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Genetic diversity and population structure of common bean (Phaseolus vulgaris) landraces from China revealed by a new set of EST-SSR markers
Biochemical Systematics and Ecology 57 (2014) 250e256
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Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Genetic diversity and population structure of common bean (Phaseolus vulgaris) landraces from China revealed by a new set of EST-SSR markers Shengchun Xu a, 1, Guofu Wang b, 1, Weihua Mao c, Qizan Hu a, Na Liu a, Lingwei Ye a, Yaming Gong a, * a b c
Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, PR China Department of Life Science, Yuanpei College, Shaoxing University, Shaoxing 312000, PR China Center of Analysis and Measurement, Zhejiang University, Hangzhou 310058, PR China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 April 2014 Accepted 22 August 2014 Available online
A new set of EST-SSR markers were developed and employed to analyze the genetic diversity and population structure of Phaseolus vulgaris in China. A total of 2452 microsatellites were identified in 2144 unigenes assembled from P. vulgaris ESTs, indicating that merely 6.9% of the 30,952 unigene sequences contained SSRs. Seventeen of 153 randomly designed EST-SSR primer pairs successfully amplified polymorphic products in 31 landraces from six major production provinces of China, with the mean number of alleles per locus of 2.700 and polymorphism information content of 0.378. The observed and expected heterozygosity ranged from 0.100 to 0.954 and 0.081 to 0.558, respectively. Using these markers, both an unrooted neighbor-joining tree and principal coordinates analysis showed that almost all of the landraces were separated according with their regional distribution. Moreover, population structure analysis revealed that all genotypes formed into three distinct clusters (k ¼ 3), suggesting that geographic and climatic factors could provide diverse degrees of selection pressure. Accordingly, germplasm collection and cross breeding among different regions are suggested to accelerate the process of diverse germplasm creation and broaden germplasm resources of Chinese common bean. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Expressed sequence tag (EST) Genetic diversity Microsatellite Phaseolus vulgaris Population structure
1. Introduction Common bean (Phaseolus vulgaris) is one of the most popular food legumes in the world and is grown worldwide for its wide consumer acceptability and diverse environmental suitability (Singh, 2001). Two major gene pools have been identified, Andean and Mesoamerican, each represented by different races (Blair et al., 2006). The races derived from the different gene pools were introduced into China around the 16th century (Zheng, 1997). Subsequently, a longer and thinner pod type variant of common bean, called snap bean, arose due to mutation of the hard pod layer gene after long-term domestication and cultivation. Based on this, a previous study proposed China to be a center of diversification (Yan and Lu, 1994). In recent years, China has become the largest producer of snap beans in the world.
* Corresponding author. Tel./fax: þ86 571 86404179. E-mail address: [email protected] (Y. Gong). 1 These two authors contributed equally to this work. http://dx.doi.org/10.1016/j.bse.2014.08.012 0305-1978/© 2014 Elsevier Ltd. All rights reserved.
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In addition to its economic and nutritional importance, the self-pollinating trait and small genome of common bean render it an excellent species for genetic analysis (Perseguini et al., 2011). A number of studies have been devoted to characterizing genetic diversity in germplasm collections, including those of Mesoamerican and Andean gene pools from Central and South America, in order to support P. vulgaris breeding and management of important genetic resources based on morphological and molecular markers (Beebe et al., 2001; Blair et al., 2012). Similar analyses have been carried out for some secondary diversity centers, e.g. east African highlands and the Iberian Peninsula (Ocampo et al., 2005; Asfaw et al., 2009). However, molecular breeding efforts in common bean have been limited in China because of insufficient knowledge of the molecular characteristics of the landraces found there. It is currently difficult to identify the extent of genetic differences among the landraces due to the introgression of Central America gene pools following domestication. Recently, different types of molecular markers have been used to broaden our knowledge of the molecular genetics of common bean (Hanai et al., 2010; Cortes et al., 2011). Due to their co-dominant inheritance, high level of transferability, close association with genes of known function, and low cost of development, microsatellite (SSR) markers derived from expressed sequence tags (ESTs) have proven to be fast and efficient tools for analyses of genetic diversity and population structure, as well as genetic mapping and marker assisted selection (Varshney et al., 2005; Kalia et al., 2011). A large set of ESTs from P. vulgaris have been deposited in public databases (http://www.ncbi.nlm.nih.gov/Genbank/), which facilitates development of EST-SSR markers (Blair et al., 2011; Garcia et al., 2011). However, most of these databases still have not been extensively screened nor have SSR markers been characterized, making placement of appropriate markers difficult. Accordingly, EST-SSR markers for common bean are still scarce (Gonçalves-Vidigal and Rubiano, 2011). The limited numbers of EST-SSR markers hampers our understanding of diversity and genetic structure in common bean germplasm collections in China. To date, few comprehensive assessments have been performed on the comparative diversity and regional differentiation of common bean landraces from different regions in China (Zhang et al., 2008). The present research therefore was carried out with the objectives to (1) develop a new set of EST-SSRs to increase the number of markers available for genetic studies in present and future applications in P. vulgaris, and (2) assess the genetic diversity and population structure among snap bean landraces from major production regions in China. The results may help in understanding and managing this species, as well as promoting marker-assisted selection and breeding programs. 