Development of genomic SSR markers for fingerprinting lettuce (Lactuca sativaL.) cultivars and mapping genes
© Rauscher and Simko; licensee BioMed Central Ltd. 2013
Received: 28 September 2012
Accepted: 8 January 2013
Published: 22 January 2013
Lettuce (Lactuca sativa L.) is the major crop from the group of leafy vegetables. Several types of molecular markers were developed that are effectively used in lettuce breeding and genetic studies. However only a very limited number of microsattelite-based markers are publicly available. We have employed the method of enriched microsatellite libraries to develop 97 genomic SSR markers.
Testing of newly developed markers on a set of 36 Lactuca accession (33 L. sativa, and one of each L. serriola L., L. saligna L., and L. virosa L.) revealed that both the genetic heterozygosity (UHe = 0.56) and the number of loci per SSR (Na = 5.50) are significantly higher for genomic SSR markers than for previously developed EST-based SSR markers (UHe = 0.32, Na = 3.56). Fifty-four genomic SSR markers were placed on the molecular linkage map of lettuce. Distribution of markers in the genome appeared to be random, with the exception of possible cluster on linkage group 6. Any combination of 32 genomic SSRs was able to distinguish genotypes of all 36 accessions. Fourteen of newly developed SSR markers originate from fragments with high sequence similarity to resistance gene candidates (RGCs) and RGC pseudogenes. Analysis of molecular variance (AMOVA) of L. sativa accessions showed that approximately 3% of genetic diversity was within accessions, 79% among accessions, and 18% among horticultural types.
The newly developed genomic SSR markers were added to the pool of previously developed EST-SSRs markers. These two types of SSR-based markers provide useful tools for lettuce cultivar fingerprinting, development of integrated molecular linkage maps, and mapping of genes.
KeywordsData resolution statistics Genotyping Lactuca Linkage map Marker distribution Microsatellites
Cultivated lettuce (Lactuca sativa L.) is a self-fertilizing diploid species from the family of Compositae (Asteraceae) with 2n = 2x = 18 chromosomes. Several horticultural types of lettuce are cultivated worldwide for human consumption. Classification of lettuce cultivars into horticultural types is generally based on head and leaf shape, size, and structure and stem length. The seven types include crisphead (combined iceberg and Batavia-type lettuces), romaine, butterhead, Latin, leaf, stem, and oil lettuces.
Several types of biochemical and molecular markers have been applied for lettuce genotyping, such as isozymes , restriction fragment length polymorphism - RFLP , random amplified polymorphic DNA - RAPD , amplified fragment length polymorphism - AFLP , simple sequence repeats - SSR , target region amplification polymorphism - TRAP [6, 7], expressed sequence tag based SSR - EST-SSR , single nucleotide polymorphism – SNP , and single position polymorphism – SPP . Genotyping with molecular markers is used for cultivar fingerprinting, detection of genetic diversity, assessment of population structure, mapping genes of interest, and for selection of desirable genotypes in breeding programs. Fingerprinting of plant cultivars is frequently carried out with SSR markers (microsatellites) because they are co-dominant, multi-allelic and thus more informative than dominant-types of markers. However, development of SSR markers is costly and time-consuming and therefore only a very limited number of SSR markers are publicly available for lettuce . Previously, we have developed a set of EST-SSR markers  from approximately twenty thousand unigenes of L. sativa and its close wild relative prickly lettuce (L. serriola L.). In the present work we describe the development of SSR markers from genomic DNA for fingerprinting lettuce cultivars. To develop this set of novel SSR markers we used the method of enriched microsatellite libraries [11–13].
Objectives of the present work were to 1) develop a set of genomic SSR markers; 2) test marker polymorphism on a diverse set of lettuce cultivars; and 3) integrate the SSR markers into the molecular linkage map of lettuce.
Development of genomic SSR markers
Genomic SSR markers were developed from L. sativa cv. Salinas according to the protocols of Glenn and Schable  and Farias et al. , with some modifications. The procedure consists of DNA extraction, DNA digestion with a restriction enzyme, ligation of linkers to DNA fragments, PCR-enrichment for microsatellite-containing fragments, hybridization to microsatellite-specific probes, recovery of microsatellite-containing fragments, and cloning and sequencing of products.
