Plant Biotechnology
Electronic Journal of Biotechnology ISSN: 0717-3458 Vol. 13 No. 6, Issue of November 15, 2010
© 2010 by Pontificia Universidad Católica de Valparaíso -- Chile Received June 14, 2010 / Accepted September 22, 2010
DOI: 10.2225/vol13-issue6-fulltext-14 How to reference this article

Genetic mapping of EST-SSR, SSR and InDel to improve saturation of genomic regions in a previously developed sunflower map

Paola Talia1 · Verónica Nishinakamasu1 · Horacio Esteban Hopp1, 2 · Ruth Amelia Heinz1, 2 · Norma Paniego*1

1Instituto de Biotecnología, CICVyA, CNIA, INTA Castelar, Buenos Aires, Argentina
2Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina

*Corresponding autor:

Financial support: This work was supported by INTA-AEBIO1330 /241331, and granted from the Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT/ PAV2004-137), Argentina.

Keywords: EST-SSR, InDels, linkage map, SSR, sunflower.

Abstract   Reprint (PDF)

In order to saturate a sunflower genetic map and facilitate marker-assisted selection (MAS) breeding for stress response, it is necessary to enhance map saturation with molecular markers localized in linkage groups associated to genomic regions involved in these traits. This work describes the identification and characterization of 1,134 simple sequence repeat (SSR) containing expressed sequence tags (EST) from unigenes available databases. Twelve of these functional markers as well as 41 public SSR markers were successfully localized in linkage groups, thus contributing to the saturation of specific regions on a reference genetic-linkage-map derived from recombinant inbred lines (RIL) mapping population from the cross between PAC2 x RHA266 lines. The enriched map includes 547 markers (231 SSR, 9 EST-SSR, 3 insertions/deletions (InDel) and 304 amplified fragment length polymorphisms (AFLP) distributed in 17 linkage groups (LG), spanning genetic size to 1,942.3 cM and improving its mean density to 3.6 cM per locus. As consequence, no gaps longer than 13.2 cM remain uncovered throughout the entire map, which increases the feasibility of detecting genes or traits of agronomic importance in sunflower.


Microsatellite markers have been widely used in genetic analysis of crop plants based on their ubiquitous presence in genomes and their genetic characteristics of being co-dominant, frequently multi-allelic and chromosome-specific. Although non-genic simple sequence repeats (SSR) are more polymorphic than genic SSR (Cho et al. 2000; Lee et al. 2004), the estimated frequency of genic SSR is high (Morgante et al. 2002) and their location within putative candidate genes make them particularly interesting as functional markers. They can be adapted to high-throughput genotyping and thus, become suitable for the construction of high-density linkage maps, gene mapping and marker-assisted selection. However, their development is expensive, labor intensive and time consuming, especially if they derived from genomic libraries (Paniego et al. 2002; Varshney et al. 2002; Tang et al. 2003). Alternatively, SSR can be identified by mining expressed sequence tags (EST) databases and used for SSR marker development (Morgante and Olivieri, 1993). These markers are commonly referred as EST-SSR and they have been applied to genetic studies in different plant species as Eragrostis (Cervigni et al. 2008) sugarcane (Cordeiro et al. 2001), wheat (Eujayl et al. 2002; Leigh et al. 2003; Yu et al. 2004; Zhang et al. 2005), barley (Thiel et al. 2003; Chabane et al. 2005), rye (Hackauf and Wehling, 2002), melon (Fernandez-Silva et al. 2008), quercus (Ueno et al. 2008), oil palm (Singh et al. 2008), tea (Sharma et al. 2009) eucalyptus (Acuña el al. 2010) and sunflower (Kumpatla and Mukhopadhyay, 2005; Pashley et al. 2006; Heesacker et al. 2008).

Over recent years there has been an increase in the availability of ESTs for a wide range of plant species, including sunflower (Gentzbittel et al. 1999; Fernández et al. 2003; Heesacker et al. 2008) enabling identification and genetic characterization of functional markers (Kolkman et al. 2004; Kumpatla and Mukhopadhyay, 2005; Lai et al. 2005; Liu and Burke, 2006; Pashley et al. 2006; Kolkman et al. 2007; Fusari et al. 2008; Heesacker et al. 2008).

