Genetic diversity and core collection construction of glutinous rice landraces in 'He' cultivation zone of Guizhou using SSR sequencing

Wenhui Yang; Mingyi Mao; Jianquan Qin; Jamal Nasar; Zongdong Pan; Lijie Zhou; Quanzhi Zhao

doi:10.1371/journal.pone.0343623

Abstract

Kam Sweet Rice (KSR), a distinctive group of glutinous rice landraces, has evolved over millennia through agro-ecological adaptation by the Dong ethnic group in the 'He' cultivation zone of Southeast Guizhou, China. This study examined the genetic diversity of 388 glutinous rice landraces from the region, comprising 325 KSR and 63 non-KSR varieties, using Simple Sequence Repeat (SSR) sequencing. Results revealed that non-KSR germplasm exhibited significantly higher genetic diversity than KSR germplasm. Collectively, diversity patterns were strongly shaped by the numerical predominance of genetically similar KSR germplasms, resulting in an uneven distribution of genetic diversity between KSR and non-KSR groups. Five strategies were applied to construct and evaluate core collections (see Methods for full details). Among them, the simulated annealing algorithm (SA)-based Allelic Richness Maximization Strategy (SANA) (20% sampling intensity) demonstrated superior performance in preserving genetic diversity, except for the number of alleles (Na) and observed heterozygosity (Ho), where the Modified Heuristic Sampling (M-HS) strategy (13.66% sampling intensity) performed better at lower sampling intensities. By optimizing both approaches, a core collection of 65 germplasms was established, capturing 90.86% of alleles and retaining key genetic parameters. This core set effectively represents the genetic diversity of the entire collection, providing a strong foundation for future germplasm innovation and utilization.

Citation: Yang W, Mao M, Qin J, Nasar J, Pan Z, Zhou L, et al. (2026) Genetic diversity and core collection construction of glutinous rice landraces in 'He' cultivation zone of Guizhou using SSR sequencing. PLoS One 21(3): e0343623. https://doi.org/10.1371/journal.pone.0343623

Editor: Muhammad Abdul Rehman Rashid, Government College University Faisalabad, PAKISTAN

Received: October 18, 2025; Accepted: February 9, 2026; Published: March 10, 2026

Copyright: © 2026 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All raw sequencing data files are available from the Genome Sequence Archive (GSA) database (accession number: CRA027773; URL: https://ngdc.cncb.ac.cn/gsa/browse/CRA027773).

Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 32260444), Congjiang Terraced Wetland Ecosystem Observation and Research Station of Guizhou Province (Grant No. Qian-Ke-He YWZ [2024]004), Innovative Talent Team in Rice Crop Science and Technology in Karst Mountainous Areas of Guizhou Province (Grant No. Qian-Ke-He-Platform-talent BQW [2024]001), and Foundation of Guizhou University (Grant No. Guidarenjihezi [2015]09). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Glutinous rice (Oryza sativa), known for its sticky texture, plays an important role in the dietary and cultural traditions in East and Southeast Asia, contributing to what has been termed the "Glutinous Rice Cultural Zone" [1]. The southern mountainous region of Qiandongnan Miao and Dong Autonomous Prefecture(QDN) in Guizhou province, China, represents a significant area for glutinous rice cultivation. Local ethnic minorities have traditionally depended on glutinous rice as a staple food, developing a distinctive cultural tradition centered on this crop. The Dong people, in particular, have developed and maintained ecological rice varieties known as 'He', which have adapted to the region's mountainous climate through a combination of natural domestication and over a thousand years of artificial selection [2,3].

The cultivation of 'He' rice is primarily concentrated in the border region of Guizhou, Hunan, and Guangxi provinces, with the largest planting areas located in Congjiang, Liping, and Rongjiang counties of Guizhou. This distribution formed a distinct 'He' cultivation zone, where glutinous varieties dominate [4]. Local Dong people named this glutinous rice "Oux Yag" in their native language, and is internationally referred to as "Kam Sweet Rice" [5]. Kam Sweet Rice (KSR) is prized for its favorable agronomic and culinary traits, including a rich, mellow aroma, high stickiness, cold tolerance, and resistance to pests and diseases [3]. Later, the Food and Agriculture Organization of the United Nations (FAO) has recognized KSR as a specialty rice of global significance [6]. Besides the wide range of KSR varieties, the 'He' rice Cultivation Zone in Guizhou is also a home to diverse array of non-KSR glutinous rice landraces. These two groups differ not only in name but also in both harvesting and post-harvest processing. For example, KSR varieties are difficult to thresh under natural conditions and require traditional milling methods, specifically, the use of mortar and pestle, to effectively remove husk. In contrast the non-KSR varieties are relatively easy to thresh using conventional methods [7].

