Genetic diversity assessment of sesame core collection in China by phenotype and molecular markers and extraction of a mini-core collection
© Zhang et al.; licensee BioMed Central Ltd. 2012
Received: 30 March 2012
Accepted: 3 October 2012
Published: 15 November 2012
Sesame (Sesamum indicum L.) is one of the four major oil crops in China. A sesame core collection (CC) was established in China in 2000, but no complete study on its genetic diversity has been carried out at either the phenotypic or molecular level. To provide technical guidance, a theoretical basis for further collection, effective protection, reasonable application, and a complete analysis of sesame genetic resources, a genetic diversity assessment of the sesame CC in China was conducted using phenotypic and molecular data and by extracting a sesame mini-core collection (MC).
Results from a genetic diversity assessment of sesame CC in China were significantly inconsistent at the phenotypic and molecular levels. A Mantel test revealed the insignificant correlation between phenotype and molecular marker information (r = 0.0043, t = 0.1320, P = 0.5525). The Shannon-Weaver diversity index (I) and Nei genetic diversity index (h) were higher (I = 0.9537, h = 0.5490) when calculated using phenotypic data from the CC than when using molecular data (I = 0.3467, h = 0.2218). A mini-core collection (MC) containing 184 accessions was extracted based on both phenotypic and molecular data, with a low mean difference percentage (MD, 1.64%), low variance difference percentage (VD, 22.58%), large variable rate of coefficient of variance (VR, 114.86%), and large coincidence rate of range (CR, 95.76%). For molecular data, the diversity indices and the polymorphism information content (PIC) for the MC were significantly higher than for the CC. Compared to an alternative random sampling strategy, the advantages of capturing genetic diversity and validation by extracting a MC using an advanced maximization strategy were proven.
This study provides a comprehensive characterization of the phenotypic and molecular genetic diversities of the sesame CC in China. A MC was extracted using both phenotypic and molecular data. Low MD% and VD%, and large VR% and CR% suggested that the MC provides a good representation of the genetic diversity of the original CC. The MC was more genetically diverse with higher diversity indices and a higher PIC value than the CC. A MC may aid in reasonably and efficiently selecting materials for sesame breeding and for genotypic biological studies, and may also be used as a population for association mapping in sesame.
Sesame (Sesamum indicum L.) has been cultivated in Asia for over 5000 years. In China, sesame is one of the four major oil crops, along with rapeseed, soybean, and peanut. On average (from 2001 to 2010), over 627,000 hectares of sesame are harvested annually, producing over 663,000 tons of sesame seeds, representing about 20% of the world’s production . Furthermore, China has been identified as one of the five sesame diversity centers in classical studies [2, 3]. As of 2012, the national gene bank of China has collected, reproduced, and preserved 5550 accessions of sesame.
Abundant plant germplasm resources provide a broad genetic foundation for plant breeding and genetic research. However, large germplasm resources are also difficult to preserve, evaluate, and use . Establishing a core collection (CC) is a favored approach for the efficient exploration and utilization of novel variation in genetic resources [5, 6]. The concept of a CC was first proposed by Frankel  and later developed by Brown . It involves the selection of a subset from the whole germplasm by certain methods in order to capture the maximum genetic diversity of the whole collection while minimizing accessions and redundancy. To date, CC have been established for many plant species around the world, including peanut [9, 10], barley , ryegrass , soybean [13, 14], safflower , rice [6, 16], olive [17, 18], Brassica rapa, Cornus officinalis, Arabidopsis thaliana, Medicago truncatula, and Vitis vinifera. To increase the usefulness of CC, genetic information must be clearly identified and documented .
To further reduce the duplication of some accessions in a CC, a ‘mini-core collection’ (MC) can serve as a small, representative subset of the CC. MC have been developed and evaluated for chickpea , peanut [26, 27], pigeon pea , maize , sorghum , rice [6, 31, 32], and other crops, promoting the utilization of genetic resources for these plants. For example, Upadhyaya  investigated the variability in drought resistance-related traits in the 184 entries of a MC for peanut. The results suggested certain accessions that can be used in peanut improvement programs to develop cultivars with a broad genetic base. Chamberlin et al.  evaluated a U.S. peanut MC using a molecular marker for resistance to Sclerotinia minor Jagger. They identified 39 accessions as new potential sources for resistance and targets for further evaluation. Using association analysis, Li et al.  mapped quantitative trait loci (QTLs) for improving grain yield using the USDA rice MC. Wang et al.  conducted association analysis of seed quality traits in a U.S. peanut (Arachis hypogaea L.) MC. In addition, Sharma et al.  identified new sources of resistance to Fusarium wilt and sterility mosaic disease using a pigeon pea MC and found that the diverse accessions with resistance would be useful in pigeon pea resistance breeding programs.