Approximately 100 mg of tissue from young leaves of a month-old, greenhouse-grown plant was collected and immediately lyophilized. The sample was ground to fine powder using a TissueLyser mill before extracting DNA with DNeasy Plant Mini Kit (both from Qiagen, Valencia, CA). The DNA concentration and quality was analyzed with an ND-1000 Spectrometer (NanoDrop Technologies, Wilmington, DE). Three μg of genomic DNA was digested with BfuCI, an isoschizomer of Sau3AI (New England Biolabs Ipswich, MA) according to the manufacturer’s instructions. The enzyme was deactivated at 80°C for 20 min and a 5 μl aliquot was run on a 0.8% agarose gel to verify the digestion. The linkers were created by hybridizing two oligonucleotides: Er1BhGATCSticky 5′-GAT CGG CAG GAT CCA CTG AAT TCG C-3′ and Er1Bh1Blunt 5′-GCG AAT TCA GTG GAT CCT GCC-3′. These linkers were then ligated to the fractioned DNA using T4 DNA ligase (New England Biolabs, Ipswich, MA) following the manufacturer’s instructions.
PCR conditions (initial denaturation, number of cycles, denaturation, annealing, elongation, and a final extension step)
Enrichment for microsatellite-containing fragments
94°C for 2 min, followed by 12 cycles of 94°C for 15 sec, 55°C for 35 sec, 72°C for 90 sec
Recovery of microsatellite-containing fragments
35 cycles of 94°C for 15 sec, 55°C for 35 sec, 72°C for 30 sec, and a final extension at 72°C for 5 min
Preparation of products for cloning
94°C for 2 min, followed by 15 cycles of 94°C for 15 sec, 55°C for 35 sec, 72°C for 30 sec, and a final extension at 72°C for 5-10 min
Confirmation of cloned products
96°C for 2 min, followed by 33 cycles of 94°C for 40 sec, 57°C for 12 sec, 72°C for 30 sec, and a final extension at 72°C for 5 min
Genotyping with SSRs
96°C for 2 min, followed by 33 cycles at 94°C for 35 sec, annealing temperature* for 15 sec, 72°C for 30 sec, and a final extension at 72°C for 5 min
Once the fragment recovery was verified, a second PCR-enrichment was set up to prepare sequences for cloning. Four reactions were set up with 0.8 mM dNTPs, 1× PCR buffer, 0.4 μM Er1Bh1Blunt primer, 2.5 U Taq Polymerase and 1 μl of the hybridization product (Table 1). The PCR products were pooled, cleaned with QiaQuick columns (Qiagen, Valencia, CA), and cloned using Topo TA cloning kit for sequencing and E. coli Mach1-T1R cells (Invitrogen, Grand Island, NY), according to the manufacturer’s instructions. Transformed cells were passed to 96 well plates with lysogeny broth (LB) containing 50 mg/ml ampicillin, and grown for at least 4 hours at 37°C. A confirmation PCR was carried out using standard M13 forward and reverse primers and 2–3 μl of the LB medium with bacterial growth as a template. Bovine serum albumin in the concentration of 25 μg/ml was added to the PCR; all other reagents were used in concentrations described above. E. coli colonies that contained products of expected size were transferred to Wu Broth supplemented with ampicillin and submitted for sequencing to the USDA-ARS Genomics and Bioinformatics Research Unit in Stoneville, MS. Sequencing data were cleaned up from vector contamination and assembled in contigs using CLC DNA workbench 5.0 (CLCBio Aarhus, Denmark). The SSRs with the minimal length of 14 bp were identified using WebSat . Primers for SSR amplification were designed by Primer3 software  integrated into WebSat. Primer quality analysis was performed with OligoAnalizer 3.1 (Integrated DNA Technologies Inc, Coralville, IA). When sequences contained multiple SSRs, different primer-pairs were designed for each SSR. If amplification with the Primer 3-designed primers did not yield expected products, a second pair of primers was designed using CLC DNA workbench. Sequences of SSR-containing fragments were compared in January 2012 to the GenBank database (http://www.ncbi.nlm.nih.gov) using CLC DNA workbench 5.0. The ‘blastn’ option of the BLAST algorithm  was applied to search the nucleotide collection (nr) of the viridiplantae database using low complexity filter to avoid spurious hits based on microsatellite sequence only. The threshold of significance to report similarity was set at 1e-4.