Genetic resources are a vital component of biodiversity, forming the foundation for agricultural productivity, resilience, and adaptability. They play a crucial role in promoting sustainable agricultural development and ensuring global food security [8]. To improve the management and utilization of large germplasm resources, Frankel et al. [9] proposed the concept of a core collection, a subset designed to capture the genetic diversity of the entire collection using the smallest number of representative samples. Brown et al. [10] demonstrated that for germplasm collection exceeds 3,000 accessions, sampling 10% of the total can retain around 70% of the overall genetic diversity. Typically, core collections are developed using 5% to 30% of the original collection, a range shown to be effective for preserving genetic variation and improving the efficiency of resource use [11,12]. The optimal sampling proportion depends on factors such as the total population size, the level of genetic diversity, and the specific sampling strategy used [13]. Various strategies have been developed to construct core collections based on different objectives. For example, Maximization (M strategy) [14], Allele Coverage (CV) [15], and simulated annealing algorithm (SA)-based strategies have been adopted: one maximizing allelic richness (SANA) for allele representation, and the other maximizing genetic diversity (SAGD) for genetic diversity optimization [16]. Additionally, the A-NE strategy is used to optimize the average genetic distance between germplasm and the closest core set entry [17]. These strategies with appropriate weighting can help achieve both comprehensive allele representation and high genetic diversity in core collections [18,19].

Accurate assessment of genetic diversity is critical for the effective construction of core collections [20]. Compared to traditional phenotypic assessment, molecular marker-based approaches offer distinct advantages. They are not influenced by environmental conditions and more accurately reflect the inherent genetic variation within germplasm, providing a reliable basis for core collection development [21]. Among molecular markers, Simple Sequence Repeats (SSRs), short tandem repeat sequences widely distributed throughout plant genomes, are particularly valuable due to their high polymorphism, co-dominant inheritance, reproducibility, and ease of use [22,23]. However, traditional SSR detection techniques, which rely mainly on electrophoresis, are limited in resolution and accuracy because they only measure fragment length and lack detailed sequence information [24]. The Advent of Next-Generation Sequencing (NGS) technology has enabled the development of SSR sequencing, which integrates SSR markers with high-throughput sequencing. This approach allows for precise detection of SSR repeat units, allele frequency, and relative abundance, thereby significantly improving resolution and accuracy [25,26]. Unlike traditional methods, SSR sequencing enhances the detection of allelic variation and reveals greater levels of genetic polymorphism [27]. This technology has been successfully applied in genetic studies of various crop germplasm resources, including cucumber [26], radish [28], and Camellia oleifera [27].

In this study, we analyzed and compared the genetic diversity of Kam Sweet Rice (KSR) and non-KSR glutinous rice collected from the 'He' cultivation zone Qiandongnan (QDN) region. Based on SSR sequencing, we constructed a core collection of glutinous rice using different strategies. These findings provide important insights into the genetic structure of glutinous rice in the 'He' cultivation zone and offer a valuable reference for developing core collections using integrated strategies frameworks.

Materials and methods

Rice materials

A total of 388 glutinous rice germplasm samples, including 325 KSR and 63 non-KSR glutinous rice landraces, were collected from the 'He' cultivation zone by the Qiandongnan Miao and Dong Autonomous Prefecture Academy of Agricultural Sciences in Guizhou Province, China (S1 Table). Among them, 223 accessions originated from Liping County, 150 from Congjiang County, and 11 from Rongjiang County in Guizhou Province, while four accessions were collected from Sanjiang County, Guangxi Province. Fig 1 shows the geographic distribution of the sampling sites at the provincial and county levels. In addition, four representative indica (9311, Minghui 63, IR50, Huanghuazhan) and japonica varieties (Nipponbare, Daohuaxiang, Wuyujing 3, Koshihikari) were used as controls for clustering to inaugurate indica-japonica differentiation.

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Fig 1. Geographic distribution of sampling counties for glutinous rice germplasms in the 'He' cultivation zone of Southwest China.

