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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agricultue Vol.27 No.5 pp.639-647

Genetic Diversity Analysis of Rice Accessions Collected from South and Southeast Asia Using SSR Markers

Min-Young Yoon*, Yong-Jin Park*,**
*Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan 32439, Korea
**Legume Bio-Resource Center of Green Manure, Kongju National University, Yesan 32439, Korea
Corresponding author
April 5, 2013 December 21, 2015 December 21, 2015


Genetic diversity and population genetic structure of 85 rice accessions collected from 10 South and Southeast Asian countries were studied based on 29 simple sequence repeat markers (SSRs). In total, 342 alleles were identified; the number of alleles per locus ranged from 2 to 28, with a mean value of 11.8 alleles. Polymorphic information content (PIC) values ranged from 0.11 to 0.93, with an average of 0.71, revealing an excess of heterozygous individuals at 29 SSR markers and an excess of homozygous individuals at two SSR markers. The means of expected heterozygosity (HE) and genetic diversity were both 0.73, ranging from 0.12 to 0.93 and 0.11 to 0.94, respectively. As a result of a modelbased structure analysis, three distinct groups consistent with clustering results based on genetic distance were identified. This study explored the genetic diversity and population genetic structure of domesticated rice collected from South and Southeast Asia. This collection can be used for future germplasm conservation and crossbreeding programs.

SSR 마커를 이용한 아시아 지역에서 수집된 벼 유전자원의 유전적 다양성 및 집단구조 분석

윤 민영*, 박 용진*,**
*공주대학교 식물자원학과
**공주대학교 두과녹비자원 연구센터


    Ministry of Agriculture, Food and Rural Affairs


    Rice domestication is perennial and widely distributed in the tropical regions of the monsoon areas in South and Southeast Asia, where rice production has increased quickly in recent years (Mutert & Fairhurst, 2000). This increase has occurred mostly in irrigated areas, and proceeding has been much slower in areas where the irrigated area scale is very small. Therefore, more suitable to environmental restraints, new cultivated varieties with higher yield and more disease resistance are necessary for the evolution of rice planting, and the assessment of genetic variability can offer new insights into the evolutionary history and genetic relationships (Ge et al., 1999). To date, most studies have concentrated on assessing genetic variation and the domestication process of cultivated rice, but determining the population structure and genetic diversity of nature-domesticated rice may be the first step in achieving these goals.

    Microsatellites, also called simple sequence repeats (SSRs), are tandem repeats of short nucleotide motifs that usually exhibit high polymorphism and species-specific characteristics in repeat number (Yu et al., 2009). In addition to a relatively low cost, high genetic resolution, and simple technical execution, microsatellites have advantages over other markers. For example, microsatellites are codominant, abundant, disseminate over the genome, and display high polymorphism in plant species (McCouch et al., 1997; Gwag et al., 2010; Cho et al., 2011). Recently, SSRs have become a popular marker choice for population, genetic diversity, and evolutionary studies in many plants. It has also been used to research cultivated rice varieties, including quantitative trait locus analysis (Xiao et al., 1996; Ishii et al., 2001; Moe et al., 2011), variety resource conservation, and cultivar identification, and has been used to research wild kindred rice (Olufowote et al., 1997; Garland et al., 1999; Zhou et al., 2003).

    Our previous research illustrated the genetic diversity and population structure of rice collected from East Asia (Zhao et al., 2009) and America (Lu et al., 2009), but we know little about South and Southeast Asia collections. As a model organism with a fully sequenced genome, the aim of the present research was to explore the genetic diversity and population structure of 85 accessions from 10 countries of the above areas using 29 SSR markers.


    Plant Materials

    The plant materials used in this study consisted of 85 accessions acquired from the National Agrobiodiversity Center of the Rural Development Administration (RDA) in the Republic of Korea. The accessions were mainly collected from the following South and Southeast Asian areas: Bangladesh (4), Bhutan (2), India (17), Laos (2), Sri Lanka (5), Myanmar (2), Pakistan (5), the Philippines (28), Thailand (13), and Vietnam (7).

    DNA Extraction and SSR Genotyping

    Total DNA was extracted from fresh, young leaves of the 85 accessions using a Qiagen DNA Extraction Kit (Qiagen, Seoul, Republic of Korea). The relative purity and concentration of the extracted DNA was estimated with a Nanodrop ND-1000 (Nanodrop Technologies, Inc., Wilmington, DE, USA), and the final concentration of each DNA sample was adjusted to 20 ng/μL.

