Genetics Research International

Genetics Research International / 2016 / Article

Research Article | Open Access

Volume 2016 |Article ID 7052323 | https://doi.org/10.1155/2016/7052323

Shamshad Ul Haq, Pradeep Kumar, R. K. Singh, Kumar Sambhav Verma, Ritika Bhatt, Meenakshi Sharma, Sumita Kachhwaha, S. L. Kothari, "Assessment of Functional EST-SSR Markers (Sugarcane) in Cross-Species Transferability, Genetic Diversity among Poaceae Plants, and Bulk Segregation Analysis", Genetics Research International, vol. 2016, Article ID 7052323, 16 pages, 2016. https://doi.org/10.1155/2016/7052323

Assessment of Functional EST-SSR Markers (Sugarcane) in Cross-Species Transferability, Genetic Diversity among Poaceae Plants, and Bulk Segregation Analysis

Academic Editor: Norman A. Doggett
Received26 Nov 2015
Revised13 Apr 2016
Accepted26 Apr 2016
Published01 Jun 2016

Abstract

Expressed sequence tags (ESTs) are important resource for gene discovery, gene expression and its regulation, molecular marker development, and comparative genomics. We procured 10000 ESTs and analyzed 267 EST-SSRs markers through computational approach. The average density was one SSR/10.45 kb or 6.4% frequency, wherein trinucleotide repeats (66.74%) were the most abundant followed by di- (26.10%), tetra- (4.67%), penta- (1.5%), and hexanucleotide (1.2%) repeats. Functional annotations were done and after-effect newly developed 63 EST-SSRs were used for cross transferability, genetic diversity, and bulk segregation analysis (BSA). Out of 63 EST-SSRs, 42 markers were identified owing to their expansion genetics across 20 different plants which amplified 519 alleles at 180 loci with an average of 2.88 alleles/locus and the polymorphic information content (PIC) ranged from 0.51 to 0.93 with an average of 0.83. The cross transferability ranged from 25% for wheat to 97.22% for Schlerostachya, with an average of 55.86%, and genetic relationships were established based on diversification among them. Moreover, 10 EST-SSRs were recognized as important markers between bulks of pooled DNA of sugarcane cultivars through BSA. This study highlights the employability of the markers in transferability, genetic diversity in grass species, and distinguished sugarcane bulks.

1. Introduction

Sugarcane is a bioenergy crop belonging to the genus Saccharum L. of the tribe Andropogoneae (family: Poaceae). This tribe comprises grass species which have high economic value. The noble sugarcane varieties are developed from interspecific hybridization of Saccharum officinarum L. () which has high sugar content with less disease tolerance and Saccharum spontaneum ( to 120) which provides stress, disease tolerance, and high fiber content for biomass. The taxonomy and genetic constitution of sugarcane are complicated due to complex interspecific aneupolyploid genome which makes chromosome numbers range from 100 to 130 [1]. Moreover, six Saccharum spp. (S. spontaneum, S. officinarum, S. robustum, S. edule, S. barberi, and S. sinense) and four Saccharum related genera (Erianthus, Miscanthus, Sclerostachya, and Narenga) have purportedly undergone interbreeding, forming the “Saccharum complex” [2, 3]. The interbreeding has made their genome more complex and added to multigenic and/or multiallelic nature for most agronomic traits that made sugarcane breeding a more difficult task [4].

A vast array of genomic tools has been developed which has opened new ways to define the genetic architecture of sugarcane and helped to explore its functional system [1, 5]. Among the molecular markers, microsatellites are most favored for a variety of genetic applications due to their multiallelic nature, high reproducibility, cross transferability, codominant inheritance, abundance, and extensive genome coverage [68]. Microsatellites or simple sequences repeats (SSRs) are monotonous repetitions of very short (one to six) nucleotide motifs, which occur as interspersed repetitive elements in all eukaryotic and prokaryotic genomes. However, transcribed regions of the genome also contain enormous range of microsatellites that correspond to genic microsatellites or EST-SSRs. Therefore, expressed sequence tags (ESTs) are the short transcribed portions and involved in the variety of metabolic functions. The presence of the microsatellites in genes as well as ESTs unveils the biological significance of SSR distribution, expansion, and contraction on the function of the genes themselves [9].

Presently, huge amounts of expressed sequence tags have been deposited in public database (NCBI). In silico approaches to retrieve EST sequences from NCBI and functional annotations provide more constructive EST-SSRs or gene-based SSR (genic SSRs) marker development besides own EST libraries development. This method of the EST-SSR markers development provides the easiest way to reduce cost, time, and labours along with more meaningful marker identifications [10]. The presence of microsatellites in the genic region is found to be more conserved due to which they possess high reproducibility and high interspecific/intraspecific transferability. Hence, EST-SSR could be used for polymorphism, genetic diversity, cross transferability, and comparative mapping in different plant species. Accordingly, several genetic studies were done on sugarcane using microsatellite markers to decipher polymorphism, cross transferability, genetic diversity, informative marker detection through bulk segregation analysis (BSA), and comparative genomics [8, 1113]. The objective of the present study was to retrieve EST sequences for more informative EST-SSR development and their genetic assessment within and across the taxa through cross transferability, genetic relationships, and bulk segregation analysis.

2. Materials and Methods

2.1. EST Sequences Retrieving, ESTs Assembling, and Microsatellites Identification

Total 10000 EST sequences of the Saccharum spp. were downloaded in Fasta format from National Centre for Biotechnology Information (NCBI) for microsatellites deciphering. Further, ESTs assembling was carried out using CAP3 programme (http://mobyle.pasteur.fr/cgi-bin/portal.py#forms::cap3) for minimization of sequences redundancy. Microsatellite identification was carried out using MISA software (http://pgrc.ipk-gatersleben.de/misa/) and the criteria for SSR detection were 6, 4, 3, 3, and 3 repeat units for di-, tri-, tetra-, penta-, and hexanucleotides, respectively. SSR primer pairs (forward and reverse) were designed for the selected EST sequences having microsatellites using online web tool, batch primer 3 pipeline [14].

2.2. EST-SSR Sequences Annotation

Assessment of EST sequences having SSR was done through blastn/blastx analysis for homology search and against nonredundant (nr) protein at the NCBI. Furthermore, functional annotation pipeline was also run at online tool for gene ontology (GO) which was intended for different GO functional classes like biological process, cellular component, and molecular function [15].

2.3. PCR Amplification and Electrophoresis

PCR reactions were carried out in a total of 10 μL volume containing 25 ng template DNA, 1.0 μL (10 pmol/μL) of each forward and reverse primer, 100 mM of dNTPs, 0.5 U of Taq DNA polymerase, and 1.0 μL of 10x PCR buffer with 2.5 mM of MgCl2. Amplification was performed in a thermal cycler (Bio-Rad) in the following conditions: initial denaturation at 94°C for 5 min followed by 30 amplification cycles of denaturation for 1 min at 94°C followed by annealing temperature () for 1 min and then extension for 2 min at 72°C; final extension at 72°C for 7 min was allowed. The PCR conditions particularly the annealing temperatures (varying from 52°C to 58°C) for each primer were standardized and amplified products were stored at 4°C. The PCR products were analyzed on a 7% native PAGE in vertical gel electrophoresis unit (Bangalore Genei) using TBE buffer. The sizes of amplified fragments were estimated using 50 bp DNA ladder (Fermentas). Gels were documented using ethidium bromide (EtBr) stained dye.