(A) Location of Qiandongnan Miao and Dong Autonomous Prefecture (pink, Guizhou Province) and Liuzhou City (orange, Guangxi Province). (B) Distribution of the four sampling counties, including Liping, Congjiang, and Rongjiang Counties in Qiandongnan Prefecture (Guizhou Province), and Sanjiang County in Liuzhou City (Guangxi Province). Maps were created with ArcGIS Pro 3.6.0. The administrative boundary data were obtained from the GADM database (https://gadm.org/).

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DNA extraction and quality control

For each sample, ten seeds from a single panicle of each landrace were germinated under controlled indoor conditions. Fresh leaf tissue (20 mg) was collected from each sample, ground in liquid nitrogen, and genomic DNA was extracted using the DNA secure Plant Kit (Tiangen Biotech, China). DNA concentration and purity were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA), with acceptable samples showing OD260/280 values between 1.7 and 1.9, and OD260/230 values above 2.0. DNA integrity was confirmed via agarose gel electrophoresis.

SSR primer screening and genotyping

Initially, 348 SSR markers distributed across the 12 rice chromosomes [29,30] were assessed for suitability in SSR sequencing. Selection criteria for optimal markers includes: (1) repeat units of at least 3 bp; (2) avoidance of homopolymer-rich sequences (e.g., only GC or AT); (3) no nearby SSR loci within the flanking region; and (4) fewer than 10 repeat units. Based on preliminary bioinformatics analysis, 80 SSR markers were deemed suitable. Further screening prioritized marker polymorphism and even chromosomal distribution, resulting in the selection of 37 SSR primer pairs (see S2 Table).

Genotyping was employed using SSR sequencing technology on the Illumina HiSeq 4000 platform (paired-end 2 × 150 bp) [27] by Genesky Biotechnologies Inc. (Shanghai, China). The 37 SSR markers were grouped into two multiplex PCR panels (18 or 19 markers per panel). Amplicons from each panel were pooled based on fragment intensity and count, with standard and GC-rich PCR systems processed in parallel. Following initial amplification, Index PCR added sample-specific barcodes. Indexed amplicons were pooled, gel-purified, and verified using an Agilent 2100 Bioanalyzer (Illumina, San Diego, CA, USA). Raw sequencing reads were first assessed using FastQC to evaluate base quality scores and nucleotide composition. Paired-end reads were merged using FLASH, and only successfully merged sequences were retained for subsequent analyses. The merged reads were then aligned to primer-captured reference sequences using BLASTn, and only high-confidence, locus-specific reads were defined as effective reads and used for genotyping. For each sample and SSR locus, the number of effective reads was calculated to ensure adequate sequencing depth. To further evaluate the specificity and efficiency of target locus enrichment, merged reads were aligned to the reference genome using BLASTn, and the proportion of reads mapping to target regions was calculated. Potential genotyping errors, including allele dropout, PCR stutter interference, and null alleles, were assessed indirectly based on read depth distribution, locus-specific missing rates, and allelic read count balance within heterozygous genotypes. Loci or samples showing extremely low effective read counts, excessive missing data, or highly unbalanced allelic read proportions were excluded from downstream analyses to reduce the influence of unreliable genotypes. Allele frequencies and allele counts were then calculated from the filtered dataset to generate the final SSR genotyping matrix for subsequent population genetic analyses.

Construction of core collections

Five sampling strategies were used to construct preliminary core collections based on SSR data: (1) Modified Heuristic Sampling (M-HS) Strategy (PowerCore v1.0) [31]: Uses a modified heuristic algorithm to reduce redundancy and capture 100% of allele diversity without manual sampling ratio adjustment, (2) SANA Strategy (PowerMarker v3.25) [16]: Simulated annealing algorithm (SA) that maximizes allele richness, (3) A-NE Strategy and (4) E-NE Strategy (Core Hunter v3.0) [17,32]: Optimize average (A-NE) and extreme (E-NE) genetic distances, using the Modified Rogers distance, and (5) CV Strategy (Core Hunter v3.0) [15]: Focuses on maximizing allele coverage with minimal sample size [33]. While the M-HS strategy determines the sampling ratio automatically, the other four strategies were evaluated at fixed sampling proportions of 5%, 10%, 15%, 20%, 25%, and 30%.

