically pure, while those below were categorized as intermixed. Similarly, 48 sunflower genotypes were divided into five subpopulations, with the following representation percentages: 20.83% (subpopulation I, 10 genotypes), 22.91% (subpopulation II, 11 genotypes), 11.25% (subpopulation III, 9 genotypes), 12.50% (subpopulation IV, 6 genotypes), and 25.00% (subpopulation V, 12 genotypes) represented in Fig. 7. Net nucleotide distances among subpopulations revealed varying degrees of genetic divergence, with subpopulations II and V exhibiting the highest distance (0.056) and subpopulations I and IV showing the lowest (0.014). This diversity was further emphasized by expected heterozygosity variations: subpopulation IV showcased the highest diversity (0.352), while subpopulation III displayed the lowest, accompanied by a low Fst value (0.173). Subpopulation II showed the highest Fst mean percentage and the lowest expected heterozygosity (0.2277), while subpopulation IV had the lowest Fst mean percentage and the highest expected heterozygosity. Significant divergence (Fst) is evident among sub-populations represented in (Fig. 8 and Table 3) and plotting the structure of significant divergence among sub-populations triangular plot, histogram distribution of alpha and histogram distribution of Fst presented in (Fig. 9 and Figs. S7–S8).

Table 2:

Summary of molecular variance within sunflower populations was evaluated through Analysis of Molecular Variance (AMOVA).

Source	df	SS	MS	Est. var.	%	P-value
Among Pops	4	326.917	81.729	4.711	11%	0.001**
Within Pops	43	1625.979	37.813	37.813	89%	0.001**
Total	47	1952.896	119.542	42.524	100%

DOI: 10.7717/peerj.18205/table-2

Notes:

df

Degree of Freedom

SS

Sum Square

MS

Mean sum square

Est. Var

Estimated variance

Two asterisks (**) indicate significance at the 1% probability level.

Genetic divergence based on morphological data

Principal Component Analysis (PCA) and cluster analysis are powerful multivariate techniques utilized to explore genetic diversity, trace crop evolutionary pathways, select parental lines, and assess diversity. PCA was employed to assess the relationships between various parameters. Nine principal components (PCs), corresponding to the number of traits, were identified through PCA (Table 5). With four PCs exhibiting eigenvalues greater than 1 as shown in (Fig. 11). Among these PC1 explained 36.67% of the variation, followed by PC2 (24.04%), PC3 (14.67%), and PC4 (11.34%). Traits positive factor loading (PFL) with PC1 included VW, while PC2 exhibited PFL with DF, DM, PH, HSW, VW, and OC. Similarly, PC3 showed PFL with VW, OC, and OYP, while PC4 exhibited PFL with DF, DM, and SYP. The biplot of PC1 vs. PC2 visually depicts trait variability, with genotypes positioned towards the positive or negative ends of principal components reflecting favourable traits. DF, DM, HD, OYP, SYP and HSW these trait contribution to divergence of sunflower represented in (Fig. 12).The biplot also illustrates overlapping vectors for traits such as HD, OYP, PH, OC, HSW, OC, DM, DF, and SYP, indicating strong association among them. Genotypes such as RHA 273, COSFV5, RHA102, IB 80, GMU 325, and GP6 1089 are positioned away from the centroid, while the remaining genotypes are clustered closer to the centroid (Fig. 13). Hierarchical Cluster Analysis (HCA) grouped the 48 sunflower genotypes, including checks, into three clusters using PCs, eigenvalues surpassing 1, and principal factor scores (PF scores) for various traits. The analysis unveiled that cluster I is comprised of 19 genotypes, cluster II encompassed 15, and cluster III included 14 genotypes. Cluster I displayed the highest diversity among genotypes, while those in cluster III, exhibiting the lowest diversity (Fig. 14 and Table S7). Mean performance for various traits was observed based on morphological data early flowering and early maturity were noted in RHA 102, IB 80, CMS 107B, PM 36, GP6 912, and RHA 378. High head diameter was recorded in RHA 102, CMS 1103B, RHA GPR 58, COSF 13B, GMU 336, and GMU 1181. High oil content was observed in CMSNDCMS2B, GMU 477, GMU 450, PM 36, RHA 378, and COSF 13B. High seed yield per plant was found in ARM 248B, GMU 1181, RHA 102, CMS 1103B, GMU 336, RHA GPR 58, COSF 13B, GMU 477, and GMU 450.

Table 3:

Substantial divergence (Fst) observed between sub-populations, along with average distances (expected heterozygosity) among sunflower populations, and net nucleotide distances among sub-populations.