2. Materials and methods 2.1. Plant materials Thirty-one landraces of Phaseolus vulgaris were collected from natural populations in Hebei (38 104800 N, 114 280 4800 E), Heilongjiang (45 440 2500 N, 126 39'2300 E), Zhejiang (30140 3500 N, 120 90 3600 E), Neimenggu (40 490 2000 N, 111400 200 E), Yunnan (25 20 5400 N, 102 420 3000 E) and Guizhou (26 34'1200 N, 106 42'3600 E) provinces, representing six major production regions in China. For the materials, YN001113 (No.6) and GZ001208 (No.16) were used as check genotypes representing the Mesoamerica gene pools. Other genotypes were recorded with the origin from the cultivated Mesoamerican gene pools, which were more suitable to the Chinese eating habits (Table S1). Total genomic DNA from leaves at the four-leaf stage of each individual was extracted using the CTAB method (Doyle, 1991). The DNA concentration was estimated by comparison with a standard DL2000 DNA marker (TaKaRa, Dalian, Liaoning, China) using agarose gel electrophoresis. 2.2. Primer definition and PCR reaction The unigene sequences of P. vulgaris were downloaded from PlantGDB database (http://www.plantgdb.org/), and screened for the presence of microsatellites with SSRIT software (http://www.gramene.org/gramene/searches/ssrtool), with criteria of 8, 5, 4, 3 repeating units for di-, tri-, tetra-, penta- and higher order nucleotides, respectively. A set of unigenes with candidate microsatellites were found suitable for primer design using the Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, California, USA), with a length of 17e24 bp, annealing temperature of 50e60 C, and product sizes ranging from 100 to 400 bp. The forward primers of each pair were labeled with fluorescent dye 6-FAM or HEX (TaKaRa, Dalian, Liaoning, China). Polymerase chain reactions (PCRs) were carried out in 20 mL reaction volumes containing 20 ng genomic DNA, 2 mL 1 PCR buffer, 1 unit Taq DNA polymerase (TaKaRa, Dalian, Liaoning, China), 2 mM MgCl2, 0.2 mM each primer, and 0.2 mM dNTPs. PCRs were performed on a PTC-225 Peltier Thermal Cycler (MJ Research, Waltham, Massachusetts, USA) with an initial denaturation at 94 C for 5 min, followed by 35 cycles of 94 C for 30 s, appropriate annealing temperature (Table S2) for 30 s, 72 C for 1 min, and a final extension at 72 C for 10 min. The PCR products were diluted 5-fold with distilled water and detected using a MegaBACE 1000 DNA analysis system (Amersham Biosciences, Piscataway, New Jersey, USA) at the Center of Analysis and Measurement in Zhejiang University. Sizes of amplified fragments were quantified using the ET550-R size standards and Genetic Profiler version 2.2 (GE Healthcare, Piscataway, New Jersey, USA). 2.3. Data analysis The polymorphic level of EST-SSR markers was evaluated by analyzing the genotypes in six populations. The genetic statistics based on six populations of 31 genotypes were calculated using GenAlEx 6.5 software (Peakall and Smouse, 2012)
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including the number of polymorphic alleles (N), observed heterozygosity (HO), and expected heterozygosity (HE). Polymorphism information content (PIC) was calculated using the formula developed by Anderson et al. (1993). The population allele frequencies and inbreeding index (Fis) were also calculated using the program GenAlEx 6.5. To infer the distribution of genetic diversity, principal coordinates analysis (PCoA) was carried out using GenAlEx 6.5 software. A neighbor-joining dendrogram was generated using MEGA 4.0 (Tamura et al., 2007) based on pairwise genetic distances among individuals. The model-based program STRUCTURE (version 2.3.4) was used to infer population genetic structure (Pritchard et al., 2000). In order to identify the number of populations (K) capturing the major structure in the data, we used a burn-in period of 10,000 Markov Chain Monte Carlo (MCMC) iterations and 100,000 run length with 5 replicate runs for each value of K (1e10). An admixture model following HardyeWeinberg equilibrium and correlated allele frequencies were chosen for each run. Then, using the results in the STRUCTURE output file, the most appropriate K value was determined by Structure Harvester (Earl and Vonholdt, 2012), based on the posterior probability of the data for a given K and DK. 3. Results 3.1. Frequencies and distribution of EST-SSRs from P. vulgaris A total of 30,952 P. vulgaris unigene sequences were obtained from the PlantGDB database (http://www.plantgdb.org/), and searched for SSRs. From these, 2144 unigenes were found to contain 2452 microsatellites (Table 1). Among all sequences, 1884 unigenes contained one SSR, followed by unigenes with two to five SSRs, gradually. The most abundant SSRs from P. vulgaris were tri-nucleotide repeats, accounting for 40.4% of all SSRs, followed by hexa-, penta-, di- and tetra-nucleotide repeats (Fig. 1). The repeat unit numbers at SSR loci ranged from 3 to 53, and the length of SSRs ranged from 15 to 106 bp, with a mean value of 18.38 bp. The different types of identified SSR units are described in Table S2. For example, among the tri-nucleotide repeats, GAA/TTC was the most abundant, with a frequency of 7.1%. TTTC/GAAA (9.0%) predominated in the tetranucleotide type. 3.2. Polymorphism of EST-SSR markers To screen the polymorphism of EST-SSRs, a total of 153 EST-SSR primer pairs were designed. Of these, 71 (46.4%) were found to amplify products of the expected size, while 34 (22.2%) generated unexpected products and 48 (31.4%) failed to amplify any products. Among the successful primer pairs, seventeen showed clearly polymorphic patterns based on 6 populations (Table S3). Eleven (64.7%) of them showed significant similarity to proteins of known function, such as transcription factors, transfer proteins, and stress-related proteins (Table S2). Because of their association with the coding regions of the genome, these EST-SSRs should be directly applicable for marker trait association. The 17 EST-SSR markers revealed a total of 46 alleles in the 31 genotypes, and the number of alleles (N) ranged from 2 (PVSat003, PVSat007, etc.) to 6 (PVSat040) with an average of 2.7 per locus (Table 2). The observed heterozygosity (HO) and expected heterozygosity (HE) varied from 0.100 to 0.954 and 0.081 to 0.558, respectively, while polymorphism information content (PIC) changed from 0.138 to 0.606, with the average of 0.378. All of these indexes indicated that the polymorphic ESTSSRs developed in the present study would be useful in evaluation of the genetic variation of P. vulgaris germplasm resources. 3.3. Genetic diversity assessment The parameters of genetic diversity among populations were presented in Table 3. The highest levels of N were found in population YN. The mean values of HO and HE were 0.626 and 0.368, with the highest level both in population ZJ. The values of Fis, were negative in all populations with the value from 0.853 (HB) to 0.465 (YN), indicating a high frequency of heterozygous loci in these populations. An unrooted neighbor-joining (NJ) tree was constructed based on the pairwise genetic distances. The genotypes were largely, but not completely, separated in accord with the aforementioned natural regional distributions (Fig. 2). The check genotypes, No. 6 from Yunnan and No. 16 from Guizhou were clustered together. Furthermore, principal coordinates analysis
Table 1 Summary of in silico mining of unigene sequences of Phaseolus vulgaris. Parameter
Number
Total unigene sequences Total SSRs identified Sequences containing one SSR Sequences containing two SSRs Sequences containing three SSRs Sequences containing four SSRs Sequences containing five SSRs Total SSR-containing unigene sequences
30,952 2452 1884 224 27 6 3 2144
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Fig. 1. Frequency distribution of SSRs of different repeat types (2-6 bp motif units) identified in unigene sequences of Phaseolus vulgaris.
(PCoA) was performed with EST-SSR data on the 31 genotypes. The first and second components of the PCoA analysis accounted for 34.1% and 21.9% of the total variation, respectively. Although the landraces were mostly distributed in different areas of the plot in accord with their natural regional distributions, the results revealed four distinct clusters for the germplasm types (Fig. 3).
Table 2 Characteristics of the 17 EST-SSR markers and the diversity detected in the 31 Phaseolus vulgaris landraces in China. Locus
N
HO
HE
PIC
PVSat003 PVSat007 PVSat018 PVSat021 PVSat023 PVSat029 PVSat031 PVSat033 PVSat039 PVSat040 PVSat042 PVSat047 PVSat049 PVSat056 PVSat057 PVSat061 PVSat068 Mean
2 2 2 4 2 2 2 3 2 6 2 4 5 2 2 2 2 2.7
0.872 0.778 0.100 0.428 0.139 0.954 0.933 0.918 0.872 0.936 0.167 0.222 0.744 0.952 0.478 0.750 0.167 0.627
0.494 0.470 0.150 0.299 0.081 0.500 0.487 0.520 0.481 0.553 0.083 0.239 0.558 0.500 0.313 0.449 0.083 0.369
0.374 0.368 0.180 0.598 0.138 0.375 0.374 0.419 0.371 0.555 0.371 0.293 0.606 0.375 0.365 0.358 0.300 0.378
N: number of polymorphic alleles; HO: observed heterozygosity; HE: expected heterozygosity; PIC: polymorphism information content.
Table 3 Estimates of genetic diversity for each population based on 17 EST-SSR markers. Population ID
N
HO
HE
Fis
HB YN ZJ GZ HLJ NMG Mean
1.765 2.118 1.941 1.824 1.941 1.824 1.902
0.694 0.529 0.718 0.600 0.618 0.600 0.626
0.373 0.351 0.389 0.369 0.373 0.354 0.368
0.853** 0.465** 0.840** 0.573** 0.611** 0.660** 0.669
N: number of alleles per locus; HO: observed heterozygosity; HE: expected heterozygosity; Ap: number of population-specific alleles; Fis: inbreeding coefficient. **P < 0.01.
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Fig. 2. Unrooted neighbor-joining (NJ) tree of the 31 landraces. Color codes represent the germplasms from different provinces in China.
3.4. Population structure analysis In order to further elucidate the genetic structure of these landraces, population structure was investigated using the STRUCTURE program to estimate the number of genetically distinct populations (K). The result showed there was a clear peak of DK for K ¼ 3, suggesting the existence of three major clusters in present P. vulgaris accessions (Figure S1A; Figure S1B). All landraces formed three clusters, with cluster 1 consisting of populations YN and GZ, cluster 2 including population ZJ and cluster 3 containing populations HLJ, NMG and HN. Dominated by different gene pools, the three clusters showed significant genetic division from each other. The populations were mapped to their respective original distribution areas and presented with pie charts according to mean membership coefficient values (Figure S1C). The results indicated that different clusters showing specific genotypes corresponded closely to the geographic regions in which the populations were found (the northeast, middle east, and southwest of China). 4. Discussion 4.1. Evaluation of EST-SSR markers Compared with screening for SSR markers from conventional genomic libraries, the generation of EST-SSR markers is relatively easy and inexpensive because they originate from EST data that are publicly available (Varshney et al., 2005). In present research, a large number of non-redundant unigenes assembled from P. vulgaris EST sequences were used to mine for SSRs. Merely 6.9% of unigenes contained microsatellite motifs, similar frequency as previously found for some Leguminosae family ESTs (Gong et al., 2010, 2011). However, this rate was higher than that (5.9%) previously reported in common bean
Fig. 3. Principal coordinates analysis (PCoA) of the 31 landraces based on results with 17 EST-SSR primer pairs. Coord. 1 and Coord. 2 refer to the first and second principal components, respectively.