The usefulness of EST-SSR markers arises from their close linkage to potentially important genes, helping to identify candidate genes for quantitative trait loci (QTL). Moreover, these markers could also be of great assistance for comparative studies in related species due to higher heterologous conservation.

Over the past decades, several genetic linkage maps that differ in length and density were developed for cultivated sunflower, based on different molecular markers such as restriction fragment length polymorphism (RFLP) and/or random amplification of polymorphic DNA (RAPD) markers for the first reported maps (Berry et al. 1995; Gentzbittel et al. 1995; Berry et al. 1997; Jan et al. 1998; Rieseberg, 1998; Gentzbittel et al. 1999; Berry et al. 2003). Later, the addition of amplified fragment length polymorphisms (AFLPs) markers allowed further saturation of genetic linkage maps (Peerbolte and Peleman, 1996; Gedil et al. 2001; Al-Chaarani et al. 2004). More recently, the concomitant development of a large number of SSR markers and the automatization of the mapping procedures (Paniego et al. 2002; Tang et al. 2002; Tang et al. 2003) lead to the generation of SSR-anchorage linkage maps for different populations including those relevant for sunflower (Paniego et al. 2002; Tang et al. 2002; Tang et al. 2003; Yu et al. 2003; Kiani et al. 2007). Most of the SSR markers used in sunflower mapping are neutral (usually located in intergenic genomic regions), as they were developed from genomic libraries using microsatellite motives as hybridization probes (Paniego et al. 2002; Tang et al. 2002; Tang et al. 2003). In recent years, due to the rapid increase of sequence information, the generation of EST-SSR and single nucleotide polymorphisms (SNPs) markers has become an attractive alternative to complement existing SSR marker collections (Pashley et al. 2006, Heesacker et al. 2008) allowing the inclusion of functional markers in genetic maps (Lai et al. 2005).

The purposes of this work were to mine the unigene sunflower database (Helianthus annuus Gene Index, HAGI) developed for TIGR (now DFCI Gene Index Database) for the identification and characterization of novel EST-derived SSR in terms of frequency, type, and motifs of repetition, and their inclusion along with new genomic SSR and insertion/deletions (InDels) on a previously developed sunflower reference map (Kiani et al. 2007), exploring their colocalization with biotic and abiotic stress tolerance QTL.

Materials and Methods

Plant material

Sunflower genomic DNAs extracted from PAC2, RHA266, HA89 and RHA801 inbred lines were used for amplification of EST-SSR and polymorphism detection considering that these lines constitute the parentals of two mapping populations used for characterization of stress response traits. A segregant population of 94 recombinant inbred lines (RILs), derived from the crossing of sunflower PAC2 and RHA266 (Roath et al. 1981; Gentzbittel et al. 1995) was used for linkage mapping.

EST-SSRs survey in the TIGR sunflower unigene database

In order to identify EST-SSR, the database of sunflower TIGR unigenes (HAGI), currently maintained and updated at the Dana-Farber Cancer Institute ( was screened using the CUGI-SSR Server (Clemson University Genome Institute, USA) for the presence of repeat tandem sequences with di, tri, tetra and penta-nucleotide motives. The database used for this analysis included 36,743 unigenes (Release 4.0).

The conditions set to identify EST-SSR were the following: repetition length ≥18 bases for the di-and trinucleotides and ≥20 bases for tetra and pentanucleotides. The output file was manually evaluated and those primers amplifying more than one simple sequence motif (compound microsatellites) or amplifying imperfect repetitions were ruled out of further analysis. A subset of 127 identified EST-SSR were selected and evaluated for polymorphism detection in four sunflower genotypes.

Genomic SSR and identification of InDels

Two sets of SSR, the set of 50 SSR named ‘ORS’ (Tang et al. 2002; Tang et al. 2003) selected by its position in a composite map (Yu et al. 2003) and a set of 117 SSR named ‘HA’, previously developed in the lab and without genomic localization (Paniego et al. 2002) were evaluated for polymorphism in PAC2 and RHA266 parental lines.

DNA isolation and PCR amplification

Genomic DNA was isolated from lyophilized young leaves of the four parental inbred lines and 94 RILs using the CTAB method (Saghai-Maroof et al. 1984).