2.4. Evaluation of Saccharum EST-SSR across the Taxa through Cross Transferability

The cross transferability of Saccharum derived EST-SSR markers was evaluated among the 20 accessions comprising seven cereals (wheat, maize, barley, rice, pearl millet, oat, and Sorghum), four Saccharum related genera (Erianthus, Miscanthus, Narenga, and Sclerostachya), three Saccharum species (51NG56 (S. robustum), N58 (S. spontaneum), and two clones of S. officinarum (Bandjermasin Hitam and Gunjera)), and five Saccharum commercial cultivars (CoS 88230, CoS 92423, UP 9530, CoS 8436, and CoS 91230). All genotypes were collected from the Sugarcane Research Institute Farm, UPCSR, Shahjahanpur, India. Furthermore, genomic DNA from young juvenile, disease-free, immature leaves was isolated for each genotype using CTAB (cetyl trimethylammonium bromide) method [16]. Isolated DNA samples were treated with RNAase for 1 h at 37°C and purified by phenol extraction (25 phenol : 24 chloroform : 1 isoamyl alcohol, v/v/v) followed by ethanol precipitation [17] and stored at −80°C. DNA was quantified on 0.8% agarose gel and the working concentration of 25 ng/μL was obtained by making final adjustment in 10 mM TE buffer.

2.5. Genetic Diversity Analysis

The assessment of EST-SSRs in genetic diversity analysis was done among 20 plants belonging to distinct groups comprising cereals, Saccharum related genera, Saccharum species, and Saccharum cultivars. The allelic data of 63 EST-SSR primers were used to ascertain the genetic relationships between 20 genotypes by clustering analysis. Amplified bands were scored as binary data in the form of present (1) or absent (0). Dendrogram was constructed by neighbour-joining and Jaccard’s algorithm using FreeTree and TreeView software [18, 19]. The polymorphic information content (PIC) values were calculated for each primer by using the online resource of PIC Calculator (http://www.liv.ac.uk/~kempsj/pic.html).

2.6. Informative Assessment of Functional EST-SSR Markers between Bulks

Plant materials were used as F2 mapping population comprising 209 genotypes of the sugarcane cultivars which were developed from cross between CoS 91230 (Parent; CoS 775 × Co 1148) with CoS 8436 (Parent; MS 68/47 × Co 1148) from September to March (2010-2011). Grouping of genotypes was done according to their stem diameter (contrasting high and low stem diameter genotypes) into two sets. DNA extractions were carried out from both sets and equal quantities of genomic DNA from 10 extreme high stem diameter and 10 extreme low stem diameter genotypes were pooled into two bulks. PCR amplification was done in both bulks with newly developed EST-SSR primers for informative markers identifications through bulk segregation analysis (BSA) [20].

3. Results and Discussion

3.1. Mining of Microsatellites in EST Sequences and SSRs Characterization

Total 10,000 EST sequences related to Saccharum spp. were examined from NCBI for the simple sequence repeat (SSR) identification and characterization using computational approach. Prior to the marker deciphering, sequence assembly was performed and 6201 (4201 kb) nonredundant sequences were detected comprising 1752 contigs and 4449 singlets, wherein 406 SSRs were identified with 360 perfect SSRs and 37 sequences containing more than 1 SSR and 30 SSRs in compound formation. Therefore, computational and experimental approach to ascertain microsatellites in EST libraries from public database (NCBI) turned to be very cost effective and reduces time and labour besides expense of own libraries development. EST-SSRs are a more preferable DNA marker in the variety of genetic analysis and found to be more conserved as present in the transcribed region of the genome. These were found to be more transferable across the taxonomic boundaries and could be evaluated as most informative markers for variety of genomics applications [10, 21]. These are more adapted in plants comparative genetic analysis for gene identification, gene mapping, marker-assisted-selection, transferability, and genetic diversity [7, 2224]. Also, a variety of studies have been reported on sugarcane using EST-SSR markers for desired genetic analysis [8, 13, 25, 26].

The frequency of SSR in EST sequences was 6.4% including all the repeats except mononucleotide repeats. This result is comparatively higher compared to previous studies on sugarcane [8, 2729]. Contrary to this, Singh et al. [13] reported higher frequency (9.3%) in sugarcane. Kumpatla and Mukhopadhyay [30] also observed high range (2.65% to 10.62%) of SSR frequency in different plant species. In general, about 5% of ESTs contained SSR which has been reported in many plant species [31]. These variations in microsatellite frequency could be attributed to the “search criteria” used, type of SSR motif, size of sequence data, and the mining tools used [24, 32]. In other words, the density of the microsatellites was one SSR per 10.45 kb which is closely comparable to earlier studies in sugarcane with densities 1 SSR/10.9 kb [8] and 1/9 kb SSR [13].

Analysis revealed that trinucleotide repeats (66.74%) were found to be more frequent followed by di- (26.10%), tetra- (4.67%), penta- (1.5%), and hexanucleotide (1.2%) repeats. Our observation of high frequency of trinucleotide repeats is in agreement with previous reports on sugarcane [8, 13, 2729, 33]. Several other studies have also represented high frequency of trinucleotide repeats in different plant species [24, 31, 3436]. A total of 33 different types of motifs were identified of which four belonged to dinucleotide, eight belonged to trinucleotides, twelve belonged to tetranucleotide, five belonged to pentanucleotide, and two belonged to hexanucleotide repeats (Figure 1). We observed that motifs AG/CT and AT/AT were more frequent in dinucleotide repeat followed by motifs CCG/CGG, AGC/CTG, AGG/CCT, and ACG/CGT in trinucleotide repeat, motif AAAG/CTTT in tetranucleotide repeats, motif ACAGG/CCTGT in pentanucleotide repeats, and AACACC/GGTGTT in hexanucleotide repeats. The presence of motif CCG/CGG was also observed in sugarcane by different authors [13, 27]. Kantety et al. [37] also reported CCG/CGG motif as most abundant in wheat and Sorghum. Similarly, both Lawson and Zhang [38] and Da Maia et al. [39] also observed abundance of motif CCG/CGG in different member of the grass family. Victoria et al. [35] also decoded motif CCG/CGG in the lower plants (C. reinhardtii and P. patens). Thus, this predominance of CCG/CGG motif frequency has been related to a high GC-content [5]. Some motifs which are responsible for making unusual DNA folding structure (hairpin formed, bipartite triplex formed, and simple loop folding) also have effect on gene expressions and regulations mechanism, namely, CCT/AGG, CCG/GGC, GGA/TTC, and GAA/TTC motifs [40, 41]. Moreover, the presence of trinucleotide repeats in the coding region formed a distinct group and encoded amino acid tracts within the peptide [42]. We also observed predictable twenty different types of amino acids including stop codon. Alanine, arginine, glycine, proline, and serine were most frequent (Figure 2). This is in agreement with previous studies that reported on different plant species [11, 35, 43].