Sub population	Fst value	Percent mean value of Fst within population	Genotypes	Exected heterozygosity	Percentage of genotypes	Net nucleotide distance
						Sub-population I	Sub-population II	Sub-population III	Sub-population IV	Sub-population V
I	0.183	0.2109	10	0.3019	20.83	–	0.0539	0.049	0.0141	0.045
II	0.202	0.388	11	0.2277	22.91	0.0539	–	0.0474	0.0339	0.0557
III	0.173	0.3292	9	0.2655	18.75	0.049	0.0474	–	0.0254	0.0467
IV	0.188	0.0004	6	0.352	12.5	0.0141	0.0339	0.0254	–	0.0179
V	0.254	0.2275	12	0.2816	25	0.045	0.0557	0.0467	0.0179	–

DOI: 10.7717/peerj.18205/table-3

Table 4:

Genetic diversity of sunflower population from using following parameters (number of observed alleles (na), number of effective alleles (ne), Nei’s gene diversity (h), Shannon’s information index (I), Nei’s gene diversity (h) and unbiased diversity (uh) and gene flow (Nm).

Population	Mean					Polymorphic %	Nm
	Na	Ne	I	h	uh
1	1.54	1.43	0.38	0.25	0.30	69.29%	4.01
2	1.24	1.34	0.29	0.20	0.25	51.31%
3	1.42	1.40	0.35	0.23	0.28	62.55%
4	1.74	1.48	0.43	0.29	0.31	83.52%
5	1.25	1.37	0.32	0.21	0.27	54.68%
Total	1.44	1.40	0.35	0.24	0.28	64.27%
SE	0.05	0.02	0.02	0.01	0.01

DOI: 10.7717/peerj.18205/table-4

Notes:

Na

No. of Different Alleles

Ne

No. of Effective Alleles

I

Shannon’s Information Index

h

Diversity

uh

Unbiased Diversity

Discussion

Molecular markers are versatile tools for studying genetic diversity among plant populations. Traditionally, germplasms are selected based on biometrical data, which is time-consuming and less reliable due to environmental influences. Molecular markers, such as microsatellites (SSR), offer a more efficient and reliable alternative. SSRs, with their multiallelic and polymorphic nature, allow for the exploration of dissimilarity in parental lines of breeding material. Their stability, unaffected by environmental effects, provides a major gain over conventional methods using DNA markers. The number and size of DNA fragments depended on primer sequences, with reactions repeated for consistency. Only well-defined and bright DNA bands were counted. These results highlight SSR markers’ utility in sunflower parent identification, superior genotypes selection. Traditional methods like grow-out trials are time-consuming and inclined by environmental factors. Molecular markers offer a faster and more reliable substitute for varietal identification, hybrid confirmation, and parental selection in sunflower breeding programs. Genetic impurity in cross-pollinated crops like sunflower poses a widespread challenge, and relying solely on phenotype-based descriptions is cumbersome and less dependable (Iqbal et al., 2011; Zeinalzadeh-Tabrizi et al., 2018; Ahmed et al., 2022).

Studying genetic variance is crucial for breeders seeking to pinpoint promising genotypes. In this investigation, SSR DNA markers were employed to scrutinize genetic connections among sunflower genotypes. The prevalence of alleles per locus indicates natural outcrossing in sunflowers, a crop that relies on cross-pollination. Primer Index (PI), Resolving Power (Rp), Effective Multiplex Ratio (EMR), and Marker Index (MI) evaluate the efficiency of molecular markers in genetics, these metrics help to select effective markers and primer sets, optimizing genetic analysis. However, not all markers are equally effective for genotyping and varietal identification; their utility primarily hinges on their PIC value, Rp, and MI values, which signify their efficacy for genotypic differentiation (Lavudya et al., 2024). Ibrar et al. (2022) reported a range of 1 to 4 alleles, with an average of 2.1 alleles. Similarly, Antonova et al. (2006) employed 10 SSR primer pairs to assess the genetic diversity of 17 sunflower hybrids and inbred lines, revealing an average of 2.2 alleles. Most robust markers with high average PIC value indicates a highly informative marker that can clearly distinguish between individuals and populations, making it crucial for selecting markers in genetic studies. It’s recommended to steer clear of markers with low PIC values when conducting genomic studies, favoring those with high or moderate values instead. Ibrar et al. (2022) pinpointed markers like ORS-605 and ORS-700 as fitting for assessing genetic diversity among germplasms. Similarly, Sahranavard Azartamar et al. (2015) emphasized markers HA3040 and ORS-733, noted for their high PIC values, as adept for analyzing genetic diversity in sunflowers. The most potent marker, ORS-453, was singled out by Ahmed et al. (2022). While diversity profiling through SSR primers indicated a moderate level of genotypic diversity, the PIC values closely mirrored those reported by Lochner (2011) (ranging from 0.06 to 0.75) and Darvishzadeh et al. (2010) (ranging from 0.09 to 0.62).

