Molecular analysis of genetic diversity among vine accessions using DNA markers

A.F. da Costa; P.E. Teodoro; L.L. Bhering; F.D. Tardin; R.F. Daher; W.F. Campos; A.P. Viana; M.G. Pereira

doi:10.4238/gmr16029586

Research Article

Molecular analysis of genetic diversity among vine accessions using DNA markers

Published: April 13, 2017
Genet.Mol.Res. 16(2): gmr16029586
DOI: 10.4238/gmr16029586

Abstract

Viticulture presents a number of economic and social advantages, such as increasing employment levels and fixing the labor force in rural areas. With the aim of initiating a program of genetic improvement in grapevine from the State University of the state of Rio de Janeiro North Darcy Ribeiro, genetic diversity between 40 genotypes (varieties, rootstock, and species of different subgenera) was evaluated using Random amplified polymorphic DNA (RAPD) molecular markers. We built a matrix of binary data, whereby the presence of a band was assigned as “1” and the absence of a band was assigned as “0.” The genetic distance was calculated between pairs of genotypes based on the arithmetic complement from the Jaccard Index. The results revealed the presence of considerable variability in the collection. Analysis of the genetic dissimilarity matrix revealed that the most dissimilar genotypes were Rupestris du Lot and Vitis rotundifolia because they were the most genetically distant (0.5972). The most similar were genotypes 31 (unidentified) and Rupestris du lot, which showed zero distance, confirming the results of field observations. A duplicate was confirmed, consistent with field observations, and a short distance was found between the variety ‘Italy’ and its mutation, ‘Ruby’. The grouping methods used were somewhat concordant.

Introduction

Leuciscus leuciscus baicalensis, a freshwater species of the Cyprinidae family (subfamily Leuciscinae), is widely distributed in Xinjiang, China (Huo et al., 2011). A few studies have been conducted on the artificial reproduction and development of L. leuciscus baicalensis and Leuciscus idus (Nowosad et al., 2014; Witeska et al., 2014; Siddique et al., 2016). Moreover, Chang et al. (2014) and Cui et al. (2015) have reported the differential gene expression of Leuciscus waleckii. Their studies indicate that transcriptome changes play a role in spawning migration and in acid-base homeostasis in fish under alkaline stress. L. leuciscus baicalensis is one of the most important commercial fish in Xinjiang, but in recent years, fishery resources have fallen sharply owing to water conservation projects, unreasonable utilization, and invasion by alien species. Therefore, researchers have begun to explore artificial domestication. Studies on the management of cultivation conditions and the impact of fishery drugs on fry seem to suggest that artificial culture is not difficult and could be popularized (Liu et al., 2015a; Lin and Tang, 2016).

Codominant simple sequence repeats (SSRs) are regarded as one of the most effective molecular markers for the examination of genetic diversity within and between populations, and they provide abundant genetic information. With the rapid development of next-generation sequencing (NGS) technology, both expressed sequence tag (EST)-SSRs and genomic SSRs (gSSRs) can be obtained cheaply and efficiently (Gao et al., 2012; Zheng et al., 2013; Liu et al., 2015b). In the present study, we developed novel SSR markers from both genomic DNA libraries and EST libraries, and focused on L. leuciscus baicalensis sampled from the Irtysh River, even though Dubut et al. (2009) have generated some polymorphic SSR markers based on European L. leuciscus baicalensis. According to the decryption of the evolutionary history and the genetic differentiation of the subfamily (Costedoat et al., 2006; Boron et al., 2009; Perea et al., 2010; Hu et al., 2015), Leuciscinae species, particularly L. leuciscus baicalensis and L. idus, are assumed to be closely related. Therefore, in the present study we tested the cross utility of polymorphic SSR primers mined from L. leuciscus baicalensis in other species to investigate transferability.

These novel SSR markers provide useful information for phylogenetic analysis and studies on population genetics. Moreover, they are appropriate for studies involving fingerprinting, gene flow, genetic diversity, population structure, germplasm characterization research, and molecular-assisted breeding in L. leuciscus baicalensis and related species.