3.2. Expressed Sequence Tags Annotation and Primers Development

All EST sequences having SSRs were examined by functional annotation (blastn, blastx, and gene ontology). After-effect, sixty-three ESTs having SSRs were successfully identified on the basis of their involvement in the various metabolic processes (Figure 3). After-effect, sixty-three EST-SSRs primer pairs were designed for polymorphic nature, cross transferability, bulk segregation analysis, and genetic diversity in the test plants (Table 1). These selected EST-SSRs comprised all types of repeat motifs (excluding mononucleotide repeat), and among trinucleotide repeats they were highly frequent with GCT/CGA, TCC/AGG, and GGT/CCA repeat motifs. Similarly, Sharma et al. [44] also used functional annotation pipelines for the more prominent molecular markers development related to gene transcripts. Selected EST-SSRs were associated with various pathways of metabolic process, namely, GO:0006281 DNA repair, GO:0006301 postreplication repair, GO:0016070 RNA metabolic process, GO:0016070 RNA metabolic process, GO:0006446 regulation of translational initiation, GO:0015991 ATP hydrolysis coupled proton transport, GO:0006629 lipid metabolic process, GO:0015031 protein transport, GO:0005667 transcription factor complex, GO:0005815 microtubule organizing centre, GO:0003743 translation initiation factor activity, GO:0017005 3′-tyrosyl-DNA phosphodiesterase activity, GO:0030042 actin filament depolymerization, and GO:0015078 hydrogen ion transmembrane transporter activity, and so forth (see the complete details of the most promising hits of gene ontology of EST-SSRs in the supplementary table available online at http://dx.doi.org/10.1155/2016/7052323).


Serial number TypePrimer sequenceAnnealing temperatureSSR motifPIC value-valuePutative identities (blastn/blastx)