PCR was carried out in 12 µl (final volume) using a Mastercycler ep-gradient (Eppendorf, Germany). Each reaction was accomplished using 30 ng of genomic DNA in 1 x PCR buffer including 1.5 mM MgCl2, 0.2 mM dNTPs, 0.25 µM of each primer, 0.75 U of Taq DNA polymerase (Invitrogen, USA). All fragments were amplified using the following touch-down PCR profile: an initial denaturing step of 4 min at 95ºC; 13 cycles of 30 sec at 94ºC, 30 sec at 63ºC, and 1 min at 72ºC, annealing temperature decreasing to 55ºC by 1ºC per cycle; followed by 30 cycles of 30 sec at 94ºC, 30 sec at 50ºC, 1 min at 72ºC, followed by 10 min at 72ºC.

Amplified SSR and EST-SSRs fragments of parental lines were evaluated by conventional and automatic methods. Conventional method consisted on 6% denaturing polyacrylamide gels (AA:BIS = 19:1) and 7 M urea in 0.5 x TBE buffer. PCR fragment were mixed with equal volumes of loading buffer (formamide containing 0.8 mM EDTA and 5 x bromophenol blue and xylene cyanol), denatured at 95ºC for 5 min and cooled on ice. Samples were then loaded on pre-heated Gibco BRL Sequencing System (Life Technologies, USA), and run at 2,000 V for 3 up to 4 hrs, depending on the fragment length. After the run, the fragments were visualized by silver staining. For this aim the gel was fixed for 15 min in 10% acetic acid, rinsed in deionized water, stained for 30 min in 0.3% (w/v) silver nitrate, rinsed again in deionized cold water, and developed for approximately 15 min until the bands became visible. The gel was then fixed for 15 min in 10% acetic acid. Scoring was done by visual inspection.

Amplified polymorphic fragments from genomic SSR and EST-SSR were alternatively analyzed using fluorescent labelled PCR primers according to the procedures described by Tang et al. (2003) and Kiani et al. (2007). Polymerase chain reaction (PCR) fragments were resolved using electrophoresis through an ABI 3130xl DNA analyzer (Applied Biosystems, USA). Fragment sizing was done using the ROX 500 internal-lane standard (Applied Biosystems; ROX, 6-carboxy-x-rhodamine). GeneMapper 3.0 software (Applied Biosystems, USA) was used to score SSR alleles.

After evaluating allele profiles between the parental lines RHA266 and PAC2, useful polymorphisms were genotyped in the 94 RILs.

Three polymorphic InDel described by Fusari et al. (2008) between PAC2 and RHA266 (Fusari et al. 2008) were genotyped in the present work as well, using fluorescent labelled primers.

Gene Ontology (GO) annotations

Gene ontology (GO), Ashburner et al. 2000) and Enzyme Commission (EC) annotations for the accessions shown in Table 1 were obtained using Blast2GO (Conesa and Götz, 2008). The BLASTX (Altschul et al. 1990) analysis against NCBI's NR Database was performed with an e-value threshold set at 1e-5. For the annotation step the e-value hit filter was 1e-6 and the annotation cut-off was 55 (Table 1).

Linkage analysis and map construction

Genotyping data from 94 RILs were included in a previously developed data matrix composed by 343 AFLP and 191 SSR markers (Kiani et al. 2007). Segregation pattern of each marker allele in the progeny was assessed using the GQMOL program (Schuster and Cruz, 2004, available at by FDR (False Discovery Rate) test (Benjamin and Hocheberg, 1995).

The map was constructed using Carthagene 0.999 (Schiex and Gaspin, 1997) and Mapmaker 3.0 (Lander et al. 1987). Loci were assembled into groups using likelihood odds (LOD) ratios with a LOD threshold of 4.0 and a maximum recombination frequency threshold of 0.35 (Kiani et al. 2007). The likelihoods of different locus-order possibilities were compared and the one having the highest likelihood was selected for each linkage group. Kosambi mapping function (Kosambi, 1994) was used to calculate map distances (cM) from recombination frequency. Locus order and map distance were also tested using MapMaker 3.0. Finally, the genetic map was drawn using the Mapchart 2.1 program (Voorrips, 2002).


Search for ESTs containing SSR motifs

HAGI database was used to identify EST-SSRs containing di, tri- tetra and penta-nucleotide with a minimum length of 18 (di- and tri-) to 20 (tetra-penta) bases. These parameters were chosen based on previous studies performed on different species that indicate that the polymorphism level decreases with repetitions shorter than 18 bases (Cho et al. 2000). CUGI-SSR Server identified 1,134 repetition motifs with at least one repeat from a total of 36,741 analyzed sequences (3.08%).