Testing of marker polymorphism
List of 36 Lactuca accessions genotyped with genomic SSR markers
Horticultural type or species
Bibb, Big Boston, Dark Green Boston, Margarita
Calmar, Empire, Great Lakes 54, Iceberg, La Brillante, Reine des Glaces, Salinas, Salinas 88, Vanguard, Winterhaven
Eruption, Little Gem
Australian, Grand Rapids, Lolla Rossa, Prizehead, Red Oak Leaf, Red Salad Bowl, Salad Bowl
Clemente, Heart’s Delight, Paris Island Cos, PI 207490, Triple Threat, Valmaine
Balady Aswan, Celtuce, Da Ye Wo Sun
Genotyping with SSR markers: The PCR conditions for SSR amplification were optimized for each primer pair. The optimal PCR conditions are described in Additional file 1. In general, the reactions were set up using 0.2 μM of each primer, 5 ng of DNA template, and 1× of Taq PCR master mix (New England Biolabs, Ipswich, MA) in a final volume of 10 μl (Table 1). The PCR products were separated using eGene HDA-GT12 DNA analyzer (currently known as QIAxcel System, from Qiagen, Valencia, CA) and scored by Biocalculator software (eGene, Irvine, CA).
Analysis of genetic heterozygosity: The statistical analyses of SSR data were performed with GenAlEx 6.1  for codominant markers and GenoDive v.2.0b20 . Missing data and null alleles were excluded from the analysis. The unbiased estimate of genetic heterozygosity UHe and observed number of different alleles Na were used to measure marker informative value (GenAlEx 6.1). Genetic distances (F st )  between all pairs of horticultural types with at least two accessions, analysis of molecular variance (AMOVA) , and principal components analysis (PCA) were calculated using GenoDive v.2.0b20. The significance of the differences between the EST-based  and genomic SSRs were tested with Student’s t-test.
Consistency of molecular marker datasets: Data resolution (DR) statistics were used to evaluate the internal consistency of the SSRs dataset with the program written by van Hintum . DR values can be in the range from 0 to 1; where higher values indicate higher internal consistency of the data. The number of replications was set to 10,000.
Identification of genotypes: The software MultiLocus ver. 1.3b  was used to estimate the number of different genotypes that can be identified in a set of 36 accessions with a gradually increasing number of markers. This analysis shows whether scoring more markers leads to increasing number of identified genotypes. One thousand samplings of markers were performed at random from 1 to m-1, where m is the total number of markers. The relative number of identified genotypes was calculated by dividing the number of identified genotypes by the total number of accessions.
Integrating SSR markers into the molecular linkage map of lettuce
Newly developed genomic SSRs were integrated into the L. sativa (cv. Salinas) × L. serriola (accession UC96US23) molecular linkage map . A framework linkage map consisted of SNP and AFLP markers spaced approximately 5–10 cM apart and covering all nine lettuce linkage groups. These framework markers were selected from the integrated SNP/AFLP linkage map, and the marker information was downloaded in April 2010 from the Compositae Genome Project website (compgenomics.ucdavis.edu/compositae_LettMap.php). Both parental genotypes and 96 F8 recombinant inbred lines (RILs) from the interspecific L. sativa × L. serriola mapping population were genotyped with SSRs. DNA isolation and genotyping with SSRs was carried out as described above. Integration of the SSR markers into the framework linkage map was performed using MapManager QTX version 0.30 . Program settings included SelfRI for linkage evaluation, the Kosambi mapping function, inference of missing data, and the command for marker distribution with p-value ≤ 0.001. In addition to genomic SSRs, EST-SSR markers previously developed in our laboratory  were also integrated into this molecular linkage map.
Modeling and analysis of marker distribution
To analyze distribution of the SSR markers on the molecular linkage map, we compared the observed distribution of markers with a model that assumes a random distribution of markers. This model was developed by randomly placing markers on nine linkage groups of the molecular linkage map. One thousand models were generated for each linkage group populated with genomic SSRs and EST-SSRs. Analyses of marker distribution were based on 1) the length of intervals between two successive markers and 2) the clustering of markers. The length of intervals (in cM) between two successive markers was calculated from the linkage map (or modeled data). Subsequently, the intervals were grouped into bins containing intervals of similar size (in 10 cM increase). Evaluation of clustering was carried out by dividing each of the nine linkage groups into segments 20 cM long. The number of markers per 20 cM-long segment was counted for both the real and modeled data.
Goodness of fit between observed and modeled distributions of markers was analyzed both with Kolmogorov–Smirnov (K-S) test, and the Pearson’s Chi-square (χ2) test. Modeling and statistical analyses were performed with Microsoft Excel v.14.1.4 (Microsoft, Redmond, WA) and JMP 6.0.3 (SAS Institute, Cary, NC, USA).
