Skip to main content
PLOS One logoLink to PLOS One
. 2015 Apr 29;10(4):e0125235. doi: 10.1371/journal.pone.0125235

Development of SSR Markers and Genetic Diversity in White Birch (Betula platyphylla)

Wei Hao 1, Shengji Wang 1, Huajing Liu 1,2, Boru Zhou 1, Xinwang Wang 1,3,*, Tingbo Jiang 1,*
Editor: Niranjan Baisakh4
PMCID: PMC4414481  PMID: 25923698

Abstract

In order to study genetic diversity of white birch (Betula platyphylla), 544 primer pairs were designed based on the genome-wide Solexa sequences. Among them, 215 primer pairs showed polymorphism between five genotypes and 111 primer pairs that presented clear visible bands in genotyping 41 white birch plants that were collected from 6 different geographical regions. A total of 717 alleles were obtained at 111 loci with a range of 2 to 12 alleles per locus. The results of statistic analysis showed that polymorphic frequency of the alleles ranged from 17% to 100% with a mean of 55.85%; polymorphism information content (PIC) of the loci was from 0.09 to 0.58 with a mean of 0.30; and gene diversity between the tested genotypes was from 0.01 to 0.66 with a mean of 0.36. The results also indicated that major allele frequency ranged from 0.39 to 1.00 with an mean of 0.75; expected heterozygosity from 0.22 to 0.54 with a mean of 0.46; observed heterozygosity from 0.02 to 0.95 with a mean of 0.26; Nei's index from 0.21 to 0.54 with a mean of 0.46; and Shannon's Information from 0.26 to 0.87 with a mean of 0.66. The 41 white birch genotypes at the 111 selected SSR loci showed low to moderate similarity (0.025-0.610), indicating complicated genetic diversity among the white birch collections. The UPGMA-based clustering analysis of the allelic constitution of 41 white birch genotypes at 111 SSR loci suggested that the six different geographical regions can be further separated into four clusters at a similarity coefficient of 0.22. Genotypes from Huanren and Liangshui provenances were grouped into Cluster I, genotypes from Xiaobeihu and Qingyuan provenances into Cluster II, genotypes from Finland provenance into Cluster III, and genotypes from Maoershan into Cluster IV. The information provided in this study could help for genetic improvement and germplasm conservation, evaluation and utilization in white birch tree breeding program.

Introduction

Simple sequence repeats (SSRs), or microsatellite DNA, are short tandem repeats (1–6 bp long) of DNA sequence motifs that are widely distributed in eukaryotic organisms genomes [12]. The number of SSR motifs among different species shows polymorphism because of differences in repeated unit numbers [3]. SSRs are PCR-based markers that require low DNA amounts in the amplification of genomic DNA. Since SSR markers are generally co-dominant, multi-allelic, reproducible, and highly polymorphic [46], they have been widely applied in genetic linkage mapping, germplasmic resource investigation, phylogenetic analysis, DNA fingerprinting, and other genetic studies [79]. It has been demonstrated that SSR markers are suitable for studying genetic diversity and relationships between plant species, populations, and individuals [1011].

White birch (Betula platyphylla Suk.), a deciduous broadleaf tree species, is widely distributed in the northeast and northwest of China, in where it plays an important role in timber production [12]. Because of its fast growth and easy regeneration, white birch is a typical pioneer tree as the secondary forest in these regions. In addition, white birch trees have an indispensable ecological role in the colonization of forest lands after harvesting and protection of wild fire damages in north China. They are also valuable for timber industries because of the compact and spotless qualities of wood [13].

Like other trees, white birch breeding takes a long time to develop a new variety by using phenotype-based traditional breeding methods because of its long life cycle. Because DNA sequence polymorphisms are directly associated with genotypes, a marker-assisted selection (MAS) strategy has been proposed and could be used to directly select desired progenies with target genotype. This method has incomparable superior to traditional breeding methods that infer genotypes from the phenotypes.

As aforementioned, SSR markers are among the best biomarkers in plant breeding programs. Therefore, white birch breeders attempted to use different methods to explore SSR markers in white birch genome. For example, Wu et al. [14] obtained 13 SSR markers from the genomic DNA library of B. platyphylla by using a PCR method. Ogyu et al. [15] obtained 184 SSR-contained clones from the SSR-enriched DNA library of B. maximowicziana and tested 15 SSR primer pairs, of which 8 SSR markers were successfully amplified polymorphic fragments. Kulju et al. [16] screened 38 SSR-contained clones from 17,300 clones in the genomic DNA library of B. pendula and developed 23 polymorphic SSR markers. Truong et al. [17] obtained 17 SSR-contained clones from 8,000 clones in the genomic DNA library of B. pubescens and found 3 polymorphic SSR markers. Recently, the expressed sequence tag (EST) has been widely used to develop SSR markers. Wang et al. [18] found 260 SSR motif-contained EST sequences from 2,548 ESTs (10.2%) in B. platyphylla and designed 45 EST-SSR primers that amplified polymorphic fragments in white birch genome. Lu et al. [19] obtained 331 SSR-contained EST from 3,028 EST sequences of B. platyphylla and developed 28 EST-SSR primers that successfully amplified polymorphic fragments.

One of the SSR applications is the genetic linkage mapping. By using 19 SSRs and 145 AFLP markers, Pekkinen et al. [20] built the first genetic linkage map of B. pendula genome. Jiang et al. [21] constructed high density genetic linkage maps in B. platyphylla and B. pendula species using AFLP and RAPD markers. To date, the numbers of SSR markers used for linkage mapping in B. platyphylla are limited. The numbers of SSR markers can saturate a high density genetic map, which is the foundation of cloning important genes of interested agronomic traits in white birch breeding program. High-throughput sequencing technologies make it possible to develop a large number of SSR markers base on the whole genome sequence information. In order to accelerate the process of germplasm evaluation and cultivar/or breeding line identification in white birch breeding program[22], the present study was to develop SSR markers based on white birch genome Solexa sequences and used these markers to genotype 41 white birch plants that were collected from six geographical regions in north China and Finland.