    Markers were chosen according to their location on the rice genetic map and their suitability for high-throughput genotyping. In all, 29 microsatellite markers distributed on 12 chromosomes, including 26 rice microsatellite markers obtained from GRAMENE and three microsatellite markers related to starch synthesis (Bao et al., 2002), were used to analyze population structure. PCR product size was measured following the M13-tail PCR method of Schuelke et al. (2000). Amplification reactions were carried out in a total volume of 20 μL, which contained 200 ng of template DNA (about 10 μL DNA sample), 1× PCR buffer, 0.2 mM of each dNTP, 1U Taq DNA polymerase, 8 pmol of each reverse and fluorescent-labeled M13(-21) primer, and 2 pmol of the forward primer with the M13(-21) tail at its 5’- end. PCR amplification was conducted at 94? for 3 min, then 30 cycles each were performed at 94? for 30 s, 55? for 45 s, and 72? for 1 min, followed by 10 cycles at 94? for 30 s, 53? for 45 s, and 72? for 1 min, and a final extension at 72? for 10 min. SSR alleles were resolved with ABI Prism 3100 DNA sequencer (Ap Biosystems, Foster City, CA, USA) using GeneScan software (version 3.7; Applied Biosystems) and sized precisely against the 6-carboxy-Xrhodamine (ROX) molecular size standard using Genotyper software (version 3.7; Applied Biosystems).

    Data Analysis

    Basic statistics were used to measure diversity at each SSR marker using PowerMarker, version 3.0 (Liu & Muse, 2005), including the total number of alleles (NA), polymorphism information content (PIC), allele frequency, genetic distance, and genetic diversity (GD). Expected heterozygosity (HE) was calculated using the genetic analysis package POPGENE, version 1.31 (Yeh et al., 2000). The unweighted pair group method with arithmetic mean (UPGMA) tree was constructed from shared allele frequencies using the program MEGA 4.0 (Tamura et al., 2007), which was exported from the PowerMarker.

    The analysis of population structure and the identification of ancestral or hybrid forms within the accessions were performed using Structure 2.2 model-based software (Pritchard et al., 2000; Falush et al., 2003) In this model, several populations (K) were assumed to be present, each of which was characterized by a set of allele frequencies at each locus. All loci were assumed to be independent, and each K population was assumed to follow Hardy–Weinberg equilibrium (HWE). Individuals were also allowed to be products of an admixture between two or more of the populations. We routinely employed three iterations for estimation after a burn-in period of 100,000. An individual having more than 70% of its genome fraction value was assigned to a group.


    SSR Polymorphism in Entire Accessions

    All 29 SSR markers showed polymorphism, producing a total of 342 alleles among 85 accessions (Table 1). The allelic richness per locus varied widely, ranging from 2 to 28, with an average of 11.8 alleles. The two SSR markers, RM206 and RM6144, produced the highest and lowest number of alleles at each locus. PIC ranged from 0.11 (RM6144) to 0.93 (RM206), with an average of 0.71, revealing an excess of heterozygous individuals at 29 SSR markers, and an excess of homozygous individuals at two markers (RM6144 and RM12676). HE values for genetic diversity varied from 0.12 (RM6144) to 0.93 (RM206), with an average of 0.73 (Table 2). Detecting the degree of polymorphism using SSR markers did not display significance with the number of alleles, HE, and PIC values. Some SSR markers producing similar allele numbers varied greatly in their HE and PIC values (e.g., marker RM048 and marker SSS produced five equal alleles, but the PIC value for RM048 was 0.25 and that for SSS was 0.69, which were significantly different). The database of allelic frequencies showed that rare alleles (frequency < 0.05) comprised 65.5% of all alleles, whereas common alleles (0.05 < frequency < 0.5) and abundant alleles (frequency > 0.5) comprised 33.6% and 0.9%, respectively, of all detected alleles (Table 3). These results indicated that the presence of a significantly high frequency of rare alleles made a greater contribution to the genetic diversity of the collection (Roussel et al., 2004; Yifru et al., 2006). Hence, clarifying rare alleles is important for maximizing the genetic variations in the gene bank collections and to utilize them in a breeding program.

    Genetic Diversity and Phylogenetic Relationships

    The shared allele frequencies were used to calculate the genetic distance between all pairwise combinations among the 85 accessions, and an UPGMA tree was constructed using the MEGA 4.0 program (Tamura et al., 2007) imported through the PowerMarker; the resulting dendrogram revealed a complex accession distribution pattern.

