GENE-40450; No. of pages: 9; 4C: Gene xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
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Honglin Chen a,1, Ling Qiao a,b,1, Lixia Wang a, Suhua Wang a, Matthew Wohlgemuth Blair c, Xuzhen Cheng a,⁎
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Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers
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Article history: Received 29 December 2014 Received in revised form 15 April 2015 Accepted 16 April 2015 Available online xxxx
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Keywords: Simple sequence repeat (SSR) markers Genetic diversity Population structure
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National Key Facility for Crop Gene Resources Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China College of Agriculture, Shanxi Agricultural University, Taigu 030801, China Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209, USA
a b s t r a c t
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Mung bean is an important legume crop in tropical and subtropical countries of Asia and has high nutritional and economic value. However the genetic diversity of mung bean is poorly characterized. In this study, our goal was to develop and use microsatellite simple sequence repeat (SSR) markers for germplasm evaluation. In total, 500 novel expression sequence tag EST-based SSRs (eSSRs) and genomic SSRs (gSSRs) were developed from mung bean transcriptome and genome sequences. Of these, only 58 were useful for diversity evaluation in a panel of 157 cultivated and wild mung bean accessions from different collection sites in East Asia. A total of 2.66 alleles were detected on average per locus which shows that polymorphism is generally low for the species. The average polymorphic information content (PIC) of gSSRs was higher than eSSRs and most of the polymorphic gSSRs were composed of di- and tri-nucleotide repeats (52.4% and 38.1% of all loci, respectively). The genotypes were differentiated into nine subgroups by cluster analysis, and the wild mung bean accessions separated well from the cultivated accessions. Analysis of molecular variance indicated that 22% of variance was observed among populations and 78% was due to differences within populations. Clustering, population structure analyses showed that non-Chinese cultivated and wild mung bean accessions were separated from Chinese accessions, but no geographical distinctions existed between genotypes collected in China. Interestingly, the average PIC value of cultivated mung bean (0.36) was higher than that of wild mung bean (0.25) showing that further collecting and wide crosses are necessary for mung bean improvement. © 2015 Published by Elsevier B.V.
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Mung bean [Vigna radiata (L.) Wilczek] is an important food legume that is a source of protein and complex carbohydrates for consumers in Asian countries. It is widely grown and consumed in India, China, Myanmar and Thailand. The global annual production of mung bean is about 6 million tons. India is the world's largest producer of mung bean, followed by China and Myanmar (Nair et al., 2012; Kang et al., 2014). The whole grain is rich in fiber and minerals such as iron, potassium and zinc as well as vitamins A and B. Meanwhile, sprouted mung beans are rich in vitamins C in addition to vitamin B, folate and thiamin (Anwar et al., 2007). As a grain legume crop, mung bean is particularly interesting because of its early maturity, high tolerance to drought, and adaptation
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1. Introduction
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Abbreviations: SSR, simple sequence repeat; EST-SSRs (eSSRs), expressed sequence tagsimple sequence repeats; gSSRs, genomic SSRs; MAS, marker assisted selection; NGS, next generation sequencing; He, expected heterozygosity; Na, number of alleles; Ne, effective number of alleles; Ho, observed heterozygosity; PIC, polymorphic information content; UPGMA, unweighted pair group method with arithmetic mean. ⁎ Corresponding author. E-mail address: [email protected] (X. Cheng). 1 These authors contributed equally to this work.
to poor soil. Like other legumes, mung bean can fix atmospheric nitrogen through nodulation with Rhizobium species. High biomass production suggests that N fixation in mung beans is equal to or superior to soybean making it very important for crop rotations in some low soil fertility regions of Asia and potentially worldwide. Genetically, mung bean is a self-pollinated diploid crop (2n = 2x = 22) and possesses a relatively small genome (~560 Mb) (Van et al., 2013). Although the genome of mung bean has been sequenced and compared to other Vigna species (Kang et al., 2014), revealing a compact gene organization and cytogenetic correlations, genetic studies of mung bean are still scarce. Previous marker development for mung bean concentrated on RFLP (Young et al., 1992), RAPD (Lakhanpaul et al., 2000), AFLP (Moe et al., 2012) and CAPS (Chen et al., 2007) loci, but these marker technologies have many limitations for applicability and reproducibility. SSR markers have been widely used in various crop species as molecular tools for genetic diversity detection (Somta et al., 2008), gene mapping (Chankaew et al., 2011; Gupta and Gopalakrishna, 2013), markerassisted selection (MAS) (Kumar et al., 2011) and genome-wide association analysis (Bohra et al., 2014). SSRs along with SNP markers are therefore a more promising marker technology for mung bean. In the sequencing of the mung bean genome, a large number of SSRs have been discovered that could be useful for genotyping. In total,
http://dx.doi.org/10.1016/j.gene.2015.04.043 0378-1119/© 2015 Published by Elsevier B.V.
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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A total of 157 mung bean accessions were used in this study, all from the National Genebank of China (NGC) at Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China. The genotypes included 124 cultivated mung bean accessions from
98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 Q10123 Q9 124 125 126 127 128
133 134 135
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96 97
E
94 Q8 95
R
92 93
R
90 91 Q7
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88 89 Q6
C
86 87
N
84 85
U
82 83
2.2. SSR mining and primer design
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A total of 48,693 unigenes were generated from transcriptome sequencing of mung bean (GenBank accession number SRP043316) and were evaluated for eSSR loci. These EST sequences were searched for the presence of SSR repeats using the MIcroSAtellite (MISA) software (http://pgrc.ipk-gatersleben.de/misa), with criteria of 10, 6, 5, 4, 4 and 3 minimum repeat units for mono-, di-, tri-, tetra-, penta-, and hexanucleotide repeats, respectively. Next, 300 eSSR targeting primer pairs were designed using Primer 3.0 with the following criteria: primer length ranging from 18 to 22 bp, product sizes of 100 to 300 bp and approximately even and balanced GC content in the priming targets. Additionally, 200 gSSRs were designed with the same criteria as mentioned above based on the genomic sequence of mung bean available from GenBank/EMBL/DDBJ under the accession code JJMO00000000 (Kang et al., 2014).
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2.1. Plant materials and DNA extraction
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131
79
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2. Materials and methods
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136 137
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130
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China, 17 wild mung beans from Asia and 16 exotic genotypes from other countries (Table S1). Total genomic DNA was extracted for each genotype using the modified CTAB-based method (Englen and Kelley, 2000). In each case, fresh leaves of five seedlings were used as DNA source tissue for each accession. DNA quantity and quality were checked on 1% agarose gels run in 1 × TAE buffer along with spectrophotometric evaluation and standardized to 10 ng/μL for subsequent molecular analysis.
2.3. PCR amplification and SSR detection
138 139 140 141 142 143
146 147 148 149 150 151 152 153 154 155 156 157 158
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PCR amplification was performed in a total reaction volume of 10 μL containing 1 μL of genomic DNA at 10 ng/μL concentration, 1 U of Taq DNA polymerase, 2 μL of 10 × Taq Buffer (Mg2 +), 0.2 mM of each dNTPs and 0.25 μmol of reverse and forward primers. PCR amplification was carried out with the following conditions: one cycle of 94 °C for 4 min, 33 cycles of 94 °C for 30 s, 55–60 °C for 30 s and 72 °C for 30 s, followed by a final extension at 72 °C for 5 min. The PCR products were loaded onto 8% Polyacrylamide non-denaturing gels, separated by electrophoresis and detected by silver nitrate staining (Conesa et al., 2005). A 1 kb size marker was used as a DNA ladder to estimate the SSR allele band sizes (Promega, Madison, WI, USA).
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2.4. Statistical analysis
171
The eSSRs and gSSRs alleles were scored for presence or absence of bands. Each marker was then evaluated for the number of alleles (Na), the effective number of alleles (Ne), the observed heterozygosity (Ho), and the expected heterozygosity (He) using POPGEN v.1.32 (Krawczak et al., 2006). Nei's genetic diversity and polymorphism information content (PIC) were calculated using PowerMarker 3.25 (Liu and Muse, 2005). To determine the associations between genotypes, an unweighted pair group method with arithmetic mean (UPGMA) tree was constructed based on shared allele frequencies using MEGA 4.0 (Tamura et al., 2007). Population structure was assessed by STRUCTURE software v. 2.34 based on an admixture model. Models were tested for K-values ranging from 1 to 15, with ten independent runs each and 100,000 MCMC (Markov Chain Monte Carlo) iterations. The most likely number of clusters was chosen by plotting the LnP(D) values against Δk values with the best K value selected according to the Evanno Test (Evanno et al., 2005; Zhang et al., 2014). The genetic structure of accessions was further investigated by analysis of molecular variance (AMOVA) using GenAlEx 6.1 (Peakall and Smouse, 2012). The correlation of geographic and genetic distances from principal coordinate analysis (PCoA) was performed using NTSYS software v. 2.2 (Rohlf, 2000).
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200,808 potential loci were found but many were short to medium mono-nucleotide repeats with a total of 17,898 predicted as useful based on motif classification (Kang et al., 2014). However, there are few reports on the validation of SSR markers in mung bean to date. This has been true of genomic SSR (gSSR) markers but very much so of EST-based (EST-SSR or eSSR) markers. Though significant efforts have been made to develop SSR markers in recent years for many crops as discussed above, no more than 100 polymorphic SSR markers have been detected and published for mung bean, with most of them being gSSRs due in part to low diversity levels. Polymorphic eSSR markers are presently rare for mung bean, which hinders its genetic characterization. In previous work, Somta et al. (2008) tested more than 200 mung bean SSRs for polymorphism among 30 accessions, finding only 12 markers or 5.7% of the total polymorphic. Seehalak et al. (2009) designed 78 primers and 8 polymorphic loci were detected among 22 cultivated accessions. In total, 493 potential SSR markers were identified from sequenced fragments, 192 primer pairs were tested and detecting 60 loci that were polymorphic in 17 mung bean accessions (Sithichoke et al. 2009). EST-derived SSRs can be related to functional genes, as opposed to genomic SSRs. Therefore they are a potential tool for molecular marker assisted selection breeding (MAS), with molecular markers either originating from a gene for a desirable agronomic trait, or co-segregating with gene of interest. However, very few molecular markers have been found which were linked to a desirable gene locus in legume crops to date, especially for mung bean, except for MYMIV-resistance marker loci, YR4 and CYR1. The lack of tightly linked markers for agronomically important genes limit their utilization in selection of interested traits in mung bean MAS breeding. Although considerable efforts have been made in developing genomic resources (Kang et al., 2014; Chen et al., 2015). Identification of a linked marker with the objective traits is an essential prerequisite for MAS breeding program. Next generation sequencing (NGS) technologies are very valuable in producing millions or billions of sequence reads at once at a lower cost than traditional Sanger sequencing. Therefore, NGS is increasingly popular for obtaining high-throughput sequence data in a rapid and costeffective manner with high resolution and accuracy in genome-wide analyses (Marx, 2013). In recent years, mung bean gSSRs and eSSRs discovery has accelerated through NGS sequencing of genomic (Tangphatsornruang et al., 2009; Kang et al., 2014) and EST (Moe et al., 2011) sequences, respectively, but testing and validation remains a bottleneck. The objectives of this study were 1) to develop novel gSSRs and eSSRs for mung bean to increase the number of validated and polymorphic markers for the crop and 2) to assess the genetic diversity detectable with the new SSRs in a panel of wild and cultivated mung bean. We used highly variable nuclear microsatellite markers, which have been proven to be a powerful tool for revealing geographical population structure (O'Connell & Slatkin 1993; Estoup & Cornuet 1999). We discuss the importance of diversity as a genetic resource used to guarantee species success in various environments. Emphasis is placed on the significance of genetic diversity in breeding for high yield as the magnitude of heterosis depends on the degree of genetic divergence between parental stocks. The more diverse the parents the more are the chances of an increased spectrum of variability in the resulting segregants.