SYMS28FGCGTCAGAGTGTTAAAACAAG53(GCT)40.819.39E − 42Protein transport protein Sec61 beta
SYMS28RGTGTAGAACTGGAGCATTGAG
SYMS29FGGGCAAGCAAGAAACCAC52(TCC)40.911.62E − 24Protein translation factor SUI1
SYMS29RGAAGAGGTCAACCAAGAACTC
SYMS30FGCGTCAGAGTGTTAAAACAAG53(GCT)40.861.00E − 21Preprotein translocase Sec
SYMS30RGTGTAGAACTGGAGCATTGAG
SYMS31FGAAGCTCCCAAGCTGCTA53(AGCT)30.762.00E − 12Predicted: uncharacterized protein
SYMS31RCCTACAGGAAAGATTTTAGGG
SYMS32FGTCTCTTCTCCAGTTCTCCTT55(TGCG)40.842.46E − 63Predicted: actin-depolymerizing factor
SYMS32RGCTCAACAAATGTCTCCCTA
SYMS33FTGCACTAACATGGTTGATGT54(GAAG)30.862.82E − 90Hypothetical protein SORBIDRAFT_03g046450
SYMS33RGGTGATTGTAAGGGTCATCTT
SYMS34FGTTAATGGTGGTTCCGTTC53(GGC)60.884E − 20Predicted: uncharacterized protein LOC101783547
SYMS34RATTATCAGCGCAGAGACATC
SYMS35FGCGTCAGAGTGTTAAAACAAG52(GCT)40.751.00E − 21Preprotein translocase
SYMS35RGTGTAGAACTGGAGCATTGAG
SYMS36FGGACTGTACAAGGACGACAG53(GCT)40.701.14E − 41Protein transport protein Sec61 beta subunit
SYMS36RTCTGCTTTCTTGGATATGGTA
SYMS37FAAGAAGGATGCAAAGAAGAAG54(GAT)40.903.08E − 81Hypothetical protein SORBIDRAFT_03g046450
SYMS37RAGGCTTAGTAACAGCAGGTTT
SYMS38FAAGAAGGATGCAAAGAAGAAG56(AGA)40.869.00E − 37Hypothetical protein
SYMS38RAGGCTTAGTAACAGCAGGTTT
SYMS39FGGACTGTACAAGGACGACAG(GCT)41.14E − 41Protein transport protein
SYMS39RTCTGCTTTCTTGGATATGGTA
SYMS40FGGACTGTACAAGGACGACAG(GCT)41.25E − 40Preprotein translocase
SYMS40RTCTGCTTTCTTGGATATGGTA
SYMS41FGGACTGTACAAGGACGACAG(GCT)49.62E − 42Protein transport protein Sec61 beta subunit
SYMS41RTCTGCTTTCTTGGATATGGTA
SYMS42FCCAAAGAGATCTTGCAGACTA(ATG)41.78E − 53Jasmonate-induced protein
SYMS42RCCCAACACAACAACCAAT
SYMS43FCCACACAAGCAAGAAATAAAC(GGT)48.57E − 74Dirigent-like protein
SYMS43RTCGAACACTATGGTAAAGGTG
SYMS44FGGACTGTACAAGGACGACAG(GCT)41.15E − 41Homeodomain-like transcription factor
SYMS44RTCTGCTTTCTTGGATATGGTA
SYMS45FGCGTCAGAGTGTTAAAACAAG53(GCT)40.699.86E − 42Protein transport protein
SYMS45RGACTCTGCTTTCTTGGATATG
SYMS46FAGCTATCTTTAGTGGGGACAT52(CGT)40.901.82E − 44Hypothetical protein SORBIDRAFT_09g006220
SYMS46RGAGGTCTCATCGGAGCTTA
SYMS47FAGGTCGTTTTAATTCCTTCC53(GTTTT)30.771.00E − 21Preprotein translocase Sec
SYMS47RCGTAAATATGAACGAGGTCAG
SYMS48FAGGTCGTTTTAATTCCTTCC53(TTTA)60.904.00E − 20TPA: hypothetical protein
SYMS48RCGTAAATATGAACGAGGTCAG
SYMS49FGGACTGTACAAGGACGACAG(GCT)41.15E − 41Zinc finger A20 and AN1 domains-containing protein
SYMS49RTCTGCTTTCTTGGATATGGTA
SYMS50FTCCAAGGATTTAGCTATGGAT(TGT)106.79E − 13TPA: seed maturation protein
SYMS50RTTCAACTACACCCTTCTGTTG
SYMS51FGCGTCAGAGTGTTAAAACAAG(GCT)41.22E − 41Hypothetical protein
SYMS51RATTGTCACTTGCTATCCATTT
SYMS52FCACCTTCTTTCCTTCTCCTC(CGC)43.32E − 47V-type proton ATPase 16 kDa proteolipid subunit
SYMS52RGTAGATACCGAGCACACCAG
SYMS53FTCAGTTCAGGGATGACAATAG56(CCGTGG)30.872.59E − 78Homeodomain-like transcription factor superfamily protein
SYMS53RGGATAGACTGAAATCTGCTCA
SYMS54FCAACTCGACTCTTTTCTCTCA(CTC)54.13E − 08Protein transport protein SEC31
SYMS54RGGAGGTGGAACTTCCTGA
SYMS55FGGACTGTACAAGGACGACAG(GCT)41.12E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS55RTCTGCTTTCTTGGATATGGTA
SYMS56FGGACTGTACAAGGACGACAG(GCT)48.01E − 42Protein transport protein Sec61 subunit beta-like isoform
SYMS56RTCTGCTTTCTTGGATATGGTA
SYMS57FAAACGATCAGATACCGTTGTA(CG)67.84E − 27Caltractin
SYMS57RATCAAAGAGATCAAAGGCTTC
SYMS58FCATTTCGAAGCTCCTCCT52(CCTCCG)60.745.97E − 66Zinc finger A20 and AN1 domains-containing protein
SYMS58RTAGGCTGCACAACAATAGTCT
SYMS59FCTCCCCCATTTCTCTTCC53(GCAGCC)60.804.02E − 65Predicted: reticulon-like protein B1
SYMS59RCAAGTACTCCAGCAGAGATGT
SYMS60FCTTTTCCCTCTTCCTCTCTC(CCG)51.24E − 45Predicted: uncharacterized tRNA-binding protein
SYMS60RTGTCACTAACACGAATCACAA
SYMS61FCCCTCTCCCTGCTCTTTC54(TCC)50.794.14E − 57Actin-depolymerizing factor 3
SYMS61RCAGTCACAAAGTCGAAATCAT
SYMS62FACAACTCTTCAGTCTTCACGA54(CAAC)30.854.40E − 66Truncated alcohol dehydrogenase
SYMS62RCCAATCTTGACATCCTTGAC
SYMS63FGCACGGTGAAGTTCTAGTTC54(TCGAT)40.673.11E − 31Hypothetical protein SORBIDRAFT_08g002800
SYMS63RCAGCTTCACTCATGAATTTTT
SYMS64FGGACTGTACAAGGACGACAG(GCT)41.08E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS64RTCTGCTTTCTTGGATATGGTA
SYMS65FAACACAAGCAAGAAATAAACG53(GGT)40.513.42E − 74Dirigent-like protein
SYMS65RAACACTATGGTCAAGGTGGTA
SYMS66FGCGTCAGAGTGTTAAAACAAG52(GCT)40.581.01E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS66RGAAATCGCTCTATAAGGTTCC
SYMS67FTCTCTCTGAAGATGATGCTTT52(AAG)50.904.25E − 83Hypothetical protein SORBIDRAFT_03g005100
SYMS67RGTTAAGAGGCTTCCAAAGAAC
SYMS68FCAGCTCGTCGTCTTCTTTT(GTC)52.00E − 55Putative ubiquitin-conjugating enzyme family
SYMS68RGTGGCTTGTTTGGATATTCTT
SYMS69FGGACTGTACAAGGACGACAG54(GCT)40.799.28E − 42Protein transport protein Sec61 subunit beta-like isoform
SYMS69RCGTCAGACGTACTGAAATGTT
SYMS70FAACACAAGCAAGAAATAAACG53(GGT)40.771.58E − 73Putative dirigent protein
SYMS70RAACACTATGGTCAAGGTGGTA
SYMS71FGGACTGTACAAGGACGACAG(GCT)49.86E − 42Protein transport protein Sec61 subunit beta-like isoform
SYMS71RTCTGCTTTCTTGGATATGGTA
SYMS72FCCCTCTCCCTGCTCTTTC55(TCC)40.894.36E − 57Actin-depolymerizing factor 3
SYMS72RCAGTCACAAAGTCGAAATCAT
SYMS73FGGACTGTACAAGGACGACAG55(GCT)40.831.09E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS73RTCTGCTTTCTTGGATATGGTA
SYMS74FGGACTGTACAAGGACGACAG52(GCT)40.889.51E − 42Preprotein translocase Sec
SYMS74RTCTGCTTTCTTGGATATGGTA
SYMS75FGCACCCCCAATTCGAACG52(ACG)30.931.78E − 68TPA: general regulatory factor 1
SYMS75RCGGTAGTCCTTGATGAGTGT
SYMS76FGGACTGTACAAGGACGACAG52(GCT)40.784.79E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS76RTCTGCTTTCTTGGATATGGTA
SYMS77FCACGCAACGCAAGCACAG55(CCAT)30.938.34E − 70Hypothetical protein SORBIDRAFT_10g030160
SYMS77RAAGTTGATTCACCCTCATTCT
SYMS78FCACGCAACGCAAGCACAG53(CGATC)30.921.04E − 41Translocon-associated protein alpha subunit precursor
SYMS78RAAGTTGATTCACCCTCATTCT
SYMS79FGGACTGTACAAGGACGACAG53(GCT)40.911.04E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS79RTCTGCTTTCTTGGATATGGTA
SYMS80FCTTGATCCTTGACAAAAGAGA52(AG)60.872.25E − 59Predicted: ubiquitin-conjugating enzyme E2
SYMS80RATTGCTGTTGATATTTGGATG
SYMS81FGCGTCAGAGTGTTAAAACAAG53(GCT)40.878.01E − 42Protein transport protein Sec61 subunit beta-like isoform
SYMS81RGTGTAGAACTGGAGCATTGAG
SYMS82FTATCAACAAGCCTTCCATTC53(GTG)40.901.12E − 30Glycine-rich RNA-binding protein 2
SYMS82RGGCTATAGTCACCACGGTAG
SYMS83FCGACAGGGAGAAGAGTACAG55(GCT)40.879.39E − 42Protein transport protein Sec61 subunit beta-like isoform
SYMS83RGACTCTGCTTTCTTGGATATG
SYMS84FGCGTCAGAGTGTTAAAACAAG53(GCT)40.751.14E − 41Protein transport protein Sec61 subunit beta-like isoform
SYMS84RAATCGCTCTATAAGGTTCCTC
SYMS85FCTCTTCTTCACCAATTCCTCT(CCG)61.14E − 51Protein transport protein Sec61 subunit beta-like isoform
SYMS85RCAAACCTCATAAAGAGTGCAG
SYMS86FGGGCAAGCAAGAAACCAC54(TCC)4 0.931.16E − 28TPA: translation initiation factor 1
SYMS86RCGTACATGAACGTAGTCCTTT
SYMS87FGCGTCAGAGTGTTAAAACAAG(GCT)41.19E − 41Protein transport protein Sec61 beta subunit
SYMS87RAATCGCTCTATAAGGTTCCTC
SYMS88FTTATAAGGAAATCCCCCACT(GCC)47.71E − 55Hypothetical protein SORBIDRAFT_09g000970
SYMS88RCACCAAGTACTCATCCATCAT
SYMS89FCATCTCCTGCTAACAATTCAC55(TGC)40.919.64E − 60Predicted: NAC domain-containing protein
SYMS89RATTTATAGGTTGGCACCAGAG
SYMS90FGCGTCAGAGTGTTAAAACAAG53(GCT)40.851.00E − 22Protein transport protein Sec61 subunit beta-like isoform
SYMS90RGTGTAGAACTGGAGCATTGAG

3.3. Assessment of EST-SSR Marker in Selected Plants

A set of 63 EST-SSR primers were evaluated for PCR optimization, polymorphism, and cross amplification in twenty genotypes belonging to cereals plants and Saccharum related genera and Saccharum species and their commercial cultivars, of which 42 EST-SSR primers produced successful amplifications with both expected and unexpected sizes (Figure 4). Among 42 EST-SSRs, twenty-eight belonged to trinucleotide repeats with then seven of tetra-, three of penta-, three of hexa-, and one of dinucleotide repeats. Meanwhile, PCR amplifications produced 519 alleles (expected size) at 180 loci with an average of 2.88 alleles per locus. This result is comparable with earlier studies that reported on various plant species, namely, 2.79 alleles/locus in rice varieties [45], 2.9 to 6.0 alleles per locus in maize [46], and 3.04 alleles/locus in rye [47]. However, our result of alleles per locus is lower compared to previous studies that reported on sugarcane, that is, 6.04 alleles/locus [28], 7.55 alleles/locus [29], and 6.0 alleles/locus [48]. The polymorphic information content (PIC) was extended from 0.51 to 0.93 with an average of 0.83. It could be encompassed that low and high range of allelic amplifications with EST-SSRs correspond to marker polymorphism and low level of polymorphism from EST-SSRs might be due to possible selection against alterations in the conserved sequences of EST-SSRs [49, 50].