Materials and Methods

No specific permissions were required for these locations/activities in this paper. And we confirm that the field studies did not involve endangered or protected species.

1.1 Materials

Seeds of 41 white birch genotypes were collected from 6 different geographical regions in Heilongjiang and Liaoning provinces in China and Finland (S1 Table) and sown in the greenhouse at the Tree Breeding Base of Northeast Forestry University, Harbin, China. Of them, 36 genotypes were from Huanren (3), Qingyuan (7), Xiaobeihu (7), Maoershan (15), and Liangshui (4) in Heilongjiang and Liaoning provinces, China, and the other five genotypes were imported from Finland. Young leaves were collected from the trees in the growing season and stored at -80°C for DNA extraction.

1.2 DNA Extraction

Total genomic DNA was extracted using Universal Genomic DNA Extraction Kit (TaKaRa, Dalian, China) following the manufacture’s instruction. DNA concentration and quality were checked and quantified using a NanoDrop 2000c Spectrophotometer. The DNA was stored at -20°C for sequencing and PCR analysis.

1.3 Solexa Sequences and SSR Primer Design

Sequencing of white birch genome was implemented by BGI (Shenzhen Company Ltd., Shenzhen, China) using the Solexa next-generation sequencing technology (Illumina GA). The short sequence reads were cleaned and then assembled by using the SOAPdonova software. The genome of B. platyphylla was estimated approximately 440 million base pairs across 28 chromosomes. The clean, assembled sequences were used to search SSRs by using software SSRIT [23]. Repeats containing dimer, trimer, tetramer, pentamer, and hexamer motifs which are longer than 20bp in general were selected for SSR primer design using Primer Premier 5.0 [23] by following standard parameters: target amplicon length of 100–500 bp, annealing temperatures of 50°C—70°C, GC contents of 50%- 70%, and primer size of 18–24 bp. The SSR primer pairs were synthesized at Sangon Biotech (Shanghai, China).

1.4 PCR assay and Detection

In order to detect SSR polymorphism, a feasible PCR condition was optimized. The total reaction mixture of 20 μl included 50 ng DNA, 1.0 μl of 10 μmol forward primer, 1.0 μl of 10 μmol reverse primer, 0.5μl of 10 mmol dNTP, 2 μl of 10× buffer (100 μmol Tris–HCl, 500 mmol KCl, 0.8% Nonidet P40), 2 μl of 25 mmol MgCl2, and 0.2 μl of Taq polymerase (5 U/μl). PCR amplification was performed in an MJ Research PTC-200 thermocycler (MJ Research, MA, USA), starting with an initial denaturation step of 94°C for 4 min, followed by 35 cycles of denaturation at 94°C for 1 min, annealing at appropriate temperature (depending on SSR primers) for 1 min and extension at 72°C for 30 sec, with a final extension step at 72°C for 10 min. The PCR products were subject to electrophoresis on 6% polyacrylamide denaturing gels in 1x TBE buffer and visualized by silver staining.

The PCR products were eluted from the gel using MiniBEST Agarose Gel DNA Extraction Kit Ver.3.0 (TaKaRa, Dalian, China) and cloned into pMD19-T Vector. The recombinant plasmid were transferred into E. coli by using a hot shock method and sequenced by GENEWIZ (Suzhou, China) using M13F (-47) and M13R (-48) primers.

1.5 Statistical Analysis

The visible band of each genotype was recorded as binary data: 1 = present of band and 0 = absent of band. Statistical components, including major allele frequency, polymorphism information content (PIC), gene diversity, observed heterozygosity (Ho), expected heterozygosity (He), Nei's index (1973), and Shannon's Information index, were computed by using the POPGENE (version 1.32) program. In order to generate a dendrogram showing the relationships of genetically diversified samples, cluster analyses were performed using the unweighted pair group method average (UPGMA) method (NTSYS-pc2.11a software) [24]. The dendrogram was visualized with the TreeView 1.6.6 [25].

Results and Discussion

Molecular markers are widely used in plant genetics, breeding, biological diversity analysis, and cultivar identification since they can directly manifest genetic differences at the DNA level. SSR motifs are polymorphic, abundant, and randomly distributed in eukaryotic genomes [1]. Compared to other biomarkers, such as RAPDs and AFLPs, SSR markers are stable, co-dominant, and low cost. Therefore, they have been widely used in genetic analysis and genomic linkage mapping.

High-throughput Solexa sequencing technology has provided an efficient tool to develop SSR markers. In the present study, 544 SSR primer pairs were designed from the white birch genomes (S2 Table) and tested polymorphism among five white birch genotypes. Of them, 215 showed polymorphisms with visible bands, indicating that 39.5% of SSR loci could be used for white birch genotyping. It also suggests that development of SSR markers from the high-throughput whole genome sequences is more efficient than from genomic DNA library and EST sequences, because the SSR markers from whole genome sequences are more wide distributed and then show higher rates of polymorphism. Of the 215 polymorphic loci, 111 solid loci were selected to genotype the 41 white birch genotypes (Table 1). As results, a total of 717 alleles were visualized across these 41 genotypes. The SSR allele numbers varied by loci ranged from 2 (Loci BP-016, BP-022, BP-080, BP-121, and BP-301) to 12 (BP-210). Fig 1 showed the PCR amplification profile of the locus BP-293 across the 41 white birch genotypes.

Table 1. Summary of polymorphism of 111 SSR markers selected from Solexa sequences of white birch genome.