Results and discussion
A total of 217 products were amplified from 548 bacterial colonies grown on a selective media (LB with ampicillin). One hundred and fifty-four of these products originated from mix 2, 24 from mix 3, and 39 from mix 4. Out of 217 products, 192 were sequenced, yielding 117 unique sequences that contained microsatellites. Sequencing revealed that some fragments contain more than one microsatellite. In such case, an attempt was made to design primers that would amplify each microsatellite individually. The microsatellite-containing sequences were named based on their origin ( L actuca s ativa cv. Salinas) followed by a plate code (A or B) and a consecutive number (LSSA ## or LSSB ##).
Seventy-nine percent of sequenced products contained dinucleotide repeats; 14% of products contained trinucleotide repeats; 3% of products contained tetranucleotide repeats, and 4% of products contained repeats consisting of five or more nucleotides. Two separate repeats were detected in 24% of products and imperfect repeats were found in 22% of products.
Results of sequence homology searches
Marker informative value and analysis of accessions
Analysis of molecular variance (AMOVA) calculated from genomic SSR markers
Source of variation
Percentage of variation
Among horticultural types
Pairwise differentiation ( F st ) among horticultural types calculated from genomic SSR markers
Consistency of datasets and genotypic diversity
Distribution and clustering of SSR markers on the interspecific molecular linkage map
We have developed a set of 97 genomic SSRs and placed 54 of them on the interspecific molecular linkage map of lettuce. The SSR markers appear to be mostly randomly distributed in the genome with a possible cluster of markers in a single region on LG 6. Based on a sample of genotyping results, the maximum estimated genotyping error per sample is up to 8%. The highest error rate was observed when a difference in the size of analyzed alleles is below 3 bp. This rate of error is similar to that reported on maize , though it is higher than in some other reports [39, 40]. Generally, genotyping of lettuce with genomic SSRs produces a higher error rate than genotyping with EST-SSR . The factors increasing error rate involve a presence of stutter bands, high number of alleles per locus, and large product size . The other possibility for a relatively high error rate observed in our genotyping system is that eGene DNA analyzer has a lower resolution than some other instruments used for SSR analysis . The newly developed set of genomic SSRs in combination with previously developed EST-SSRs will be useful for cultivar fingerprinting, construction of integrated molecular linkage maps, and mapping genes of interest .
Described sequences have been submitted to GenBank database under accession numbers JX474909 to JX474987.
Analysis of molecular variance
Expressed sequence tag
- K-S test:
Number of loci per SSR marker
Principal component analysis
Polymerase chain reaction
Resistance gene candidates
Simple sequence repeat
Unbiased estimate of genetic heterozygosity.
The authors would like to thank R. Michelmore, M. Truco, O. Ochoa, L. McHale and R. Hayes for providing seeds, and/or marker information, B. Scheffler for sequencing services, and A. Atallah, A. Folck, and M. Estrada for technical assistance. This work was partly supported by the California Leafy Greens Research Program. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.
- Kesseli RV, Michelmore RW: Genetic variation and phylogenies detected from isozyme markers in species of Lactuca. J Hered. 1986, 77: 324-331.Google Scholar
- Kesseli R, Ochoa O, Michelmore R: Variation at RFLP loci inLactucaspp. and origin of cultivated lettuce (L. sativa). Genome. 1991, 34: 430-436. 10.1139/g91-065.View ArticleGoogle Scholar
- Kesseli RV, Paran I, Michelmore RW: Analysis of a detailed genetic linkage map of Lactuca sativa (lettuce) constructed from RFLP and RAPD markers. Genetics. 1994, 136: 1435-1446.PubMedPubMed CentralGoogle Scholar