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H. Chen et al. / Gene xxx (2015) xxx–xxx
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Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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H. Chen et al. / Gene xxx (2015) xxx–xxx t1:1 t1:2
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Table 1 Characterization of 24 eSSR (E) and 34 gSSR (G) markers in mung bean. Primer name
Primer sequence (5′–3′) F/R
Repeat motif
Expected size (bp)
Naa
Neb
Hoc
Hed
PICe
t1:4
E31003
(CCA)6
185
3
1.93
0.103
0.483
0.3675
t1:5
E11659
(CACCGT)3
133
2
1.72
0.133
0.4214
0.3280
t1:6
E22860
(GAAGCT)3
248
2
1.927
0.1074
0.4827
0.3659
t1:7
E26637
(GAA)6
265
2
1.9281
0.0345
0.483
0.3658
t1:8
E55107
(CTGATC)3
211
2
1.0713
0.0276
0.0668
0.0762
t1:9
E9587
(AAAG)3
134
3
1.509
0.0822
0.3385
0.2785
t1:10
E51985
(T)10
262
2
1.7809
0.1119
0.4401
0.3403
t1:11
E21522
(T)10
224
2
1.8349
0.1667
0.4565
0.3496
t1:12
E56315
(GCT)6
203
2
1.4292
0.0903
0.3014
0.2521
t1:13
E64504
(GATGAA)3
123
t1:14
E115962
(GTG)7
134
t1:15
E37870
(AACA)3
153
t1:16
E33094
(CCAACA)3
262
t1:17
E15212
(TTG)6
153
t1:18
E27164
(GCCACC)3
155
t1:19
E52717
(CTTCTC)3
167
t1:20
E13673
t1:21
E16266
t1:22
E10675
t1:23
E14180
t1:24
E34120
t1:25
E19823
t1:26
E15159
t1:27
E24080
t1:28
G3627
t1:29
G241
t1:30
G2740
t1:31
G2436
t1:32
G2208
t1:33
G1510
t1:34
G3835
t1:35
G3203
t1:36
G21063
t1:37
G2637
t1:38
G3598
t1:39
G3310
t1:40
G2624
AAGCAAGATGACACGGAGCA ATGGTTGAAGAAGCGGGAGG ACGCTCGAAATATCACCCCC GGGTCTCGAGTTTGTGAGGG AGCATGGTATAAGAATGAGAGGGT TTTCTTCAAGCTGGGGTGCT AAGCGTGGAAGTGGAGTGAG ACCGACTTAACGTTATTGAAAAGAGG ACCCTATGTGCAGTGCAACA TCTCCTCCCTTGAGAGAGGC AACAGTGACACGTGGCAGAT ACACAGAGCTGCCATTCTCC GCGATCGAGTCACTCTACGG TCATCCGCCACAACCTCTTC AGCAGGCAATTCACGCAAAG GCTGGCATCTTCTTCTTGTCG ATCCTTCGTGGTCTCCGAGA GGGGTTACAGGACCAGAAGC CTCCTGAGGGCACTGAACTG GCTTCTGCAACGAGTTTCAACT CTCCTGGGCACATTTCCACT ACCACCCACATCATTTCCCC TTGTGGTGTCCGTAGTGAGC TCGCTTCGGAAAGTGCTTCT ATTGCCACCCCCATTTCCAT AGCAGTCCACCACTCTCTCT CATGGGTCATGCACTTTCGT CGCATCCATTGAAGACCAAGC CTCAACAAGTTCCTCAGCGC CCAGAACCGGTGGAAGTCTC ACGAAATCAACGAGGCATATGA ACTTTTGTTGCGGAGGGGAA CCTTGCTCTTGTGTGCCTCT CTCCACAGCATTGACCCCAT AGAACCATGCCACGTGACAT GTCCAACCACGCAAACTCAC GCAGAAGGAAGCTCAAGATCG GCTTCCCACAACTCCCAGAA CAGATTCCAACCCGAAGCCA GCGAAAGAAGCTCGTCCTCT ACTGAGTCTCACCAGAGCCA ATTCTCCGGCACTCAACAGG ACAACAAGGATCACCGTCCC TCAGTCTCTCCAGCTCCGAA ACGTGTAGAGAGACCACCGA CCAAGCAGCAGAACCAACAC GCTCCCATTCACCATCCCAA TCCGGTTCCTTCCCACAATG TCAGGTCCCTGTTCATCAAA CCCATCTTTTCAATCAACAACA AAATTAGGGGATCCGCATGT TGCGTGTGTGTTTGTGTTTG TTGATGGCATATCCCCTAGC CTGAGGAGCTCCATGATTGC GACCATTGGTTCAACGAGAAA CCTCAGTCTGGTACCGTAACTACTTC CAAGTGCAAAGGGAAAAGGA TGTGATCACTGTCACCCACTT TACAGGCACTCGAGGATCAA AATTGTTGGAAGTGGCTTGC ACTTGAAGAAGACAGGGCCA CCAACATGGTGTTGTTCAGC TAGCTTGGCCTACTTGCGAT TGGTCACAGGAGGTGTGGTA GAATTTTCTGGAAACGCACC CCATCTGGTTTCCTGTGCTT TGAAGCCTAATATGCAAGCAA TCATTTTTGGTGAACCTTTTGA GAGCCATGGGTATGTATAGATTTTT CCTCTCCCTCTTTTTCAGCC AAGGGGGCTAGAAGTGGAAA CCACAGCAACCACAAATGAC TCCTCTGATAGAAGCAGCTACG
O
0.2206
0.3612
0.2928
2
1.3709
0.0484
0.2716
0.2392
2
1.3846
0.1111
0.2787
0.2403
4
1.0636
0.0272
0.06
0.0585
2
1.3126
0.0263
0.2389
0.2098
2
1.9892
0
0.4989
0.3728
2
1.9976
0.0903
0.5011
0.3747
2
1.074
0.0065
0.0691
0.0669
271
2
1.9648
0.126
0.493
0.37
(AAGTG)5
144
3
1.7407
0.1527
0.4272
0.3663
(GGAAGA)10
141
4
1.7967
0.156
0.445
0.3733
E
R O
1.5622
165
2
1.9952
0.0559
0.5006
0.3742
(AAATC)4
189
2
1.7056
0.1056
0.4151
0.3298
(CAT)7
217
2
1.9836
0.0758
0.4978
0.3721
(A)16
218
2
1.919
0.2603
0.4805
0.3727
(TTG)5
163
3
1.5916
0.0846
0.3721
0.3234
(AC)6
150
3
1.7634
0.0647
0.4345
0.3509
(TC)6
121
3
1.9408
0.0634
0.4865
0.37
(AG)17
103
4
3.0662
0.1088
0.6762
0.61
(GA)17
115
2
1.9951
0.0993
0.5005
0.3742
(A)11
137
2
1.9697
0.0661
0.4944
0.37
(CTT)6
210
2
1.6
0.0714
0.3765
0.3034
(ACA)8
106
4
1.7472
0.0915
0.4292
0.3968
(GT)7
128
3
2.0716
0.0308
0.5193
0.4317
(GT)8
119
2
1.3281
0.0634
0.2479
0.2089
(AGA)9
155
2
1.6475
0.1429
0.3943
0.3158
(TGT)7
93
2
1.5672
0.0949
0.3632
0.2976
(TG)10
120
2
1.2958
0.0705
0.229
1.1986
C
(TCA)6
D
E 243
T
(TAC)6 (ATA)6
P
2
R
R
N C O
U
F
t1:3
(continued on next page)
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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H. Chen et al. / Gene xxx (2015) xxx–xxx
t1:45
G3302
t1:46
G3427
t1:47
G3429
t1:48
G264
t1:49
G23823
t1:50
G1378
t1:51
G3424
t1:52
G4010
t1:53
G3044
t1:54
G1191
t1:55
G3505
t1:56
G2306
t1:57
G3108
t1:58
G7472
t1:59
G3212
t1:60
G0483
t1:61
G1242
t1:62
Mean
t1:63 t1:64 Q1 t1:65 t1:66 t1:67
a
d e
2
1.922
0.1042
0.4814
0.3639
(CA)6
140
4
1.6091
0.0979
0.3799
0.351
(TGT)6
95
2
1.7627
0.1439
0.4343
0.342
(A)12
169
2
1.9339
0.1986
0.4846
0.3655
(TCA)5
136
2
1.9231
0.1379
0.4817
0.3656
(CAC)6
224
2
1.9893
0.1533
0.499
0.3734
(TTC)9
111
2
1.5766
0.1752
0.367
0.3047
(AG)11
122
2
1.5955
0.1418
0.3745
0.3013
(AG)16
266
2
1.9999
0.1214
0.5018
0.3748
(GA)14
186
2
1.6379
0.0662
0.3908
0.3127
(GA)17
258
7
2.9389
0
0.6673
0..6136
(AG)15
221
4
3.1978
0.0083
0.6901
0.6315
(AG)20
175
6
2.2843
0
0.5644
0.5423
(CA)17
210
4
2.3611
0
0.5788
0.5151
(GA)17
166
2
1.9978
0
0.5015
0.3748
(AG)20
134
4
2.0933
0
0.5245
0.477
(AC)18
102
5
3.0137
0
0.6712
0.6089
171
3
2.3457
0.6423
0.5758
0.4848
102
2
1.8603
0
0.4642
0.3584
117
2
1.9484
0.7132
0.4886
0.3669
123
5
3.0037
0
0.6639
0.6158
2.67
1.87
0.1047
0.4367
0.3575
(AG)16 (AG)18 (AG)15 (GA)16
The number of number of alleles. The number of effective number of alleles (Kimura and Crow, 1964). The number of expected heterozygosity. The number of observed heterozygosity. Polymorphic information content.