Primer ID Forward/Reverse primer sequences Allele number Annealing temperature / °C Expected fragment size/ bp Allele Frequency Gene Diversity PIC Ho He Nei's (1973) Shannon's Information index
BP-001 GAATGGAGATTGCTTCTCTCAGG 8 60 259 0.6341 0.464 0.3564 0.3659 0.4384 0.4331 0.6246
GCCCCCAAATGTCCCAAATCCC 64
BP-002 GGGTATGGGGATGAAATGGTTGG 3 62 192 0.9268 0.1356 0.1264 0.0732 0.5035 0.4973 0.6905
CCAAATAAGCCCTAAGCCCAC 60
BP-003 CGCGCTCTGATTGGACCACGCTC 7 67 235 0.8049 0.3141 0.2648 0.1707 0.4914 0.4854 0.6785
CTGACCCTAACCCCAACCCTGAG 66
BP-005 CAAAATGTCGGAGGCAGTGTCG 7 62 211 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
CCCGTTGCAAACCCTAAATCAC 60
BP-008 GCACCTTTCGCAAGGAGAAACCGG 8 65 363 0.7805 0.3427 0.2839 0.2195 0.4818 0.4759 0.6689
CTACTGTGGCCCATCAGCATTAGC 64
BP-009 CAACGGCAATGACCTAGCGATACG 7 64 185 0.7317 0.3926 0.3155 0.2683 0.4697 0.464 0.6567
CTTGTGTTACGAGGCCATAAGCC 62
BP-010 GTCTGATAGTCATCGATCGAGCGAGGTTCGCTCTCACCTCCATCAAAGG 9 64 219 0.8049 0.3141 0.2648 0.1951 0.4869 0.481 0.674
64
BP-012 GGCTTACACCAAACCACGTTGCAG 5 64 229 0.7317 0.3926 0.3155 0.2683 0.4697 0.464 0.6567
CTTTTCTCAGTCTCAGAGTGGGG 62
BP-014 GACCGATTTAAACCCTCGCAGTG 8 62 294 0.5366 0.5378 0.4366 0.561 0.3975 0.3926 0.5816
CAGCCATGTTTGCCTCATTCCATC 62
BP-015 CGGTTGGTAGGGTAACCAAAG 8 60 198 0.8293 0.2832 0.2431 0.0976 0.5014 0.4952 0.6884
CTGTCTCTCAAACCCCTGTTTC 60
BP-016 GCTTCATTTCCTGGGACCTGATG 2 62 277 0.7317 0.4212 0.3743 0.9512 0.2867 0.2832 0.457
CCTTCTTCAAGGATCACGGTAGACC 64
BP-019 GCTTGGTTCGCTTGTTCGTCCATG 4 64 335 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
CATTCCGATCCGTTTCTCCCACC 64
BP-022 GCTGGTGGACAACGATGGTTGCAG 2 65 219 0.5122 0.5889 0.5069 0.122 0.4986 0.4926 0.6857
GTGAAGCGAGAGAACATGGCACC 64
BP-028 GTTCTGAGTCTTGGGTAGTGGTG 3 62 190 0.7561 0.3795 0.3237 0.2439 0.5056 0.4994 0.766
CCTTCAGTCCAACAAACCCTTC 60
BP-029 GAGCCATGGATTCGTTGGTATCG 9 62 188 0.8293 0.2832 0.2431 0.1707 0.4914 0.4854 0.6785
GCCTCACCATATCTTCACTCTCC 62
BP-030 GATGAGGAGTAGAGAAAGCTCGG 9 62 189 0.561 0.4926 0.3713 0.3659 0.4384 0.4331 0.6246
CGCGAAGGAGAGTTAACTGTGAG 62
BP-034 GGGAAAGGGACAAGTATGAGCTTG 5 62 259 0.4878 0.6163 0.5401 0.1951 0.4869 0.481 0.674
GAAAAAGAGAGGGTGGGGGGTTTC 64
BP-041 GTATGAAGTGACTGGATGGGCAG 8 62 285 0.9268 0.1356 0.1264 0.0976 0.505 0.4988 0.692
CCCATCTCCATCTCATTTGCAG 60
BP-044 CCCTCACACGAAGCACCATTTAG 8 62 205 0.7317 0.3926 0.3155 0.3659 0.4553 0.4497 0.642
CAGACACTCCGTCCATTCACAAC 62
BP-050 CCCCAATCGAATGGAGAGAAAGAG 5 62 191 0.7805 0.3427 0.2839 0.2195 0.4818 0.4759 0.6689
CTCTACACCCAACCAGTTCTTCCTC 64
BP-053 GGCATGGCTCTTGTTTGTGCAG 9 62 251 0.9268 0.1356 0.1264 0.122 0.4986 0.4926 0.6857
CAGGGATTCTGAAAAGTGGTCC 60
BP-061 CGAGTCTCAGACAGACAGGAAGAG 8 64 206 0.7073 0.414 0.3283 0.2927 0.4628 0.4572 0.6497
GTGAACTTGGGAAGTCACCCGTC 64
BP-063 CACGTGCAGTGGATCGATAATC 8 60 134 0.9024 0.1761 0.1606 0.0732 0.5035 0.4973 0.6905
GATCCACAGAGAGAATTCAGGC 60
BP-065 GAGGATCCAATGCGGGAATGAAG 6 62 224 0.6829 0.4688 0.4076 0.3171 0.542 0.5354 0.8717
CCCCAAGGACTGTCTTTGGTGAC 64
BP-066 GGGTTTTTATGATGGGTTCGGG 3 60 213 0.8293 0.2832 0.2431 0.2439 0.4761 0.4703 0.6631
GGAGTACATCTGGGTGCCCAATCC 65
BP-067 GAGCCTGAGAGATGATTTGCAG 3 60 160 0.7317 0.3926 0.3155 0.1707 0.4914 0.4854 0.6785
CTGGAAAAATCCAACCCCACCG 62