India, China, and Korea are the world’s leading countries for sesame germplasm collection and preservation, as well as research on sesame CC establishment. Bisht et al.  investigated 19 phenotypic and agronomic traits in 3129 sesame accessions from seven eco-geographical regions in India and established a sesame CC consisting of 362 accessions in India. Kang et al.  investigated 12 agronomic traits in 2246 sesame accessions from ten agro-climate zones preserved in the Rural Development Administration (RDA) Genebank in Korea and established a sesame CC of 475 accessions. In China, a systematic study of technical methods for the establishment of a sesame CC was conducted in cooperation with the International Plant Genetic Resources Institute (IPGRI). A sesame CC containing 453 accessions was established from the basic collection (BC) of 4251 accessions collected in China and 15 other countries using Ward’s clustering method and a stratified sampling strategy based on data for 14 phenotypic traits . The major objective for establishing sesame germplasm CC in China, Korea, and India has been to utilize germplasm more effectively. However, up until now, no comprehensive study of sesame CC genetic diversity has been carried out at either a phenotypic level or molecular level.
This study examined the genetic diversity of accessions from the sesame CC in China. The objectives of this investigation were to comprehensively explore the characteristics of genetic diversity at both a phenotypic level and a molecular level, and then to provide a theoretical foundation for effectively protecting and utilizing sesame genetic resources. Furthermore, a MC was extracted using an advanced maximization strategy based on both phenotypic and molecular data with the aim of promoting reasonable and efficient applications of sesame accessions in breeding and genotypic biological studies.
Polymorphism detected by molecular markers
A total of 36 sequence-related amplified polymorphism (SRAP) primer combinations and ten simple sequence repeat (SSR) primer pairs were employed randomly to screen polymorphism between 12 accessions (typical accessions from each group). Of these, 11 SRAP primer combinations and 3 SSR primer pairs amplified abundant, clear, and repeatable fragments. They were then employed to evaluate the genotypic diversity of 453 accessions in the CC. In total, 175 amplified fragments were detected and 126 of them were polymorphic, with a polymorphism rate of 72%. The number of fragments detected by each primer ranged from 2 (GBssr-sa-173) to 26 (Me07Em06), with an average of 12.5. The number of polymorphic fragments detected by each primer ranged from 2 (GBssr-sa-173) to 17 (Me08Em05), with an average of 9.
Genetic diversity analysis of the CC
Euclidean genetic distance and Jaccard similarity coefficient for each group
Euclidean genetic distance based on phenotypic data
Jaccard similarity coefficient based on molecular data
The pairwise GS between all 453 of the accessions in the nine groups ranged from 0.4000 to 0.9892, with an average of 0.7060. The average GS (0.7865) was the highest for group III, followed by that for group VIII (0.7486). The lowest average GS was found for group VII (0.6857), indicating that genetic relationships were the closest between accessions for group III, and the second closest for group VIII. Accessions in group VII had the most distant genetic relationship, as evaluated by molecular data.
Principal coordinate (PCO) analysis of the CC
Scatter plot based on (a) phenotypic data and (b) molecular data. Accessions from groups I, II, III, IV, V, VI, VII, VIII, and IX are marked with ♦, ⋄, ▴, △, ★, ☆, ▾, ⊚, and ▪, respectively.
MC extraction and comparison with CC
Distribution frequency comparison and representation analysis of accessions from the CC and MC among the nine groups
The mean difference percentage (MD, %), coincidence rate of range (CR, %), variance difference percentage (VD, %), and variable rate of coefficient of variance (VR, %) are used to comparably evaluate the properties of MC with CC. Over all the 14 phenotypic traits, the MD% between the CC and MC ranged from 0.64% to 2.84% in the nine groups (Table 2), with an average of 1.64%, far less than the significance level of 20% . The VD% ranged from 11.79% to 29.66%, with an average of 22.58%, slightly higher than the significance level of 20% . The VR% compares the coefficient of variation values for the phenotypic traits measured in the CC, with a representative general subset, and determines how well the variance is being represented in the representative subset . More than 100% of VR% is required for a subset to be representative of its original CC . The resulting average VR% of the MC was 114.86% (with a range of 106.50% to 121.82%), indicating that good representation of the original CC was achieved. The CR% indicates whether the distribution ranges of each trait in the MC are well represented when compared to the CC. The resulting average CR% in the nine groups was 95.76% (with a range of 92.54% to 100%), indicating homogeneous distribution ranges for the phenotypic traits, because CR was greater than 80% .