R
c
115
O
b
(CA)7
3. Results
194
3.1. Characterization of polymorphic SSR markers in mung bean
195 196
A total of 13,134 primer pairs could be developed form mung bean transcriptome sequences using Primer 3. Among them, 500 SSR primer pairs were randomly selected for PCR amplification for polymorphism. A total of 58 SSR markers were polymorphic among the 157 mung bean accessions from diverse geographical locations. These included 24 out of 300 eSSRs (8% polymorphism rate) and 34 out of 200 gSSRs (10.5% polymorphism rate) as detailed in Table 1. The polymorphism ratio of the eSSR markers varied with repeat motif and was 7.9%, 39.5%, 7.9%, 7.9% and 36.8%, respectively, for the di-, tri-, tetra-, penta-, and hexa-nucleotide repeat based markers. Most of the polymorphic eSSRs were tri- and hexanucleotides, while most of the polymorphic gSSRs were composed of mono- and di-nucleotide repeats, accounting for 16.7% and 80.6% overall, respectively. Among mono-nucleotide repeats, the A/T motif was the most abundant repeat, accounting for 91.7% of these mono-nucleotide repeats. Among di-nucleotide repeats of gSSR
199 200 201 202 203 204 205 206 207 208 209 210
U
N
C
193
197 198
O
G1671
PICe
R O
t1:44
Hed
P
G3335
Hoc
D
t1:43
Neb
E
G2999
Naa
T
t1:42
CATGCTCACTACTTTGTTGATCC GCTTCCACTTTATACATATTACGCA TATGCTTGCAAGAGTGTGGG CCCATGCAATATATCACCCA TGCGTGTATGCGTCTGTGTA ATGTTTGAGGCATTTCCCTG ATCAGGCAACAACAACCACA CCAGATCAAGAACCTACCACAA GGGACATTAGAGATTCCCCA AAAACTTGTCCAGACCACGG CCCTTTTGTTTGTGGCACTT GCTTCTGCACAACCCTCTTC CCCTACATTTCAGCAACCGT GGAATGAGTTTCTTGCAGGG GAAGGAGGAGGAGCTTCCAT AAGGTTGAGAATCGCAGGTG ACACGACGTGCTGCTAACTG AAATTGGGGATTTGCATGTG CTCTCTCCTCACATTCTCTGACA AACAACCCAAAAGCCTCCTT CCCACGCTAAGATTGAGCTA AACCAATCGACACACGTGAA CCTTCGTAGAGGTGCGCT AACCTGTGTTGGGTGTTTCTT CAAAGACACCCACTTGTAGCC AACGGGTTTCCTTCTGGTCT GTTAGGTCGTTCCTTCCCGT AAGCATGAAAGTTTGGCAGG AATTCACTTGGCTTGATTTGAA AAGGGAGTGATAGCGCAAAA ACCAACCAAACCAACGTTAC AAGTTAGTCAGAACTTTGGGCA CTAACAACAACGAACAAGAAGAGA AATATCCAAATTCAAGCGCA GTGTCGTGTCGTGTGTCGT GCGAGCGGAATCAGAATAAC CGTAACCGTAACCTTACCTACTTC TACCGAAATCCGTACGAAGC TCTCTCTTGTTTCTTCGTTGTTC TGTAGAAAAGCAAAAACCAACAAA AGGTAGATGACATGCTCGCC TGTCATTCTCTAGGGTTCAGCA ACTCGTCGGAGGTACGTTCT
Expected size (bp)
C
G2516
Repeat motif
E
t1:41
Primer sequence (5′–3′) F/R
R
Primer name
F
Table 1 (continued)
markers, the AG motif was the most abundant repeat, accounting 211 for 73.6% of the total in this group. 212
3.2. Assessment of genetic diversity in mung bean accessions
213
The data from the 58 polymorphic SSR markers were used to evaluate the genetic diversity of the 157 mung bean accessions in the diversity panel. According to POPGEN analysis, 154 alleles were found for the 58 loci. The observed number of alleles per locus (Na) ranged from 2 to 7 with a mean of 2.66, while the effective number of alleles per locus (Ne) varied from 1.07 to 3.19 with a mean of 1.87. The average Na was 2.05 and 2.67 for wild and cultivated mung bean, respectively (Table 2). The polymorphic information content (PIC) ranged from 0.06 to 0.63 with a mean of 0.36. The PIC of cultivated mung bean was higher (0.36) than that of wild mung bean (0.25). Meanwhile, the average PIC of gSSRs (0.40) was higher than for eSSRs (0.30). As an indicator of genetic diversity, the average He was 0.24 in wild mung bean accessions and 0.44 in cultivated accessions.
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Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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2 2 2 2 1 1 1 1 1 1 1 2 1 1 2 2 1 2 2 2 1 2 2 1 3 2 2 3 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 4 5 3 2 2 3 3 2 2 3 2.05
0.50 0.44 0.49 0.45 0.07 0.37 0.46 0.48 0.33 0.39 0.31 0.29 0.07 0.26 0.49 0.50 0.08 0.50 0.44 0.45 0.50 0.44 0.50 0.49 0.30 0.37 0.50 0.69 0.49 0.50 0.37 0.39 0.51 0.23 0.42 0.37 0.24 0.46 0.41 0.45 0.49 0.49 0.50 0.38 0.39 0.50 0.37 0.63 0.68 0.54 0.56 0.49 0.50 0.68 0.53 0.48 0.49 0.69 0.44
0.12 0.21 0.19 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.37 0.11 0.00 0.09 0.25 0.43 0.00 0.11 0.16 0.00 0.42 0.20 0.21 0.25 0.12 0.27 0.46 0.51 0.59 0.35 0.07 0.21 0.17 0.31 0.12 0.15 0.29 0.21 0.50 0.15 0.20 0.19 0.51 0.62 0.64 0.70 0.59 0.13 0.52 0.48 0.61 0.17 0.52 0.31 0.24
0.38 0.34 0.37 0.35 0.09 0.29 0.35 0.36 0.27 0.31 0.26 0.25 0.07 0.23 0.37 0.37 0.07 0.37 0.38 0.37 0.37 0.34 0.37 0.37 0.27 0.31 0.38 0.62 0.37 0.37 0.30 0.36 0.42 0.19 0.32 0.30 0.20 0.35 0.37 0.35 0.37 0.37 0.37 0.32 0.31 0.37 0.30 0.59 0.63 0.54 0.49 0.37 0.45 0.62 0.44 0.36 0.37 0.63 0.36
0.15 0.21 0.28 0.25 0.00 0.19 0.20 0.19 0.06 0.06 0.00 0.11 0.00 0.00 0.29 0.30 0.00 0.23 0.21 0.36 0.12 0.19 0.13 0.13 0.43 0.26 0.15 0.21 0.30 0.26 0.31 0.37 0.44 0.30 0.20 0.16 0.19 0.25 0.16 0.13 0.23 0.19 0.37 0.12 0.16 0.32 0.38 0.60 0.56 0.45 0.56 0.20 0.38 0.41 0.51 0.29 0.37 0.35 0.25
The number of number of alleles. The number of effective number of alleles (Kimura and Crow, 1964). Polymorphic information content.
A dendrogram was constructed using the UPGMA method (Fig. 1), and was found to somewhat reflect the origins of the different accessions. Cluster analysis indicated that wild and non-Chinese mung bean accessions separated from Chinese accessions. However, mung bean cultivars from within China did not group according to region and accessions within subgroups had scattered origins across the country.
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3 2 2 2 2 3 2 2 2 2 2 2 4 2 2 2 2 2 3 4 2 2 2 2 3 3 3 4 2 2 2 4 3 2 2 2 2 2 4 2 2 2 2 2 2 2 2 7 4 6 4 2 4 5 3 2 2 5 2.67
The population structure of the 157 mung bean accessions were analyzed with the alleles detected by the 58 SSR markers, and firstly inferred using STRUCTURE 2.3.4 (Evanno et al., 2005). Admixture model-based simulations were carried out by varying K from 2 to 15 with 15 iterations using all 261 genotypes which showed the most suitable ΔK is 9, showed the most suitable number of populations (Pops) to be nine (Fig. 2A). In total, 52 accessions (33.1%) were assigned to population 1, which is composed of most accessions from North China, mainly including Shandong and Hebei provinces. Pops 2, 3, 4, 5, 6, 7, 8, and 9 consisted of 15, 8, 19, 27, 11, 6, 9 and 10 accessions, respectively. Most wild and foreign mung bean accessions were assigned to Pop 4 and Pop 8, respectively. The results demonstrated that wild and foreign mung bean accessions were assigned to different populations from each other and from the Chinese cultivars. Different genetic distances between Pops were listed in Table 3, which ranged from 0.0661 (between Pop 4 and Pop 1) to 0.7044 (between Pop 4 and Pop 7). The Pop 5 (genetic diversity = 0.3937, I = 0.6174) showed the highest genetic diversity level. The Pop 7 (genetic diversity = 0.1981, I = 0.3086) showed the lowest genetic diversity level, and the sample size, genetic diversity and Shannon's information index of each group is shown in Table 4. A dendrogram was constructed to show the genetic relationship of the subgroups (SGs) via UPGMA clustering analysis (Fig. 2B). Based on the genetic distance values of the nine subgroups, SG1 was found to be closest to the wild mung beans (SG4) was closest to the foreign mung beans (SG 8).