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(Blair et al., 2011). The different frequencies might depend on the criteria and the datasets used for identifying SSRs, in addition to the different redundancy levels between the unigene and unassembled EST sequences (Yan et al., 2008). The type and abundance of motif repeats are variable and have different distributions in different plants. Among all SSRs detected in the present research, tri-nucleotide repeats accounted for the greatest proportion, in agreement with results in other plants (Xin et al., 2012; Zhao et al., 2012). For tri-nucleotide repeats, GAA/TTC was observed most frequently, consistent with previous studies on common bean and soybean (Tian et al., 2004; Hanai et al., 2007). By contrast, the AAG/CTT motif was the most abundant type in faba bean (Gong et al., 2011), and GGA/TCC was predominant in asparagus bean (Xu et al., 2010). All of these discrepancies indicated differences in SSR patterns among diverse plants and even different libraries of the same plant. Based on the information from these EST-derived microsatellite types, we attempted to develop a new set of EST-SSR markers. In total, 23.9% of successfully amplified primer pairs were found to be polymorphic. In addition, the unigenes corresponding to these newly developed EST-SSR markers showed a high rate of significant similarity to known functional proteins (Table S3). Accordingly, these markers offer marker-trait association advantages over genomic SSRs, compensating for the low level of polymorphism of EST-SSRs. Using the 17 polymorphic EST-SSR markers, we found an average of 2.7 alleles per locus and an average PIC value of 0.378, indicating a medium level of polymorphism for these markers (Babaei et al., 2012). Interestingly, these values were similar to those of a previous study on genomic-SSR markers, for which 2.4 alleles per locus were found with a mean PIC value of 0.45 (Hanai et al., 2007). Therefore, according to these variability parameters, these new EST-SSR markers represent a useful tool for genetic diversity assessment, germplasm conservation and comparative mapping. 4.2. Genetic diversity and population structure analysis in Chinese common bean Genetic diversity assessment of a species can facilitate the establishment of conservation strategies, the use of genetic resources in breeding programs, and the study of the crop evolution. To date, research on the genetic diversity of common bean in China has been relatively limited. Previous studies have revealed possible relationships between Chinese common bean germplasms and suggested that there are two main gene pools (Zhang et al., 2008). Here, we focused on revealing the genetic diversity and relationships of representative landraces which were improved from the Mesoamerica gene pools among or within major production regions in China, in order to promote breeding projects for new desirable germplasms. In generally, HE and HO, are important measurements of gene diversity (Slatkin and Barton, 1989). In our study, HE was lower than HO at 15 EST-SSR loci, indicating a significant excess of heterozygotes at these loci. Similar results were also observed at the population level. The negative values of Fis, combining with the deviations between the values of HE and HO, suggested that excess heterozygotes across the different major production regions could to be attributable to hybridization origins among multiple related landraces or species over a long period at different geographic locations, rather than one species existing at a particular region for a short period of time. The NJ tree showed that almost all of the common bean landraces were separated in accord with their natural population distribution, which was confirmed by the principal coordinates analysis (PCoA). Moreover, it is worth noting that the two check genotypes from Mesoamerican gene pools were clustered together with differentiation from other landrances, which implicated genetic changes took place on this vegetable bean in China over last 500 years. The results would be considered as an evidence of the hypothesis that China is a center of diversification for this species. Investigation of genetic structure among different genotypes of one species could illuminate our understanding of environmental factors affecting their adaptation and selection forces, and help to avoid false positives or spurious associations in analyses of natural populations (Du et al., 2012). The current study revealed that common bean genotypes from six natural populations formed into three major genetic clusters, which were dominated by three gene pools. The distribution pattern might be attributable to the geographical origins of the landraces. The agricultural regions of China are divided into two major climate types by the Yangtze River, which hinders genetic exchange in common bean at different geographical regions. The north-east group included populations HLJ, NMG and HB, which were dominated by gene pool 3, located in the temperate continental climate zone. Populations ZJ, YN and GZ, locating in south of the Yangtze River, belonged to the eastern subtropics monsoon climate region. In addition, elevation might play another major role in population structure. Populations YN and GZ, located at a mean elevation of more than 1000 m above sea level, were dominated by gene pool 1. However, population ZJ, dominated by gene pool 2, was found at a much lower elevation than populations YN and GZ. These results support the idea that different altitudes within a geographical region could provide diverse degrees of selection pressure for adaptation and could accelerate population differentiation (Lopez-Gartner et al., 2009). The differentiation among gene pools suggests that cross breeding among these different regions will accelerate the process of diverse germplasm creation and broaden germplasm resources of Chinese common bean. Accordingly, efforts are being made to collect samples from other regions, and more effective markers will be developed in order to elucidate thoroughly the genetic diversity, population structure and other details of population variability in this agronomically important species. Acknowledgments The research was supported by the National Natural Science Foundation of China (31000676, 31372072), Zhejiang Provincial Natural Science Foundation of China (LY12C15004), and Zhejiang Provincial Important Science & Technology Specific Projects (2012C12903).