Genotypes were classified using DARWIN v.6.0 software, cluster III exhibits a greater number of genotypes, suggesting higher genetic diversity within that cluster. Conversely, Cluster IV displays fewer genotypes, indicating lower genetic diversity, and implying that the genotypes within it are more similar to each other. The maximum jaccard’s pairwise coefficient revealed a determined genetic dissimilarity, indicating significant variation between genotypes. Conversely, there was a minimum genetic dissimilarity, suggesting significantly less variation between genotypes. Ahmed et al. (2022) conducted a study where the dendrogram analysis distinctly separated maternal and paternal genotypes, as well as their hybrids, into three primary clusters (A, B & C). Molecular characterization using SSR divided the sunflower lines into two main groups of Restorer and CMS lines, consistent with the results of morphological characterization (Ibrar et al., 2022). Similarly, Pandey et al. (2018) classified thirty-one genotypes into three major clusters. Jannatdoust et al. (2016) studied the hierarchical clustering of individuals using the Neighbor Joining method in DARwin5 software, resulting in the subdivision of individuals into three groups.

The resulting two-dimensional PCoA plot truly captures the groupings observed in the Neighbor Joining method, employing Jaccard’s pairwise distance matrix for cluster analysis, suggests that these axes are important for understanding the underlying patterns or structure within the data, although there is still a considerable amount of variation not captured by these three axes alone. In PCoA, genotypes far from the cluster centroid suggest significant genetic differences from the cluster’s average profile, potentially indicating outliers or distinct genetic traits within the population, genotypes close to the cluster centroid reflect genetic profiles that closely match the average characteristics of the cluster, implying population homogeneity or similarity. In a study by Perveen et al. (2023), the first two principal axes contribute 18.12 and 12.31percent to the total genetic variation, separately, resulting in a cumulative variation of 30.43%. Hladni et al. (2018) demonstrate that principal coordinates provide insight into 58.4% of the total variability. The utility of PCoA in genetic diversity analysis is emphasized by Jannatdoust et al. (2016), where the first and second components account for 7.86% and 6.16% of the total variance, respectively.

AMOVA analyzed molecular variance within sunflower populations, revealing substantial within-population variation, largely influenced by genetic drift and gene flow within groups. A smaller proportion of variation among populations suggests limited genetic differentiation, possibly due to gene flow, historical migration, or shared ancestry. Similarly, Gogoi et al. (2023) found high variation within populations(87%) and comparatively less variation among populations (13%). Jannatdoust et al. (2016) observed a higher level of genetic variation within populations (70%) compared to between populations (30%). This higher variation amid specific plants may be attributed to the high allogamous nature of sunflower.

STRUCTURE uses Bayesian methods to classify individuals into subpopulations and identify consistent subgroups. It reflects genetic interactions, evolutionary history, and gene migration, focusing on population structure, admixture, and genetic clustering. Mandel et al. (2011) observed a maximum of K = 2 clusters for population structure in 433 sunflower genotypes, while Ahmed et al. (2022) found a maximum of K = 3, subdividing the population into 3 subpopulations. The genetic structure of plant populations reflects factors such as individual genetics, evolutionary history, and gene migration within and between species. Nineteen germplasms met the purity threshold (0.80), each subpopulation contained genetically pure germplasms, identified through membership probabilities. Net nucleotide distances among subpopulations varied, with subpopulations II and V having the highest distance, indicating significant genetic differentiation possibly due to factors like geographic isolation or selection pressure. Conversely, subpopulations I and IV showed the lowest distance, suggesting minimal genetic divergence likely facilitated by gene flow or shared ancestry. The fixation index (Fst) measures population differentiation on a scale from 0 to 1. Expected heterozygosity varied: subpopulation IV showed highest diversity, subpopulation III exhibited the lowest diversity with a low Fst value, indicating low genetic differentiation among populations, indicating high gene flow and similar genetic characteristics. Subpopulation IV displayed the highest Fst value, indicating significant genetic differentiation among populations. This implies limited gene flow between populations, resulting in distinct genetic characteristics. (Patel et al., 2024) studied subpopulation III shown extreme Fst value (0.3591), indicating significant diversity among subpopulations. Mandel et al. (2011) noted pairwise Fst values spanning from 0.016 to 0.183 across 12 cultivar classes. The most significant values were observed between INRA-RHA lines and the remaining gene pool, as well as between RHA-oil lines and other cultivated classifications, suggesting genetic substructure within the germplasm. Genetic distance between populations is crucial for germplasm conservation, with hybrids from genetically distant parents often exhibiting superior traits. Consequently, genetic analysis of population structure delineates breeding subpopulations (Rauf et al., 2020). A triangular plot shows individuals’ genetic composition across subpopulations, with each corner representing a subpopulation and each point indicating an individual’s genetic makeup, visualizing admixture and genetic similarity/dissimilarity. A histogram of alpha values illustrates the distribution of admixture coefficients, showing genome proportions from each subpopulation. A histogram of Fst values displays genetic differentiation between subpopulations, assessing genetic divergence or similarity and providing insights into population differentiation and genetic exchange.