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Cultivated
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Wild
235
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Cultivated
3.3. Population structure and population genetic distance
238 239 240 241 Q12 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 Q13 260
3.4. Principal coordinate analysis and analysis of molecular variance 261 (AMOVA) 262
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a
Wild
T
t2:64 t2:65 Q2 t2:66
Cultivated
C
E31003 E11659 E22860 E26637 E55107 E9587 E51985 E21522 E56315 E64504 E115962 E37870 E33094 E15212 E27164 E52717 E13673 E16266 E10675 E14180 E34120 E19823 E15159 E24080 G3627 G241 G2740 G2436 G2208 G1510 G3835 G3203 G21063 G2637 G3598 G3310 G2624 G2516 G2999 G3335 G1671 G3302 G3427 G3429 G264 G23823 G1378 G3424 G4010 G3044 G1191 G3505 G2306 G3108 G7472 G3212 G0483 G1242 Mean
PICc
E
t2:5 t2:6 t2:7 t2:8 t2:9 t2:10 t2:11 t2:12 t2:13 t2:14 t2:15 t2:16 t2:17 t2:18 t2:19 t2:20 t2:21 t2:22 t2:23 t2:24 t2:25 t2:26 t2:27 t2:28 t2:29 t2:30 t2:31 t2:32 t2:33 t2:34 t2:35 t2:36 t2:37 t2:38 t2:39 t2:40 t2:41 t2:42 t2:43 t2:44 t2:45 t2:46 t2:47 t2:48 t2:49 t2:50 t2:51 t2:52 t2:53 t2:54 t2:55 t2:56 t2:57 t2:58 t2:59 t2:60 t2:61 t2:62 t2:63
Heb
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t2:4
Naa
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Marker
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t2:3
Table 2 Polymorphism of SSR markers in wild and cultivated mung bean.
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A cluster graph produced by two-dimensional principle coordinate analysis (PCoA) showed similar results as for UPGMA clustering, also indicating that wild mung bean accessions were clustered together and separated from other accessions (Fig. 3). Similar to the results of STRUCTURE, the wild mung bean and foreign accessions were separated from others accessions. PCoA showed that large diversity existed in mung bean collection. The percentage of variation explained by the first three axes was 71.89% (axis 1 — 31.44%, axis 2 — 21.56% and axis 3 — 18.89%). An AMOVA analysis was used to evaluate within and among population diversity components. Significant genetic differentiation among the nine population structure classified subgroups was detected by AMOVA at a p value of 0.0001. The results of AMOVA indicated that majority of the variance occurring within populations accounted for 78% of the total variation, and 22% of variation was attributed to differences among populations (Table 5). The Fst was observed to be 0.024, indicating the low differentiation between populations.
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For eSSR markers developed from coding regions, it can detect diversity not found at other locus types, while possessing a good degree of conservation within closely related species, increasing transferability to closely related species. They have been widely used for crossspecies and comparative mapping of related crops or phylogenetic studies of wild and cultivated crop accessions (Tautz and Renz, 1984; Zietkiewicz et al., 1994; Kumari et al., 2013). In comparison with other crops, relatively few SSR markers have been developed for the genus Vigna, reflecting the limited sequence resources available. In the present study, we developed 58 polymorphic mung bean SSRs, with 24 based on eSSR loci and 34 based on gSSR loci. The mung bean eSSRs were preferentially based on certain repeat motifs, which were more common within the transcriptome, namely tri-nucleotide repeats (37.5%), and followed by hexa- (33.3%), mono- (12.5%), tetra(8.3%) and penta- (8.3%) nucleotide repeats. The relative abundance of
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Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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tri-nucleotide repeats in EST sequences has been observed in mung bean (Gupta et al., 2014) and many other legumes including pea (Gong et al., 2010), cowpea (Gupta and Gopalakrishna, 2010), chickpea (Choudhary et al., 2009), common bean (Blair and Hurtado, 2013) and horse gram (Bhardwaj et al., 2013). A new finding is that hexanucleotide repeats are about equally common as tri-nucleotide repeats. Meanwhile, di-nucleotide repeats tend to be the most abundant in genomic sequences of many legumes (Blair et al., 2011), followed by tri-nucleotide repeats (53.4% and 38.1%, respectively, in mung bean). Recently, 200,808 gSSRs were detected in the mung bean genome sequence with many being mono-nucleotide repeats but 17,898 predicted to be useful for genotyping (Kang et al., 2014). Mono-nucleotide repeats were common in our previous analysis but are not so useful for marker development and therefore most microsatellites are based on either dior tri-nucleotide repeats from genomics sequences and tri- or higher numbered repeats from EST sequences. The primer pairs and markers designed in this study were independent and different from previous SSRs discovered in silico or in vitro. Several studies have found gene-based eSSRs to be superior to gSSRs for their level of conservation, and suitable for cross-species amplifications (Zhang et al., 2014) but their level of polymorphism is often lower. Previously, 27 eSSR markers have been used to study the genetic relationship among 20 mung bean genotypes, and 21 of the markers (78%) displayed polymorphism (Gupta et al., 2014). In that study, the number of alleles ranged from two to six with an average PIC value of 0.34. In a second study, a total of 60 gSSRs revealed polymorphism in 17 mung bean accessions with PIC values ranging from 0.056 to 0.691 with an average of 0.259 with high transfer rates to various Vigna species and other related legumes (Tangphatsornruang et al., 2009).
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Fig. 1. Dendrogram of 157 mung bean accessions based on SSR marker data at 58 loci generated from Nei's genetic distance matrix by UPGMA.
In our study, we did not evaluate transferability outside of the Vigna genus but did find 58 markers that could amplify DNA from wild mung beans, and which had moderate PIC values averaging 0.36, similar to the previous group of gSSRs. Our evaluation of the genetic variation in mung bean suggested that cultivated mung bean showed higher polymorphism than wild mung bean, which is in contrast to a previous study in wild barley (Zhang et al., 2014). This divergence is probably because most of the wild mung bean accessions we evaluated originated from Anhui province, while the cultivated mung bean accessions originated from 24 provinces across China. Expected heterozygosity (He) was 0.44 and 0.24 for cultivated and wild mung bean, respectively, and was correlated with the higher PIC values for cultivated accessions. The findings of population structure analysis in our study demonstrated that the newly developed eSSRs and gSSRs could distinguish among non-Chinese versus Chinese cultivars and wild versus domesticated mung beans. The 157 genotypes were divided into nine subgroups (SGs) with SG 4 containing wild mung bean accessions and SG 8 including most of the foreign mung bean accessions. However, the lack of genetic clustering of accessions according to geographical areas indicated that individuals from different geographical areas in China are not different genetically based on origin. In other words, there was no direct relationship between genetic diversity and geographic distribution. This may be due to seed movement and/or high rates of gene flow between the populations. AMOVA analysis revealed 78% of the variation to be within populations, and 22% among populations. Analysis of pairwise genetic differentiation revealed that Fst values were small. According to our obtained Fst index (0.024), this represents the low differentiation among populations. The geographic genetic diversity within populations was much
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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Fig. 2. STRUCTURE analysis (K = 9) and UPGMA dendrogram for nine populations of mung bean based on Nei's genetic distance. A. The population structure of 157 mung bean accessions in k = 9 clusters. B. The dendrogram of the nine subpopulations according to the genetic distance using UPGMA clustering analysis.
t3:1 t3:2
Table 3 Nei's unbiased measures of genetic identity and genetic distance based on 58 SSR markers.
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Supplementary data to this article can be found online at http://dx. 375 doi.org/10.1016/j.gene.2015.04.043 376
Pop 2 Pop 3 Pop 4 Pop 5 Pop 6 Pop 7 Pop 8 Pop 9
Pop 1
Pop 2
Pop 3
Pop 4
Pop 5
Pop 6
Pop 7
Pop 8
0.1056 0.1849 0.0661 0.1623 0.3428 0.4523 0.2805 0.1355
0.1079 0.2214 0.0895 0.1904 0.2538 0.2399 0.1232
0.2595 0.1681 0.2612 0.3545 0.3079 0.1435
0.3164 0.5537 0.7044 0.4533 0.2656
0.1055 0.1635 0.1763 0.0949
0.2143 0.2347 0.2383
0.2816 0.2425
0.2701
377
Conflict of interests
The authors declare that they have no conflicts of personal, commu- 378 nication or financial interests. 379 380 Q15
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greater than that among populations. In addition, high levels of genetic variation were indirectly deduced among the tested genotypes. Although the actual contribution of human activity to the rate of gene flow is unknown, the low levels of differentiation among geographical groups might reflect human activities in different regions resulting in germplasm exchange. In summary, eSSR and gSSR markers are two valuable tools for studying population structure, genetic diversity and comparative mapping. Here we used a mixture of both marker types to analyze 157 accessions with 58 markers finding them to be accurate in distinguishing wild and cultivated genotypes as well as accession of different origins. Gwag et al. (2010) analyzed the population genetic structure of 692 mung bean accessions using 15 gSSR markers and found similar low levels of diversity. It is obvious that mung bean diversity studies require the use of up to 100 SSRs or even more SNP markers to be accurate. Therefore, the validated markers described here will be a valuable resource together with markers from other projects for further diversity analysis. In addition, the new SSR markers could be used for constructing genetic maps and mapping of genes related to important agronomic traits. Finally, these markers can be important for identifying functional diversity of unique germplasm with interesting adaptive traits.
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Murty and Arunachalan, 1966 Sato et al., 2010 Acknowledgments
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This study was funded by the Agricultural Science and Technology Innovation Program (ASTIP) in CAAS, grants from the Ministry of Agriculture of China (the earmarked fund for China Agriculture Research System [CARS-09]), and the Agricultural Science and Technology Innovation Program (ASTIP) of CAAS. MWB was supported by the Evans Allen Fund of the USDA to TSU. The funding agencies had no role in
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Table 4 Summary statistics of genetic diversity for model-based groups.
t4:1 t4:2
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Population
Sample size
Genetic diversity
Shannon's Information index (I)
t4:3
1 2 3 4 5 6 7 8 9
52 15 8 19 27 11 6 9 10
0.3525 0.3521 0.2740 0.2451 0.3937 0.2691 0.1981 0.3197 0.3689
0.5681 0.5558 0.4331 0.4128 0.6174 0.4267 0.3086 0.5009 0.5606
t4:4 t4:5 t4:6 t4:7 t4:8 t4:9 t4:10 t4:11 t4:12
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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Table 5 Analysis of molecular variance (AMOVA) based on 58 SSR markers and 157 mung bean genotypes divided into populations by structure analysis. Source of variation
d.f.
Sum of squares deviations
t5:4 t5:5
Among Pops Within Pops
8 148
2073.567 6763.431
Estimates of variance components
Percentage of variation
p value
13.257 45.699
22 78
0.001 0.001
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Fig. 3. Principal Coordinate Analysis (PCoA) of 157 mung bean accessions based on 58 SSR markers. The numbers in the figure are code numbers of the 157 mung bean accessions detailed in Table S1.