Genetic diversity indices and PIC values among the nine groups in the MC and CC
Genetic diversity index based on phenotypic data
Genetic diversity index based on molecular data
PIC value from molecular data
The polymorphism information content (PIC) value of each group in the MC ranged between 0.1668 and 0.2289, with an average of 0.2037, whereas PIC ranged from 0.1482 to 0.2111 in the CC, with an average of 0.1818. Results of pairwise t-tests indicated that the PIC value in the MC was significantly (P<0.0001) higher than that in the CC (Table 3). In addition to the genetic diversity indices and the PIC values, we also compared the loci and alleles of molecular markers between the MC and the CC. The allele frequency was diverse, from 0.44% to 100% in the CC, with an average of 50.78%, whereas it ranged between 1.09% and 100% in the MC, with an average of 49.98%. No allele was missing, and 100% of the allelic diversity at the tested loci was represented in the MC.
Validation of the sampling strategy for extracting the MC
Genetic diversity indices of accessions in MC extraction using two different strategies and a significance of difference analysis based on t -tests
Genetic diversity index based on phenotypic data
Genetic diversity index based on molecular data
Significance of the phenotypic and molecular genetic diversities of the sesame CC
The sesame CC in China is one of only three sesame CC in the world and differs from the other two collections [41, 42] in that it was developed from a basic collection (BC) with a larger quantity (4251 accessions), broader origin (16 countries), more diverse types (landrace, cultivar, special material), and more genetic diversity. Since sesame CC in China was established in 2000 , no study has examined its genetic diversity either at a phenotypic or molecular level. The present study comprehensively characterized the phenotypic and molecular genetic diversities of this CC. Our results will provide both technical guidance and a theoretical basis not only for further collection of sesame germplasm (from areas or agro-ecological zones with higher diversity) and the effective protection of sesame accessions (with rare alleles), but also for reasonable application and comprehensive analysis of sesame genetic resources. Information from a combined phenotypic and molecular genetic analysis of the CC can also be used to design parental crosses that maximize genetic polymorphisms for important traits.
Genetic diversity assessments of phenotypic and molecular data were significantly inconsistent
This study assessed genetic diversity in the nine groups of sesame CC in China both at the phenotypic and molecular levels, but the results were inconsistent. Evaluated based on phenotypic data, the Euclidean genetic distance indicated that genetic relationships were the closest between accessions in group IX and the most distant in group VIII. The Shannon-Weaver and Nei genetic diversity indices indicated that accessions in group III were genetically more diverse than in other groups, while group I displayed the least genetic diversity. Evaluated using molecular data, the Jaccard genetic similarity coefficient and genetic diversity indices indicated that the genetic relationships were nearest between accessions in group III, followed by accessions in group VIII. Accessions in group VII had the most distant genetic relationship. Furthermore, the phenotypic-based cluster did not correspond with the molecular-based cluster; the correlation coefficient (r = 0.0043) for the two clustering matrices tested by a Mantel test showed an insignificant correlation between phenotype and molecular marker information. This result is much lower than that found in safflower (r = 0.12) by Johnson et al.  and also much lower than that found by Reed and Frankham  in 71 datasets (r = 0.217). Reed and Frankham  suggested that molecular measures of genetic diversity have a very limited ability to predict quantitative genetic variability. Therefore, the combination of phenotypic-based and molecular-based analyses in genetic diversity assessments of the sesame CC is very important.
Why were genetic diversity indices of the CC much higher when evaluated using phenotypic data?
Both the Shannon-Weaver diversity index and the Nei genetic diversity index were much higher (I = 0.9537, h = 0.5490) when calculated using phenotypic data in the CC than when using molecular data (I = 0.3467, h = 0.2218). The sesame CC used in this study was extracted from the BC based on phenotypic traits data. Molecular data were not referred to because of the lack of molecular marker techniques; therefore genetic diversity was comparatively higher when examined on a phenotypic level. Genetic diversity on a molecular level was not considered in the establishing the CC. The high genotypic coefficient of similarity and the small phenotypic coefficient of distance between some accessions indicated that there were duplicates or near duplicates included within the CC. Therefore, in this study, extraction of a MC from the CC was performed based on both phenotypic and molecular data, using a diversity maximization strategy. Some genetically similar accessions were removed from the CC by further reduction; thus the genetic diversity indices evaluated by molecular data in the MC (I = 0.3861, h =0.2519) were significantly higher than in the CC, and the genetic diversity indices evaluated by phenotypic data (I = 0.9677, h =0.5530) were also enhanced.