    As shown in Fig.1, most accessions were clearly classified into three groups (SI, SII, and SIII). SI consisted of 25 accessions, and 21 accessions belonged to Southeast Asia, and 4 accession from South Asia. SII included 21 accessions that predominated in the Indian (11), and were all from Southeast Asia. SIII consisted of 39 accessions originating from 8 countries, predominantly Southeast Asian countries (31 accessions), and the others belonged to South Asia. In this dendrogram, there are 5 admixture accessions.

    The genetic distance matrix of the rice population from 10 countries was also used to construct an UPGMA tree (Fig. 2). The similarity coefficients ranged from 0.227 to 1, with an average of 0.725. The rice populations could be clustered into two branches (BI and BII). BI consisted of four populations from Sri Lanka, India, Bangladesh, and Pakistan—all South Asian countries. BII contained five populations from Myanmar, Bhutan, Vietnam, the Philippines, and Thailand—all Southeast Asian countries—only Laos was out of this group. The dendrogram indicated that the genetic diversity of the rice populations was characterized by an average of 3.4 alleles, varying from 1.3 in Myanmar to 7.0 in the Philippines, and the average major frequency per locus was 0.58, ranging from 0.44 in the Philippines to 0.81 in Myanmar (Table 4). The results presented in this study suggest that rice breeders could select germplasm polymorphisms for use in a breeding plan.

    Population Structure

    Effective protection and administration strategies for rice accessions demand a basic understanding of their population structure. Once the total accessions were selected, the model-based clustering method for inferring population structure was performed using 85 accessions and a total of 29 SSR markers (Pritchard et al., 2000). The estimated likelihood values for a given K showed consistent results with three independent runs, but the distribution of LnP(D) did not show a clear pattern for the true K because of their behaviors, which were expected when factors such as inbreeding and departures from HWE were present (Falush et al., 2003). These factors could generate an overestimation of the number of a population’s K. Thus, another ad hoc quantity (ΔK) was used to overcome the difficulty of interpreting the real K values (Evanno et al., 2005). ΔK represented a clear peak at the true value of K. Base on this, we selected ΔK = 3 and all rice accession studies fell into three genetic groups (Fig. 3). The relatively small value of the alpha parameter (α = 0.028) demonstrated that most of the accessions originated from one primary ancestor, with a few admixed individuals (Ostrowski et al., 2006).

    The model-based structure analysis used here revealed the presence of three populations in the selected core set. When clustering based on genetic distance and structure analyses based on the model were compared, similar patterns of groupings of accessions were discovered (Fig. 4). From the figure we can see Population 1 consisted of 19 accessions, mainly from Southeast Asia, predominated in the Philippines (6) and Vietnam (3). 24 accessions collected from 9 countries of both South and Southeast Asia formed population 2. Population 3 consisted of 37 accessions, mainly from the Philippines (17). The occurrence of some admixed and introgression events and new gene combinations between domestic cultivars and their wild or weedy relatives were important for the evolution of domesticated plant species (Jarvis et al., 1999). Of the 85 rice accessions, 80 (94.1%) shared at least 75% membership with one of the three populations and were classified as the members of that population, whereas five accessions (5.9%) were categorized as admixed forms with varying levels of membership shared among the three genetic groups (Table 5). In our result, some of the rice accessions collected from the same geographic locations did not cluster together. This result indicated that significant variations of rice could be generated diversity. Rice germplasm is freely distributed through the geographical barriers. Moreover, we can conclude our result will fulfill the corner of background information for rice crop improvement programs.

    적 요

    본 연구는 효율적인 자원보존과 유전자원의 작물육종 활용 성 제고를 위한 기초정보제공을 목적으로, 29개 SSR marker 를 이용하여 아시아지역의 10개 국가에서 수집된 벼 유전자원 에 대해 유전적 다양성 및 집단 구조 분석을 수행하였다.

    1. 총 85점의 벼 유전자원이 수집되었으며, 29개 SSR 마커 에 의해 증폭된 allele 수는 총 342개 였다. Allele 수는 2 개에서 28개 범위로 나타났으며, 마커당 평균 allele 수는 11.8 개 였다. 유전적 다양성을 나타내는 genetic diversity와 PIC 값의 범위는 각각 0.12-0.93과 0.11-0.93으로 나타났고, 평균은 각각 0.73과 0.71로 나타났다.