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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Honglin Chen a,1, Ling Qiao a,b,1, Lixia Wang a, Suhua Wang a, Matthew Wohlgemuth Blair c, Xuzhen Cheng a,⁎
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Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers
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Article history: Received 29 December 2014 Received in revised form 15 April 2015 Accepted 16 April 2015 Available online xxxx
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Keywords: Simple sequence repeat (SSR) markers Genetic diversity Population structure
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National Key Facility for Crop Gene Resources Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China College of Agriculture, Shanxi Agricultural University, Taigu 030801, China Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209, USA
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Mung bean is an important legume crop in tropical and subtropical countries of Asia and has high nutritional and economic value. However the genetic diversity of mung bean is poorly characterized. In this study, our goal was to develop and use microsatellite simple sequence repeat (SSR) markers for germplasm evaluation. In total, 500 novel expression sequence tag EST-based SSRs (eSSRs) and genomic SSRs (gSSRs) were developed from mung bean transcriptome and genome sequences. Of these, only 58 were useful for diversity evaluation in a panel of 157 cultivated and wild mung bean accessions from different collection sites in East Asia. A total of 2.66 alleles were detected on average per locus which shows that polymorphism is generally low for the species. The average polymorphic information content (PIC) of gSSRs was higher than eSSRs and most of the polymorphic gSSRs were composed of di- and tri-nucleotide repeats (52.4% and 38.1% of all loci, respectively). The genotypes were differentiated into nine subgroups by cluster analysis, and the wild mung bean accessions separated well from the cultivated accessions. Analysis of molecular variance indicated that 22% of variance was observed among populations and 78% was due to differences within populations. Clustering, population structure analyses showed that non-Chinese cultivated and wild mung bean accessions were separated from Chinese accessions, but no geographical distinctions existed between genotypes collected in China. Interestingly, the average PIC value of cultivated mung bean (0.36) was higher than that of wild mung bean (0.25) showing that further collecting and wide crosses are necessary for mung bean improvement. © 2015 Published by Elsevier B.V.
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Mung bean [Vigna radiata (L.) Wilczek] is an important food legume that is a source of protein and complex carbohydrates for consumers in Asian countries. It is widely grown and consumed in India, China, Myanmar and Thailand. The global annual production of mung bean is about 6 million tons. India is the world's largest producer of mung bean, followed by China and Myanmar (Nair et al., 2012; Kang et al., 2014). The whole grain is rich in fiber and minerals such as iron, potassium and zinc as well as vitamins A and B. Meanwhile, sprouted mung beans are rich in vitamins C in addition to vitamin B, folate and thiamin (Anwar et al., 2007). As a grain legume crop, mung bean is particularly interesting because of its early maturity, high tolerance to drought, and adaptation
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1. Introduction
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37 35 34
Abbreviations: SSR, simple sequence repeat; EST-SSRs (eSSRs), expressed sequence tagsimple sequence repeats; gSSRs, genomic SSRs; MAS, marker assisted selection; NGS, next generation sequencing; He, expected heterozygosity; Na, number of alleles; Ne, effective number of alleles; Ho, observed heterozygosity; PIC, polymorphic information content; UPGMA, unweighted pair group method with arithmetic mean. ⁎ Corresponding author. E-mail address: [email protected] (X. Cheng). 1 These authors contributed equally to this work.
to poor soil. Like other legumes, mung bean can fix atmospheric nitrogen through nodulation with Rhizobium species. High biomass production suggests that N fixation in mung beans is equal to or superior to soybean making it very important for crop rotations in some low soil fertility regions of Asia and potentially worldwide. Genetically, mung bean is a self-pollinated diploid crop (2n = 2x = 22) and possesses a relatively small genome (~560 Mb) (Van et al., 2013). Although the genome of mung bean has been sequenced and compared to other Vigna species (Kang et al., 2014), revealing a compact gene organization and cytogenetic correlations, genetic studies of mung bean are still scarce. Previous marker development for mung bean concentrated on RFLP (Young et al., 1992), RAPD (Lakhanpaul et al., 2000), AFLP (Moe et al., 2012) and CAPS (Chen et al., 2007) loci, but these marker technologies have many limitations for applicability and reproducibility. SSR markers have been widely used in various crop species as molecular tools for genetic diversity detection (Somta et al., 2008), gene mapping (Chankaew et al., 2011; Gupta and Gopalakrishna, 2013), markerassisted selection (MAS) (Kumar et al., 2011) and genome-wide association analysis (Bohra et al., 2014). SSRs along with SNP markers are therefore a more promising marker technology for mung bean. In the sequencing of the mung bean genome, a large number of SSRs have been discovered that could be useful for genotyping. In total,
http://dx.doi.org/10.1016/j.gene.2015.04.043 0378-1119/© 2015 Published by Elsevier B.V.
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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A total of 157 mung bean accessions were used in this study, all from the National Genebank of China (NGC) at Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China. The genotypes included 124 cultivated mung bean accessions from
98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 Q10123 Q9 124 125 126 127 128
133 134 135
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96 97
E
94 Q8 95
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92 93
R
90 91 Q7
O
88 89 Q6
C
86 87
N
84 85
U
82 83
2.2. SSR mining and primer design
144
A total of 48,693 unigenes were generated from transcriptome sequencing of mung bean (GenBank accession number SRP043316) and were evaluated for eSSR loci. These EST sequences were searched for the presence of SSR repeats using the MIcroSAtellite (MISA) software (http://pgrc.ipk-gatersleben.de/misa), with criteria of 10, 6, 5, 4, 4 and 3 minimum repeat units for mono-, di-, tri-, tetra-, penta-, and hexanucleotide repeats, respectively. Next, 300 eSSR targeting primer pairs were designed using Primer 3.0 with the following criteria: primer length ranging from 18 to 22 bp, product sizes of 100 to 300 bp and approximately even and balanced GC content in the priming targets. Additionally, 200 gSSRs were designed with the same criteria as mentioned above based on the genomic sequence of mung bean available from GenBank/EMBL/DDBJ under the accession code JJMO00000000 (Kang et al., 2014).
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F
2.1. Plant materials and DNA extraction
80 81
O
131
79
R O
2. Materials and methods
77 78
136 137
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130
75 76
China, 17 wild mung beans from Asia and 16 exotic genotypes from other countries (Table S1). Total genomic DNA was extracted for each genotype using the modified CTAB-based method (Englen and Kelley, 2000). In each case, fresh leaves of five seedlings were used as DNA source tissue for each accession. DNA quantity and quality were checked on 1% agarose gels run in 1 × TAE buffer along with spectrophotometric evaluation and standardized to 10 ng/μL for subsequent molecular analysis.
2.3. PCR amplification and SSR detection
138 139 140 141 142 143
146 147 148 149 150 151 152 153 154 155 156 157 158
159
PCR amplification was performed in a total reaction volume of 10 μL containing 1 μL of genomic DNA at 10 ng/μL concentration, 1 U of Taq DNA polymerase, 2 μL of 10 × Taq Buffer (Mg2 +), 0.2 mM of each dNTPs and 0.25 μmol of reverse and forward primers. PCR amplification was carried out with the following conditions: one cycle of 94 °C for 4 min, 33 cycles of 94 °C for 30 s, 55–60 °C for 30 s and 72 °C for 30 s, followed by a final extension at 72 °C for 5 min. The PCR products were loaded onto 8% Polyacrylamide non-denaturing gels, separated by electrophoresis and detected by silver nitrate staining (Conesa et al., 2005). A 1 kb size marker was used as a DNA ladder to estimate the SSR allele band sizes (Promega, Madison, WI, USA).
160
2.4. Statistical analysis
171
The eSSRs and gSSRs alleles were scored for presence or absence of bands. Each marker was then evaluated for the number of alleles (Na), the effective number of alleles (Ne), the observed heterozygosity (Ho), and the expected heterozygosity (He) using POPGEN v.1.32 (Krawczak et al., 2006). Nei's genetic diversity and polymorphism information content (PIC) were calculated using PowerMarker 3.25 (Liu and Muse, 2005). To determine the associations between genotypes, an unweighted pair group method with arithmetic mean (UPGMA) tree was constructed based on shared allele frequencies using MEGA 4.0 (Tamura et al., 2007). Population structure was assessed by STRUCTURE software v. 2.34 based on an admixture model. Models were tested for K-values ranging from 1 to 15, with ten independent runs each and 100,000 MCMC (Markov Chain Monte Carlo) iterations. The most likely number of clusters was chosen by plotting the LnP(D) values against Δk values with the best K value selected according to the Evanno Test (Evanno et al., 2005; Zhang et al., 2014). The genetic structure of accessions was further investigated by analysis of molecular variance (AMOVA) using GenAlEx 6.1 (Peakall and Smouse, 2012). The correlation of geographic and genetic distances from principal coordinate analysis (PCoA) was performed using NTSYS software v. 2.2 (Rohlf, 2000).
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200,808 potential loci were found but many were short to medium mono-nucleotide repeats with a total of 17,898 predicted as useful based on motif classification (Kang et al., 2014). However, there are few reports on the validation of SSR markers in mung bean to date. This has been true of genomic SSR (gSSR) markers but very much so of EST-based (EST-SSR or eSSR) markers. Though significant efforts have been made to develop SSR markers in recent years for many crops as discussed above, no more than 100 polymorphic SSR markers have been detected and published for mung bean, with most of them being gSSRs due in part to low diversity levels. Polymorphic eSSR markers are presently rare for mung bean, which hinders its genetic characterization. In previous work, Somta et al. (2008) tested more than 200 mung bean SSRs for polymorphism among 30 accessions, finding only 12 markers or 5.7% of the total polymorphic. Seehalak et al. (2009) designed 78 primers and 8 polymorphic loci were detected among 22 cultivated accessions. In total, 493 potential SSR markers were identified from sequenced fragments, 192 primer pairs were tested and detecting 60 loci that were polymorphic in 17 mung bean accessions (Sithichoke et al. 2009). EST-derived SSRs can be related to functional genes, as opposed to genomic SSRs. Therefore they are a potential tool for molecular marker assisted selection breeding (MAS), with molecular markers either originating from a gene for a desirable agronomic trait, or co-segregating with gene of interest. However, very few molecular markers have been found which were linked to a desirable gene locus in legume crops to date, especially for mung bean, except for MYMIV-resistance marker loci, YR4 and CYR1. The lack of tightly linked markers for agronomically important genes limit their utilization in selection of interested traits in mung bean MAS breeding. Although considerable efforts have been made in developing genomic resources (Kang et al., 2014; Chen et al., 2015). Identification of a linked marker with the objective traits is an essential prerequisite for MAS breeding program. Next generation sequencing (NGS) technologies are very valuable in producing millions or billions of sequence reads at once at a lower cost than traditional Sanger sequencing. Therefore, NGS is increasingly popular for obtaining high-throughput sequence data in a rapid and costeffective manner with high resolution and accuracy in genome-wide analyses (Marx, 2013). In recent years, mung bean gSSRs and eSSRs discovery has accelerated through NGS sequencing of genomic (Tangphatsornruang et al., 2009; Kang et al., 2014) and EST (Moe et al., 2011) sequences, respectively, but testing and validation remains a bottleneck. The objectives of this study were 1) to develop novel gSSRs and eSSRs for mung bean to increase the number of validated and polymorphic markers for the crop and 2) to assess the genetic diversity detectable with the new SSRs in a panel of wild and cultivated mung bean. We used highly variable nuclear microsatellite markers, which have been proven to be a powerful tool for revealing geographical population structure (O'Connell & Slatkin 1993; Estoup & Cornuet 1999). We discuss the importance of diversity as a genetic resource used to guarantee species success in various environments. Emphasis is placed on the significance of genetic diversity in breeding for high yield as the magnitude of heterosis depends on the degree of genetic divergence between parental stocks. The more diverse the parents the more are the chances of an increased spectrum of variability in the resulting segregants.