Sampling strategy for the MC extraction and representation
Using a heuristic algorithm, Kim et al.  developed the PowerCore program, which selects a subset of accessions with a higher diversity representing the total coverage of marker alleles and trait states present in the entire collection, applying an advanced maximization strategy. Zhao et al.  selected 50 rice cultivars from each of Korea, China, and Japan from the RDA Genebank using the Powercore program and analyzed their genetic diversity and population structure using SSR Markers. Belaj et al.  developed a CC of olive (Olea europaea L.) based on molecular markers and agronomic traits, using the PowerCore and MSTRAT programs . Their results suggested that the CC extracted by PowerCore may be of special interest for genetic conservation applications in olive, owing to PowerCore’s high efficiency at capturing all of the allele/trait states found in the entire collection. Subsequent applications of PowerCore also suggested its effectiveness in establishing a CC that retains all characteristics of qualitative traits and all classes of quantitative ones .
In this study, PowerCore was used to extract a sesame MC containing 184 accessions from the CC and the resulting collection was compared to that obtained by random sampling. The advantage of the advanced maximization strategy of PowerCore in capturing genetic diversity and in validating MC extraction was illustrated. An MC with low MD% and VD% and large VR% and CR% can be considered to provide a good representation of the genetic diversity in the initial CC [41, 42]. The similarity of diversity index distributions between the MC and CC among the nine groups also showed that the selected MC provides a sound description of the sesame genetic diversity found in China. In addition, the χ2 test results showed homogeneous distribution frequencies between the MC and CC from group I to group VII, with two exceptions (groups VIII and IX). The distribution frequencies for Group VIII were 3.97% in the CC and 7.61% in the MC, whereas those for group IX were 6.84% in the CC and 10.87% in the MC. The distribution frequencies of these two groups from the MC were significantly higher than from the CC, which may be attributed to their special accession types. Accessions from group VIII were made up of 18 cultivars released in China, and accessions from group IX were 31 exotic landraces from 15 countries. The accession numbers were both very limited, and to maintain the genetic diversity of these two groups, it was necessary to increase their frequencies in the MC.
Limitations of this study and future development of the sesame MC
This study established a MC, but continued research on sesame germplasm is necessary. More accessions are being added to the BC, and with these accessions must also be updated in the CC and MC in a dynamic manner. The sesame MC presented here will be useful for efficiently selecting accessions with maximum diversity for sesame breeding, for selecting parents to generate mapping populations, or for further evaluation and restructuring of the CC. Furthermore, the MC, with its smaller number of accessions than the CC, can aid in comprehensive investigations of important traits and molecular markers in sesame. The combination of phenotype and molecular marker data can be used directly for association mapping and for developing key molecular markers associated with important traits.
This study presented a comprehensive characterization of the phenotypic and molecular genetic diversities of sesame CC in China. We extracted an MC containing 184 accessions from the CC based on both of phenotypic and molecular data. Low MD% and VD% and large VR% and CR% suggested that the MC provided a good representation of the genetic diversity of the original CC; it was more genetically diverse with higher diversity indices and a higher PIC value than the CC. The development of a MC may aid in reasonably and efficiently selecting materials for sesame breeding and for genotypic biological studies, and may also be used as a population for association mapping in sesame.
Materials and methods
Description of the 453 sesame accessions used in this study
Northeast and northwest of China
agro-ecological zone I
agro-ecological zone II
The Yellow River and Huai River valley
agro-ecological zone III
The Yangtze and Han River valley
agro-ecological zone IV
Middle and lower of Yangtze valley
agro-ecological zone V
South-central of China and southern China
agro-ecological zone VI
Southwest of China
agro-ecological zone VII
Phenotype data mining and analysis
The following 14 genetically stable and important agronomical traits were investigated: growth period, plant type, number of locules, flower number per nod, stem hairiness, flower color, seed color, capsule dehiscence, tolerance of water-logging, resistance to stem spot wilt, resistance to wilt, 1000-seed weight, oil content, and protein content. Values of these 14 traits recorded in a database  were used to conduct the following statistical analysis. Quantification of qualitative traits followed the method of Zhang et al. . Phenotype data were standardized first using the standardization program of NTSYS-pc software version 2.1 . Genetic distance (GD) was calculated using the Interval data program and the EUCLID (Euclidean) coefficient, which is likely the most commonly used type of distance. Principal coordinate (PCO) analysis was undertaken using principal component analysis programs such as DCENTER and EIGEN based on genetic distance matrices, and a scatter plot was generated. Additionally, the Shannon-Weaver diversity index and the Nei genetic diversity index were estimated using POPGENE version 1.32 .