The relative abundance of di-, tri-, tetra-, and penta-nucleotide was 30.28%, 51.65%, 9.32% and 8.74% respectively (Figure 1).

Mononucleotides were not taken into account in this study. One hundred and forty six unigenes (12.9%) out of 1,134 SSR exhibited more than one SSR motif.

The GA/CT motif (23.2%) was the most abundant dinucleotide SSR, while the AC/GT (3.8%), AT/TA (3.2%) and CG/GC (0.08%) were detected at a lower frequency. All possible combinations of trinucleotide repeat motifs were detected in the sunflower unigenes, showing AAG/CTT (11.9%), ACC/GGT (9.2%), and AAT/ATT (7.8%) higher frequencies (Figure 2).

BLASTX analysis and GO mapping were performed for functional annotation of the genotyped EST-SSR markers. Fifteen out of seventeen sequences had at least one significant BLAST hit and eleven accessions were mapped to one or more GO terms each (Table 1).

Amplification and polymorphism degree of EST-SSR and genomic SSR

Among the 127 EST-SSR primer pairs tested in four parental lines, 70 showed clear, easy scoring banding patterns, 50 failed to amplify, 3 amplified null alleles (0/1 or 1/0) and the remaining ones generated unspecific amplification or complex amplification patterns. Twenty three EST-SSR markers were polymorphic within the parents of one or both mapping populations.

The analysis of 167 genomic SSR performed within the parents of the PAC2 x RHA266 mapping population showed 37.7% polymorphic markers, 40.6% of monomorphic, 15% of null alleles and 6.7% failed the amplification reaction for both alleles.

Genetic mapping

Sixty three SSR, 14 EST-SSR and the three previously described InDels displaying clear polymorphism between parental lines were assayed in 94 RIL. A total of 53 new markers could be positioned on the sunflower linkage map described by Kiani et al. (2007). Of these, 41 markers were genomic SSR (30 HAx and 11 ORSx) and 12 correspond to new functional markers (9 EST-SSR and 3 InDel) developed in this work (Table 2).

Molecular marker distribution among the linkage groups in the resulting map is presented in Table 3. The map now contains 547 markers (304 AFLP, 231 SSR, 9 EST-SSR and 3 InDel) placed in 17 linkage groups (Figure 3)

This linkage map was constructed using a minimum LOD score of 4.0 and a maximum recombination value of 0.35. The total map length is 1,942.3 cM with a mean density of 3.6 cM per locus (Table 3). Linkage group 10 presented the highest density value (2.9 cM/locus), while LG 17 presented the lowest one (4.9 cM/locus). The groups ranged in length from 71.9 (LG 7) to 203 cM (LG 10) and carried between 20 (GL 13) to 70 (LG 10) markers. Twenty two SSR and five EST-SSR markers remained unlinked to any group.

Among the newly identified functional markers, some are predicted to derive from key genes as those coding for transcription factors, kinases that may play important roles in signal transduction, stress resistance and metabolism (Table 1).


A strategy for identification and characterization of EST-SSR from a sunflower unigene database was implemented to unravel new functional markers which were further mapped along with other genomic SSR and InDel, in a previously developed reference map (Kiani et al. 2007).