- Hill M, Witsenboer H, Zabeau M, Vos P, Kesseli R, Michelmore R: PCR-based fingerprinting using AFLPs as a tool for studying genetic relationships in Lactuca spp. Theor Appl Genet. 1996, 93: 1202-1210. 10.1007/BF00223451.PubMedView ArticleGoogle Scholar
- Van de Wiel C, Arens P, Vosman B: Microsatellite retrieval in lettuce (Lactuca sativa L.). Genome. 1999, 42: 139-149.PubMedView ArticleGoogle Scholar
- Hu J, Ochoa OE, Truco MJ, Vick BA: Application of the TRAP technique to lettuce (Lactuca sativa L.) genotyping. Euphytica. 2005, 144: 225-235. 10.1007/s10681-005-6431-1.View ArticleGoogle Scholar
- Simko I, Hu J: Population structure in cultivated lettuce and its impact on association mapping. J Am Soc Hort Sci. 2008, 133: 61-68.Google Scholar
- Simko I, Pechenick DA, McHale LK, Truco MJ, Ochoa OE, Michelmore RW, Scheffler BE: Association mapping and marker-assisted selection of the lettuce dieback resistance gene Tvr1. BMC Plant Biol. 2009, 9: 135-10.1186/1471-2229-9-135.PubMedPubMed CentralView ArticleGoogle Scholar
- Kwon SJ, Truco MJ, Hu J: LSGermOPA, a custom OPA of 384 EST-derived SNPs for high-throughput lettuce (Lactuca sativa L.) germplasm fingerprinting. Mol Breed. 2012, 29: 887-901. 10.1007/s11032-011-9623-5.View ArticleGoogle Scholar
- Stoffel K, van Leeuwen H, Kozik A, Caldwell D, Ashrafi H, Cui X, Tan X, Hill T, Reyes-Chin-Wo S, Truco MJ, Michelmore RW, Van Deynze A: Development and application of a 6.5 million feature affymetrix genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.). BMC Genomics. 2012, 13: 185-10.1186/1471-2164-13-185.PubMedPubMed CentralView ArticleGoogle Scholar
- Edwards KJ, Barker JHA, Daly A, Jones C, Karp A: Microsatellite libraries enriched for several microsatellite sequences in plants. Biotechniques. 1996, 20: 758-760.PubMedGoogle Scholar
- Farias IP, Hrbek T, Brinkmann H, Sampaio I, Meyer A: Characterization and isolation of DNA microsatellite primers for Arapaima gigas, an economically important but severely over-exploited fish species of the Amazon basin. Mol Ecol Notes. 2003, 3: 128-130. 10.1046/j.1471-8286.2003.00375.x.View ArticleGoogle Scholar
- Glenn TC, Schable NA: Isolating microsatellite DNA loci. Methods Enzymol. 2005, 395: 202-222.PubMedView ArticleGoogle Scholar
- Martins WS, Lucas DC, Neves KF, Bertioli DJ: WebSat - A web software for microsatellite marker development. Bioinformation. 2009, 3: 282-283. 10.6026/97320630003282.PubMedPubMed CentralView ArticleGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods in Molecular Biology, vol. 132: Bioinformatics Methods and Protocols. Edited by: Misener S, Krawetz SA. Humana Press, Totowa NJ, 2000, 365-386.Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-410.PubMedView ArticleGoogle Scholar
- Peakall R, Smouse PE: GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006, 6: 288-295. 10.1111/j.1471-8286.2005.01155.x.View ArticleGoogle Scholar
- Meirmans PG, Van Tienderen PH: GENOTYPE and GENODIVE: Two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes. 2004, 4: 792-794. 10.1111/j.1471-8286.2004.00770.x.View ArticleGoogle Scholar
- Nei M: Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978, 89: 583-590.PubMedPubMed CentralGoogle Scholar
- Michalakis Y, Excoffier L: A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics. 1996, 142: 1061-1064.PubMedPubMed CentralGoogle Scholar
- Excoffier L, Smouse PE, Quattro JM: Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics. 1992, 131: 479-491.PubMedPubMed CentralGoogle Scholar
- Simko I: Development of EST-SSR markers for the study of population structure in lettuce (Lactuca sativa L.). J Hered. 2009, 100: 256-262.PubMedView ArticleGoogle Scholar
- van Hintum TJL: Data resolution: A jackknife procedure for determining the consistency of molecular marker datasets. Theor Appl Genet. 2007, 115: 343-349. 10.1007/s00122-007-0566-5.PubMedView ArticleGoogle Scholar