DNA extraction, PCR amplification and electrophoresis
Sequences of primers used in this study
Forward primer code
Reverse primer code
Molecular marker data mining and analysis
All of the major DNA fragments were recorded as either 1 or 0 representing the presence or absence of the band, respectively. The pairwise genetic similarity coefficient (GS) was calculated using Jaccard coefficient  by the SIMQUAL program of NTSYS-pc software version 2.1 . Principal coordinate (PCO) analysis was conducted using principal component analysis programs such as DCENTER and EIGEN of the NTSYS-pc based on genetic similarity matrices to generate a scatter plot. The Shannon-Weaver and Nei genetic diversity indices were estimated using POPGENE version 1.32 . The relationship between the phenotypic-based clustering matrix and the genotypic-based clustering matrix was tested using a Mantel test [55, 56]. An unweighted pair group method with arithmetic mean (UPGMA) dendrogram was created using MEGA version 4.1 . Polymorphism information content (PIC) was calculated using PowerMarker version 3.25 .
Extracting the MC from the CC
On the basis of the phenotype and molecular marker data, a MC was extracted from the CC using PowerCore software , which can represent all the alleles identified by the molecular markers and classes of the phenotypic observations. The software applies an advanced maximization strategy implemented through a modified heuristic algorithm. The resulting MC was compared with the original CC to assess its homogeneity as follows. Chi-squared (χ2) tests were used to contrast the similarity of the distribution frequency between the MC and the CC between each group, and homogeneity was further evaluated using the mean difference percentage (MD, %), coincidence rate of range (CR, %), variance difference percentage (VD, %), and variable rate of the coefficient of variance (VR, %) according to Hu et al.  and Kim et al. . The Shannon-Weaver diversity index and the Nei diversity index of the MC were estimated using phenotypic data and molecular data, and the significance of genetic diversity index differences between the MC and CC was analyzed using pairwise t-tests.
This work was supported by the Project of National Plant Germplasm Resources Protection (no. NB2012-2130135), the Project of youth science and technology morning plan in Wuhan (no. 201150431067), the Project of National Science and Technology Infrastructure Construction (no. 2005DKA21001-20) and the National Basic Research Program of China (973 Program) (no. 2011CB109304-2).
- FAOSTAT: 2012, http://faostat.fao.org/site/567/default.aspx#ancor,
- Zeven A, Zhukovsky P: Dictionary of cultivated plants and their centres of diversity. 1975, PUDOC: WageningenGoogle Scholar
- Hawkes J: The diversity of crop plants. 1983, Cambridge: Harvard University PressView ArticleGoogle Scholar
- Holden JHW: The second ten years. Crop Genetic Resources Conservation and Evaluation. Edited by: Holden JHW, Williams JT. 1984, London: George Allen and Unwin Publication, 277-285.Google Scholar
- Hodgkin T, Brown AHD, Hintum TJLV, Morales EAV: Core collections of plant genetic resources. 1995, United Kingdom: A co-publication with the international plant genetic resources institute (IPGRI) and Sayce publishingGoogle Scholar
- Zhang HL, Zhang DL, Wang MX, Sun JL, Qi YW, Li JJ, Wei XH, Han LZ, Qiu ZE, Tang SX, Li ZC: A core collection and mini core collection of Oryza sativa L. in China. Theor Appl Genet. 2011, 122: 49-61. 10.1007/s00122-010-1421-7.View ArticlePubMedGoogle Scholar
- Frankel OH: Genetic perspectives of germplasm conservation. Genetic Manipulation: Impact on Man and Society. Edited by: Arber WK, Llimensee K, Peacock WJ, Starlinger P. 1984, Cambridge: Cambridge University Press, 161-170.Google Scholar
- Brown AHD: The case for core collection. Genome. 1989, 1989 (31): 818-824.View ArticleGoogle Scholar