Data analysis

Genetic diversity parameters, including the number of alleles (Na), effective alleles (Ne), Shannon's information index (I), observed heterozygosity (Ho) and expected heterozygosity (He) were analyzed using GenAlEx v.6.51b2 software [34]. The major allele frequency (MAF), polymorphism information content (PIC), and fixation index (F) were analyzed using PowerMarker v.3.25 software [16]. The Shapiro-Wilk test was used to evaluate the normality of the genetic diversity parameters. The results indicated that all genetic diversity parameters significantly (P < 0.01) deviated from normality in both the entire germplasm collection and KSR. Therefore, the Mann-Whitney U test (SPSS version 27; IBM, Armonk, NY, USA) was used to assess differences between KSR and non-KSR groups, and between the core collection and the full germplasm set. Retention rate was calculated as the mean of locus-wise parameter values in the core collection divided by the mean of locus-wise parameter values in the whole collection (37 loci). Population genetics parameters, including intra-population inbreeding coefficient (Fis), total inbreeding coefficient (Fit), population differentiation index (Fst), and gene flow (Nm), were calculated using Popgene v1.32 [35]. AMOVA (Analysis of Molecular Variance) was conducted using Arlequin v3.5.2.2 [36].

Clustering analysis based on Nei's genetic distance was performed using the UPGMA method in PowerMarker v3.25, and visualized using Evolview [37]. Population structure analysis was carried out using STRUCTURE v2.3.4 [38], with a burn-in of 10,000 iterations and 50,000 MCMC replications. The number of subpopulations (K) ranged from 1 to 11, with 10 replicates per K. STRUCTURE HARVESTER v0.6.91 [39] was used to determine the optimal K using the ΔK method. Q-values were averaged across replicates using CLUMPP v1.1.2 [40]. Individuals with Q ≥ 0.6 were assigned to a specific group; those with all Q-values < 0.6 were designated as admixed or unassigned. To evaluate core collection representativeness, six genetic parameters (Na, Ne, I, Ho, He, and PIC) were compared between core subsets and the full collection. Principal Coordinate Analysis (PCoA) was also conducted using GenAlEx v6.51b2 to visualize genetic relationships and confirm the genetic representativeness of the core collections.

Results

Genetic diversity based on SSR sequencing

A genetic diversity analysis was conducted on 388 glutinous rice germplasms from the 'He' cultivation zone using 37 SSR markers derived from SSR sequencing (Table 1). In total 186 alleles were identified across all loci. The number of alleles per locus (Na) ranged from 2 to 16, with an average 5.0. This number for Ne ranged from 1.0536 to 6.4317 (mean = 1.8453). The index I, reflecting allele distribution uniformity and evenness, ranged from 0.1554 to 2.1573, averaging 0.6970. MAF values ranged from 0.2964 to 0.9741 (mean = 0.7663), higher values indicating the dominance of certain alleles across the population. Ho ranged from 0.0026 to 0.5902, averaging 0.0406, while He ranged from 0.0509 to 0.8445 (mean = 0.3442). PIC values ranged from 0.0505 to 0.8291, averaging 0.3166. Overall, the Na and I values reflect allelic variation across loci, whereas the relatively high MAF and low Ho (and comparatively lower He) indicate skewed allele-frequency distributions and low heterozygosity, as expected in a predominantly self-pollinating crop such as rice.

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Table 1. Genetic diversity analysis of 388 glutinous rice germplasm using SSR sequencing.

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A comparative analysis of genetic diversity between KSR and non-KSR germplasms is summarized in Table 1, S3 and S4 Tables. There was no significant difference in Na between the KSR and non-KSR groups (Table 1). However, non-KSR germplasms showed significantly higher Ne (1.3463 [1.3087–1.9385] vs. 1.2268 [1.1905–1.9059]; P = 0.0001), I (0.4938 [0.4007–0.8150] vs. 0.3851 [0.2972–0.7444]; P = 0.0002), and He (P < 0.001), along with significantly lower MAF (0.8544 [0.6671–0.8634] vs. 0.8969 [0.6841–0.9123]; P = 0.0002). Notably, Ho was lower in both groups. The number of highly polymorphic sites (PIC ≥ 0.50) were greater in non-KSR (12) than in KSR (7). Moderately polymorphic sites (0.25 ≤ PIC < 0.50) were 19 in non-KSR and 6 in KSR (S3 and S4 Tables). These loci accounted for 83.78% and 35.14% of all markers in non-KSR and KSR, respectively. Overall, non-KSR accessions exhibited significantly higher with-group diversity (Ne, I, He and PIC) than KSR, indicating that panel-level diversity estimates are strongly influenced by the numerical predominance and lower within-group diversity of KSR accessions in this regional collection.