3.4. Cross Transferability

The potentials of EST-SSR primers were examined for cross transferability among 20 plant species belonging to cereals and Saccharum related genera and Saccharum species and their cultivars under the same PCR conditions. However, 42 EST-SSRs showed successful amplifications among all the selected plants. The cross transferability was estimated to be 27.22% in wheat, 27.22% in maize, 47.22% in barely, 46.66% in rice, 36.11% in pearl millet, 55.55% in oat, 26.11% in Sorghum, 88.33% in Narenga, 98.88% in Sclerostachya, 71.11% in Erianthus, 60.0% in Miscanthus, 73.33% in Bandjermasin Hitam, 55.55% in Gunjera, 75.55% in 51NG56, 55.0% in N58, 50.56% in CoS 92423, 58.88% in CoS 88230, 51.11% in UP 9530, 52.78% in CoS 91230, and 60.0% in CoS 8436. Meanwhile, the frequency distributions of cross transferability of EST-SSRs ranged from 26.11% for Sorghum to 98.88% for Sclerostachya, with an average of 55.86% (Table 2). Saccharum related genera (79.58%) and Saccharum species (64.86%) showed high rate of cross transferability compared to other groups. This is in agreement with previous studies reported on Saccharum species and Saccharum related genera [12, 13, 51]. Several earlier studies related to cross transferability have been reported on distinct plant groups from different families using EST-SSRs markers [7, 52, 53]. This suggests that transferring ability of genic markers makes it compatible to determine genetic studies across the taxa for utilization in mapping of genes from related species along with genera and identification of suspended hybridization. This can also aid vigilance of the introgression of genetic entity from wild relatives to cultivated, comparative mapping and establishing evolutionary relationship between them. Thus, microsatellites derived from expressed region of the genome are expected to be more conserved and more transferable across taxa.


S. number/lane1234567891011121314151617181920Primer
polymorphism (%)

SY 284561230255323233132395
SY 296359312351267787767668100
SY 301040070566334323232380
SY 310000000232112220220050
SY 321001020243130010000055
SY 330022353764655523023385
SY 340000000000032545559985
SY 350001040521001112212165
SY 360000000030052220200235
SY 3797566559121011888867656100
SY 380003020781002212003050
SY 450000020510121100000035
SY 460144100440100000000035
SY 470133200100000000000025
SY 4800005308120533213220060
SY 5300011331292112213213185
SY 580001103210000000000025
SY 590000000403300100000020
SY 610375403456341535555590
SY 620001020453220000000035
SY 630000000114161110102155
SY 650000000001110123222150
SY 660000000100031200322140
SY 671256330256320020000060
SY 691520100312111112103280
SY 701001010520010020330045
SY 721020000145085813646870
SY 730020120220131410300360
SY 742110113132141510300280
SY 751023300063976760460065
SY 760000020121400010000030
SY 772566843969304434302085
SY 782256464576410910328759100
SY 790000100352051772100655
SY 801104010456254553545585
SY 810001260668733433333280
SY 8200103143911654353443290
SY 831206234272132313323395
SY 840001230020033103333360
SY 86156325458116531151276710100
SY 89965417374310445734735100
SY 905032523434043644236490
Average of transferability 27.2227.2247.2246.6736.1155.5626.1188.3398.8971.1160.073.3355.5675.5655.050.5658.8951.1152.7860.0

3.5. Genetic Diversity Analysis by EST-SSRs

In order to evaluate the potential of EST-SSRs, the genetic analysis was done among 20 genotypes belonging to 7 cereals (wheat, maize, barley, rice, pearl millet, oat, and Sorghum), 4 Saccharum related genera (Erianthus, Miscanthus, Narenga, and Sclerostachya), 3 Saccharum species (51NG56 (S. robustum), N58 (S. spontaneum), and two of S. officinarum clones (Gunjera and Bandjermasin Hitam)), and 5 sugarcane commercial cultivars (CoS 8436, CoS 91230, CoS 88230, UP 9530, and CoS 92423). The generated allelic data were used for genetic relationships analysis by making dendrogram based on Jaccard’s and neighbour-joining algorithm using FreeTree and TreeView software. The dendrogram fell into three major clusters with several edges, cluster I with eight genotypes comprising most of Saccharum species and their commercial cultivars, cluster II encompassing six genotypes of most of cereals species, and cluster III with six species comprising most of the Saccharum related genera along with some interventions (Figure 5). This relationship is in agreement with previous studies reported by other authors [12, 13, 51, 54]. Our EST-SSRs markers showed close syntenic relationship and their evolutionary nature among the 20 genotypes into three major clusters with some genotypes divergence. These relationships have resulted from the expansion and contraction of SSRs in conserved EST sequences within the same group of plant species along with some variation having resulted from higher evolutionary divergence among them. Several earlier studies also reported on genetic diversity analysis within and across the plant taxa using molecular marker [7, 8, 24, 48, 52, 5557]. Thus, microsatellite markers distinguished all the genotypes to certain extent and also provided the realistic estimate of genetic diversity among them.

3.6. Bulk Segregation Analysis (BSA) in Sugarcane

All the 42 EST-SSR markers were evaluated in pooled DNA bulks of contrasting trait of sugarcane cultivars (CoS 91230 (CoS 775 × Co 1148) cross with CoS 8436 (MS 68/47 × Co 1148)) for the identification of reporter EST-SSR markers based on their allelic differences between them. Interestingly, 10 markers showed polymorphic nature and apparently discriminating potential between bulks through bulk segregation analysis (Figure 6). Among these, markers SYMS30, SYMS53, SYMS82, and SYMS89 showed a better response to discriminating the bulks. BSA is the strategy that involves the identification of genetic markers associated with character or trait which are based on their allelic differences between bulks [20]. Earlier studies have been established in sugarcane for the most prominent molecular markers detection linked to desirable traits through BSA. For example, molecular markers apparently linked to high fiber content in Saccharum species [5860] and molecular markers used for QTL analysis and utilized for generating genetic maps around resistance genes in sugarcane against diseases and pests through BSA [12, 61, 62]. Several other studies also reported on selection of different agronomic traits in sugarcane for breeding programme with the development of molecular markers through BSA [1, 6365]. Alternatively, BSA approach has been recently used for various purposes against the identification of differential expressed gene associated with both qualitative and quantitative using of the cDNA-AFLP approach [6669]. Thus, BSA approach provides the easiest way in the direction of trait linked marker identification and also makes it possible to select informative markers beside evaluations of each marker in the whole progeny.