- Holbrook CC, Anderson WF, Pittman RN: Selection of a core collection from the U.S. germplasm collection of peanut. Crop Sci. 1993, 33: 859-861. 10.2135/cropsci1993.0011183X003300040044x.View ArticleGoogle Scholar
- Upadhyaya HD, Ortiz R, Bramel PJ, Singh S: Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Genet Resour Crop Evol. 2003, 50: 139-148. 10.1023/A:1022945715628.View ArticleGoogle Scholar
- Hintum TJL: Comparison of marker systems and construction of a core collection in a pedigree of European spring barley. Theor Appl Genet. 1995, 89: 991-997.Google Scholar
- Charmet G, Balfourier F: The use of geostatistics for sampling a core collection of perennial ryegrass populations. Genet Resour Crop Evol. 1995, 42: 303-309. 10.1007/BF02432134.View ArticleGoogle Scholar
- Zhao LM, Dong YS, Liu B, Hao S, Wang KJ, Li XH: Establishment of a Core Collection for the Chinese annual wild soybean (Glycine Soja). Chin Sci Bull. 2005, 50: 989-996. 10.1360/982004-657.View ArticleGoogle Scholar
- Wang LX, Guan Y, Guan RX, Li YH, Ma YS, Dong ZM, Liu X, Zhang HY, Zhang YQ, Liu ZX, Chang RZ, Xu HM, Li LH, Lin FY, Luan WJ, Yan Z, Ning XC, Zhu L, Cui YH, Piao RH, Liu Y, Chen PY, Qiu LJ: Establishment of Chinese soybean (Glycine max) core collections with agronomic traits and SSR markers. Euphytica. 2006, 151: 215-223. 10.1007/s10681-006-9142-3.View ArticleGoogle Scholar
- Dwivedi SL, Upadhyaya HD, Hegde DM: Development of core collection using geographic information and morphological descriptors in safflower (Carthamus tinctorius L.) germplasm. Genet Resour Crop Evol. 2005, 52: 821-830. 10.1007/s10722-003-6111-8.View ArticleGoogle Scholar
- Yan WG, Rutger JN, Bryant RJ, Bockelman HE, Fjellstrom RG, Chen MH, Tai TH, McClung AM: Development and evaluation of a core subset of the USDA rice (Oryza sativa L.) germplasm collection. Crop Sci. 2007, 47: 869-878. 10.2135/cropsci2006.07.0444.View ArticleGoogle Scholar
- Haouane H, Bakkali AE, Moukhli A, Tollon C, Santoni S, Oukabli A, Modafar CE, Khadari B: Genetic structure and core collection of the World Olive Germplasm Bank of Marrakech: towards the optimized management and use of Mediterranean olive genetic resources. Genetica. 2011, 139: 1083-1094. 10.1007/s10709-011-9608-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Belaj A, Dominguez-García MC, Atienza SG, Urdíroz NM, Rosa RD, Satovic Z, Martín A, Kilian A, Trujillo I, Valpuesta V, Río CD: Developing a core collection of olive (Olea europaea L.) based on molecular markers (DArTs, SSRs, SNPs) and agronomic traits. Tree Genet & Genomes. 2012, 8: 365-378. 10.1007/s11295-011-0447-6.View ArticleGoogle Scholar
- Carpio DPD, Basnet RK, De Vos RCH, Maliepaard C, Visser R, Bonnema G: The patterns of population differentiation in a Brassica rapa core collection. Theor Appl Genet. 2011, 122: 1105-1118. 10.1007/s00122-010-1516-1.View ArticleGoogle Scholar
- Li GS, Zhang LJ, Bai CK: Chinese Cornus officinalis: genetic resources, genetic diversity and core collection. Genet Resour Crop Evol. 2012, 10.1007/s10722-011-9789-z.Google Scholar
- McKhann HI, Camilleri C, Bérard A, Bataillon T, David JL, Reboud X, Le Corre V, Caloustian C, Gut IG, Brunel D: Nested core collections maximizing genetic diversity in Arabidopsis thaliana. Plant J. 2004, 38: 193-202. 10.1111/j.1365-313X.2004.02034.x.View ArticlePubMedGoogle Scholar
- Ronfort J, Bataillon T, Santoni S, Delalande M, David JL, Prosperi JM: Microsatellite diversity and broad scale geographic structure in a model legume: building a set of nested core collection for studying naturally occurring variation in Medicago truncatula. BMC Plant Biol. 2006, 6: 28-10.1186/1471-2229-6-28.PubMed CentralView ArticlePubMedGoogle Scholar