Genetic population structure

UPGMA clustering analysis divided 388 glutinous rice germplasms and eight reference varieties (4 indica and 4 japonica) into two major genetic clusters corresponding to the indica and japonica groups (Fig 2A). One cluster comprises all 4 japonica reference varieties and 335 landraces, while the other contains 4 indica reference varieties and 53 landraces. Japonica glutinous rice dominates the 'He' cultivation zone, comprising 86.3% of the total. Differences were observed between KSR and non-KSR germplasm types. Among non-KSR samples, 25 were indica and 38 were japonica out of 63. In contrast, 91.4% of KSR varieties (297 out of 325) belonged to the japonica group (Fig 2B), in agreement with previous findings [2].

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Fig 2. UPGMA clustering of glutinous rice germplasms (4 indica and 4 japonica reference controls).

(A) UPGMA clustering diagram of 388 glutinous rice germplasms alongside 8 indica/japonica references. (B) The indica/japonica composition in KSR and non-KSR germplasms.

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STRUCTURE analysis further confirmed the population structure. Structure Harvester analysis revealed ΔK value trends as K increased from 1 to 11, with a significant ΔK peak occurring at K = 2 (Fig 3A), indicating that the 388 glutinous rice germplasm resources could be categorized into two distinct subpopulations based on genetic structure. The hierarchical genetic structure analysis with K = 2 resulted in one subpopulation of 332 germplasms and another of 53 germplasms (Fig 3B). Among these germplasms, Niumaohe, Dongsui, and Gaicaohe (with Q-values < 0.6; S5 Table) exhibited admixed ancestry and were not assigned to either subpopulation. Comparing results from the Bayesian model-based STRUCTURE analysis with UPGMA clustering analysis based on Nei's genetic distance revealed that 385 rice germplasms showed completely consistent groupings, except for three KSR germplasms (Niumaohe, Dongsui, and Gaicaohe).

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Fig 3. Population genetic structure analysis of 388 glutinous rice germplasms.

(A) Estimation of the optimal K value based on ΔK analysis. (B) Inferred population structure of 388 samples at K = 2, where each vertical bar represents an individual sample. The numbers in parentheses indicate that 1 denotes non-KSR and 2 denotes KSR. Colors indicate genetic clusters (green for subpopulation Ⅰ and red for subpopulation Ⅱ), with segment lengths proportional to membership probabilities (Q-values).

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Construction of core collections

Five strategies such as M-HS, SANA, A-NE, E-NE, and CV, were used to construct primary core collection from 388 glutinous rice germplasms based on SSR sequencing. The M-HS strategy automatically selected 53 accessions (13.66%) as core subset. The other sampling strategies such as SANA, A-NE, E-NE, and CV generated subset at six sampling intensities (i.e., 5%, 10%, 15%, 20%, 25%, and 30%), respectively, resulting in core sets of 19–116 accessions (Table 2).

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Table 2. Comparison of Genetic diversity parameters between primary core subsets compared with the entire collection.

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Comparison of different methods for core collection construction

To evaluate the representativeness, genetic diversity parameters (i.e., Na, Ne, I, He, Ho, and PIC) were compared between 25 subsets and the entire collection (Table 2). The M-HS strategy core subset did not show any significant differences in Na and Ho, but did differ significantly (P < 0.01) in Ne, I, He and PIC at 13.66% sampling intensity. For the SANA strategy, subsets at 5% and 10% sampling intensities showed significant differences (P < 0.01) in Na and/or Ho. However, subsets with 15% to 30% sampling intensities exhibited no significant (P > 0.05) differences across all six genetic diversity parameters. In contrast, core subsets generated via A-NE and E-NE strategies at various sampling intensities showed significant difference from the entire collection. Among CV strategy subsets, only the 30% sampling intensity subset showed no significant difference.

Genetic diversity retention rates for representative core subsets are shown in Table 3. The CV strategy core subset (30% sampling intensity) demonstrated retention rates of 100% (Na), 106.54% (Ne), 108.39% (I), 115.72% (Ho), 108.89% (He), and 108.44% (PIC), with the higher sampling intensity more effectively capturing the diversity concentrated in the entire collection. The SANA strategy core subset (20% sampling intensity) showed over 100% genetic diversity for Ne, I, He, and PIC, but lower retention Na (85.48%) and Ho (96.13%). The M-HS strategy achieved 100% Na and 115.04% Ho retention at 13.66% sampling intensity, showing no significant difference in these parameters compared to the entire collection (Table 3).

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Table 3. Genetic diversity retention rates for representative core germplasm subsets.