4. Conclusion

The present study was intended for identification and characterization of SSR in Saccharum spp. expressed sequence tag which is retrieved from public database (NCBI). Further, functional annotation was feasible to identify the most eminent EST-SSR markers selection. Therefore, this is the bypass way for EST-SSR markers development which reduces cost and time and provides an efficient way to analyze the transcribed portion of genome besides expense of own libraries development. A total of 63 EST-SSR markers were developed and experimentally validated for cross transferability along with their genetic relationships and also used for differentiation between pooled DNA bulks of Saccharum cultivars. These markers showed successful transferability rate among the twenty genotypes and established genetic diversity among cereals, Saccharum species/cultivars, and Saccharum related genera with some inconsistency. Further, some prominent marker also distinguished pooled DNA bulks of sugarcane cultivars based on stem diameter. Consequently, these EST-SSR markers were found to be more convenient which made it easy for us to use them as informative markers in further genetic studies in sugarcane breeding programme.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

Authors are highly grateful to the Division of Biotechnology, UP Council of Sugarcane Research, for providing an opportunity and facilities for research works. Authors are also grateful to Director, UP Council of Sugarcane Research, Shahjahanpur, UP, India for their moral support. Authors also acknowledge University of Rajasthan for providing DBT-IPLS and DBT-BIF facilities.

Supplementary Materials

Supplementary Table: Brief details of best promising hits obtained through gene ontology for ESTs having SSR which were categorized into biological process, cellular component and molecular function.