- Agapow PM, Burt A: Indices of multilocus linkage disequilibrium. Mol Ecol Notes. 2001, 1: 101-102. 10.1046/j.1471-8278.2000.00014.x.View ArticleGoogle Scholar
- Truco MJ, Antonise R, Lavelle D, Ochoa O, Kozik A, Witsenboer H, Fort SB, Jeuken MJW, Kesseli RV, Lindhout P, Michelmore RW, Peleman J: A high-density, integrated genetic linkage map of lettuce (Lactuca spp.). Theor Appl Genet. 2007, 115: 735-746. 10.1007/s00122-007-0599-9.PubMedView ArticleGoogle Scholar
- Manly KF, Cudmore RH,Jr Meer JM: Map Manager QTX, cross-platform software for genetic mapping. Mamm Genome. 2001, 12: 930-932. 10.1007/s00335-001-1016-3.PubMedView ArticleGoogle Scholar
- Goremykin VV, Lockhart PJ, Viola R, Velasco R: The mitochondrial genome of Malus domestica and the import-driven hypothesis of mitochondrial genome expansion in seed plants. Plant J. 2012, 71: 615-626. 10.1111/j.1365-313X.2012.05014.x.PubMedView ArticleGoogle Scholar
- Michelmore RW, Meyers BC: Clusters of resistance genes in plants evolve by divergent selection and a birth-and-death process. Genome Res. 1998, 8: 1113-1130.PubMedGoogle Scholar
- Scott KD, Eggler P, Seaton G, Rossetto M, Ablett EM, Lee LS, Henry RJ: Analysis of SSRs derived from grape ESTs. Theor Appl Genet. 2000, 100: 723-726. 10.1007/s001220051344.View ArticleGoogle Scholar
- Cho YG, Ishii T, Temnykh S, Chen X, Lipovich L, McCouch SR, Park WD, Ayres N, Cartinhour S: Diversity of microsatellites derived from genomic libraries and GenBank sequences in rice (Oryza sativa L.). Theor Appl Genet. 2000, 100: 713-722. 10.1007/s001220051343.View ArticleGoogle Scholar
- Eujayl I, Sorrells ME, Baum M, Wolters P, Powell W: Isolation of EST-derived microsatellite markers for genotyping the A and B genomes of wheat. Theor Appl Genet. 2002, 104: 399-407. 10.1007/s001220100738.PubMedView ArticleGoogle Scholar
- Pashley CH, Ellis JR, McCauley DE, Burke JM: EST databases as a source for molecular markers: Lessons from Helianthus. J Hered. 2006, 97: 381-388.PubMedView ArticleGoogle Scholar
- Liewlaksaneeyanawin C, Ritland CE, El-Kassaby YA, Ritland K: Single-copy, species-transferable microsatellite markers developed from loblolly pine ESTs. Theor Appl Genet. 2004, 109: 361-369.PubMedView ArticleGoogle Scholar
- Simko I, Eujayl I, van Hintum TJL: Empirical evaluation of DArT, SNP, and SSR marker-systems for genotyping, clustering, and assigning sugar beet hybrid varieties into populations. Plant Sci. 2012, 184: 54-62.PubMedView ArticleGoogle Scholar
- Young WP, Schupp JM, Keim P: DNA methylation and AFLP marker distribution in the soybean genome. Theor Appl Genet. 1999, 99: 785-792. 10.1007/s001220051297.View ArticleGoogle Scholar
- Röder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW: A microsatellite map of wheat. Genetics. 1998, 149: 2007-2023.PubMedPubMed CentralGoogle Scholar
- Semagn K, Bjørnstad Å, Skinnes H, Marøy AG, Tarkegne Y, William M: Distribution of DArT, AFLP, and SSR markers in a genetic linkage map of a doubled-haploid hexaploid wheat population. Genome. 2006, 49: 545-555. 10.1139/G06-002.PubMedView ArticleGoogle Scholar
- Jones ES, Sullivan H, Bhattramakki D, Smith JSC: A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor Appl Genet. 2007, 115: 361-371. 10.1007/s00122-007-0570-9.PubMedView ArticleGoogle Scholar
- Hoffman JI, Amos W: Microsatellite genotyping errors: Detection approaches, common sources and consequences for paternal exclusion. Mol Ecol. 2005, 14: 599-612.PubMedView ArticleGoogle Scholar
- Selkoe KA, Toonen RJ: Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol Lett. 2006, 9: 615-629. 10.1111/j.1461-0248.2006.00889.x.PubMedView ArticleGoogle Scholar
- Wang X, Rinehart TA, Wadl PA, Spiers JM, Hadziabdic D, Windham MT, Trigiano RN: A new electrophoresis technique to separate microsatellite alleles. Afr J Biotechnol. 2009, 8: 2432-2436.Google Scholar
- Rauscher G, Hayes R, Simko I: QTL mapping of resistance to powdery mildew in lettuce. Phytopathology. 2009, 99: S107-Google Scholar