BP-069 GGATTGAGGGAATGCGGGATTGAG 7 64 191 0.3902 0.6591 0.5849 0.2683 0.4697 0.464 0.6567
CAATGGGGTGATAGTTTGAGAGGG 62
BP-071 GCTCAACTCTGGCGGAACCGAACC 9 67 284 0.561 0.5318 0.4349 0.4146 0.4818 0.4759 0.6689
CCCGTCTAAACTCGGCGATGTTCTC 65
BP-072 GCCGAACATGAAACCGTACCTG 3 62 291 0.8537 0.2499 0.2186 1 0.2529 0.2499 0.4163
CCATGTTTGGTTCCCGAGAAACC 62
BP-073 GGCTTACTCGGGCGCCATGCTTGAG 3 68 178 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
GGTCCCTTAGGGCGTCTCCTCAGC 69
BP-075 GCTTGAGTGCCACGAATTTGTCAC 5 62 221 0.9268 0.1356 0.1264 0.0488 0.505 0.4988 0.692
GGGATGGTAGTTTGAGGGATCTG 62
BP-076 GAAAGGGGAAAGGGAGTTGGGGATC 8 65 210 0.7073 0.414 0.3283 0.2927 0.4628 0.4572 0.6497
GTCCCAAGCATTATTGGCGGTGGC 65
BP-078 GAACCTCAATCCATCGCATACC 7 60 182 0.7073 0.414 0.3283 0.7073 0.2529 0.2499 0.4163
GTCTTGAAGGCGAAACCACCTC 62
BP-079 GTTGTTGAGCGTCTCGAACTTGAG 5 62 302 0.7561 0.3688 0.3008 0.3415 0.4472 0.4417 0.6337
CGCGAAGTTTGACTAAGACCTCTC 62
BP-080 CTGGTCAGAGGATCAGATGGTG 2 62 249 0.8293 0.2903 0.26 0.1707 0.5191 0.5128 0.7799
CGGCCAGAGTTCATCTGATTTG 60
BP-081 GAATCCCACAGTTTCTCCGGTTG 9 62 236 0.8537 0.2499 0.2186 0.1951 0.4869 0.481 0.674
GCTGTTCTTGAATCTTGACCAGGC 62
BP-085 CCCAAAGAAAAGACCTCCGCAGTG 5 64 261 0.8049 0.3141 0.2648 0.1951 0.4869 0.481 0.674
GTTTGCTCGTGAGAGGAACATACC 62
BP-089 GAAGTTGGCCATGGCCATGAAAG 8 62 167 0.8293 0.2903 0.26 0.1951 0.5014 0.4952 0.6884
CTCCTTGTTCCTCCTCCTCATTG 62
BP-097 CTCCCATGAGAATCTCTGCACTG 4 62 175 0.8537 0.2594 0.242 0.122 0.5059 0.4997 0.6928
GCGTGTTATTGGGAGAAAAGGAGC 62
BP-098 CACAGAATGCTCCTTTGATGCGAC 8 62 201 0.7317 0.3926 0.3155 0.1951 0.4869 0.481 0.674
CGAGAGTTAGTGATGGAACGAAGC 62
BP-102 CTCAGCAACCATACAGGAGGTAC 8 62 141 0.7561 0.3688 0.3008 0.2195 0.4914 0.4854 0.6785
CAGAAGCCGAAAGAAAGCGTAG 60
BP-110 GAGCGAGATTTGGTGGTCATACC 7 62 177 0.6098 0.4759 0.3627 0.3902 0.4291 0.4239 0.6149
GTGGAGTAATGCCCACCTTATGC 62
BP-111 GGCCAGGAGCAAGAAGAGAGAAAG 8 64 118 0.439 0.6496 0.5765 0.4146 0.4553 0.4497 0.642
CTTCCCACTTCCCACATCCTCTTC 64
BP-113 CACACTGCTGCCTGA 6 54 168 0.5122 0.4997 0.3749 0.5122 0.3734 0.3688 0.5555
TCATAAAACCCTCAAAGAAT 50
BP-115 TCTACGCTGTGACCAGTC 4 57 187 0.561 0.4926 0.3713 0.439 0.4086 0.4036 0.5934
AGAATCCTAGCCTTTTCAAT 52
BP-116 AATGCAGCATCTCTTACC 8 53 139 0.9024 0.1761 0.1606 0.122 0.4986 0.4926 0.6857
CACGCAATAATATGGAAA 48
BP-121 CCTTGTGTACTTGAGTAGTGC 2 54 152 0.4878 0.5449 0.4406 0.5122 0.4432 0.4378 0.7364
TTGATCCCACCAGTTTATTGC 54
BP-123 TCTCACCAAACCACTCACTCA 3 58 215 0.9512 0.0928 0.0885 0.0488 0.505 0.4988 0.692
AAGAGCGTGGCAATGAACTC 58
BP-124 CAGACGACAAAGCAAGCTGA 5 58 213 0.8049 0.3141 0.2648 0.1707 0.4914 0.4854 0.6785
CATGCTCACATACAAGGCAAA 56
BP-127 GAGAGAACCAAAACAGTAGACAGAGA 6 60 168 1 0.0092 0.0902 0.0488 0.505 0.4988 0.692
GGCCTGTTCTTGATGACGAT 58
BP-128 GGGGGTTGCTCTTCATTTTT 3 56 222 0.6829 0.4593 0.3895 0.9024 0.2168 0.2142 0.3708
GGTTTCCTCGTCGGTTATGA 58
BP-130 GTTAAGAAGGTGCGCCAGTC 5 60 254 0.7317 0.414 0.3597 0.2683 0.5276 0.5211 0.8287
ACTAACCGCGCATAAACTGC 58
BP-207 CAGCCTTCCTGCCTGCATGTGTG 9 66 167 0.8293 0.2832 0.2431 0.1707 0.4914 0.4854 0.6785
CGAAGTCAGTTGTCAGCTTGTGG 62
BP-208 GAGCTAGAGAGATGGGTGTGGCAG 7 65 199 0.6829 0.4331 0.3393 0.2927 0.4628 0.4572 0.6497
CTCGTAACCAGTAACGTACCCACG 64
BP-210 CCCTCTCCCCATGGTAATTGCATG 12 64 170 0.561 0.4926 0.3713 0.439 0.4086 0.4036 0.5934