- Le Cunff L, Fournier-Level A, Laucou V, Vezzulli S, Lacombe T, Adam-Blondon AF, Boursiquot JM, This P: Construction of nested genetic core collections to optimize the exploitation of natural diversity in Vitis vinifera L. subsp. sativa. BMC Plant Biol. 2008, 8: 31-10.1186/1471-2229-8-31.PubMed CentralView ArticlePubMedGoogle Scholar
- Hokanson S, Szewc-McFadden A, Lamboy W, McFerson J: Microsatellite (SSR) markers reveal genetic identities, genetic diversity and relationships in a Malus xdomestica Borkh. core subset collection. Theor Appl Genet. 1998, 97: 671-683. 10.1007/s001220050943.View ArticleGoogle Scholar
- Upadhyaya HD, Ortiz R: A mini core subset for capturing diversity and promoting utilization of chickpea genetic resources in crop improvement. Theor Appl Genet. 2001, 102: 1292-1298. 10.1007/s00122-001-0556-y.View ArticleGoogle Scholar
- Upadhyaya HD, Bramel PJ, Ortiz R, Singh S: Developing a mini core of peanut for utilization of genetic resources. Crop Sci. 2002, 42: 2150-2156. 10.2135/cropsci2002.2150.View ArticleGoogle Scholar
- Holbrook CC, Dong WB: Development and evaluation of a mini core collection for the U.S. peanut germplasm collection. Crop Sci. 2005, 45: 1540-1544. 10.2135/cropsci2004.0368.View ArticleGoogle Scholar
- Upadhyaya HD, Reddy LJ, Gowda CLL, Reddy KN, Singh S: Development of a mini core subset for enhanced and diversified utilization of pigeonpea germplasm resources. Crop Sci. 2006, 46: 2127-2132. 10.2135/cropsci2006.01.0032.View ArticleGoogle Scholar
- Yu YT, Wang RH, Shi YS, Song YC, Wang TY, Li Y: Genetic diversity and structure of the core collection for maize lines in China. Maydica. 2007, 52: 181-194.Google Scholar
- Upadhyaya HD, Pundir RPS, Dwivedi SL, Gowda CLL, Reddy VG, Singh S: Developing a minicore collection of sorghum for diversified utilization of germplasm. Crop Sci. 2009, 49: 1769-1780. 10.2135/cropsci2009.01.0014.View ArticleGoogle Scholar
- Agrama HA, Yan WG, Lee F, Fjellstrom R, Chen MH, Jia M, McClung A: Genetic assessment of a mini–core subset developed from the USDA rice Genebank. Crop Sci. 2009, 49: 1336-1346. 10.2135/cropsci2008.06.0551.View ArticleGoogle Scholar
- Borba TCO, Brondani RPV, Rangel PHN, Brondani C: Microsatellite marker–mediated analysis of the EMBRAPA rice core collection genetic diversity. Genetica. 2009, 137: 293-304. 10.1007/s10709-009-9380-0.View ArticleGoogle Scholar
- Upadhyaya HD: Variability for Drought Resistance Related Traits in the Mini Core Collection of Peanut. Crop Sci. 2005, 45: 1432-1440. 10.2135/cropsci2004.0389.View ArticleGoogle Scholar
- Chamberlin KDC, Melouk HA, Payton ME: Evaluation of the U.S. peanut mini core collection using a molecular marker for resistance to Sclerotinia minor Jagger. Euphytica. 2010, 172: 109-115. 10.1007/s10681-009-0065-7.View ArticleGoogle Scholar
- Li XB, Yan WG, Agrama H, Jia LM, Shen XH, Jackson A, Moldenhauer K, Yeater K, McClung A, Wu DX: Mapping QTLs for improving grain yield using the USDA rice mini-core collection. Planta. 2011, 234: 347-361. 10.1007/s00425-011-1405-0.View ArticlePubMedGoogle Scholar
- Wang ML, Sukumaran S, Barkley NA, Chen ZB, Chen CY, Guo BZ, Pittman RN, Stalker HT, Holbrook CC, Pederson GA, Yu JM: Population structure and marker–trait association analysis of the US peanut (Arachis hypogaea L.) mini-core collection. Theor Appl Genet. 2011, 123: 1307-1317. 10.1007/s00122-011-1668-7.View ArticlePubMedGoogle Scholar
- Sharma M, Rathore A, Mangala UN, Ghosh R, Sharma S, Upadhyay HD, Pande S: New sources of resistance to Fusarium wilt and sterility mosaic disease in a mini-core collection of pigeonpea germplasm. Eur J Plant Pathol. 2012, 133: 707-714. 10.1007/s10658-012-9949-9.View ArticleGoogle Scholar
- Bisht IS, Mahajan RK, Loknathan TR, Agrawal RC: Diversity in Indian sesame collection and stratification of germplasm accessions in different diversity groups. Genet Resour Crop Evol. 1998, 45: 325-335. 10.1023/A:1008652420477.View ArticleGoogle Scholar