    2. 국가별 벼 자원의 유전적 거리를 기초로 유연관계를 분 석한 결과 2개 그룹으로 구분되었다. BⅠ 그룹에는 남아시아 지역에 속하는 스리랑카, 인도, 방글라데시, 파키스탄이 포함 되었고, BII 그룹에는 라오스를 제외한 동남아시아 지역인 미 얀마, 부탄, 베트남, 필리핀, 태국이 포함되었다.

    3. 수집 국가별 마커당 평균 allele 수는 미얀마가 1.28개로 가장 낮았고, 필리핀이 7.03개로 가장 높았으며, PIC 값 역시 필리핀이 0.64로 가장 높은 값을 보였고, 미얀마는 가장 낮은 0.15로 관찰됐다.

    4. 유전적 거리와 Structure ver2.2를 이용하여 집단의 구조 를 분석한 결과, 75%의 확률에서 85개 자원 중 80개 자원 (64.1%)는 3개의 subpopulation으로 나눌 수 있었으며, 5개 자원(5.9%)은 유전적으로 혼입된 형태를 나타냈다.

    5. 각각의 subpopulation은 수집 국가의 특성과 일치하지 않 았으며, 대부분 자원은 각각의 subpopulation에 불규칙적으로 분포되었다.


    This research was supported by Bio-industry Technology Development Program Ministry for Food, Agriculture, Forestry and Fisheries, Republic of Korea. (No.110136-5)



    UPGMA dendrogram showing the genetic relationships among 85 accessions collected from South and Southeast Asia.


    UPGMA dendrogram showed genetic relationships among different rice populations from different countries.


    a :( Log) likelihood of data (n=85), L(K), as a function of K; b: value of ΔK, with its model value detecting a true K of the 3 groups(ΔK=3). For each K value, three independent runs were considered and the data were averaged over the replicates.


    Model based clustering for each of the 85 accessions examined based on the 29 SSR markers. Group for each panel is represented by colors. Each individual bar represents each accession.


    Information of rice accessions (85) used in this study.

    Frequency distribution of different alleles in 29 SSR markers among 85 accessions.

    a: Map position,
    b: Number of rare alleles,
    c:Size range,
    d: Major allele frequency,
    e: Number of alleles,
    f: Genetic diversity,
    g: Expected heterozygosity,
    h: Polymorphism information content

    Distribution of alleles according to frequency range.

    Genetic parameter values at 29 SSR markers among rice populations from 10 countries.

    a: State abbreviation: BGD, Bangladesh; BTN, Bhutan; IND, India; LAO, Laos; LKA, Sri Lanka; MMR, Myanmar; PAK, Pakistan; PHL, Philippines; THA, Thailand; VNM, Vietnam;
    b: Major allele frequency
    c: Allele number
    d: Genetic diversity
    e: Expected heterozygosity
    f: Polymorphic information content

    Distribution of accessions from different countries to each population and admixture