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Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Genetic diversity and population structure of common bean (Phaseolus vulgaris) landraces from China revealed by a new set of EST-SSR markers Shengchun Xu a, 1, Guofu Wang b, 1, Weihua Mao c, Qizan Hu a, Na Liu a, Lingwei Ye a, Yaming Gong a, * a b c
Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, PR China Department of Life Science, Yuanpei College, Shaoxing University, Shaoxing 312000, PR China Center of Analysis and Measurement, Zhejiang University, Hangzhou 310058, PR China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 April 2014 Accepted 22 August 2014 Available online
A new set of EST-SSR markers were developed and employed to analyze the genetic diversity and population structure of Phaseolus vulgaris in China. A total of 2452 microsatellites were identified in 2144 unigenes assembled from P. vulgaris ESTs, indicating that merely 6.9% of the 30,952 unigene sequences contained SSRs. Seventeen of 153 randomly designed EST-SSR primer pairs successfully amplified polymorphic products in 31 landraces from six major production provinces of China, with the mean number of alleles per locus of 2.700 and polymorphism information content of 0.378. The observed and expected heterozygosity ranged from 0.100 to 0.954 and 0.081 to 0.558, respectively. Using these markers, both an unrooted neighbor-joining tree and principal coordinates analysis showed that almost all of the landraces were separated according with their regional distribution. Moreover, population structure analysis revealed that all genotypes formed into three distinct clusters (k ¼ 3), suggesting that geographic and climatic factors could provide diverse degrees of selection pressure. Accordingly, germplasm collection and cross breeding among different regions are suggested to accelerate the process of diverse germplasm creation and broaden germplasm resources of Chinese common bean. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Expressed sequence tag (EST) Genetic diversity Microsatellite Phaseolus vulgaris Population structure
1. Introduction Common bean (Phaseolus vulgaris) is one of the most popular food legumes in the world and is grown worldwide for its wide consumer acceptability and diverse environmental suitability (Singh, 2001). Two major gene pools have been identified, Andean and Mesoamerican, each represented by different races (Blair et al., 2006). The races derived from the different gene pools were introduced into China around the 16th century (Zheng, 1997). Subsequently, a longer and thinner pod type variant of common bean, called snap bean, arose due to mutation of the hard pod layer gene after long-term domestication and cultivation. Based on this, a previous study proposed China to be a center of diversification (Yan and Lu, 1994). In recent years, China has become the largest producer of snap beans in the world.
* Corresponding author. Tel./fax: þ86 571 86404179. E-mail address: [email protected] (Y. Gong). 1 These two authors contributed equally to this work. http://dx.doi.org/10.1016/j.bse.2014.08.012 0305-1978/© 2014 Elsevier Ltd. All rights reserved.
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In addition to its economic and nutritional importance, the self-pollinating trait and small genome of common bean render it an excellent species for genetic analysis (Perseguini et al., 2011). A number of studies have been devoted to characterizing genetic diversity in germplasm collections, including those of Mesoamerican and Andean gene pools from Central and South America, in order to support P. vulgaris breeding and management of important genetic resources based on morphological and molecular markers (Beebe et al., 2001; Blair et al., 2012). Similar analyses have been carried out for some secondary diversity centers, e.g. east African highlands and the Iberian Peninsula (Ocampo et al., 2005; Asfaw et al., 2009). However, molecular breeding efforts in common bean have been limited in China because of insufficient knowledge of the molecular characteristics of the landraces found there. It is currently difficult to identify the extent of genetic differences among the landraces due to the introgression of Central America gene pools following domestication. Recently, different types of molecular markers have been used to broaden our knowledge of the molecular genetics of common bean (Hanai et al., 2010; Cortes et al., 2011). Due to their co-dominant inheritance, high level of transferability, close association with genes of known function, and low cost of development, microsatellite (SSR) markers derived from expressed sequence tags (ESTs) have proven to be fast and efficient tools for analyses of genetic diversity and population structure, as well as genetic mapping and marker assisted selection (Varshney et al., 2005; Kalia et al., 2011). A large set of ESTs from P. vulgaris have been deposited in public databases (http://www.ncbi.nlm.nih.gov/Genbank/), which facilitates development of EST-SSR markers (Blair et al., 2011; Garcia et al., 2011). However, most of these databases still have not been extensively screened nor have SSR markers been characterized, making placement of appropriate markers difficult. Accordingly, EST-SSR markers for common bean are still scarce (Gonçalves-Vidigal and Rubiano, 2011). The limited numbers of EST-SSR markers hampers our understanding of diversity and genetic structure in common bean germplasm collections in China. To date, few comprehensive assessments have been performed on the comparative diversity and regional differentiation of common bean landraces from different regions in China (Zhang et al., 2008). The present research therefore was carried out with the objectives to (1) develop a new set of EST-SSRs to increase the number of markers available for genetic studies in present and future applications in P. vulgaris, and (2) assess the genetic diversity and population structure among snap bean landraces from major production regions in China. The results may help in understanding and managing this species, as well as promoting marker-assisted selection and breeding programs. 