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Although several single-strategy core subsets showed no significant differences from the entire collection, they generally required relatively high sampling intensities to achieve overall representativeness (e.g., SANA at ≥15% and CV at 30%). In contrast, the M-HS strategy achieved complete allelic richness (Na) retention at a much smaller sampling intensity (13.66%) but still differed from the entire collection in other diversity parameters. Therefore, no single strategy simultaneously satisfied high Na coverage, overall representativeness across diversity parameters, and a practical core size. To address this trade-off, we used the above comparative results to guide a results-driven optimization (Fig 4). Based on the observed differences among strategies in allelic richness retention, overall diversity representativeness, and sampling efficiency, we prioritized candidate solutions that integrated complementary strengths across these evaluation criteria. Multiple alternative core sets were then compared within a reduced candidate space to identify statistically acceptable and sampling-efficient solutions. Candidate core sets were evaluated against the entire collection using Mann–Whitney U tests for Na, Ne, I, Ho, He, and PIC, together with diversity retention rates. Core sets showing no significant differences across all parameters were considered acceptable, and when multiple candidates met this criterion, lower sampling intensity was prioritized while maintaining high Na retention. The final selection was further interpreted in light of population composition to avoid adding redundant accessions with limited contribution to overall diversity.

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Fig 4. Results-driven workflow for identifying the final core germplasm collection.

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Optimized construction strategy for the final core collection

Based on the optimization framework described above, we combined two preliminary subsets to form an enriched candidate pool for final core selection: 120 germplasms in total, selected from the M-HS (53) and SANA (77) strategies. Ten germplasms (Heironghe, Goubadang, Zaogaonuo, Heimaohe, Goulieshibei, Huangmaozaohan, Liuyuegu, Zhuyanuo-2, Nuopanggu, and Shuba) were common to between the two, while 110 germplasms were unique. From the established ten core sets, Core set 1 was generated using the M-HS strategy based on the 110 unique accessions, yielding 49 unique accessions, to which the 10 shared accessions were added (total n = 59). Core Sets 2–10 were developed using the SANA strategy with incremental sampling intensities (10%–90% of the 110 unique accessions), with the same 10 shared accessions added back to each set (Table 4).

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Table 4. Genetic diversity parameters for different optimized core sets compared with the entire collection.

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Diversity parameters for all core sets are summarized in Table 4. Among the ten optimized core sets, only Core sets 6 and 9 showed no significant differences (P > 0.05; Mann–Whitney U test) from the entire collection across all six genetic diversity parameters (Na, Ne, I, Ho, He, and PIC) and were therefore considered acceptable candidates. Both core sets also showed allele coverage above 90% and retention rates above 100% for Ne, I, Ho, He, and PIC (Table 5) outperforming most preliminary core subsets (Tables 3 and 5). The two acceptable candidates differed in sampling efficiency and Na retention: Core set 6 achieved 16.75% sampling intensity with 90.86% Na retention, whereas Core set 9 required a higher sampling intensity(25.26%) but retained 97.85% of Na. Considering that non-KSR accessions exhibited significantly higher within-group diversity (Ne, I, He and PIC) than KSR, and that panel-level diversity estimates in this regional collection are strongly influenced by the numerical predominance and lower within-group diversity of KSR accessions, increasing sampling intensity primarily tends to add redundant KSR genotypes, thereby offering limited additional contribution to overall diversity representation. Therefore, among the two acceptable candidates, we selected the more sampling-efficient Core set 6 as the final core collection.

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Table 5. Genetic diversity retention rates of core set 6 and 9.

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PCoA analysis further confirmed that the core sets 6 and 9 reflected the genetic structure of the entire glutinous rice population, with even distribution across principle coordinates (Fig 5). Based on sampling efficiency, genetic diversity parameters, and population structure representation, Core set 6 comprising 65 germplasms (S6 Table), was determined to be the optimal core collection for the 'He' cultivation zone.

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Fig 5. PCoA Analysis of Core sets 6(A) and 9(B).

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Discussion

Genetic resources are the foundation of crop improvement, with genetic variation directly affecting breeding outcomes. Historic breakthroughs in rice breeding, such as the "Green Revolution" and hybrid rice development, stemmed from discovery and utilization of key genetic resources [41,42]. Despite their importance, the vast volume of genetic resources poses challenges for their effective conservation and utilization [43]. Preserving and harnessing effective genetic diversity remains essential for maximizing their breeding potential.