Materials and Methods

Fish materials and extraction of genomic DNA

Ninety-six specimens of L. leuciscus baicalensis comprising three populations were collected randomly using gill nets from the natural river systems of Xinjiang Province, including the tributary streams of the Habahe River (HBH: N48°04'546'', E086°20'686'') and the Buerjin River (BEJ: N47°42'875'', E086°50'169''), and the Beiwan section of the main stream (BW: N48°01'486'', E085°33'060''). All samples were examined and classified according to Ren et al. (2002). Based on previous studies on the effects of sample size on genetic diversity estimates in populations using SSR markers (Yan and Zhang, 2004; Pruett and Winker, 2008; Ou et al., 2009), we determined that approximately 30 individuals for each population was sufficient. The samples were preserved in 95% ethanol. Total genomic DNA was isolated from the fins by proteinase K digestion followed by the standard phenol/chloroform method, and visualized on a 1.5% agarose gel (Wang et al., 2011).

Primer design, SSR marker development, and detection

The assembled contigs and expressed sequences (from unpublished data) from NGS were used to detect SSR loci with a Perl script known as Microsatellite (MISA, http://pgrc.ipk-gatersleben.de/misa ). The EST-SSR loci and gSSR loci were only considered if they contained at least six repeats for dinucleotides, five repeats for trinucleotides, and four repeats for tetranucleotides, pentanucleotides, and hexanucleotides. Differences in mononucleotide repeats were excluded for EST-SSRs, and at least 10 repeats were required for mononucleotides in gSSRs. The maximal number of bases interrupting two SSRs in a compound microsatellite was set at 100 bp. The primers flanking the SSR core sequences were designed using Primer Premier 5.0 software (Kamel and Abd-Elsalam, 2003).

Initially amplification was conducted to optimize the annealing temperature of the SSR markers. Polymerase chain reaction (PCR) amplifications were performed in a final volume of 10 μL [0.5 μL DNA, 0.5 μL each primer (Tsingke Biological Technology, Beijing, China), 5 μL 2X Es Taq Master Mix (CWBIO, Beijing, China), and 3.5 μL ddH₂O]. The optimized SSR primers were used to amplify DNA in 24 individuals from the Beiwan (BW) population. The PCR products were analyzed using 8% polyacrylamide gel electrophoresis (PAGE) and stained with silver to distinguish the polymorphisms (Creste et al., 2001). Each forward polymorphic primer was marked with 5-FAM, HEX, TET, or TAMRA fluorescent dye at the 5' end. Polymorphic loci were tested in the 96 individuals from the three populations (28 from HBH, 26 from BEJ, 42 from BW) with fluorescent primers. The accurate sizes of the target fragments were measured with GeneMarker version 1.51 software using an ABI 3730 capillary sequencer.

Cross-species transferability

To assess the transferability of polymorphic SSR loci developed above, cross-species amplifications were conducted in nine other Cyprinidae fish: Leuciscus idus, Rutilus lacustris, Abramis brama orientalis, Phoxinus ujmonensis, Phoxinus brachyurus, and Tinca tinca from Leuciscinae, Gymnodiptychus dybowskii and Diptychus maculates from Schizothoracinae, and Cyprinus carpio from Cyprinidae. All of these species were also distributed in Xinjiang, and 12 individuals were sampled during our investigation. All the amplification systems and procedures were the same as above.

Evaluation of SSR polymorphism and genetic diversity analysis

We evaluated the following genetic parameters for both SSRs and populations using POPGENE version 1.31: the number of alleles (N_A), the number of effective alleles (N_E), the expected heterozygosity (H_E), the observed heterozygosity (H_O), and the genetic distance (Yeh et al., 1999). Polymorphism information content (PIC) was calculated by applying the PIC_CALC software package (version 0.6). A dendrogram was constructed by UPGMA clustering analysis on the basis of genetic distance (Pavlícek et al., 1999). The F-statistic (F_ST) was calculated using the Arlequin software package (version 3.11).

Results and Discussion

Characterization of various SSRs in the genome

A total of 1,839,008 assembled genomic contigs were generated via Illumina for MiSeq^TM 2000 sequencing, and 1373 of these contigs were longer than 1000 bp, as shown in Table 1. From 10,168 sequences, 1686 potential SSRs were identified through MISA. The largest groups of repeat motifs were mononucleotides and dinucleotides (both 38.7%), followed by trinucleotides (11.2%), tetranucleotides (6.4%), pentanucleotides (4.6%), and hexanucleotides (less than 1%) (Figure 1).

geneticsmr-Analysis-genetic-diversity-Tetranucleotide

Figure 1: Distribution to different repeat type classes of gSSRs in Leuciscus leuciscus baicalensis. Mononucleotide (1), Dinucleotide (2), Trinucleotide (3), Tetranucleotide (4), Pentanucleotide (5), Hexanucleotide (6).