2. Materials and methods 2.1. Plant materials Thirty-one landraces of Phaseolus vulgaris were collected from natural populations in Hebei (38 104800 N, 114 280 4800 E), Heilongjiang (45 440 2500 N, 126 39'2300 E), Zhejiang (30140 3500 N, 120 90 3600 E), Neimenggu (40 490 2000 N, 111400 200 E), Yunnan (25 20 5400 N, 102 420 3000 E) and Guizhou (26 34'1200 N, 106 42'3600 E) provinces, representing six major production regions in China. For the materials, YN001113 (No.6) and GZ001208 (No.16) were used as check genotypes representing the Mesoamerica gene pools. Other genotypes were recorded with the origin from the cultivated Mesoamerican gene pools, which were more suitable to the Chinese eating habits (Table S1). Total genomic DNA from leaves at the four-leaf stage of each individual was extracted using the CTAB method (Doyle, 1991). The DNA concentration was estimated by comparison with a standard DL2000 DNA marker (TaKaRa, Dalian, Liaoning, China) using agarose gel electrophoresis. 2.2. Primer definition and PCR reaction The unigene sequences of P. vulgaris were downloaded from PlantGDB database (http://www.plantgdb.org/), and screened for the presence of microsatellites with SSRIT software (http://www.gramene.org/gramene/searches/ssrtool), with criteria of 8, 5, 4, 3 repeating units for di-, tri-, tetra-, penta- and higher order nucleotides, respectively. A set of unigenes with candidate microsatellites were found suitable for primer design using the Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, California, USA), with a length of 17e24 bp, annealing temperature of 50e60 C, and product sizes ranging from 100 to 400 bp. The forward primers of each pair were labeled with fluorescent dye 6-FAM or HEX (TaKaRa, Dalian, Liaoning, China). Polymerase chain reactions (PCRs) were carried out in 20 mL reaction volumes containing 20 ng genomic DNA, 2 mL 1 PCR buffer, 1 unit Taq DNA polymerase (TaKaRa, Dalian, Liaoning, China), 2 mM MgCl2, 0.2 mM each primer, and 0.2 mM dNTPs. PCRs were performed on a PTC-225 Peltier Thermal Cycler (MJ Research, Waltham, Massachusetts, USA) with an initial denaturation at 94 C for 5 min, followed by 35 cycles of 94 C for 30 s, appropriate annealing temperature (Table S2) for 30 s, 72 C for 1 min, and a final extension at 72 C for 10 min. The PCR products were diluted 5-fold with distilled water and detected using a MegaBACE 1000 DNA analysis system (Amersham Biosciences, Piscataway, New Jersey, USA) at the Center of Analysis and Measurement in Zhejiang University. Sizes of amplified fragments were quantified using the ET550-R size standards and Genetic Profiler version 2.2 (GE Healthcare, Piscataway, New Jersey, USA). 2.3. Data analysis The polymorphic level of EST-SSR markers was evaluated by analyzing the genotypes in six populations. The genetic statistics based on six populations of 31 genotypes were calculated using GenAlEx 6.5 software (Peakall and Smouse, 2012)
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including the number of polymorphic alleles (N), observed heterozygosity (HO), and expected heterozygosity (HE). Polymorphism information content (PIC) was calculated using the formula developed by Anderson et al. (1993). The population allele frequencies and inbreeding index (Fis) were also calculated using the program GenAlEx 6.5. To infer the distribution of genetic diversity, principal coordinates analysis (PCoA) was carried out using GenAlEx 6.5 software. A neighbor-joining dendrogram was generated using MEGA 4.0 (Tamura et al., 2007) based on pairwise genetic distances among individuals. The model-based program STRUCTURE (version 2.3.4) was used to infer population genetic structure (Pritchard et al., 2000). In order to identify the number of populations (K) capturing the major structure in the data, we used a burn-in period of 10,000 Markov Chain Monte Carlo (MCMC) iterations and 100,000 run length with 5 replicate runs for each value of K (1e10). An admixture model following HardyeWeinberg equilibrium and correlated allele frequencies were chosen for each run. Then, using the results in the STRUCTURE output file, the most appropriate K value was determined by Structure Harvester (Earl and Vonholdt, 2012), based on the posterior probability of the data for a given K and DK. 3. Results 3.1. Frequencies and distribution of EST-SSRs from P. vulgaris A total of 30,952 P. vulgaris unigene sequences were obtained from the PlantGDB database (http://www.plantgdb.org/), and searched for SSRs. From these, 2144 unigenes were found to contain 2452 microsatellites (Table 1). Among all sequences, 1884 unigenes contained one SSR, followed by unigenes with two to five SSRs, gradually. The most abundant SSRs from P. vulgaris were tri-nucleotide repeats, accounting for 40.4% of all SSRs, followed by hexa-, penta-, di- and tetra-nucleotide repeats (Fig. 1). The repeat unit numbers at SSR loci ranged from 3 to 53, and the length of SSRs ranged from 15 to 106 bp, with a mean value of 18.38 bp. The different types of identified SSR units are described in Table S2. For example, among the tri-nucleotide repeats, GAA/TTC was the most abundant, with a frequency of 7.1%. TTTC/GAAA (9.0%) predominated in the tetranucleotide type. 3.2. Polymorphism of EST-SSR markers To screen the polymorphism of EST-SSRs, a total of 153 EST-SSR primer pairs were designed. Of these, 71 (46.4%) were found to amplify products of the expected size, while 34 (22.2%) generated unexpected products and 48 (31.4%) failed to amplify any products. Among the successful primer pairs, seventeen showed clearly polymorphic patterns based on 6 populations (Table S3). Eleven (64.7%) of them showed significant similarity to proteins of known function, such as transcription factors, transfer proteins, and stress-related proteins (Table S2). Because of their association with the coding regions of the genome, these EST-SSRs should be directly applicable for marker trait association. The 17 EST-SSR markers revealed a total of 46 alleles in the 31 genotypes, and the number of alleles (N) ranged from 2 (PVSat003, PVSat007, etc.) to 6 (PVSat040) with an average of 2.7 per locus (Table 2). The observed heterozygosity (HO) and expected heterozygosity (HE) varied from 0.100 to 0.954 and 0.081 to 0.558, respectively, while polymorphism information content (PIC) changed from 0.138 to 0.606, with the average of 0.378. All of these indexes indicated that the polymorphic ESTSSRs developed in the present study would be useful in evaluation of the genetic variation of P. vulgaris germplasm resources. 