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Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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H. Chen et al. / Gene xxx (2015) xxx–xxx t1:1 t1:2
3
Table 1 Characterization of 24 eSSR (E) and 34 gSSR (G) markers in mung bean. Primer name
Primer sequence (5′–3′) F/R
Repeat motif
Expected size (bp)
Naa
Neb
Hoc
Hed
PICe
t1:4
E31003
(CCA)6
185
3
1.93
0.103
0.483
0.3675
t1:5
E11659
(CACCGT)3
133
2
1.72
0.133
0.4214
0.3280
t1:6
E22860
(GAAGCT)3
248
2
1.927
0.1074
0.4827
0.3659
t1:7
E26637
(GAA)6
265
2
1.9281
0.0345
0.483
0.3658
t1:8
E55107
(CTGATC)3
211
2
1.0713
0.0276
0.0668
0.0762
t1:9
E9587
(AAAG)3
134
3
1.509
0.0822
0.3385
0.2785
t1:10
E51985
(T)10
262
2
1.7809
0.1119
0.4401
0.3403
t1:11
E21522
(T)10
224
2
1.8349
0.1667
0.4565
0.3496
t1:12
E56315
(GCT)6
203
2
1.4292
0.0903
0.3014
0.2521
t1:13
E64504
(GATGAA)3
123
t1:14
E115962
(GTG)7
134
t1:15
E37870
(AACA)3
153
t1:16
E33094
(CCAACA)3
262
t1:17
E15212
(TTG)6
153
t1:18
E27164
(GCCACC)3
155
t1:19
E52717
(CTTCTC)3
167
t1:20
E13673
t1:21
E16266
t1:22
E10675
t1:23
E14180
t1:24
E34120
t1:25
E19823
t1:26
E15159
t1:27
E24080
t1:28
G3627
t1:29
G241
t1:30
G2740
t1:31
G2436
t1:32
G2208
t1:33
G1510
t1:34
G3835
t1:35
G3203
t1:36
G21063
t1:37
G2637
t1:38
G3598
t1:39
G3310
t1:40
G2624
AAGCAAGATGACACGGAGCA ATGGTTGAAGAAGCGGGAGG ACGCTCGAAATATCACCCCC GGGTCTCGAGTTTGTGAGGG AGCATGGTATAAGAATGAGAGGGT TTTCTTCAAGCTGGGGTGCT AAGCGTGGAAGTGGAGTGAG ACCGACTTAACGTTATTGAAAAGAGG ACCCTATGTGCAGTGCAACA TCTCCTCCCTTGAGAGAGGC AACAGTGACACGTGGCAGAT ACACAGAGCTGCCATTCTCC GCGATCGAGTCACTCTACGG TCATCCGCCACAACCTCTTC AGCAGGCAATTCACGCAAAG GCTGGCATCTTCTTCTTGTCG ATCCTTCGTGGTCTCCGAGA GGGGTTACAGGACCAGAAGC CTCCTGAGGGCACTGAACTG GCTTCTGCAACGAGTTTCAACT CTCCTGGGCACATTTCCACT ACCACCCACATCATTTCCCC TTGTGGTGTCCGTAGTGAGC TCGCTTCGGAAAGTGCTTCT ATTGCCACCCCCATTTCCAT AGCAGTCCACCACTCTCTCT CATGGGTCATGCACTTTCGT CGCATCCATTGAAGACCAAGC CTCAACAAGTTCCTCAGCGC CCAGAACCGGTGGAAGTCTC ACGAAATCAACGAGGCATATGA ACTTTTGTTGCGGAGGGGAA CCTTGCTCTTGTGTGCCTCT CTCCACAGCATTGACCCCAT AGAACCATGCCACGTGACAT GTCCAACCACGCAAACTCAC GCAGAAGGAAGCTCAAGATCG GCTTCCCACAACTCCCAGAA CAGATTCCAACCCGAAGCCA GCGAAAGAAGCTCGTCCTCT ACTGAGTCTCACCAGAGCCA ATTCTCCGGCACTCAACAGG ACAACAAGGATCACCGTCCC TCAGTCTCTCCAGCTCCGAA ACGTGTAGAGAGACCACCGA CCAAGCAGCAGAACCAACAC GCTCCCATTCACCATCCCAA TCCGGTTCCTTCCCACAATG TCAGGTCCCTGTTCATCAAA CCCATCTTTTCAATCAACAACA AAATTAGGGGATCCGCATGT TGCGTGTGTGTTTGTGTTTG TTGATGGCATATCCCCTAGC CTGAGGAGCTCCATGATTGC GACCATTGGTTCAACGAGAAA CCTCAGTCTGGTACCGTAACTACTTC CAAGTGCAAAGGGAAAAGGA TGTGATCACTGTCACCCACTT TACAGGCACTCGAGGATCAA AATTGTTGGAAGTGGCTTGC ACTTGAAGAAGACAGGGCCA CCAACATGGTGTTGTTCAGC TAGCTTGGCCTACTTGCGAT TGGTCACAGGAGGTGTGGTA GAATTTTCTGGAAACGCACC CCATCTGGTTTCCTGTGCTT TGAAGCCTAATATGCAAGCAA TCATTTTTGGTGAACCTTTTGA GAGCCATGGGTATGTATAGATTTTT CCTCTCCCTCTTTTTCAGCC AAGGGGGCTAGAAGTGGAAA CCACAGCAACCACAAATGAC TCCTCTGATAGAAGCAGCTACG
O
0.2206
0.3612
0.2928
2
1.3709
0.0484
0.2716
0.2392
2
1.3846
0.1111
0.2787
0.2403
4
1.0636
0.0272
0.06
0.0585
2
1.3126
0.0263
0.2389
0.2098
2
1.9892
0
0.4989
0.3728
2
1.9976
0.0903
0.5011
0.3747
2
1.074
0.0065
0.0691
0.0669
271
2
1.9648
0.126
0.493
0.37
(AAGTG)5
144
3
1.7407
0.1527
0.4272
0.3663
(GGAAGA)10
141
4
1.7967
0.156
0.445
0.3733
E
R O
1.5622
165
2
1.9952
0.0559
0.5006
0.3742
(AAATC)4
189
2
1.7056
0.1056
0.4151
0.3298
(CAT)7
217
2
1.9836
0.0758
0.4978
0.3721
(A)16
218
2
1.919
0.2603
0.4805
0.3727
(TTG)5
163
3
1.5916
0.0846
0.3721
0.3234
(AC)6
150
3
1.7634
0.0647
0.4345
0.3509
(TC)6
121
3
1.9408
0.0634
0.4865
0.37
(AG)17
103
4
3.0662
0.1088
0.6762
0.61
(GA)17
115
2
1.9951
0.0993
0.5005
0.3742
(A)11
137
2
1.9697
0.0661
0.4944
0.37
(CTT)6
210
2
1.6
0.0714
0.3765
0.3034
(ACA)8
106
4
1.7472
0.0915
0.4292
0.3968
(GT)7
128
3
2.0716
0.0308
0.5193
0.4317
(GT)8
119
2
1.3281
0.0634
0.2479
0.2089
(AGA)9
155
2
1.6475
0.1429
0.3943
0.3158
(TGT)7
93
2
1.5672
0.0949
0.3632
0.2976
(TG)10
120
2
1.2958
0.0705
0.229
1.1986
C
(TCA)6
D
E 243
T
(TAC)6 (ATA)6
P
2
R
R
N C O
U
F
t1:3
(continued on next page)
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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H. Chen et al. / Gene xxx (2015) xxx–xxx
t1:45
G3302
t1:46
G3427
t1:47
G3429
t1:48
G264
t1:49
G23823
t1:50
G1378
t1:51
G3424
t1:52
G4010
t1:53
G3044
t1:54
G1191
t1:55
G3505
t1:56
G2306
t1:57
G3108
t1:58
G7472
t1:59
G3212
t1:60
G0483
t1:61
G1242
t1:62
Mean
t1:63 t1:64 Q1 t1:65 t1:66 t1:67
a
d e
2
1.922
0.1042
0.4814
0.3639
(CA)6
140
4
1.6091
0.0979
0.3799
0.351
(TGT)6
95
2
1.7627
0.1439
0.4343
0.342
(A)12
169
2
1.9339
0.1986
0.4846
0.3655
(TCA)5
136
2
1.9231
0.1379
0.4817
0.3656
(CAC)6
224
2
1.9893
0.1533
0.499
0.3734
(TTC)9
111
2
1.5766
0.1752
0.367
0.3047
(AG)11
122
2
1.5955
0.1418
0.3745
0.3013
(AG)16
266
2
1.9999
0.1214
0.5018
0.3748
(GA)14
186
2
1.6379
0.0662
0.3908
0.3127
(GA)17
258
7
2.9389
0
0.6673
0..6136
(AG)15
221
4
3.1978
0.0083
0.6901
0.6315
(AG)20
175
6
2.2843
0
0.5644
0.5423
(CA)17
210
4
2.3611
0
0.5788
0.5151
(GA)17
166
2
1.9978
0
0.5015
0.3748
(AG)20
134
4
2.0933
0
0.5245
0.477
(AC)18
102
5
3.0137
0
0.6712
0.6089
171
3
2.3457
0.6423
0.5758
0.4848
102
2
1.8603
0
0.4642
0.3584
117
2
1.9484
0.7132
0.4886
0.3669
123
5
3.0037
0
0.6639
0.6158
2.67
1.87
0.1047
0.4367
0.3575
(AG)16 (AG)18 (AG)15 (GA)16
The number of number of alleles. The number of effective number of alleles (Kimura and Crow, 1964). The number of expected heterozygosity. The number of observed heterozygosity. Polymorphic information content.