GGAGCCTCAAGGCAAGGTAGCTTC 65
BP-212 CACGAGAGAGATCACGCTTTCCC 10 64 195 0.9024 0.1761 0.1606 0.0732 0.5035 0.4973 0.6905
CCACCGCCAGAAACCCTTTGATC 64
BP-213 CCATTGCTCTCTGAGATAAGGG 5 60 148 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
GCTCTAACGCTCTCTGACAGTTAC 62
BP-214 CCAAAGCGAAGATGCTCACCGCTTG 7 65 282 0.6829 0.4331 0.3393 0.3171 0.4553 0.4497 0.642
CTGTAGGGTTCAAGGGGCGAGAC 66
BP-215 GCTACGATGGTGGTGGTTGGGTGG 9 67 191 0.8293 0.2832 0.2431 0.1707 0.4914 0.4854 0.6785
CCTCTCTCTCTCTCTCCCTCTCTC 65
BP-219 GAGGAGAAAAGGGGAATTTGCTGG 7 62 194 0.6098 0.5366 0.4674 0.3659 0.4818 0.4759 0.6689
CTTCCTCCATGAATGAACGTCCC 62
BP-226 GAGCTCCCAAGCATAACCGATCCTG 9 65 204 0.6585 0.4497 0.3486 0.3415 0.4472 0.4417 0.6337
CCCTACACACCATACTCTCCCTCTC 65
BP-229 GTTGAAGTTCGGGCAGACATAC 8 60 259 0.8293 0.2832 0.2431 0.1707 0.4914 0.4854 0.6785
GACACAGCTGCCAAGCCTTATG 62
BP-233 GAGAGAGAGGCGGCTCATGAATG 9 64 185 0.7317 0.3926 0.3155 0.2683 0.4697 0.464 0.6567
GTGACCGGATGAGTTTTCAGTG 60
BP-235 GAGAGAAAGTGTGGTGACCGTG 6 62 197 0.439 0.6425 0.5677 0.5366 0.3857 0.381 0.5689
CCATAACTACTAACACCCACCG 60
BP-236 GTCTTTCCCGGCGGAACACAGG 5 66 261 0.5854 0.4854 0.3676 0.5854 0.3327 0.3287 0.5104
CTGTCGGCTCTGGGATACGACGC 67
BP-237 GAGAGCTAACGCACAGTCGGAGAG 8 65 171 0.7317 0.4045 0.3401 0.0244 0.5059 0.4997 0.6928
GGGTGGAAGGTGGGAAGAGGAATAC 65
BP-244 GCCTAACAGTGTGGGTATGAAGC 5 62 184 0.9268 0.1356 0.1264 0.0732 0.5035 0.4973 0.6905
GACAAAGCACTCCACCATAACC 60
BP-245 CTTAAGTGCACAGTTGCACGCAC 8 62 208 0.9512 0.094 0.0918 0.0976 0.5062 0.5 0.6931
CGATTCCTTTCTCTCTCCCTCC 62
BP-248 GAACGCCATGCATATTTCGAGG 9 60 203 0.878 0.2142 0.1912 0.122 0.4986 0.4926 0.6857
GTGGGGATGTAACATCTACGTG 60
BP-249 GATAATAGGTTAGAGCTCGAGGGG 4 62 274 0.5854 0.4854 0.3676 0.4146 0.4553 0.4497 0.642
GTGAACATGTTCTACTTCGGCGG 62
BP-250 GCAGAGAAGGAGAATTGAGGTC 8 60 182 0.7561 0.3688 0.3008 0.2195 0.4914 0.4854 0.6785
GTCCTTGAGAGTCATCGTGTTC 60
BP-251 GTAAATGACACCACTGTGGGCATG 6 63 116 0.7073 0.414 0.3283 0.3415 0.4472 0.4417 0.6337
CATTGGGTTGTCCTGAGTACCTC 62
BP-253 GTCTTTCCCTTTAAGGCGGAGC 9 62 189 0.7073 0.414 0.3283 0.3659 0.4553 0.4497 0.642
GAGCCTGAATCGCTAACGAACC 62
BP-254 GTACTTCACAGGCCAAGAGAGAG 5 62 300 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
GATCGATGTTACTAGACAGGCCC 62
BP-257 GGTGGAAATTGCAGGGGTTTTG 8 60 200 0.8293 0.2832 0.2431 0.1707 0.4986 0.4926 0.6857
CGCATGCATGCATGCATTAGTG 60
BP-258 GGTAGTAGCGTCAGTGTGAGAATG 7 62 246 0.9268 0.1356 0.1264 0.0488 0.5062 0.5 0.6931
CTCATCTGCCTCCTTCACTGCTTC 64
BP-259 GAGAGTGGGGACGTACATCAATAG 7 62 248 0.8049 0.3141 0.2648 0.2195 0.4914 0.4854 0.6785
CAGACCCGAAATCCCGAAACTATC 62
BP-268 GTCAAGCTCAAGAGATCCCTTG 8 60 304 0.7561 0.3688 0.3008 0.2683 0.4697 0.464 0.6567
GTTTCTGTCGGCAAGGAAAAGG 60
BP-269 GAGAGGTCTTTGGGTCAAGGAAG 7 62 244 0.6829 0.4331 0.3393 0.3659 0.4553 0.4497 0.642
GTTCCTCGGCTATGAACCAAAGC 62
BP-275 CAATGATGAAGCCCTAGCGACC 5 62 162 0.9756 0.0476 0.0465 0.0732 0.5059 0.4997 0.6928
GTCAGGGGTGGGAGTTTACTTAC 62
BP-276 CATTAATGGGTTTGGGCAGGCAC 5 62 211 0.7805 0.3427 0.2839 0.1463 0.5014 0.4952 0.6884
CTAAGGAGGCATCTTATGGGTCC 62
BP-279 GTCGGTGTAGGGCGACTGAGATATG 8 65 126 0.878 0.2189 0.2033 0.1463 0.4953 0.4893 0.6824
CCTCTCCCCCATTTCGTCTGAAACC 65
BP-282 CATTCCGCGTACTAAACGAGTTC 5 60 251 0.7073 0.414 0.3283 0.2439 0.4761 0.4703 0.6631