3.3. Genetic diversity assessment The parameters of genetic diversity among populations were presented in Table 3. The highest levels of N were found in population YN. The mean values of HO and HE were 0.626 and 0.368, with the highest level both in population ZJ. The values of Fis, were negative in all populations with the value from 0.853 (HB) to 0.465 (YN), indicating a high frequency of heterozygous loci in these populations. An unrooted neighbor-joining (NJ) tree was constructed based on the pairwise genetic distances. The genotypes were largely, but not completely, separated in accord with the aforementioned natural regional distributions (Fig. 2). The check genotypes, No. 6 from Yunnan and No. 16 from Guizhou were clustered together. Furthermore, principal coordinates analysis
Table 1 Summary of in silico mining of unigene sequences of Phaseolus vulgaris. Parameter
Number
Total unigene sequences Total SSRs identified Sequences containing one SSR Sequences containing two SSRs Sequences containing three SSRs Sequences containing four SSRs Sequences containing five SSRs Total SSR-containing unigene sequences
30,952 2452 1884 224 27 6 3 2144
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Fig. 1. Frequency distribution of SSRs of different repeat types (2-6 bp motif units) identified in unigene sequences of Phaseolus vulgaris.
(PCoA) was performed with EST-SSR data on the 31 genotypes. The first and second components of the PCoA analysis accounted for 34.1% and 21.9% of the total variation, respectively. Although the landraces were mostly distributed in different areas of the plot in accord with their natural regional distributions, the results revealed four distinct clusters for the germplasm types (Fig. 3).
Table 2 Characteristics of the 17 EST-SSR markers and the diversity detected in the 31 Phaseolus vulgaris landraces in China. Locus
N
HO
HE
PIC
PVSat003 PVSat007 PVSat018 PVSat021 PVSat023 PVSat029 PVSat031 PVSat033 PVSat039 PVSat040 PVSat042 PVSat047 PVSat049 PVSat056 PVSat057 PVSat061 PVSat068 Mean
2 2 2 4 2 2 2 3 2 6 2 4 5 2 2 2 2 2.7
0.872 0.778 0.100 0.428 0.139 0.954 0.933 0.918 0.872 0.936 0.167 0.222 0.744 0.952 0.478 0.750 0.167 0.627
0.494 0.470 0.150 0.299 0.081 0.500 0.487 0.520 0.481 0.553 0.083 0.239 0.558 0.500 0.313 0.449 0.083 0.369
0.374 0.368 0.180 0.598 0.138 0.375 0.374 0.419 0.371 0.555 0.371 0.293 0.606 0.375 0.365 0.358 0.300 0.378
N: number of polymorphic alleles; HO: observed heterozygosity; HE: expected heterozygosity; PIC: polymorphism information content.
Table 3 Estimates of genetic diversity for each population based on 17 EST-SSR markers. Population ID
N
HO
HE
Fis
HB YN ZJ GZ HLJ NMG Mean
1.765 2.118 1.941 1.824 1.941 1.824 1.902
0.694 0.529 0.718 0.600 0.618 0.600 0.626
0.373 0.351 0.389 0.369 0.373 0.354 0.368
0.853** 0.465** 0.840** 0.573** 0.611** 0.660** 0.669
N: number of alleles per locus; HO: observed heterozygosity; HE: expected heterozygosity; Ap: number of population-specific alleles; Fis: inbreeding coefficient. **P < 0.01.
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Fig. 2. Unrooted neighbor-joining (NJ) tree of the 31 landraces. Color codes represent the germplasms from different provinces in China.
3.4. Population structure analysis In order to further elucidate the genetic structure of these landraces, population structure was investigated using the STRUCTURE program to estimate the number of genetically distinct populations (K). The result showed there was a clear peak of DK for K ¼ 3, suggesting the existence of three major clusters in present P. vulgaris accessions (Figure S1A; Figure S1B). All landraces formed three clusters, with cluster 1 consisting of populations YN and GZ, cluster 2 including population ZJ and cluster 3 containing populations HLJ, NMG and HN. Dominated by different gene pools, the three clusters showed significant genetic division from each other. The populations were mapped to their respective original distribution areas and presented with pie charts according to mean membership coefficient values (Figure S1C). The results indicated that different clusters showing specific genotypes corresponded closely to the geographic regions in which the populations were found (the northeast, middle east, and southwest of China). 4. Discussion 4.1. Evaluation of EST-SSR markers Compared with screening for SSR markers from conventional genomic libraries, the generation of EST-SSR markers is relatively easy and inexpensive because they originate from EST data that are publicly available (Varshney et al., 2005). In present research, a large number of non-redundant unigenes assembled from P. vulgaris EST sequences were used to mine for SSRs. Merely 6.9% of unigenes contained microsatellite motifs, similar frequency as previously found for some Leguminosae family ESTs (Gong et al., 2010, 2011). However, this rate was higher than that (5.9%) previously reported in common bean
Fig. 3. Principal coordinates analysis (PCoA) of the 31 landraces based on results with 17 EST-SSR primer pairs. Coord. 1 and Coord. 2 refer to the first and second principal components, respectively.