Germplasms were categorized into five populations, with genetic variability parameters. Subpopulation IV displayed the highest values, indicate increased genetic diversity within populations and higher gene flow between populations. Sub population II exhibiting the lowest values, indicate reduced genetic diversity within populations and lower gene flow between populations. Gene flow (Nm), ranging from <1 to >4, shapes genetic diversity. Low flow drives divergence, while high flow fosters uniformity and diversity. A value of 4.01 indicates high flow, promoting genetic homogenization while maintaining diversity. Gogoi et al. (2023) observed that Population 1 in Assam showed the highest values for parameters (na, ne, h, I), confirming significant genetic diversity within the population. Moreover, significant genetic diversity was confirmed through total species diversity within the population (Ht) and gene flow (Nm), which were notably higher. Ibrar et al. (2022) reported that pairwise genetic distance estimates based on Nei’s gene distance index showed the greatest variation between CMS lines and SFP (self-pollinated lines), whereas the lowest diversity was observed between restorer lines and SFP. The high allelic distribution in subpopulation III indicates greater genetic diversity, enhancing adaptability and resilience. Conversely, the lower allelic distribution in subpopulation IV suggests reduced genetic diversity.

PCA and cluster analysis are essential multivariate tools for genetic diversity exploration, crop evolution tracing, parental line selection, diversity assessment, and environmental interaction. This highlights the substantial explanatory power of the initial four principal components, making them pivotal for understanding parameter relationships. Although the remaining components still contributed to overall variation, their impact on data variability was relatively minor. Genotypes exhibit high diversity and have been utilized across various breeding techniques, positioning them away from the centroid. Conversely, the other genotypes are clustered closer to the centroid, indicating less diversity. The principal components derived from this analysis provide valuable insights for genotype characterization and inform future breeding strategies. Dudhe et al. (2020) found that the first four PCA components explained 86.72% of variation, with subsequent components contributing 13.28% or less. Saux et al. (2020) observed high PCA percentages in seven clusters, indicating distinct resistance or sensitivity traits. Hierarchical clustering provides clearer genotype grouping, aiding in parent selection for sunflower hybrids (Ibrar et al., 2024). Cluster means for seven traits across 2149 entries were recorded (Dudhe et al., 2020). In a PCA biplot, the angle between attribute vectors’ sines provides an approximate correlation representation. An acute angle (<90°) suggests positive correlation, while a right angle (90°) implies trait independence. However, it’s important to note that biplot angles don’t directly correspond to correlation coefficients; separate calculation is necessary for accuracy (Abdi & Williams, 2010; Mohi-Ud-Din et al., 2021; Antony et al., 2021). By integrating morphological and molecular marker data with genetic distance analysis, substantial diversity was observed, with genotypes RHA 273 and GMU 325 consistently demonstrating high oil yield per plant. Genotypes GMU 477, GMU 450, COSF 13B, RHA 102, CMS 1103B, and RHA GPR 58 were identified as suitable parents for enhancing oil content in sunflower breeding programs.

Conclusion

Sunflower (Helianthus annuus L.) is a globally significant oilseed crop known for its adaptability and high yield potential. This research enhances our understanding of sunflower through insights into genetic divergence, molecular diversity, and selection cues, aiding future breeding efforts to enhance oil production and desired traits. By integrating molecular and morphological data, the study offers crucial insights into the genetic diversity and population structure of sunflower germplasm. SSR markers are effective tools for assessing genetic variability, supporting breeding programs aimed at refining sunflower cultivars for superior traits and productivity. This study utilized 267 loci to characterize genetic diversity across sunflower germplasms, setting the stage for future evolutionary and functional genomic research. Broadening germplasm representation in future studies will further enrich our knowledge of sunflower genetic diversity.

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