CATACGGAATATGAGCAACGGCG 62
BP-287 CAAACCCAACTAATCCTCGCG 7 60 275 0.878 0.2142 0.1912 0.122 0.4986 0.4926 0.6857
CGCGTACTGGTTTGAATCCAGC 62
BP-292 GCGTTTACACAGAGAGAGAGAG 6 60 219 0.9024 0.1761 0.1606 0.0976 0.5014 0.4952 0.6884
CTTCTGTCTCTCACAGGTACACG 62
BP-293 GCGAGAGGGAAAGTACACGAAAG 7 62 174 0.6829 0.4331 0.3393 0.3171 0.4553 0.4497 0.642
GGTAGATCCCAAAGGTCTCTCTC 62
BP-296 GAGAGAGATTGCAGGGGGGAGAAG 8 65 205 0.8049 0.3141 0.2648 0.1463 0.4953 0.4893 0.6824
CCACTTCCCCCCATTTTCCCATCTC 65
BP-298 CTATGGCGCACTCAAATCCTCATC 6 62 239 0.9024 0.1761 0.1606 0.0976 0.5014 0.4952 0.6884
CACTTTGTGTGAAAGGCGCTTGG 62
BP-299 CACCACCGAATGCCGTCGAAATCTC 8 65 154 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
GTGGCGTATTCCGGCGGTAGGTTTC 67
BP-300 CAGCTCAAGGACACAGCAACCAG 5 64 367 0.6829 0.4331 0.3393 0.3171 0.4553 0.4497 0.642
CAAGGGGGTGTTTCACAGCCGATC 65
BP-301 GCGGGAACGGTTATCAGAATTCG 2 62 146 0.5366 0.5544 0.462 0.4634 0.495 0.489 0.8194
GGATTTCGCCTTCTTTGAACCGC 62
BP-304 GCTCTAACGAAACCCGCCGAAAG 5 64 188 0.5122 0.4997 0.3749 0.439 0.4086 0.4036 0.5934
CTCCACCTCATCTTACCATTGGC 62
BP-305 GCTGGCTTTGTGAACCCATGTG 6 62 227 0.7317 0.3926 0.3155 0.2927 0.4628 0.4572 0.6497
GAGGTCTTGGCGCTCCAGAAAC 64
BP-306 CATGCTCCAAGAACACACCTTG 5 60 248 0.7805 0.3427 0.2839 0.2195 0.4818 0.4759 0.6689
CTGAACTAGACTCCGGGTTTCTC 62
BP-309 GCTTGGCAGATGACACTTGAAG 9 60 185 0.7805 0.3427 0.2839 0.2195 0.4818 0.4759 0.6689
GTGCAGACACTTGCATGGGAATG 62
BP-311 GCGAAGAAGATAGCAAGAACCG 8 60 220 0.9268 0.1356 0.1264 0.0732 0.5035 0.4973 0.6905
GAACCCCTGAAAGCTCTGTGTTG 62
BP-313 GAAGGTTGAAACCTTCAGCACC 4 60 208 0.6829 0.4331 0.3393 0.2927 0.4761 0.4703 0.6631
CCGGATATGGAAAGAACAGCAG 60
BP-316 CAGGTTGGGAGAATAGATCGGAG 7 62 192 0.6585 0.4783 0.4015 0.6829 0.2701 0.2668 0.4372
CTCTACATGCCACGTGTTCTCTC 62
BP-319 GAAAGAAAGCACAGAGGAGCAC 7 60 195 0.439 0.6496 0.5765 0.4878 0.3857 0.381 0.5689
GGCTGCCAACAAACAGTACTAC 60
BP-320 GGTGTTGGGTCTCATGCAAATC 7 60 168 0.878 0.2142 0.1912 0.122 0.4986 0.4926 0.6857
CCCTACCAGATCTTCAAATGGC 60
BP-325 GAGGAGGATGTAAGCGAGGTAG 7 62 181 0.9512 0.0928 0.0885 0.0488 0.5062 0.5 0.6931
CTGGACGATGAGAAGGACAACC 62
BP-329 CTGAAGACCCTCCGATGCTTAAG 10 62 150 0.6098 0.4759 0.3627 0.5366 0.3604 0.356 0.5413
CCATTTGACGAGGACTTCTGGAC 62
BP-337 CCACAACGATGTAGGCATGAGAG 6 62 187 0.8293 0.2832 0.2431 0.2439 0.4761 0.4703 0.6631
GTTTCCTTCCCATGCTGACTCTG 62
BP-338 CACTTGTGCCCGATAACTCAAG 6 60 168 0.6585 0.4914 0.4259 0.2927 0.4869 0.481 0.674
GCCACGATTTGGTCGTTCAAAC 60
BP-344 GTGGATGCGGTTATTGGCCATATC 7 62 161 0.5122 0.4997 0.3749 0.4878 0.4086 0.4036 0.5934
GATATGGCCAATAACCGCATCCAC 62
BP-346 GAAAGCATGAGACCCGTCTT 6 58 161 0.6585 0.4497 0.3486 0.3415 0.4472 0.4417 0.6337
AACCTAAACAGCCTGCCAAA 56
BP-384 GCGACACACCCTACCATCTT 5 60 219 0.8537 0.2499 0.2186 0.1463 0.4953 0.4893 0.6824
GGTGCACTTGCAGATGTGAT 58
BP-389 TCGGATTGGTGGGTCTATTT 7 56 190 0.4146 0.646 0.5706 0.3171 0.4553 0.4497 0.642
CGAAACCCCTTTGATGAGTT 56
AF310847 CAGTGTTTGGACGGTGAGAA 6 58 209 0.6098 0.4759 0.3627 0.3902 0.4291 0.4239 0.6149
CGGGTGAAGTAGACGGAACT 60
AF310856 ACGCTTTCTTGATGTCAGCC 9 58 189 0.8293 0.2832 0.2431 0.1707 0.4914 0.4854 0.6785
TCACCAAGTTCCTGGTGGAT 58
AF310866 GGCCAACAGATATAAAACGACG 6 58 301 0.6341 0.464 0.3564 0.3659 0.4384 0.4331 0.6246
TTTTAAATGCCCACCTTCCC 56