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(Blair et al., 2011). The different frequencies might depend on the criteria and the datasets used for identifying SSRs, in addition to the different redundancy levels between the unigene and unassembled EST sequences (Yan et al., 2008). The type and abundance of motif repeats are variable and have different distributions in different plants. Among all SSRs detected in the present research, tri-nucleotide repeats accounted for the greatest proportion, in agreement with results in other plants (Xin et al., 2012; Zhao et al., 2012). For tri-nucleotide repeats, GAA/TTC was observed most frequently, consistent with previous studies on common bean and soybean (Tian et al., 2004; Hanai et al., 2007). By contrast, the AAG/CTT motif was the most abundant type in faba bean (Gong et al., 2011), and GGA/TCC was predominant in asparagus bean (Xu et al., 2010). All of these discrepancies indicated differences in SSR patterns among diverse plants and even different libraries of the same plant. Based on the information from these EST-derived microsatellite types, we attempted to develop a new set of EST-SSR markers. In total, 23.9% of successfully amplified primer pairs were found to be polymorphic. In addition, the unigenes corresponding to these newly developed EST-SSR markers showed a high rate of significant similarity to known functional proteins (Table S3). Accordingly, these markers offer marker-trait association advantages over genomic SSRs, compensating for the low level of polymorphism of EST-SSRs. Using the 17 polymorphic EST-SSR markers, we found an average of 2.7 alleles per locus and an average PIC value of 0.378, indicating a medium level of polymorphism for these markers (Babaei et al., 2012). Interestingly, these values were similar to those of a previous study on genomic-SSR markers, for which 2.4 alleles per locus were found with a mean PIC value of 0.45 (Hanai et al., 2007). Therefore, according to these variability parameters, these new EST-SSR markers represent a useful tool for genetic diversity assessment, germplasm conservation and comparative mapping. 4.2. Genetic diversity and population structure analysis in Chinese common bean Genetic diversity assessment of a species can facilitate the establishment of conservation strategies, the use of genetic resources in breeding programs, and the study of the crop evolution. To date, research on the genetic diversity of common bean in China has been relatively limited. Previous studies have revealed possible relationships between Chinese common bean germplasms and suggested that there are two main gene pools (Zhang et al., 2008). Here, we focused on revealing the genetic diversity and relationships of representative landraces which were improved from the Mesoamerica gene pools among or within major production regions in China, in order to promote breeding projects for new desirable germplasms. In generally, HE and HO, are important measurements of gene diversity (Slatkin and Barton, 1989). In our study, HE was lower than HO at 15 EST-SSR loci, indicating a significant excess of heterozygotes at these loci. Similar results were also observed at the population level. The negative values of Fis, combining with the deviations between the values of HE and HO, suggested that excess heterozygotes across the different major production regions could to be attributable to hybridization origins among multiple related landraces or species over a long period at different geographic locations, rather than one species existing at a particular region for a short period of time. The NJ tree showed that almost all of the common bean landraces were separated in accord with their natural population distribution, which was confirmed by the principal coordinates analysis (PCoA). Moreover, it is worth noting that the two check genotypes from Mesoamerican gene pools were clustered together with differentiation from other landrances, which implicated genetic changes took place on this vegetable bean in China over last 500 years. The results would be considered as an evidence of the hypothesis that China is a center of diversification for this species. Investigation of genetic structure among different genotypes of one species could illuminate our understanding of environmental factors affecting their adaptation and selection forces, and help to avoid false positives or spurious associations in analyses of natural populations (Du et al., 2012). The current study revealed that common bean genotypes from six natural populations formed into three major genetic clusters, which were dominated by three gene pools. The distribution pattern might be attributable to the geographical origins of the landraces. The agricultural regions of China are divided into two major climate types by the Yangtze River, which hinders genetic exchange in common bean at different geographical regions. The north-east group included populations HLJ, NMG and HB, which were dominated by gene pool 3, located in the temperate continental climate zone. Populations ZJ, YN and GZ, locating in south of the Yangtze River, belonged to the eastern subtropics monsoon climate region. In addition, elevation might play another major role in population structure. Populations YN and GZ, located at a mean elevation of more than 1000 m above sea level, were dominated by gene pool 1. However, population ZJ, dominated by gene pool 2, was found at a much lower elevation than populations YN and GZ. These results support the idea that different altitudes within a geographical region could provide diverse degrees of selection pressure for adaptation and could accelerate population differentiation (Lopez-Gartner et al., 2009). The differentiation among gene pools suggests that cross breeding among these different regions will accelerate the process of diverse germplasm creation and broaden germplasm resources of Chinese common bean. Accordingly, efforts are being made to collect samples from other regions, and more effective markers will be developed in order to elucidate thoroughly the genetic diversity, population structure and other details of population variability in this agronomically important species. Acknowledgments The research was supported by the National Natural Science Foundation of China (31000676, 31372072), Zhejiang Provincial Natural Science Foundation of China (LY12C15004), and Zhejiang Provincial Important Science & Technology Specific Projects (2012C12903).
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