  1. Supplementary Material

References

  1. J. Y. Hoarau, G. Souza, A. D'Hont et al., Sugarcane, A Tropical Crop with a Highly Complex Genome, Plant Genomics, Sciences Publishers, Enfield, NH, USA, 2006.
  2. B. Roach and J. Daniels, “A review of the origin and improvement of sugarcane,” Copersucar International Sugarcane Breeding Workshop, vol. 1, pp. 1–31, 1987. View at: Google Scholar
  3. A. D'Hont, D. Ison, K. Alix, C. Roux, and J. C. Glaszmann, “Determination of basic chromosome numbers in the genus Saccharum by physical mapping of ribosomal RNA genes,” Genome, vol. 41, no. 2, pp. 221–225, 1998. View at: Publisher Site | Google Scholar
  4. R. E. Casu, J. M. Manners, G. D. Bonnett et al., “Genomics approaches for the identification of genes determining important traits in sugarcane,” Field Crops Research, vol. 92, no. 2-3, pp. 137–147, 2005. View at: Publisher Site | Google Scholar
  5. M. Morgante and A. M. Olivieri, “PCR-amplified microsatellites as markers in plant genetics,” Plant Journal, vol. 3, no. 1, pp. 175–182, 1993. View at: Publisher Site | Google Scholar
  6. M. Agarwal, N. Shrivastava, and H. Padh, “Advances in molecular marker techniques and their applications in plant sciences,” Plant Cell Reports, vol. 27, no. 4, pp. 617–631, 2008. View at: Publisher Site | Google Scholar
  7. S. U. Haq, R. Jain, M. Sharma, S. Kachhwaha, and S. L. Kothari, “Identification and characterization of microsatellites in expressed sequence tags and their cross transferability in different plants,” International Journal of Genomics, vol. 2014, Article ID 863948, 12 pages, 2014. View at: Publisher Site | Google Scholar
  8. S. K. Parida, A. Pandit, K. Gaikwad et al., “Functionally relevant microsatellites in sugarcane unigenes,” BMC Plant Biology, vol. 10, no. 1, article 251, 2010. View at: Publisher Site | Google Scholar
  9. X. Li, W. Gao, H. Guo, X. Zhang, D. D. Fang, and Z. Lin, “Development of EST-based SNP and InDel markers and their utilization in tetraploid cotton genetic mapping,” BMC Genomics, vol. 15, no. 1, article 1046, 2014. View at: Publisher Site | Google Scholar
  10. Q. Kong, C. Xiang, Z. Yu et al., “Mining and charactering microsatellites in Cucumis melo expressed sequence tags from sequence database,” Molecular Ecology Notes, vol. 7, no. 2, pp. 281–283, 2007. View at: Publisher Site | Google Scholar
  11. S. Gupta, K. P. Tripathi, S. Roy, and A. Sharma, “Analysis of unigene derived microsatellite markers in family solanaceae,” Bioinformation, vol. 5, no. 3, pp. 113–121, 2010. View at: Publisher Site | Google Scholar
  12. R. K. Singh, R. B. Singh, S. P. Singh, and M. L. Sharma, “Identification of sugarcane microsatellites associated to sugar content in sugarcane and transferability to other cereal genomes,” Euphytica, vol. 182, no. 3, pp. 335–354, 2011. View at: Publisher Site | Google Scholar
  13. R. K. Singh, S. N. Jena, S. Khan et al., “Development, cross-species/genera transferability of novel EST-SSR markers and their utility in revealing population structure and genetic diversity in sugarcane,” Gene, vol. 524, no. 2, pp. 309–329, 2013. View at: Publisher Site | Google Scholar
  14. F. M. You, N. Huo, Y. Q. Gu et al., “BatchPrimer3: a high throughput web application for PCR and sequencing primer design,” BMC Bioinformatics, vol. 9, article 253, 2008. View at: Publisher Site | Google Scholar
  15. T.-W. Chen, R.-C. R. Gan, T. H. Wu et al., “FastAnnotator—an efficient transcript annotation web tool,” BMC Genomics, vol. 13, no. 7, article S9, 2012. View at: Google Scholar
  16. D. Hoisington, M. Khairallah, and D. González-de-León, Laboratory Protocols: CIMMYT Applied Molecular Genetics Laboratory, CIMMYT, Mexico DF, Mexico, 2nd edition, 1994.
  17. J. Sambrook and D. W. Russell, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, NY, USA, 2001.
  18. A. Pavlíček, Š. Hrdá, and J. Flegr, “Free-tree—freeware program for construction of phylogenetic trees on the basis of distance data and bootstrap/jackknife analysis of the tree robustness. Application in the RAPD analysis of genus Frenkelia,” Folia Biologica, vol. 45, no. 3, pp. 97–99, 1999. View at: Google Scholar
  19. R. D. Page, “TreeView: an application to display phylogenetic trees on personal computers,” Computer Applications in the Biosciences, vol. 12, no. 4, pp. 357–358, 1996. View at: Google Scholar
  20. R. W. Mlchelmore, I. Paran, and R. V. Kesseli, “Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 88, no. 21, pp. 9828–9832, 1991. View at: Publisher Site | Google Scholar
  21. J. R. Ellis and J. M. Burke, “EST-SSRs as a resource for population genetic analyses,” Heredity, vol. 99, no. 2, pp. 125–132, 2007. View at: Publisher Site | Google Scholar
  22. J. Squirrell, P. M. Hollingsworth, M. Woodhead et al., “How much effort is required to isolate nuclear microsatellites from plants?” Molecular Ecology, vol. 12, no. 6, pp. 1339–1348, 2003. View at: Publisher Site | Google Scholar
  23. C. Duran, N. Appleby, D. Edwards, and J. Batley, “Molecular genetic markers: discovery, applications, data storage and visualisation,” Current Bioinformatics, vol. 4, no. 1, pp. 16–27, 2009. View at: Publisher Site | Google Scholar
  24. K. Kumari, M. Muthamilarasan, G. Misra et al., “Development of eSSR-markers in setaria italica and their applicability in studying genetic diversity, cross-transferability and comparative mapping in millet and non-millet species,” PLoS ONE, vol. 8, no. 6, article e67742, 2013. View at: Publisher Site | Google Scholar
  25. M. Rossi, P. G. Araujo, F. Paulet et al., “Genomic distribution and characterization of EST-derived resistance gene analogs (RGAs) in sugarcane,” Molecular Genetics and Genomics, vol. 269, no. 3, pp. 406–419, 2003. View at: Publisher Site | Google Scholar
  26. K. S. Aitken, P. A. Jackson, and C. L. McIntyre, “A combination of AFLP and SSR markers provides extensive map coverage and identification of homo(eo)logous linkage groups in a sugarcane cultivar,” Theoretical and Applied Genetics, vol. 110, no. 5, pp. 789–801, 2005. View at: Publisher Site | Google Scholar
  27. G. M. Cordeiro, R. Casu, C. L. McIntyre, J. M. Manners, and R. J. Henry, “Microsatellite markers from sugarcane (Saccharum spp.) ESTs cross transferable to erianthus and sorghum,” Plant Science, vol. 160, no. 6, pp. 1115–1123, 2001. View at: Publisher Site | Google Scholar
  28. L. R. Pinto, K. M. Oliveira, E. C. Ulian, A. A. F. Garcia, and A. P. De Souza, “Survey in the sugarcane expressed sequence tag database (SUCEST) for simple sequence repeats,” Genome, vol. 47, no. 5, pp. 795–804, 2004. View at: Publisher Site | Google Scholar
  29. K. M. Oliveira, L. R. Pinto, T. G. Marconi et al., “Characterization of new polymorphic functional markers for sugarcane,” Genome, vol. 52, no. 2, pp. 191–209, 2009. View at: Publisher Site | Google Scholar
  30. S. P. Kumpatla and S. Mukhopadhyay, “Mining and survey of simple sequence repeats in expressed sequence tags of dicotyledonous species,” Genome, vol. 48, no. 6, pp. 985–998, 2005. View at: Publisher Site | Google Scholar
  31. R. K. Varshney, A. Graner, and M. E. Sorrells, “Genic microsatellite markers in plants: features and applications,” Trends in Biotechnology, vol. 23, no. 1, pp. 48–55, 2005. View at: Publisher Site | Google Scholar
  32. E. Portis, I. Nagy, Z. Sasvári, A. Stágel, L. Barchi, and S. Lanteri, “The design of Capsicum spp. SSR assays via analysis of in silico DNA sequence, and their potential utility for genetic mapping,” Plant Science, vol. 172, no. 3, pp. 640–648, 2007. View at: Publisher Site | Google Scholar
  33. B. T. James, C. Chen, A. Rudolph et al., “Development of microsatellite markers in autopolyploid sugarcane and comparative analysis of conserved microsatellites in sorghum and sugarcane,” Molecular Breeding, vol. 30, no. 2, pp. 661–669, 2012. View at: Publisher Site | Google Scholar
  34. L. Qiu, C. Yang, B. Tian, J.-B. Yang, and A. Liu, “Exploiting EST databases for the development and characterization of EST-SSR markers in castor bean (Ricinus communis L.),” BMC Plant Biology, vol. 10, no. 1, article 278, 2010. View at: Publisher Site | Google Scholar
  35. F. C. Victoria, L. C. da Maia, and A. C. de Oliveira, “In silico comparative analysis of SSR markers in plants,” BMC Plant Biology, vol. 11, article 15, 2011. View at: Publisher Site | Google Scholar
  36. M. Muthamilarasan, B. V. Suresh, G. Pandey, K. Kumari, S. K. Parida, and M. Prasad, “Development of 5123 intron-length polymorphic markers for large-scale genotyping applications in foxtail millet,” DNA Research, vol. 21, no. 1, pp. 41–52, 2014. View at: Publisher Site | Google Scholar