SSR derived from the unigenes database of H. annuus were detected in 3.08% of the analyzed sequences, under the criteria set of 18 bases for the di-and trinucleotide, and 20 bases for the tetra-and pentanucleotides. This result is consistent with that found by other authors that reported rates of EST-SSR ranging between 1.5% to 4.7% for species like barley, maize, rice, sorghum and wheat (Kantety et al. 2002; Gao et al. 2003). However, in other species such as wheat and pepper, frequencies with values above 11% have been reported (Nicot et al. 2004; Yi et al. 2006). Kumpatla and Mukhopadhyay (2005) analyzed EST-SSR in 49 dicotyledonous species, including sunflower and described that the frequencies may vary from 2.65% to 16.82%. These authors analyzed 60,007 sunflowers EST deposited in GenBank database ( and found a frequency of EST-SSR of 6.18%. The criteria used for the EST-SSR identification was set in a minimum repetition value of 10 nucleotides. These authors conclude that mononucleotide SSR were the most frequent motives, followed by dinucleotide and trinucleotide SSR. On the other hand, Pashley et al. (2006) also identified SSR in sunflower EST sequences deposited in CGPDB database (, with a selection criterion of a minimum of 10 repeat units for dinucleotide and 12 for tri-and tetranucleotide motifs. They found that trinucleotides were the most abundant type of repetitions, followed by tetranucleotides and then dinucleotides. The most abundant trinucleotide motif was ATG/CAT. A selection of 188 sequences was used in their study to design primers, which were amplified in 12 species of wild H. annuus with the aim to analyze the transferability of these markers, and they identify a set of 48 functional EST-SSR. Recently, Heesacker et al. (2008) developed EST-SSR from a collection of 17,904 unigenes obtained from the assembly of 89,225 ESTs deposited in GenBank from H. annuus, H. argophyllus and H. paradoxus. Considering a repetition length ≥10 bp for dinucleotide, and a minimum number of repetitions ≥5 for the rest of the motives, they found that 10.9% of the 17,904 unigenes of H. annuus contained SSRs. Also, under these criteria, the dinucleotide motif presented the most frequent rate of recurrence, followed by trinucleotide, and finally tetranucleotide.

In general, differences in results between these studies may be attributed to the different data sets used to conduct the studies (ESTs and/or unigenes from different assembly processes), the tools and criteria used in the different analysis. In particular, the differences between the observations of this study and those of other authors can be explained based on the conditions set for the present analysis, regarding the inclusion of only perfectly matched microsatellites with a length greater than or equal to 20 bp and the exclusion of mononucleotides.

According to the procedures applied in this work, trinucleotide repeats were the most abundant type of motives in sunflower unigenes database (HAGI), represented by 51.7% of the sequences, followed by dinucleotide (30.3%), then tetranucleotides (9.3%) and finally pentanucleotides with 8.7%. This dominance of trimeric SSRs over di-, tetra-, and pentameric SSRs may be explained on the basis of the suppression of non-trimeric SSRs in coding regions due to the risk of frameshift mutations that may occur when those microsatellites alternate in length in one unit (Rungis et al. 2004). This may be because repeat numbers of trinucleotide microsatellites can change without altering the reading frame of the messenger RNA. It also could be due to the higher number of possible trinucleotide combinations compared to those for dinucleotide repeats. Morgante et al. (2002) suggested that mutation pressure and positive selection for specific single amino acid stretches could account for the doubled frequency of tri-nucleotide repeats relative to mono- and dinucleotide repeats in the genes of plant species. The most abundant dinucleotide motif found in the database analyzed was GA/CT, present in 23.2% of the sequences. The encoding triplets that can be constructed based on dinucleotide repetitions may constitute different codons, depending on the reading frame, and be translated into different amino acids. For example, GA/CT can represent codons GAG, AGA, and CUC, UCU in a population of mRNA and translated into amino acids Arg, Glu, Ala and Leu, respectively. Ala and Leu are present with high frequency in proteins, 8% and 10% respectively (Lewin, 1994). This could be one of the reasons why GA/CT is present at high frequency in the collections of ESTs. Previous research showed that the dinucleotide AG was the most common in plant ESTs (Morgante et al. 2002; Varshney et al. 2002; Kumpatla and Mukhopadhyay, 2005; Parida et al. 2006; Yi et al. 2006). The most common sunflower trinucleotide found in this work was the AAG/CTT (11.9%), followed by the ACC/GGT (9.2%) and the AAT/ATT (7.8%). According to Kumpatla and Mukhopadhyay (2005), AAG/CTT is the most abundant motive in dicot species. However, these authors reported that the most frequent trinucleotide in sunflower was GGT/ACC.

The analysis of genomic markers performed within the parents of the mapping population (PAC2 x RHA266) showed 37.7% polymorphic markers. These results agree with those reported by Tang et al. (2002), who found a polymorphism level of 41.2% between genotypes RHA280 x RHA801 and with the previous study (Kiani et al. 2007). The percentage of null alleles in the genomic analysis of the SSR was 15%, coinciding this with a study reported by Tang et al. (2002), which showed a rate of 13.7% in the loci analyzed. Validation of genic SSR in four genotypes of sunflower (RHA266, PAC2, HA89 and RHA801) resulted in amplification of 74 sequences from a total of 127 analyzed. Out of them, 13% represented polymorphic loci, 45% monomorphic, 5% null alleles and the remaining 37% showed either no amplification product, nonspecific amplification or complex or difficult to resolve banding patterns. The percentage of polymorphism observed coincides with that reported by Heesacker et al. (2008), which conclude that less than 10% of the transcribed loci in sunflower can be genetically mapped using SSR, and in agreement with reports for other species (Eujayl et al. 2004; Fraser et al. 2004; Varshney et al. 2005). The frequency of EST-SSR marker polymorphism was 18%. In general, EST-SSR has demonstrated less polymorphism compared with genomic SSR in crop plants due to higher DNA sequence conservation in transcribed regions (Cho et al. 2000; Scott et al. 2000; Rungis et al. 2004).