Kam Sweet Rice (KSR), primarily grown in the mountainous regions of southeastern Guizhou, represent a unique group of glutinous rice landraces. Beyond its value as a genetic resource, KSR is a culturally embedded landrace closely tied to the cultural identity and traditional livelihoods of the Dong people. This cultural embeddedness supports its continued on-farm cultivation and maintenance [2,3]. Introduced during the Dong people migration, KSR possess a distinct genetic background compared to other Guizhou landraces [2]. In this study, 325 KSR and 63 non-KSR rice landraces from the 'He' cultivation zone were collected and preserved by the Qiandongnan Agriculture Science Institute [44] representing the genetic diversity characteristic of this region. The genetic diversity observed in this study is consistent with previous SSR-based analyses of glutinous rice landraces in southwest China, while also revealing clear regional and population-specific differences. For instance, a SSR-based study on glutinous rice landraces and promoted cultivated varieties in Tengchong, Yunnan reported an average of 4.6 alleles per locus, a mean I of 0.398 and PIC values spanning 0.26–0.84, providing a useful regional reference for diversity estimates [45]. In contrast, a large-scale analysis of KSR from southeast Guizhou reported low within-group diversity and heterozygosity and suggested that KSR may represent a highly stabilized landrace group with a distinct genetic background [46]. Our results are consistent with these observations: non-KSR landraces in the 'He' cultivation zone exhibited higher diversity than KSR, whereas KSR accessions were more genetically similar and numerically predominant. Clustering and genetic structure analyses classified both KSR and non-KSR varieties into two subpopulations: japonica and indica, without significant differentiation between the two groups. This supports the view that natural selection, more than artificial selection, drives the main divergence between rice subspecies [47]. KSR cultivation is deeply influenced by traditional dietary culture of ethnic minorities in southeastern Guizhou [48], and continuous selection and exchange among farmers have helped shaped rice diversity [49]. Notably, Ho was extremely low across loci in both KSR and non-KSR landraces, which is expected for predominantly self-pollinating rice. This pattern may be further reinforced by on-farm seed saving/purification and localized cultivation that limit gene flow, as farmer management can affect heterozygosity in landraces [50]. For KSR, recent genomic evidence also indicates domestication-related bottlenecks and reductions in effective population size, plausibly linked to major agricultural transitions in southeastern Guizhou (the "glutinous-to-nonglutinous" shift and the adoption of hybrid rice) [2].

Genetic differentiation analysis of KSR and non-KSR reveled significant inbreeding within populations (Fis = 0.9141), moderate genetic differentiation between populations (Fst = 0.0737), and frequent gene exchange between the two populations (Nm = 37.7232) (S7 Table). AMOVA showed that 16.57% of the genetic variations was between the KSR and non-KSR populations, while 73.43% accounted among individuals within populations (S8 Table), indicating that most diversity is intra-population.

KSR exhibited lower allele richness and frequency than non-KSR (Table 1), with dominance of specific alleles likely due to the long-term ethnic selection [48]. Non-KSR landraces had higher proportion of highly/moderately polymorphic loci (83.78%) compared to KSR (35.14%), indicating greater genetic diversity, consistent with previous findings [51].

Using 37 SSR markers, we constructed a core collection of 65 germplasm resources (16.75% of total), including 51 KSR and 14 non-KSR samples (S6 Table). Five strategies (M-HS, SANA, A-NE, E-NE, and CV) were evaluated for a core collection construction of 388 glutinous rice resources. Since single genetic parameters cannot fully represent diversity, comprehensive evaluation using Ne, He and other indices is essential [52,53].