Total no. of contigs	Bases in all contigs	No. of large contigs (>1000 bp)	Bases of large contigs	Greatest length	Contig N50	Contig N90	GC percentage
1,839,008	5,099,360 bp	1373	2,085,150 bp	16,606 bp	1446 bp	1065 bp	40.89%

N50: scanned sequences were accumulated from large to small according to length. When the cumulative value was more than 50% of the entire sequence length, the length of the sequence was N50. N90: N90 was determined in the same way as N50. The average lengths of N50 and N90 expressed the stand or fall of splicing sequences more accurately.

Table 1. Reads and assembled contig information for Leuciscus leuciscus baicalensis.

Evaluation of genetic diversity

Taking many factors into account, the use of mononucleotides was abandoned in the design of the primers. In total, 160 pairs of primers (64 from genomic libraries and 96 from EST libraries) were designed and amplified using the DNA from the BW samples, in which three pairs isolated from the genomic libraries and 25 pairs isolated from the EST libraries failed to provide PCR products. The high failure rate of the EST-SSR primers was possibly due to sequencing errors and the presence of introns. Thirty polymorphic SSR markers were identified among the successfully amplified primers, comprising 19 gSSRs and 11 EST-SSRs, which indicated high conservation in the transcribed regions of the various polymorphic regions (31.1 and 15.5%, respectively). The details of the 30 polymorphic SSRs are given in Table 2.

SSR	Primer sequences (5'-3')		GenBank	Fluorescent	SSR motif	Annealing	Product size (bp)
SSR	Primer sequences (5'-3')		accession No.	marker		temperature (°C)
BJZ6	R: GGGCAGCTGTTAGTCTGAGG	F: GGCCAAGTTATGTCTTTGAAATTGC	KX197890	FAM	(AATTCA)5	60	165-200
BJZ71	R:GCGTCTCTGTCTGGTTTTGC	F: ATCTCTTCCCCTCGTCTGCT	KX197891	HEX	(CA)6(CG)6	56	193-209
BJZ78	R: AACTCTGTCCCTCCCGTCAT	F: AGTCCATGTGGTTGAGAGGC	KX197892	TET	(TCT)7	56	121-145
BJZ66	R: CTTCCACCTTAACCAGCCCT	F: TCACCATCCAGGCTTAAACGT	KX197893	FAM	(CAT)6c(AAT)6	56	219-246
BJZ62	R: GTGGAGGATTTGCATTGGGC	F: TGTCAGATGATGGGAGGCAAC	KX197894	FAM	(AGTC)5	60	173-201