R
c
115
O
b
(CA)7
3. Results
194
3.1. Characterization of polymorphic SSR markers in mung bean
195 196
A total of 13,134 primer pairs could be developed form mung bean transcriptome sequences using Primer 3. Among them, 500 SSR primer pairs were randomly selected for PCR amplification for polymorphism. A total of 58 SSR markers were polymorphic among the 157 mung bean accessions from diverse geographical locations. These included 24 out of 300 eSSRs (8% polymorphism rate) and 34 out of 200 gSSRs (10.5% polymorphism rate) as detailed in Table 1. The polymorphism ratio of the eSSR markers varied with repeat motif and was 7.9%, 39.5%, 7.9%, 7.9% and 36.8%, respectively, for the di-, tri-, tetra-, penta-, and hexa-nucleotide repeat based markers. Most of the polymorphic eSSRs were tri- and hexanucleotides, while most of the polymorphic gSSRs were composed of mono- and di-nucleotide repeats, accounting for 16.7% and 80.6% overall, respectively. Among mono-nucleotide repeats, the A/T motif was the most abundant repeat, accounting for 91.7% of these mono-nucleotide repeats. Among di-nucleotide repeats of gSSR
199 200 201 202 203 204 205 206 207 208 209 210
U
N
C
193
197 198
O
G1671
PICe
R O
t1:44
Hed
P
G3335
Hoc
D
t1:43
Neb
E
G2999
Naa
T
t1:42
CATGCTCACTACTTTGTTGATCC GCTTCCACTTTATACATATTACGCA TATGCTTGCAAGAGTGTGGG CCCATGCAATATATCACCCA TGCGTGTATGCGTCTGTGTA ATGTTTGAGGCATTTCCCTG ATCAGGCAACAACAACCACA CCAGATCAAGAACCTACCACAA GGGACATTAGAGATTCCCCA AAAACTTGTCCAGACCACGG CCCTTTTGTTTGTGGCACTT GCTTCTGCACAACCCTCTTC CCCTACATTTCAGCAACCGT GGAATGAGTTTCTTGCAGGG GAAGGAGGAGGAGCTTCCAT AAGGTTGAGAATCGCAGGTG ACACGACGTGCTGCTAACTG AAATTGGGGATTTGCATGTG CTCTCTCCTCACATTCTCTGACA AACAACCCAAAAGCCTCCTT CCCACGCTAAGATTGAGCTA AACCAATCGACACACGTGAA CCTTCGTAGAGGTGCGCT AACCTGTGTTGGGTGTTTCTT CAAAGACACCCACTTGTAGCC AACGGGTTTCCTTCTGGTCT GTTAGGTCGTTCCTTCCCGT AAGCATGAAAGTTTGGCAGG AATTCACTTGGCTTGATTTGAA AAGGGAGTGATAGCGCAAAA ACCAACCAAACCAACGTTAC AAGTTAGTCAGAACTTTGGGCA CTAACAACAACGAACAAGAAGAGA AATATCCAAATTCAAGCGCA GTGTCGTGTCGTGTGTCGT GCGAGCGGAATCAGAATAAC CGTAACCGTAACCTTACCTACTTC TACCGAAATCCGTACGAAGC TCTCTCTTGTTTCTTCGTTGTTC TGTAGAAAAGCAAAAACCAACAAA AGGTAGATGACATGCTCGCC TGTCATTCTCTAGGGTTCAGCA ACTCGTCGGAGGTACGTTCT
Expected size (bp)
C
G2516
Repeat motif
E
t1:41
Primer sequence (5′–3′) F/R
R
Primer name
F
Table 1 (continued)
markers, the AG motif was the most abundant repeat, accounting 211 for 73.6% of the total in this group. 212
3.2. Assessment of genetic diversity in mung bean accessions
213
The data from the 58 polymorphic SSR markers were used to evaluate the genetic diversity of the 157 mung bean accessions in the diversity panel. According to POPGEN analysis, 154 alleles were found for the 58 loci. The observed number of alleles per locus (Na) ranged from 2 to 7 with a mean of 2.66, while the effective number of alleles per locus (Ne) varied from 1.07 to 3.19 with a mean of 1.87. The average Na was 2.05 and 2.67 for wild and cultivated mung bean, respectively (Table 2). The polymorphic information content (PIC) ranged from 0.06 to 0.63 with a mean of 0.36. The PIC of cultivated mung bean was higher (0.36) than that of wild mung bean (0.25). Meanwhile, the average PIC of gSSRs (0.40) was higher than for eSSRs (0.30). As an indicator of genetic diversity, the average He was 0.24 in wild mung bean accessions and 0.44 in cultivated accessions.
214 215
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2 2 2 2 1 1 1 1 1 1 1 2 1 1 2 2 1 2 2 2 1 2 2 1 3 2 2 3 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 4 5 3 2 2 3 3 2 2 3 2.05
0.50 0.44 0.49 0.45 0.07 0.37 0.46 0.48 0.33 0.39 0.31 0.29 0.07 0.26 0.49 0.50 0.08 0.50 0.44 0.45 0.50 0.44 0.50 0.49 0.30 0.37 0.50 0.69 0.49 0.50 0.37 0.39 0.51 0.23 0.42 0.37 0.24 0.46 0.41 0.45 0.49 0.49 0.50 0.38 0.39 0.50 0.37 0.63 0.68 0.54 0.56 0.49 0.50 0.68 0.53 0.48 0.49 0.69 0.44
0.12 0.21 0.19 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.37 0.11 0.00 0.09 0.25 0.43 0.00 0.11 0.16 0.00 0.42 0.20 0.21 0.25 0.12 0.27 0.46 0.51 0.59 0.35 0.07 0.21 0.17 0.31 0.12 0.15 0.29 0.21 0.50 0.15 0.20 0.19 0.51 0.62 0.64 0.70 0.59 0.13 0.52 0.48 0.61 0.17 0.52 0.31 0.24
0.38 0.34 0.37 0.35 0.09 0.29 0.35 0.36 0.27 0.31 0.26 0.25 0.07 0.23 0.37 0.37 0.07 0.37 0.38 0.37 0.37 0.34 0.37 0.37 0.27 0.31 0.38 0.62 0.37 0.37 0.30 0.36 0.42 0.19 0.32 0.30 0.20 0.35 0.37 0.35 0.37 0.37 0.37 0.32 0.31 0.37 0.30 0.59 0.63 0.54 0.49 0.37 0.45 0.62 0.44 0.36 0.37 0.63 0.36
0.15 0.21 0.28 0.25 0.00 0.19 0.20 0.19 0.06 0.06 0.00 0.11 0.00 0.00 0.29 0.30 0.00 0.23 0.21 0.36 0.12 0.19 0.13 0.13 0.43 0.26 0.15 0.21 0.30 0.26 0.31 0.37 0.44 0.30 0.20 0.16 0.19 0.25 0.16 0.13 0.23 0.19 0.37 0.12 0.16 0.32 0.38 0.60 0.56 0.45 0.56 0.20 0.38 0.41 0.51 0.29 0.37 0.35 0.25
The number of number of alleles. The number of effective number of alleles (Kimura and Crow, 1964). Polymorphic information content.
A dendrogram was constructed using the UPGMA method (Fig. 1), and was found to somewhat reflect the origins of the different accessions. Cluster analysis indicated that wild and non-Chinese mung bean accessions separated from Chinese accessions. However, mung bean cultivars from within China did not group according to region and accessions within subgroups had scattered origins across the country.
236 Q11 237
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3 2 2 2 2 3 2 2 2 2 2 2 4 2 2 2 2 2 3 4 2 2 2 2 3 3 3 4 2 2 2 4 3 2 2 2 2 2 4 2 2 2 2 2 2 2 2 7 4 6 4 2 4 5 3 2 2 5 2.67
The population structure of the 157 mung bean accessions were analyzed with the alleles detected by the 58 SSR markers, and firstly inferred using STRUCTURE 2.3.4 (Evanno et al., 2005). Admixture model-based simulations were carried out by varying K from 2 to 15 with 15 iterations using all 261 genotypes which showed the most suitable ΔK is 9, showed the most suitable number of populations (Pops) to be nine (Fig. 2A). In total, 52 accessions (33.1%) were assigned to population 1, which is composed of most accessions from North China, mainly including Shandong and Hebei provinces. Pops 2, 3, 4, 5, 6, 7, 8, and 9 consisted of 15, 8, 19, 27, 11, 6, 9 and 10 accessions, respectively. Most wild and foreign mung bean accessions were assigned to Pop 4 and Pop 8, respectively. The results demonstrated that wild and foreign mung bean accessions were assigned to different populations from each other and from the Chinese cultivars. Different genetic distances between Pops were listed in Table 3, which ranged from 0.0661 (between Pop 4 and Pop 1) to 0.7044 (between Pop 4 and Pop 7). The Pop 5 (genetic diversity = 0.3937, I = 0.6174) showed the highest genetic diversity level. The Pop 7 (genetic diversity = 0.1981, I = 0.3086) showed the lowest genetic diversity level, and the sample size, genetic diversity and Shannon's information index of each group is shown in Table 4. A dendrogram was constructed to show the genetic relationship of the subgroups (SGs) via UPGMA clustering analysis (Fig. 2B). Based on the genetic distance values of the nine subgroups, SG1 was found to be closest to the wild mung beans (SG4) was closest to the foreign mung beans (SG 8).