Individual strategies proved insufficient for constructing a core collection, either due to the significant variations in genetic diversity or excessive sampling proportions. In general, increasing sampling intensity improves allele coverage but simultaneously increases redundancy among accessions, thereby reducing the efficiency of the core collection for practical utilization [10,11,54]. For example, while the CV strategy at a 30% sampling generated representative subset, the proportion was relatively high given the lower genetic diversity of the entire collection. Previous studies have suggested that an optimal sampling proportion for core collections typically ranges between 5% and 20%, providing a practical balance between manageable sample size and effective preservation of genetic variability for breeding and utilization purposes [10,11]. Moreover, minimizing redundancy while retaining maximal genetic diversity has been emphasized as a central principle in core collection construction [20]. Although a larger subset may achieve higher allele coverage, this gain often comes at the cost of a substantial increase in collection size. In this study, while a higher sampling ratio (25.26%) resulted in improved allele coverage, Core set 6 (16.75%) retained more than 90% of the alleles while exhibiting superior or comparable performance across multiple genetic diversity parameters, including Ne and distance-based measures. This indicates a more favorable balance between representativeness and redundancy for a working core collection. The final core collection was therefore optimized through the integration of multiple strategies, retaining over 90% of alleles and showing genetic diversity indices (Ne, I, Ho, He, and PIC) comparable to those of the entire germplasm set. This finding is consistent with previous reports demonstrating that combining complementary strategies enhances both efficiency and accuracy in core collection construction [19]. Overall, the optimized core collection of 65 of glutinous rice accessions effectively captured the genetic diversity of the 'He' cultivation zone. It offers a valuable foundation for future breeding efforts, enabling genetic improvement with minimal but representative germplasm resources. The core collection can support pre-breeding applications, such as prioritized multi-environment phenotyping and the selection of genetically diverse parents. Recent studies have identified agronomically relevant loci/genes and resistance-related haplotypes in KSR [2,46]. These findings underscore KSR's potential for breeding climate-resilient and high-yield rice varieties. However, trait–marker relationships are often population- and environment-dependent. Therefore, systematic phenotypic evaluation and validation of candidate alleles/markers in the core collection will be an essential next step.

Conclusion

In this study, we analyzed the genetic diversity and population structure of 388 glutinous rice germplasms from the 'He' cultivation zone using SSR sequencing data from 37 SSR loci. The result revealed an uneven distribution of genetic diversity between KSR and non-KSR landraces, with KSR accessions being more genetically similar and numerically predominant, which strongly shaped the diversity pattern at the whole-collection level. Based on these findings, a core collection was constructed using optimized sampling. The SANA method, applied at a 16.75% sampling intensity yielded a core collection of 65 samples. This core collection retained a high level of genetic diversity, with retention rate 90.86% (Na), 117.61% (Ne), 116.10% (I), 102.63% (Ho), 118.42% (He), and 117.32% (PIC). No significant differences were observed between the core set and the entire collection, confirming the effectiveness of the strategy. Principal coordinate analysis also validated its representativeness, showing an even distribution across the entire collection. These results demonstrated the feasibility of constructing a compact yet representative core collection through integrated strategies. This provides a strong foundation for future research and targeted utilization of glutinous rice genetic resources in Guizhou.

Supporting information

S1 Table. Information of 388 glutinous rice landraces.

https://doi.org/10.1371/journal.pone.0343623.s001

(XLSX)

S2 Table. Information of 37 SSR markers used in SSR sequencing.

https://doi.org/10.1371/journal.pone.0343623.s002

(XLSX)

S3 Table. Genetic diversity analysis of 325 KSR germplasms.

https://doi.org/10.1371/journal.pone.0343623.s003

(XLSX)

S4 Table. Genetic diversity analysis of 63 non-KSR germplasms.

https://doi.org/10.1371/journal.pone.0343623.s004

(XLSX)

S5 Table. The Q-values of 388 germplasms in two subpopulations.

https://doi.org/10.1371/journal.pone.0343623.s005

(XLSX)

S6 Table. Composition of the final core collection.

https://doi.org/10.1371/journal.pone.0343623.s006

(XLSX)

S7 Table. Genetic differentiation parameters between KSR and non-KSR.

https://doi.org/10.1371/journal.pone.0343623.s007

(XLSX)

S8 Table. Molecular varisance analysis (AMOVA) between KSR and non-KSR.

https://doi.org/10.1371/journal.pone.0343623.s008

(XLSX)

Acknowledgments

We are grateful to the Academy of Agricultural Sciences, Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou, China for providing the glutinous rice landraces used in this study.

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Figures

Abstract

Introduction

Materials and methods

Rice materials

DNA extraction and quality control

SSR primer screening and genotyping

Construction of core collections

Data analysis

Results

Genetic diversity based on SSR sequencing

Genetic population structure

Construction of core collections

Comparison of different methods for core collection construction

Optimized construction strategy for the final core collection

Discussion

Conclusion

Supporting information

S1 Table. Information of 388 glutinous rice landraces.

S2 Table. Information of 37 SSR markers used in SSR sequencing.

S3 Table. Genetic diversity analysis of 325 KSR germplasms.

S4 Table. Genetic diversity analysis of 63 non-KSR germplasms.

S5 Table. The Q-values of 388 germplasms in two subpopulations.

S6 Table. Composition of the final core collection.

S7 Table. Genetic differentiation parameters between KSR and non-KSR.

S8 Table. Molecular varisance analysis (AMOVA) between KSR and non-KSR.

Acknowledgments

References