    1. Bao J , Corke H , Sun M (2002) Microsatellites in starchsynthesizing genes in relation to starch physicochemical properties in waxy rice (Oryza sativa L) , Theor. Appl. Genet, Vol.105 (6) ; pp. 898-905
    2. Cho YI , Park JH , Lee CW , Ra WH , Chung JW , Lee JR , Ma KH , Lee SY , Lee KS , Lee MC (2011) Evaluation of the genetic diversity and population structure of sesame (Sesamum indicum L) using microsatellite markers , Genes & Genomics, Vol.33 (2) ; pp.187-195
    3. Evanno G , Regnaut S , Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study , Molecular Ecology, Vol.14 (8) ; pp.2611-2620
    4. Falush D , Stephens M , Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies , Genetic, Vol.164 (4) ; pp.1567
    5. Garland SH , Lewin L , Abedinia M , Henry R , Blakeney A (1999) The use of microsatellite polymorphisms for the identification of Australian breeding lines of rice (Oryza sativa L) , Euphytica, Vol.108 (1) ; pp. 53-63
    6. Ge S , Oliveira GC , Schaal BA , Gao LZ , Hong D (1999) RAPD variation within and between natural populations of the wild rice Oryza rufipogon from China and Brazil , Heredity, Vol.82 (6) ; pp.638-644
    7. Gwag JG , Dixit A , Park YJ , Ma KH , Kwon SJ , Cho GT , Lee GA , Lee SY , Kang HK , Lee SH (2010) Assessment of genetic diversity and population structure in mungbean , Genes & Genomics, Vol.32 (4) ; pp.299-308
    8. Ishii T , Xu Y , McCouch S (2001) Nuclear-and chloroplastmicrosatellite variation in A-genome species of rice , Genome, Vol.44 (4) ; pp.658-666
    9. Jarvis DI , Hodgkin T (1999) Wild relatives and crop cultivars: detecting natural introgression and farmer selection of new genetic combinations in agroecosystems , Molecular Ecology, Vol.8 (12) ; pp.159-173
    10. Liu K , Muse SV (2005) PowerMarker:an integrated analysis environment for genetic marker analysis , Bioinformatics, Vol.21 (9) ; pp.21-28
    11. Lu FH , Chung JW , Cho YI , Kim TS , Park YJ (2009) Analysis of Genetic Diversity and Population Structure of Rice Cultivars from the Americas using SSR Markers , Korean Journal of International Agriculture, Vol.21 (4) ; pp. 268-275
    12. McCouch SR , Chen X , Panaud O , Temnykh S , Xu Y , Cho YG , Huang N , Ishii T , Blair M (1997) Microsatellite marker development, mapping and applications in rice genetics and breeding , Plant Molecular Biology, Vol.35 (1) ; pp.89-99
    13. Moe K T , Cui H , Chung J W , Ra S B , Kwon S W , Park Y J (2011) ‘Sharei’ weedy collection, found in ganghwa island, maintains a unique population structure , Korean Journalof International Agriculture, Vol.23 (3) ; pp.324-334
    14. Mutert E , Fairhurst T (2000) Developments in rice production in Southeast Asia , Population, Vol.15 ; pp.2-17
    15. Olufowote JO , Xu Y , Chen X , Goto M , McCouch SR , Park WD , Beachell HM , Dilday RH (1997) Comparative evaluation of within-cultivar variation of rice (Oryza sativa L.) using microsatellite and RFLP markers , Genome, Vol.40 (3) ; pp.370-378
    16. Ostrowski MF , David J , Santoni S , Mckhann H (2006) Evidence for a large-scale population structure among accessions of Arabidopsis thaliana: possible causes and consequences for the distribution of linkage disequilibrium , Molecular Ecology , Vol.15 (6) ; pp.1507-1517
    17. Pritchard JK , Stephens M , Donnelly P (2000) Inference of population structure using multilocus genotype data , Genetics, Vol.155 (2) ; pp.945-959
    18. Roussel V , Koenig J , Beckert M (2004) Molecular diversity in French bread wheat accessions related to temporal trends and breeding programmes , TAG Theoretical and Applied Genetics, Vol.108 (5) ; pp.920-930
    19. Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments , Nature Biotechnology, Vol.18 (2) ; pp.233- 234
    20. Tamura K , Dudley J , Nei M , Kumar S (2007) MEGA4: molecular evolutionary genetics analysis (MEGA) software version 40 , Molecular biology and evolution , Vol.24 (8) ; pp.1596- 1599
    21. Wu KS , Tanksley SD (1993) Abundance, polymorphism and genetic mapping of microsatellitesin rice , Molecular and General Genetics, Vol.241 (1) ; pp.225-235
    22. Xiao J , Grandillo S , Ahn SN , McCouch SR , Tanksley SD , Li J , Yuan L (1996) Genes from wild rice improve yield , Nature, Vol.384 (6606) ; pp.223-224
    23. Yeh F , Yang R , Boyle T , Ye Z (2000) PopGene32, Microsoft Windows-based freeware for population genetic analysis, version 1 31, Molecular Biology and Biotechnology Centre, University of Alberta,
    24. Yifru T , Hammer K , Huang XQ , Roder MS (2006) Regional patterns of microsatellite diversity in Ethiopian tetraploid wheat accessions , Plant breeding, Vol.125 (2) ; pp.125-130
    25. Yu JW , Dixit A , Ma KH , Chung JW , Park YJ (2009) A study on relative abundance, composition and length variation of microsatellites in 18 underutilized crop species , Genetic Resources and Crop Evolution, Vol.56 (2) ; pp.237-246
    26. Zhao WG , Chung JW , Ma KH , Kim TS , Kim SM , Shin D , Kim CH , Koo HM , Park YJ (2009) Analysis of genetic diversity and population structure of rice cultivars from Korea, China, and Japan using SSR marker , enes& Genomics, Vol.31 (4) ; pp.283-292
    27. Zhou H , Xie Z , Ge S (2003) Microsatellite analysis of genetic diversity and population genetic structure of a wild rice (Oryza rufipogon Griff.) in China , TAG Theoretical and Applied Genetics, Vol.107 (2) ; pp.332-339