BJZ80	R: ATGTGAGGACATCTGCTGCC	F: GGAGCGAATCTGGACTGGAG	KX197895	HEX	(GAA)7	52	226-244
BJZ33	R: CACGCCAAGACATGCTGAAC	F: ACTTCGCTCCCATTTGCTGT	KX197896	TET	(GTCA)6	56	248-280
BJZ34	R: TCCTATGTGGTGATGCCCCT	F: AACACTGCGTGTAGGCTCTG	KX197897	FAM	(GTTG)6	58	271-287
BJZ46	R: ACTGAAGGTGGCAAGCCTTA	F: TGGCACTGACAACCTCATCG	KX197898	FAM	(CAA)7	56	131-146
BJZ88	R: ATCCTAGGTACCACACGGCT	F: TTTATGAAGTGCAGCGGGGT	KX197899	HEX	(AC)10	60	176-194
BJZ89	R: CATCAGCCTGAAGGGGGTTT	F: TTGATCTCGCCGCTGAAACT	KX197900	TAMRA	(GT)10	60	201-219
BJG21	R: CCTGATGCGTTACCTTCG	F: GCAATGCTCTGTTTGGGAT	KX197901	FAM	(TC)10g(CT)12	60	170-200
BJG25	R: CGCAGTGGCAGCATTTAT	F: CGGTTTAGGGTCAGGGTT	KX197902	HEX	(TG)24	58	224-258
BJG13	R: CCACCCAATCCGCATCCT	F: CCCAGCCAAACAACCACC	KX197903	HEX	(CACT)17	60	102-114
BJG20	R: CTCTGATGTGAGTGGGAAG	F: AATCGCCTGTAAGAATGAA	KX197904	FAM	(TA)14	52	129-141
BJG23	R: GGTTGCTGATGGTTTAGAT	F: TCCTCACACAGATTTAGATAGA	KX197905	FAM	(GT)16	56	120-150
BJG27	R: GACAAAAGCGTCTTCCAAAT	F: TGTAAAAGGTTAGGTGATAGCC	KX197906	HEX	(AAT)8(TAT)6	58	170-185
BJG31	R: CCTCACTCCAATGGTCTA	F: GTAATAAACAGGGGAATAAC	KX197907	TAMRA	(GT)15	52	200-260
BJG60	R: GTAGGGTTTACCAGGACACA	F: GAGAGCACGGCAGCAT	KX197908	TAMRA	(GT)9	60	214-244
BJG50	R: GCAAACAGAGCAACGATG	F: GTGAACTCAAACCAGGGG	KX197909	FAM	(CA)12	56	128-158
BJG3	R: CCGTAGACAGAAAATCAACTT	F: GCACAAAACATTCAGCCA	KX197910	FAM	(CTAT)29	60	205-257
BJG51	R: CTTCTGGTATTTCGGTAGC	F: AGTAATCAGGGGAGGAGG	KX197911	HEX	(TTG)8	56	153-168
BJG41	R: TGCTGCGTCAAATGCGT	F: CACCCCTAAACTGGGATGT	KX197912	FAM	(AT)14	54	231-275
BJG54	R: TGATTCCTTCAAATACACCG	F: CCCCTCTCTGCCAACTT	KX197913	HEX	(TTA)8	60	155-185
BJG53	R: AAGAGAAGGAACAGAAAG	F: AAGACGAAAAAGAAGACT	KX197914	FAM	(TC)12	56	268-302
BJG52	R: GTGGTGCGTCACGATTAT	F: TTGTGTGTCTGATTGGTCC	KX197915	HEX	(CA)11	60	200-252
BJG62	R: GAACGAGCAGCAATCAAG	F: ATAGTAACGCCTGTGGTG	KX197916	FAM	(AG)10	60	233-255
BJG57	R: CCTGATGGCGTCGTTACT	F: TCAAATGTTCCCCTGCTG	KX197917	HEX	(AG)12	60	90-116
BJG26	R: CATTTTCAGGTTTTCCCC	F: CCGTTTTAGACACTTTGCTC	KX197918	FAM	(TA)15a(AT)6	58	252-282
BJG28	R: TAATCAAATAAAGGCAGGCT	F: GAACCGTTACATAATCCCAT	KX197919	HEX	(TG)15	58	182-210