  37. R. V. Kantety, M. La Rota, D. E. Matthews, and M. E. Sorrells, “Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat,” Plant Molecular Biology, vol. 48, no. 5-6, pp. 501–510, 2002. View at: Publisher Site | Google Scholar
  38. M. J. Lawson and L. Zhang, “Distinct patterns of SSR distribution in the Arabidopsis thaliana and rice genomes,” Genome Biology, vol. 7, no. 2, article R14, 2006. View at: Publisher Site | Google Scholar
  39. L. C. Da Maia, V. Q. De Souza, M. M. Kopp, F. I. F. De Carvalho, and A. C. De Oliveira, “Tandem repeat distribution of gene transcripts in three plant families,” Genetics and Molecular Biology, vol. 32, no. 4, pp. 822–833, 2009. View at: Publisher Site | Google Scholar
  40. I. Fabregat, K. S. Koch, T. Aoki et al., “Functional pleiotropy of an intramolecular triplex-forming fragment from the 3′-UTR of the rat Pigr gene,” Physiological Genomics, vol. 5, no. 2, pp. 53–65, 2001. View at: Google Scholar
  41. Y.-C. Li, A. B. Korol, T. Fahima, A. Beiles, and E. Nevo, “Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review,” Molecular Ecology, vol. 11, no. 12, pp. 2453–2465, 2002. View at: Publisher Site | Google Scholar
  42. J. R. Gatchel and H. Y. Zoghbi, “Diseases of unstable repeat expansion: mechanisms and common principles,” Nature Reviews Genetics, vol. 6, no. 10, pp. 743–755, 2005. View at: Publisher Site | Google Scholar
  43. R. K. Mishra, B. H. Gangadhar, J. W. Yu, D. H. Kim, and S. W. Park, “Development and characterization of EST based SSR markers in Madagascar periwinkle (Catharanthus roseus) and their transferability in other medicinal plants,” Plant Omics Journal, vol. 4, no. 3, pp. 154–162, 2011. View at: Google Scholar
  44. R. K. Sharma, P. Bhardwaj, R. Negi, T. Mohapatra, and P. S. Ahuja, “Identification, characterization and utilization of unigene derived microsatellite markers in tea (Camellia sinensis L.),” BMC Plant Biology, vol. 9, no. 1, article 53, 2009. View at: Publisher Site | Google Scholar
  45. V. Pachauri, N. Taneja, P. Vikram, N. K. Singh, and S. Singh, “Molecular and morphological characterization of Indian farmers rice varieties (Oryza sativa L.),” Australian Journal of Crop Science, vol. 7, no. 7, pp. 923–932, 2013. View at: Google Scholar
  46. B. Stich, A. E. Melchinger, M. Frisch, H. P. Maurer, M. Heckenberger, and J. C. Reif, “Linkage disequilibrium in European elite maize germplasm investigated with SSRs,” Theoretical and Applied Genetics, vol. 111, no. 4, pp. 723–730, 2005. View at: Publisher Site | Google Scholar
  47. B. Hackauf and P. Wehling, “Identification of microsatellite polymorphisms in an expressed portion of the rye genome,” Plant Breeding, vol. 121, no. 1, pp. 17–25, 2002. View at: Publisher Site | Google Scholar
  48. T. G. Marconi, E. A. Costa, H. R. Miranda et al., “Functional markers for gene mapping and genetic diversity studies in sugarcane,” BMC Research Notes, vol. 4, no. 1, article 264, 2011. View at: Publisher Site | Google Scholar
  49. M. C. Saha, M. A. R. Mian, I. Eujayl, J. C. Zwonitzer, L. Wang, and G. D. May, “Tall fescue EST-SSR markers with transferability across several grass species,” Theoretical and Applied Genetics, vol. 109, no. 4, pp. 783–791, 2004. View at: Publisher Site | Google Scholar
  50. S. Feingold, J. Lloyd, N. Norero, M. Bonierbale, and J. Lorenzen, “Mapping and characterization of new EST-derived microsatellites for potato (Solanum tuberosum L.),” Theoretical and Applied Genetics, vol. 111, no. 3, pp. 456–466, 2005. View at: Publisher Site | Google Scholar
  51. S. K. Parida, S. K. Kalia, S. Kaul et al., “Informative genomic microsatellite markers for efficient genotyping applications in sugarcane,” Theoretical and Applied Genetics, vol. 118, no. 2, pp. 327–338, 2009. View at: Publisher Site | Google Scholar
  52. S. Gupta and M. Prasad, “Development and characterization of genic SSR markers in Medicago truncatula and their transferability in leguminous and non-leguminous species,” Genome, vol. 52, no. 9, pp. 761–771, 2009. View at: Publisher Site | Google Scholar
  53. S. K. Gupta, R. Bansal, U. J. Vaidya, and T. Gopalakrishna, “Development of EST-derived microsatellite markers in mungbean [Vigna radiata (L.) Wilczek] and their transferability to other Vigna species,” Indian Journal of Genetics and Plant Breeding, vol. 72, no. 4, pp. 468–471, 2012. View at: Google Scholar
  54. M. S. Khan, S. Yadav, S. Srivastava, M. Swapna, A. Chandra, and R. K. Singh, “Development and utilisation of conserved-intron scanning marker in sugarcane,” Australian Journal of Botany, vol. 59, no. 1, pp. 38–45, 2011. View at: Publisher Site | Google Scholar
  55. L. Y. Zhang, M. Bernard, P. Leroy, C. Feuillet, and P. Sourdille, “High transferability of bread wheat EST-derived SSRs to other cereals,” Theoretical and Applied Genetics, vol. 111, no. 4, pp. 677–687, 2005. View at: Publisher Site | Google Scholar
  56. A. Selvi, N. V. Nair, J. L. Noyer et al., “AFLP analysis of the phenetic organization and genetic diversity in the sugarcane complex, Saccharum and Erianthus,” Genetic Resources and Crop Evolution, vol. 53, no. 4, pp. 831–842, 2006. View at: Publisher Site | Google Scholar
  57. M. Raghami, A. I. López-Sesé, M. R. Hasandokht, Z. Zamani, M. R. F. Moghadam, and A. Kashi, “Genetic diversity among melon accessions from Iran and their relationships with melon germplasm of diverse origins using microsatellite markers,” Plant Systematics and Evolution, vol. 300, no. 1, pp. 139–151, 2014. View at: Publisher Site | Google Scholar
  58. B. W. S. Sobral and R. J. Honeycutt, “High output genetic mapping of polyploids using PCR-generated markers,” Theoretical and Applied Genetics, vol. 86, no. 1, pp. 105–112, 1993. View at: Publisher Site | Google Scholar
  59. N. Msomi and F. Botha, “Identification of molecular markers linked to fibre using bulk segregant analysis,” in Proceedings of the South African Sugar Technologists' Association (SASTA '94), vol. 68, pp. 41–45, 1994. View at: Google Scholar
  60. L. Nivetha, N. Nair, P. Prathima, and A. Selvi, “Identification of microsatellite markers for high fibre content in sugarcane,” Journal of Sugarcane Research, vol. 3, no. 1, pp. 14–19, 2013. View at: Google Scholar
  61. C. M. Dussle, M. Quint, A. E. Melchinger, M. L. Xu, and T. Lübberstedt, “Saturation of two chromosome regions conferring resistance to SCMV with SSR and AFLP markers by targeted BSA,” Theoretical and Applied Genetics, vol. 106, no. 3, pp. 485–493, 2003. View at: Google Scholar
  62. C. Asnaghi, D. Roques, S. Ruffel et al., “Targeted mapping of a sugarcane rust resistance gene (Bru1) using bulked segregant analysis and AFLP markers,” Theoretical and Applied Genetics, vol. 108, no. 4, pp. 759–764, 2004. View at: Publisher Site | Google Scholar
  63. J. H. Daugrois, L. Grivet, D. Roques et al., “A putative major gene for rust resistance linked with a RFLP marker in sugarcane cultivar ‘R570’,” Theoretical and Applied Genetics, vol. 92, no. 8, pp. 1059–1064, 1996. View at: Publisher Site | Google Scholar
  64. C. T. Guimarães, G. R. Sills, and B. W. S. Sobral, “Comparative mapping of Andropogoneae: Saccharum L. (sugarcane) and its relation to sorghum and maize,” Proceedings of the National Academy of Sciences of the United States of America, vol. 94, no. 26, pp. 14261–14266, 1997. View at: Publisher Site | Google Scholar
  65. R. Ming, S.-C. Liu, P. H. Moore, J. E. Irvine, and A. H. Paterson, “QTL analysis in a complex autopolyploid: genetic control of sugar content in sugarcane,” Genome Research, vol. 11, no. 12, pp. 2075–2084, 2001. View at: Publisher Site | Google Scholar
  66. J. Guo, R. H. Y. Jiang, L. G. Kamphuis, and F. Govers, “A cDNA-AFLP based strategy to identify transcripts associated with avirulence in Phytophthora infestans,” Fungal Genetics and Biology, vol. 43, no. 2, pp. 111–123, 2006. View at: Publisher Site | Google Scholar
  67. A. Fernández-del-Carmen, C. Celis-Gamboa, R. G. F. Visser, and C. W. B. Bachem, “Targeted transcript mapping for agronomic traits in potato,” Journal of Experimental Botany, vol. 58, no. 11, pp. 2761–2774, 2007. View at: Publisher Site | Google Scholar
  68. B. Kloosterman, M. Oortwijn, J. uitdeWilligen et al., “From QTL to candidate gene: genetical genomics of simple and complex traits in potato using a pooling strategy,” BMC Genomics, vol. 11, no. 1, article 158, 2010. View at: Publisher Site | Google Scholar
  69. X. Chen, P. E. Hedley, J. Morris, H. Liu, R. E. Niks, and N. R. Waugh, “Combining genetical genomics and bulked segregant analysis-based differential expression: an approach to gene localization,” Theoretical and Applied Genetics, vol. 122, no. 7, pp. 1375–1383, 2011. View at: Publisher Site | Google Scholar

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