- Kang CW, Kim SY, Lee SW, Mathur PN, Hodgkin T, Zhou MD, Lee JR: Selection of a core collection of Korean sesame germplasm by a stepwise clustering method. Breed Sci. 2006, 56: 85-91. 10.1270/jsbbs.56.85.View ArticleGoogle Scholar
- Zhang XR, Zhao YZ, Cheng Y, Feng XY, Guo QY, Zhou MD, Hodgkin T: Establishment of sesame germplasm core collection in China. Genet Resour Crop Evol. 2000, 47: 273-279. 10.1023/A:1008767307675.View ArticleGoogle Scholar
- Hu J, Zhu J, Xu HM: Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theor Appl Genet. 2000, 101: 264-268. 10.1007/s001220051478.View ArticleGoogle Scholar
- Kim KW, Chung HK, Cho GT, Ma KH, Chandrabalan D, Gwag JG, Kim TS, Cho EG, Park YJ: PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics. 2007, 23: 2155-2162. 10.1093/bioinformatics/btm313.View ArticlePubMedGoogle Scholar
- Johnson RC, Kisha TJ, Evans MA: Characterizing safflower germplasm with AFLP molecular markers. Crop Sci. 2007, 47: 1728-1736. 10.2135/cropsci2006.12.0757.View ArticleGoogle Scholar
- Reed DH, Frankham R: How closely correlated are molecular and qualitative measures of genetic variation? A meta-analysis. Evolution. 2001, 55: 1095-1103.View ArticlePubMedGoogle Scholar
- Zhao WG, Chung JW, Ma KH, Kim TS, Kim SM, Shin DI, Kim CH, Koo HM, Park YJ: Analysis of genetic diversity and population structure of rice cultivars from Korea, China and Japan using SSR markers. Genes & Genomics. 2009, 31: 283-292. 10.1007/BF03191201.View ArticleGoogle Scholar
- Gouesnard B, Bataillon TM, Decoux G, Rozale C, Schoen DJ, David JL: MSTRAT: an algorithm for building germplasm core collections by maximizing allelic or phenotypic richness. J Hered. 2001, 92: 93-94. 10.1093/jhered/92.1.93.View ArticlePubMedGoogle Scholar
- Rohlf FJ: NTSYS-pc: Numerical Taxonomy and Multivariate Analysis System, Version 2.1, User Guide. 2000, New York: Exeter SoftwareGoogle Scholar
- Yeh FC, Boyle TJB: Population genetic analysis of co-dominant and dominant markers and quantitative traits. Belgium J of Bot. 1997, 129: 157-Google Scholar
- Doyle JJ, Doyle JL: A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987, 19: 11-15.Google Scholar
- Li G, Quiros CF: Sequence-related amplified polymorphism (SRAP) a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor Appl Genet. 2001, 103: 455-461. 10.1007/s001220100570.View ArticleGoogle Scholar
- Dixit A, Jin MH, Chung JW, Yu JW, Chung HK, Ma KH, Park YJ, Cho EG: Development of polymorphic microsatellite markers in sesame (Sesamum indicum L.). Mol Ecol Notes. 2005, 5: 736-738. 10.1111/j.1471-8286.2005.01048.x.View ArticleGoogle Scholar
- Zhang YX, Lin ZX, Xia QZ, Zhang MJ, Zhang XL: Characteristics and analysis of simple sequence repeats in the cotton genome based on a linkage map constructed from a BC1 population between Gossypium hirsutum and G. barbadense. Genome. 2008, 51: 534-546. 10.1139/G08-033.View ArticlePubMedGoogle Scholar
- Lin ZX, He DH, Zhang XL, Nie YC, Guo XP, Feng CD, Stewart JM: Linkage map construction and mapping QTL for cotton fiber quality using SRAP, SSR and RAPD. Plant Breed. 2005, 124: 180-187. 10.1111/j.1439-0523.2004.01039.x.View ArticleGoogle Scholar
- Jaccard P: Nouvelles recherché sur la distribution florale. Bull Soc Vaud Sci Nat. 1908, 44: 223-270.Google Scholar
- Mantel N: The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27: 209-220.PubMedGoogle Scholar
- Mantel N, Valand RS: A technique of nonparametric multivariate analysis. Biometrics. 1970, 26: 547-558. 10.2307/2529108.View ArticlePubMedGoogle Scholar
- Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007, 24: 1596-1599. 10.1093/molbev/msm092.View ArticlePubMedGoogle Scholar
- Liu K, Muse SV: PowerMarker: integrated analysis environment for genetic marker data. Bioinformatics. 2005, 21: 2128-2129. 10.1093/bioinformatics/bti282.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.