Table 2. Characteristics of 30 polymorphic simple sequence repeat (SSR) markers isolated from Leuciscus leuciscus baicalensis.

Genetic diversity was assessed using 30 polymorphic markers in 96 individuals from the three populations. Overall, the number of alleles (N_A) varied from 4 to 27 (with an average of 11.3), the expected heterozygosity was 0.36-0.94 (average 0.75 ± 0.14), and the observed heterozygosity was 0.37-1.00 (average 0.68 ± 0.18). The high values of mean H_O and H_E suggest that there was relatively high heterozygosity. The polymorphism information content (PIC) was 0.31-0.93 (average 0.71 ± 0.15), suggesting high genetic diversity, and these markers were of good quality. Furthermore, the average values of H_E and PIC for the EST-SSR set (0.67 and 0.62, respectively) were lower than those for the genomic SSR set (0.79 and 0.72, respectively) (Table 3), which confirms the hereditary conservation in the transcribed regions and the higher polymorphism of the gSSR marker, and corroborates the previous studies (Zhan et al., 2009; Molina‐Luzόn et al., 2012; Zhang et al., 2014).

	PIC	H_E	H_O	N_E	N_A		PIC	H_E	H_O	N_E	N_A
BJZ6	0.73	0.77	0.65	4.28	7.00	BJG23	0.87	0.89	0.71	8.57	16.00
BJZ71	0.61	0.65	0.59	2.83	8.00	BJG27	0.71	0.76	1.00	4.06	8.00
BJZ78	0.65	0.68	0.65	3.07	9.00	BJG31	0.93	0.94	0.69	15.34	27.00
BJZ66	0.71	0.75	0.67	3.95	11.00	BJG60	0.50	0.57	0.55	2.29	11.00
BJZ62	0.44	0.47	0.43	1.87	7.00	BJG50	0.87	0.89	0.88	8.54	16.00
BJZ80	0.67	0.71	0.69	3.38	7.00	BJG3	0.88	0.90	0.45	9.22	13.00
BJZ33	0.70	0.73	0.53	3.59	9.00	BJG51	0.69	0.73	0.96	3.65	6.00
BJZ34	0.50	0.57	0.65	2.31	6.00	BJG41	0.90	0.91	0.74	10.61	22.00
BJZ46	0.57	0.64	0.54	2.77	6.00	BJG54	0.67	0.70	0.73	3.28	11.00
BJZ88	0.74	0.78	0.67	4.47	9.00	BJG53	0.83	0.85	0.52	6.45	17.00
BJZ89	0.53	0.58	0.37	2.38	6.00	BJG52	0.86	0.88	0.99	7.93	23.00
E-Mean ± SD	0.62 ± 0.10	0.67 ± 0.10	0.59 ± 0.11	3.17 ± 0.84	7.73 ± 1.62	BJG62	0.73	0.76	0.95	4.16	10.00
BJG21	0.70	0.74	0.57	3.86	12.00	BJG57	0.84	0.86	0.75	6.90	13.00
BJG25	0.69	0.73	0.62	3.70	12.00	BJG26	0.84	0.86	0.78	6.97	14.00
BJG13	0.31	0.36	0.39	1.56	4.00	BJG28	0.83	0.85	0.98	6.41	12.00
BJG20	0.81	0.84	0.69	5.87	7.00	g-Mean ± SD	0.76 ± 0.15	0.79 ± 0.14	0.73 ± 0.19	6.28 ± 3.33	13.37 ± 5.87
T-Mean ± SD	0.71 ± 0.15	0.75 ± 0.14	0.68 ± 0.18	5.14 ± 3.07	11.30 ± 5.47

E-Mean ± SD: mean ± SD of EST-SSR markers; g-Mean ± SD: mean ± SD of genomic markers; T-Mean ± SD: mean ± SD of all markers.

Table 3. Averages of PIC, H_E, H_O, N_E, and N_A values of the expressed sequence tag-simple sequence repeat (EST-SSR) and genomic SSR (gSSR) markers of Leuciscus leuciscus baicalensis.

The three populations displayed similar results for PIC and H_E: 0.71, 0.69, and 0.71, and 0.68, 0.69, and 0.68 for the HBH, BEJ, and BW populations, respectively. There were small differences in the N_A values: HBH (8.77), BEJ (8.57), and BW (9.47). Therefore, the three populations had almost the same level of genetic diversity. The details of the diversity parameters for the three populations are shown in Table 4.