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R O
Cultivated
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Wild
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Cultivated
3.3. Population structure and population genetic distance
238 239 240 241 Q12 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 Q13 260
3.4. Principal coordinate analysis and analysis of molecular variance 261 (AMOVA) 262
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a
Wild
T
t2:64 t2:65 Q2 t2:66
Cultivated
C
E31003 E11659 E22860 E26637 E55107 E9587 E51985 E21522 E56315 E64504 E115962 E37870 E33094 E15212 E27164 E52717 E13673 E16266 E10675 E14180 E34120 E19823 E15159 E24080 G3627 G241 G2740 G2436 G2208 G1510 G3835 G3203 G21063 G2637 G3598 G3310 G2624 G2516 G2999 G3335 G1671 G3302 G3427 G3429 G264 G23823 G1378 G3424 G4010 G3044 G1191 G3505 G2306 G3108 G7472 G3212 G0483 G1242 Mean
PICc
E
t2:5 t2:6 t2:7 t2:8 t2:9 t2:10 t2:11 t2:12 t2:13 t2:14 t2:15 t2:16 t2:17 t2:18 t2:19 t2:20 t2:21 t2:22 t2:23 t2:24 t2:25 t2:26 t2:27 t2:28 t2:29 t2:30 t2:31 t2:32 t2:33 t2:34 t2:35 t2:36 t2:37 t2:38 t2:39 t2:40 t2:41 t2:42 t2:43 t2:44 t2:45 t2:46 t2:47 t2:48 t2:49 t2:50 t2:51 t2:52 t2:53 t2:54 t2:55 t2:56 t2:57 t2:58 t2:59 t2:60 t2:61 t2:62 t2:63
Heb
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t2:4
Naa
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Marker
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t2:3
Table 2 Polymorphism of SSR markers in wild and cultivated mung bean.
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5
A cluster graph produced by two-dimensional principle coordinate analysis (PCoA) showed similar results as for UPGMA clustering, also indicating that wild mung bean accessions were clustered together and separated from other accessions (Fig. 3). Similar to the results of STRUCTURE, the wild mung bean and foreign accessions were separated from others accessions. PCoA showed that large diversity existed in mung bean collection. The percentage of variation explained by the first three axes was 71.89% (axis 1 — 31.44%, axis 2 — 21.56% and axis 3 — 18.89%). An AMOVA analysis was used to evaluate within and among population diversity components. Significant genetic differentiation among the nine population structure classified subgroups was detected by AMOVA at a p value of 0.0001. The results of AMOVA indicated that majority of the variance occurring within populations accounted for 78% of the total variation, and 22% of variation was attributed to differences among populations (Table 5). The Fst was observed to be 0.024, indicating the low differentiation between populations.
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4. Discussion
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For eSSR markers developed from coding regions, it can detect diversity not found at other locus types, while possessing a good degree of conservation within closely related species, increasing transferability to closely related species. They have been widely used for crossspecies and comparative mapping of related crops or phylogenetic studies of wild and cultivated crop accessions (Tautz and Renz, 1984; Zietkiewicz et al., 1994; Kumari et al., 2013). In comparison with other crops, relatively few SSR markers have been developed for the genus Vigna, reflecting the limited sequence resources available. In the present study, we developed 58 polymorphic mung bean SSRs, with 24 based on eSSR loci and 34 based on gSSR loci. The mung bean eSSRs were preferentially based on certain repeat motifs, which were more common within the transcriptome, namely tri-nucleotide repeats (37.5%), and followed by hexa- (33.3%), mono- (12.5%), tetra(8.3%) and penta- (8.3%) nucleotide repeats. The relative abundance of
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Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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tri-nucleotide repeats in EST sequences has been observed in mung bean (Gupta et al., 2014) and many other legumes including pea (Gong et al., 2010), cowpea (Gupta and Gopalakrishna, 2010), chickpea (Choudhary et al., 2009), common bean (Blair and Hurtado, 2013) and horse gram (Bhardwaj et al., 2013). A new finding is that hexanucleotide repeats are about equally common as tri-nucleotide repeats. Meanwhile, di-nucleotide repeats tend to be the most abundant in genomic sequences of many legumes (Blair et al., 2011), followed by tri-nucleotide repeats (53.4% and 38.1%, respectively, in mung bean). Recently, 200,808 gSSRs were detected in the mung bean genome sequence with many being mono-nucleotide repeats but 17,898 predicted to be useful for genotyping (Kang et al., 2014). Mono-nucleotide repeats were common in our previous analysis but are not so useful for marker development and therefore most microsatellites are based on either dior tri-nucleotide repeats from genomics sequences and tri- or higher numbered repeats from EST sequences. The primer pairs and markers designed in this study were independent and different from previous SSRs discovered in silico or in vitro. Several studies have found gene-based eSSRs to be superior to gSSRs for their level of conservation, and suitable for cross-species amplifications (Zhang et al., 2014) but their level of polymorphism is often lower. Previously, 27 eSSR markers have been used to study the genetic relationship among 20 mung bean genotypes, and 21 of the markers (78%) displayed polymorphism (Gupta et al., 2014). In that study, the number of alleles ranged from two to six with an average PIC value of 0.34. In a second study, a total of 60 gSSRs revealed polymorphism in 17 mung bean accessions with PIC values ranging from 0.056 to 0.691 with an average of 0.259 with high transfer rates to various Vigna species and other related legumes (Tangphatsornruang et al., 2009).
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Fig. 1. Dendrogram of 157 mung bean accessions based on SSR marker data at 58 loci generated from Nei's genetic distance matrix by UPGMA.
In our study, we did not evaluate transferability outside of the Vigna genus but did find 58 markers that could amplify DNA from wild mung beans, and which had moderate PIC values averaging 0.36, similar to the previous group of gSSRs. Our evaluation of the genetic variation in mung bean suggested that cultivated mung bean showed higher polymorphism than wild mung bean, which is in contrast to a previous study in wild barley (Zhang et al., 2014). This divergence is probably because most of the wild mung bean accessions we evaluated originated from Anhui province, while the cultivated mung bean accessions originated from 24 provinces across China. Expected heterozygosity (He) was 0.44 and 0.24 for cultivated and wild mung bean, respectively, and was correlated with the higher PIC values for cultivated accessions. The findings of population structure analysis in our study demonstrated that the newly developed eSSRs and gSSRs could distinguish among non-Chinese versus Chinese cultivars and wild versus domesticated mung beans. The 157 genotypes were divided into nine subgroups (SGs) with SG 4 containing wild mung bean accessions and SG 8 including most of the foreign mung bean accessions. However, the lack of genetic clustering of accessions according to geographical areas indicated that individuals from different geographical areas in China are not different genetically based on origin. In other words, there was no direct relationship between genetic diversity and geographic distribution. This may be due to seed movement and/or high rates of gene flow between the populations. AMOVA analysis revealed 78% of the variation to be within populations, and 22% among populations. Analysis of pairwise genetic differentiation revealed that Fst values were small. According to our obtained Fst index (0.024), this represents the low differentiation among populations. The geographic genetic diversity within populations was much
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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Fig. 2. STRUCTURE analysis (K = 9) and UPGMA dendrogram for nine populations of mung bean based on Nei's genetic distance. A. The population structure of 157 mung bean accessions in k = 9 clusters. B. The dendrogram of the nine subpopulations according to the genetic distance using UPGMA clustering analysis.
t3:1 t3:2
Table 3 Nei's unbiased measures of genetic identity and genetic distance based on 58 SSR markers.
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Supplementary data to this article can be found online at http://dx. 375 doi.org/10.1016/j.gene.2015.04.043 376
Pop 2 Pop 3 Pop 4 Pop 5 Pop 6 Pop 7 Pop 8 Pop 9
Pop 1
Pop 2
Pop 3
Pop 4
Pop 5
Pop 6
Pop 7
Pop 8
0.1056 0.1849 0.0661 0.1623 0.3428 0.4523 0.2805 0.1355
0.1079 0.2214 0.0895 0.1904 0.2538 0.2399 0.1232
0.2595 0.1681 0.2612 0.3545 0.3079 0.1435
0.3164 0.5537 0.7044 0.4533 0.2656
0.1055 0.1635 0.1763 0.0949
0.2143 0.2347 0.2383
0.2816 0.2425
0.2701
377
Conflict of interests
The authors declare that they have no conflicts of personal, commu- 378 nication or financial interests. 379 380 Q15
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greater than that among populations. In addition, high levels of genetic variation were indirectly deduced among the tested genotypes. Although the actual contribution of human activity to the rate of gene flow is unknown, the low levels of differentiation among geographical groups might reflect human activities in different regions resulting in germplasm exchange. In summary, eSSR and gSSR markers are two valuable tools for studying population structure, genetic diversity and comparative mapping. Here we used a mixture of both marker types to analyze 157 accessions with 58 markers finding them to be accurate in distinguishing wild and cultivated genotypes as well as accession of different origins. Gwag et al. (2010) analyzed the population genetic structure of 692 mung bean accessions using 15 gSSR markers and found similar low levels of diversity. It is obvious that mung bean diversity studies require the use of up to 100 SSRs or even more SNP markers to be accurate. Therefore, the validated markers described here will be a valuable resource together with markers from other projects for further diversity analysis. In addition, the new SSR markers could be used for constructing genetic maps and mapping of genes related to important agronomic traits. Finally, these markers can be important for identifying functional diversity of unique germplasm with interesting adaptive traits.
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Murty and Arunachalan, 1966 Sato et al., 2010 Acknowledgments
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This study was funded by the Agricultural Science and Technology Innovation Program (ASTIP) in CAAS, grants from the Ministry of Agriculture of China (the earmarked fund for China Agriculture Research System [CARS-09]), and the Agricultural Science and Technology Innovation Program (ASTIP) of CAAS. MWB was supported by the Evans Allen Fund of the USDA to TSU. The funding agencies had no role in
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Table 4 Summary statistics of genetic diversity for model-based groups.
t4:1 t4:2
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Population
Sample size
Genetic diversity
Shannon's Information index (I)
t4:3
1 2 3 4 5 6 7 8 9
52 15 8 19 27 11 6 9 10
0.3525 0.3521 0.2740 0.2451 0.3937 0.2691 0.1981 0.3197 0.3689
0.5681 0.5558 0.4331 0.4128 0.6174 0.4267 0.3086 0.5009 0.5606
t4:4 t4:5 t4:6 t4:7 t4:8 t4:9 t4:10 t4:11 t4:12
Please cite this article as: Chen, H., et al., Assessment of genetic diversity and population structure of mung bean (Vigna radiata) germplasm using EST-based and genomic SSR markers..., Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.04.043
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Table 5 Analysis of molecular variance (AMOVA) based on 58 SSR markers and 157 mung bean genotypes divided into populations by structure analysis. Source of variation
d.f.
Sum of squares deviations
t5:4 t5:5
Among Pops Within Pops
8 148
2073.567 6763.431
Estimates of variance components
Percentage of variation
p value
13.257 45.699
22 78
0.001 0.001
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Fig. 3. Principal Coordinate Analysis (PCoA) of 157 mung bean accessions based on 58 SSR markers. The numbers in the figure are code numbers of the 157 mung bean accessions detailed in Table S1.
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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