Here we report an improved high density genetic linkage map of sunflower constructed using a basic matrix of 584 markers (343 AFLP, 258 SSR, 14 EST-SSR and 3 EST-InDel) after incorporation of 53 new markers (41 SSR, 9 EST-SSR and 3 EST-InDel). The genome length spanned by this new linkage map is longer (1,942.3 cM) than that constructed using a 94 RILs population from a cross between RHA280 x RHA801 based on SSR/InDel markers (Tang et al. 2002), or from that constructed using SSR and SNP markers as described by Lai et al. (2005) which are 1,368.3 and 1,349.3 cM, respectively. This may be the result of using a combination of different marker types, such as AFLP and SSR which exhibit a differential distribution pattern (Sebastian et al. 2000; Mei et al. 2004; Syed et al. 2006; Zhang et al. 2006). Moreover, additional effects such as population size, marker clusters, the effects of distorted segregation markers, or the process of framework map construction could contribute to these differences (Cervera et al. 2001; Zhang et al. 2006).

Although most of the newly added markers locations spread all along the genome, 3 of them mapped to the outermost positions of their respective linkage groups when compared to the previously published map (Kiani et al. 2007). These markers were: ORS691 and TC15366 (located in LG 10) and HA3640 which is located to 0.6 cM from telomeric marker HA3448 (LG 11). In addition to enlarging the current genetic map of sunflower in telomeric regions, some other new markers helped to close previous gaps. TC23602, TC24992 and HA911 closed a gap of 10.8 cM present in LG 8, reducing the gap to 5 cM. Incorporation of a single marker (ORS420) in LG 15 allowed closing two previous gaps of 11.6 and 13.6 cM. As a consequence, no gaps longer than 13.2 cM (which is present in LG 17) can be found throughout the entire map, which increases the feasibility of detecting genes or traits of agronomic importance in sunflower.

However, the most interesting perspective of application of the newly mapped markers will be their use to better define and characterize previously published QTL in the added chromosome positions. These markers were mapped to different chromosome locations (Table 2) and some of them mapped close to QTL involved in the genetic control of water status and osmotic adjustment (Kiani et al. 2007), chlorophyll fluorescence parameters (Kiani et al. 2008), resistance to P. macdonaldi (Alfadil et al. 2007; Darvishzadeh et al. 2007), fatty acid composition in water-stressed conditions in the greenhouse and the field (Ebrahimi et al. 2008). Among the above mentioned QTL overlapping markers, some correspond to functional markers (Table 1). For example, TC26869 (which corresponds to a putative heat shock related protein gene) mapped close to a water status/osmotic adjustment QTL and TC26323 (encoding for a putative photosynthesis related protein gene) that colocalizes with a chlorophyll fluorescence determining QTL (Kiani et al. 2008). Although the functional demonstration that these genes actually are determinants of these traits is beyond the objectives of the present work, both the colocalization and the putative functions are very likely suggesting hints for their hypothetical involvement.

The fact that EST-SSRs exhibited sequence similarity to genes with a wide range of functions (Table 1) suggests that there is potential to identify markers that may be directly involved in determining agronomical important characters. For example plant peroxidases are a well-studied group of heme-containing enzymes for which many different functions have been proposed. In the majority of plant species investigated they occur as distinctive isoenzymes which can be constitutively expressed or induced in response to external factors such as wounding, stress and attack by pathogens (Veitch, 2004).

In conclusion, this map complements the previously published public map with a dense framework of AFLP, SSR, EST-SSR and EST-InDels markers loci in place for analysis of H. annuus (Kiani et al. 2007) suitable for the development of approaches that could be used to characterize traits which are difficult to manipulate through breeding programs.


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