	HBH					BEJ					BW
	PIC	H_E	H_O	N_E	N_A	PIC	H_E	H_O	N_E	N_A	PIC	H_E	H_O	N_E	N_A
BJZ6	0.73	0.79	0.71	4.37	6.00	0.74	0.79	0.50	4.36	6.00	0.73	0.76	0.69	3.97	6.00
BJZ71	0.61	0.62	0.68	2.57	5.00	0.63	0.67	0.65	2.93	7.00	0.57	0.66	0.50	2.90	7.00
BJZ78	0.65	0.76	0.77	4.00	8.00	0.63	0.67	0.58	2.88	8.00	0.72	0.62	0.63	2.55	7.00
BJZ66	0.71	0.81	0.71	4.96	9.00	0.60	0.65	0.58	2.77	7.00	0.77	0.76	0.69	4.02	7.00
BJZ62	0.44	0.39	0.30	1.61	5.00	0.44	0.48	0.50	1.88	5.00	0.36	0.51	0.48	2.03	7.00
BJZ80	0.67	0.76	0.78	3.93	7.00	0.58	0.64	0.50	2.67	6.00	0.72	0.72	0.76	3.41	6.00
BJZ33	0.70	0.68	0.59	3.00	7.00	0.66	0.70	0.38	3.22	7.00	0.64	0.76	0.59	4.08	9.00
BJZ34	0.50	0.56	0.52	2.24	4.00	0.42	0.51	0.62	2.00	3.00	0.48	0.62	0.76	2.59	6.00
BJZ46	0.57	0.69	0.57	3.10	6.00	0.58	0.66	0.62	2.81	5.00	0.62	0.61	0.48	2.53	5.00
BJZ88	0.74	0.77	0.63	4.07	8.00	0.65	0.70	0.58	3.14	7.00	0.72	0.80	0.76	4.78	8.00
BJZ89	0.53	0.58	0.19	2.31	5.00	0.54	0.61	0.56	2.48	5.00	0.52	0.58	0.38	2.35	5.00
E-Mean	0.62	0.67	0.59	3.29	6.36	0.59	0.64	0.55	2.83	6.00	0.62	0.67	0.61	3.20	6.64
±SD	±0.10	±0.13	±0.19	±1.05	±1.57	±0.09	±0.09	±0.08	±0.66	±1.41	±0.13	±0.09	±0.13	±0.89	±1.21
BJG21	0.70	0.75	0.61	3.73	10.00	0.70	0.75	0.63	3.79	9.00	0.69	0.74	0.51	3.69	10.00
BJG25	0.69	0.75	0.61	3.77	8.00	0.70	0.75	0.60	3.81	9.00	0.69	0.72	0.64	3.44	9.00
BJG13	0.31	0.31	0.36	1.44	3.00	0.40	0.49	0.62	1.91	4.00	0.28	0.30	0.26	1.42	3.00
BJG20	0.81	0.86	0.61	5.95	7.00	0.80	0.85	0.68	5.76	7.00	0.81	0.81	0.74	5.08	7.00
BJG23	0.87	0.87	0.68	7.00	10.00	0.84	0.88	0.73	7.08	13.00	0.84	0.89	0.71	8.58	13.00
BJG27	0.71	0.73	1.00	3.56	5.00	0.72	0.78	1.00	4.17	6.00	0.67	0.77	1.00	4.17	7.00
BJG31	0.93	0.94	0.58	12.26	18.00	0.90	0.92	0.72	10.68	16.00	0.91	0.95	0.73	16.93	23.00
BJG60	0.50	0.62	0.54	2.58	8.00	0.49	0.56	0.65	2.19	6.00	0.55	0.54	0.50	2.13	8.00
BJG50	0.87	0.90	0.89	8.48	14.00	0.85	0.88	0.88	7.19	13.00	0.87	0.88	0.86	7.93	12.00
BJG3	0.88	0.92	0.50	10.08	13.00	0.84	0.87	0.50	6.94	10.00	0.89	0.90	0.39	8.73	12.00
BJG51	0.66	0.75	0.96	3.73	6.00	0.70	0.75	0.92	3.84	6.00	0.69	0.71	0.98	3.30	6.00
BJG41	0.90	0.93	0.77	11.76	17.00	0.86	0.89	0.73	8.00	14.00	0.91	0.89	0.73	8.60	18.00
BJG54	0.67	0.69	0.67	3.08	9.00	0.64	0.68	0.77	3.03	9.00	0.65	0.72	0.74	3.46	10.00
BJG53	0.83	0.85	0.61	6.01	13.00	0.85	0.88	0.46	7.19	13.00	0.82	0.84	0.50	5.77	15.00
BJG52	0.86	0.86	0.96	6.56	13.00	0.88	0.91	1.00	9.01	16.00	0.83	0.87	1.00	7.03	18.00
BJG62	0.73	0.79	0.93	4.47	8.00	0.72	0.77	0.96	4.12	7.00	0.75	0.75	0.95	3.82	9.00
BJG57	0.84	0.84	0.75	5.72	10.00	0.84	0.87	0.88	7.01	11.00	0.80	0.86	0.67	6.53	10.00
BJG26	0.84	0.87	0.89	6.72	11.00	0.84	0.87	0.77	6.76	11.00	0.84	0.85	0.71	6.33	11.00
BJG28	0.83	0.86	1.00	6.43	10.00	0.82	0.85	1.00	6.06	11.00	0.83	0.85	0.95	6.18	10.00
g-Mean	0.80	0.79	0.73	5.96	10.16	0.79	0.80	0.76	5.71	10.05	0.80	0.78	0.71	5.95	11.11
±SD	±0.12	±0.15	±0.19	±3.00	±3.87	±0.12	±0.12	±0.17	±2.37	±3.52	±0.11	±0.15	±0.21	±3.45	±4.75
T-Mean	0.71	0.75	0.68	4.98	8.77	0.69	0.74	0.69	4.66	8.57	0.71	0.74	0.68	4.95	9.47
±SD	±0.15	±0.15	±0.20	±2.77	±3.68	±0.15	±0.13	±0.17	±2.37	±3.51	±0.16	±0.14	±0.19	±3.09	±4.39

E-Mean ± SD: mean ± SD of EST-SSR markers; g-Mean ± SD: mean ± SD of genomic markers; T-Mean ± SD: mean ± SD of all markers.

Table 4. Details of diversity parameters for the three Leuciscus leuciscus baicalensis populations.

Analysis of cluster and genetic differentiation

Based on the Nei’s genetic distances among the three populations, we constructed an unrooted dendrogram by UPGMA clustering analysis (Figure 2), showing an almost equal level of genetic differentiation among the populations. The three populations were located in continuous bodies of water, so the pelagic eggs were able to flow into the rivers, which led to the flow of genes. Comparatively speaking, the HBH and BW populations resembled each other most closely, possibly as a result of their geographical proximity to each other.

geneticsmr-Analysis-genetic-diversity-populations

Figure 2: Dendrogram of the three populations of Leuciscus leuciscus baicalensis according to the genetic distance using UPGMA clustering analysis.

The F-statistic is an indicat or of genetic differentiation among populations; the F_ST values for the three populations are given in Tables 5 and 6. According to the standard of Wright, the differentiation was defined as high when F_ST > 0.25, moderate when 0.15 < F_ST < 0.25, low when F_ST < 0.15, and absent when F_ST < 0.05. In this study, the F_ST values were all below 0.05, which meant the HBH, BEJ, and BW populations were almost non-differentiated. The pairwise F_ST values between populations were also less than 0.05 with a P-value above 0.05, i.e., there were no significant differences between any two populations (Tables 5 and 6). Details of the analysis of molecular variance (AMOVA) are provided in Table 7; almost all the variation was between individuals (95.81%), and very little variation occurred among populations. This result was consistent with the clustering analysis, which indicated that a high level of gene exchange occurred between the three populations.

Population	F_ST
HBH	0.00175
BEJ	0.00149
BW	0.00148

Table 5. F_ST values for the three populations.

Population	HBH	BEJ	BW
HBH	0
BEJ	0.00227	0
BW	-0.00013	0.00289	0
P > 0.05

Table 6. Pairwise F_ST values between populations.

Source of variation	Sum of squares	Variance component	Percentage of variation
Among populations	20.421	0.00833	0.09
Among individuals in populations	901.215	0.3817	4.1
Within individuals	857	8.92708	95.81
Total	1778.635	9.31711	100

Table 7. Analysis of molecular variance (AMOVA) analysis of Leuciscus leuciscus baicalensis.

Conclusion

In this study, 30 novel high-quality SSR markers were isolated to evaluate the genetic diversity of L. leuciscus baicalensis. The rate of successful amplification, the rate of polymorphism, and genetic diversity were lower in the EST-SSR markers. Among the three populations studied, the parameters N_A, N_E, H_O, H_E, and PIC showed the same level of genetic diversity, and the parameters of genetic distances and F_ST showed equal levels of genetic differentiation. With regard to cross-species transferability, the top three species were R. lacustris, L. idus, and P. ujmonensis.

Polymorphic SSR markers have been widely utilized in diverse areas (including in fish) for genetic research such as stock identification, parentage analysis, linkage map construction, evolutionary relationship analysis, and marker-assisted selection (Zhan et al., 2010; Liang et al., 2015; Wang et al., 2015; Zhang et al., 2016). Compared with gSSRs, EST-SSRs were more efficient in functional gene analysis, such as marker-related growth and immunity in aquatic animals (Zheng et al., 2014; Huang et al., 2015). The high levels of polymorphism and transferability of these novel markers for a number of important Leuciscus species, including L. leuciscus baicalensis, are very important attributes, which could be of vital significance with regard to genetic resource conservation and sustainable use in the future.

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgments

Research supported by the Special Project of the National Science and Technology Fundamental Work of China (grant #2012FY112700).

About the Authors

Corresponding Author

X.F. Ma

College of Fisheries, Huazhong Agricultural University, Wuhan, China

Email:: xufama@mail.hzau.edu.cn

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Molecular analysis of genetic diversity among vine accessions using DNA markers

Abstract

Introduction

Materials and Methods

Results and Discussion

Conclusion

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