The information of polymorphism of 111 SSR markers selected from Solexa sequences of white birch genome included primer ID, forward/reverse primer sequences, allele number, annealing temperature, expected fragment size, allele frequency, gene diversity, PIC, Ho,He, Nei's (1973), Shannon's information index.

Note: PIC, Polymorphism Information Content; HO, observed heterozygosity; HE, expected heterozygosity.

Fig 1. An example of SSR variation at the BP-293 locus across 41 white birch genotypes.

Fig 1

M, DL2000 DNA ladder Marker; 1–41, each represents one of the 41 white birch plants.

The polymorphic rates of 111 primer pairs across the 41 white birch genotypes ranged from 17% to 100% with an average of 55.85%. Eleven loci including BP-016, BP-019, BP-022, BP-028, BP-044, BP-065, BP-080, BP-097, BP-224, BP-250 and BP-301 presented 100% polymorphism, but AF310866 showed the lowest rate of polymorphism (17%). In this study, 111 selected polymorphic SSR loci amplified an average of 6.46 alleles per locus, which was higher than that reported by Wu et al.[14] (4.69 alleles per locus) and close to Kulju et al.[16]. The polymorphism information content (PIC) is determined by both allele numbers and allele frequency distribution and can be used to evaluate the variation of SSR alleles [26]. The results in this study showed that the 111 loci had low to moderate PIC, ranged from 0.09 (BP-127) to 0.58 (BP-069) with a mean of 0.30 (Table 1). Similarly, these SSR loci showed low to moderate gene diversity in a range of 0.01 (BP-127) to 0.66 (BP-069) with a mean of 0.36. The low to moderate PIC (0.30) and gene diversity (0.36) indicated that white birch genotypes from the six geographical locations had a lower genetic variation. Among the 111 SSR loci, locus BP-069 had the highest PIC (0.58) and gene diversity (0.66), which suggested that this marker can be used to differentiate most white birch genotypes in Betula breeding programs. In contrast, locus BP-127 had the lowest PIC (0.09) and gene diversity (0.01), indicating lower polymorphism and less utilization in the Betula cultivar identification. In addition, some other statistical analyses in the present study also reflected similar observations, higher major allele frequencies in a range from 0.39 to 1.00 with an average of 0.75, expected heterozygosity (He) from 0.22 to 0.54 with a mean of 0.46, observed heterozygosity (Ho) from 0.02 to 0.95 with a mean of 0.26. In addition, statistical analysis showed that Nei's index from 0.21 to 0.54 with a mean of 0.46 and Shannon's Information index from 0.26 to 0.87 with a mean of 0.66, indicating a moderate genetic distance among the 41 white birch genotypes. These results also were reflected by the genetic diversity analysis below.

To verify the genetic basis of sequence length variation, The PCR products were re-sequenced. The alignment profile of multiple sequences of BP-293 locus was illustrated in Fig 2. Sequence lengths ranged from 172 bp to 176 bp and the numbers of the SSR motifs ranged from 8 to 10. The results indicate that the PCR truly amplified the targets containing the expected SSR motif.

Fig 2. Multiple sequence alignment of BP-293 showing the position of SSR motifs, and expansion and contraction of the motif.

Fig 2

Genetic diversity is a result of gene evolution in plant species [27] and becomes a foundation of the genetic improvement of species. Analyses of genetic diversity by using molecular markers could provide better understanding of genetic background of white birch cultivars. The results of the present study indicated that the white birch trees from six geographical locations had low to moderate similarity (0.025–0.610) and could be further separated into four clusters at a similarity coefficient of 0.22 (Fig 3). Genotypes from Huanren and Liangshui were closely related and grouped into the cluster I, and the genotypes from Xiaobeihu and Qingyuan into cluster II. Genotypes from Finland, and Maoershan were apparently different from each other and from the other groups as well, and grouped into the clusters III and IV respectively. The clusters of genotypes were apparently agreed with their provenances, suggested that the SSR primers used in this study can effectively distinguish white birch germplasm. The genetic relationships between these genotypes might provide useful information for genetic improvement and germplasm conservation, evaluation and utilization in white birch tree breeding program.

Fig 3. The unweighted pair group method average (UPGMA) based dendrogram of 41 white birch genotypes from six geographical locations, based on their allelic constitution at 111 SSR loci. H1-H3, Huanren provenance, China; L1-L4, Liangshui provenance, China; X1-X7, Xiaobeihu provenance, China; Q1-Q7, Qingyuan provenance, China; M1-M15, Maoershan provenance, China; F1-F5, Finland provenance, Finland.

Fig 3

Supporting Information

S1 Table. The tested white birch materials for SSR analysis.

The information of tested white birch materials for SSR analysis.

(DOCX)

S2 Table. Information of 544 SSR primer pairs.

The details of 544 SSR primer pairs included probe accessions, primer ID, repeat motif, forward/reverse primer sequence, annealing temperature (Tm), expected product size, observed fragment sizes in genotypes 1–5, number of alleles obtained.

(DOCX)

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by National High-Tech Research and Development Program of China (2011AA100202, 2013AA102701) and The Innovation Project of State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University.

References

  • 1. Tautz D. (1989) Hypervariabflity of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Research 17: 6463–6471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S. (2001) Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Research 11: 1441–1452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Xu JS, Wu YT, Ye SJ, Wang L, Feng YZ. (2012) SSR primer screening and assessment on pear germplasm resources. Journal of Central South University of Forestry & Technology 32: 80–85. [Google Scholar]
  • 4. Birren B, Lai E. (1987) Nonmammalian Genomic Analysis A Practical Guide. Academic Press, San Diego, pp 75–134. [Google Scholar]
  • 5. Powell W, Machray GC, Provan J. (1996) Polymorphism revealed by simple sequence repeats. Trends in Plant Science 1: 215–222. [Google Scholar]
  • 6. Feingold S, Lloyd J, Norero N, Bonierbale M, Lorenzen J. (2005) Mapping and characterization of new EST-derived microsatellites for potato (Solanum tuberosum L.). Theoretical and Applied Genetics 111, 456–466. [DOI] [PubMed] [Google Scholar]
  • 7. Liu ZW, Biyashev RM, Maroof MAS. (1996) Development of simple sequence repeat DNA markers and their integration into a barley linkage map. Theoretical Applied Genetics 93: 869–876. doi: 10.1007/BF00224088 [DOI] [PubMed] [Google Scholar]
  • 8. Pérez F, Ortiz J, Zhinaula M, Gonzabay C, Calderón J, Volckaert F. (2005) Development of EST-SSR markers by data mining in three species of shrimp: Litopenaeus vannamei, Litopenaeus stylirostris, and Trachypenaeus birdy . Marine Biotechnology 7: 554–569. [DOI] [PubMed] [Google Scholar]
  • 9. Weng Y, Azhaguvel P, Michels GJ, Rudd JC. (2007) Cross-species transferability of microsatellite markers from six aphid (Hemiptera: Aphididae) species and their use for evaluating biotypic diversity in two cereal aphids. Insect Molecular Biology 16: 613–622. [DOI] [PubMed] [Google Scholar]
  • 10. Kostova A, Todorovska E, Christov N, Hristov K, Atanassov A. (2006) Assessment of genetic variability induced by chemical mutagenesis in elite maize germplasm via SSR markers. Journal of Crop Improvement 16: 37–48. [Google Scholar]
  • 11. Tu M, Lu BY, Zhu YY, Wang YY. (2007). Abundant within-varietal genetic diversity in rice germplasm from Yunnan province of China revealed by SSR fingerprints. Biochemical Genetics 45: 789–801. [DOI] [PubMed] [Google Scholar]
  • 12. Wang QY, Zhang JR, Yang CP. (2007) Phenotypic variation in wood fiber properties and genetic diversity in molecular marker among the populations of Betula platyphylla . Scientia Silvae Sinicae 43: 37–42. [Google Scholar]
  • 13. Li P, Fang GP, Sun CZ. (1995) Wood characteristics of pulpwood. Chemistry and Industry of Forest Products 15 (Suppl.): 13–18. [Google Scholar]
  • 14. Wu B, Lian C, Hogetsu T. (2002) Development of microsatellite markers in white birch (Betula platyphylla var. japonica). Molecular Ecology Notes 2: 413–415. [Google Scholar]
  • 15. Ogyu K, Tsuda Y, Sugaya T, Yoshimaru H, Ide Y. (2003) Identification and characterization of microsatellite loci in Betula maximowicziana Regel. Molecular Ecology Notes 3: 268–269. [Google Scholar]
  • 16. Kulju K.KM, Pekkinen M, Varvio S. (2004) Twenty-three microsatellite primer pairs for Betula pendula (Betulaceae). Molecular Ecology Notes 4: 471–473. [Google Scholar]
  • 17. Truong C, Palmé A, Felber F, Naciri-Graven Y. (2005) Isolation and characterization of microsatellite markers in the tetraploid birch, Betula pubescens ssp. tortuosa . Molecular Ecology Notes 5: 96–98. [Google Scholar]
  • 18. Wang YM, Wei ZG, Yang CP. (2008) Data mining for SSRs in ESTs and EST-SSR marker development in Betula platyphylla . Scientia Silvae Sinicae 44: 78–84. [Google Scholar]
  • 19. Lu YQ, Li HY, Jia Q, Huang HH, Tong ZK. (2011) Identification of SSR loci in Betula luminifera using birch EST data. Journal of Forestry Research 22: 201–204. [Google Scholar]
  • 20. Pekkinen M, Varvio S, Kulju KK, Kärkkäinen H, Smolander S, Viherä-Aarnio A, et al. (2005) Linkage map of birch, Betula pendula Roth, based on microsatellites and amplified fragment length polymorphisms. Genome 48: 619–627. [DOI] [PubMed] [Google Scholar]
  • 21. Jiang TB, Zhou BR, Gao FL, Guo BZ. (2011) Genetic linkage maps of white birches (Betula platyphylla Suk. and B. pendula Roth) based on RAPD and AFLP markers. Molecular Breeding 27: 347–356. [Google Scholar]
  • 22. Li JW. (2011) Application of SSR marker technology in potato genetic breeding. China Vegetables 20: 1–8. [Google Scholar]
  • 23. Tu DP, Wei ZW, Wu ZN, Lei YF, Zhang D, Qiu WW. (2011) Distribution characteristics and marker exploitation of EST-SSRs in Medicago truncatula . Pratacultural Science 28:746–752. [Google Scholar]
  • 24. Rohlf F.J. (1997) NTSYS-pc: Numerical taxonomy and Multivariate Analysis System, version 2.1. 2nd Ed New York: Biostatistics. [Google Scholar]
  • 25. Page RD. (1996) TreeView: An application to display phylogenetic trees on personal computers. Comput. Appl. Biosci. 124:357–358. [DOI] [PubMed] [Google Scholar]
  • 26. Botstein D, White RL, Skolnick M, Davis RW. (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Human Genet 32: 314–331. [PMC free article] [PubMed] [Google Scholar]
  • 27. Guo Y, Li YL, Huang Y, Jarvis D, Sato K, Kato K, et al. (2012) Genetic diversity analysis of hulless barley from Shangri-la region revealed by SSR and AFLP markers. Genet Resources and Crop Evolution 59: 1543–1552. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. The tested white birch materials for SSR analysis.

The information of tested white birch materials for SSR analysis.

(DOCX)

S2 Table. Information of 544 SSR primer pairs.

The details of 544 SSR primer pairs included probe accessions, primer ID, repeat motif, forward/reverse primer sequence, annealing temperature (Tm), expected product size, observed fragment sizes in genotypes 1–5, number of alleles obtained.

(DOCX)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.


Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES