Analysis of BAC-end sequences (BESs) and development of BES-SSR markers for genetic mapping and hybrid purity assessment in pigeonpea (Cajanus spp.)
- Abhishek Bohra†1, 2,
- Anuja Dubey†1, 3,
- Rachit K Saxena1, 2,
- R Varma Penmetsa4,
- KN Poornima1, 5,
- Naresh Kumar1, 6,
- Andrew D Farmer7,
- Gudipati Srivani1,
- Hari D Upadhyaya1,
- Ragini Gothalwal3,
- S Ramesh5,
- Dhiraj Singh6,
- Kulbhushan Saxena1,
- PB Kavi Kishor2,
- Nagendra K Singh8,
- Christopher D Town9,
- Gregory D May7,
- Douglas R Cook4Email author and
- Rajeev K Varshney1, 10Email author
© Bohra et al; licensee BioMed Central Ltd. 2011
Received: 5 December 2010
Accepted: 29 March 2011
Published: 29 March 2011
Pigeonpea [Cajanus cajan (L.) Millsp.] is an important legume crop of rainfed agriculture. Despite of concerted research efforts directed to pigeonpea improvement, stagnated productivity of pigeonpea during last several decades may be accounted to prevalence of various biotic and abiotic constraints and the situation is exacerbated by availability of inadequate genomic resources to undertake any molecular breeding programme for accelerated crop improvement. With the objective of enhancing genomic resources for pigeonpea, this study reports for the first time, large scale development of SSR markers from BAC-end sequences and their subsequent use for genetic mapping and hybridity testing in pigeonpea.
A set of 88,860 BAC (bacterial artificial chromosome)-end sequences (BESs) were generated after constructing two BAC libraries by using HindIII (34,560 clones) and BamHI (34,560 clones) restriction enzymes. Clustering based on sequence identity of BESs yielded a set of >52K non-redundant sequences, comprising 35 Mbp or >4% of the pigeonpea genome. These sequences were analyzed to develop annotation lists and subdivide the BESs into genome fractions (e.g., genes, retroelements, transpons and non-annotated sequences). Parallel analysis of BESs for microsatellites or simple sequence repeats (SSRs) identified 18,149 SSRs, from which a set of 6,212 SSRs were selected for further analysis. A total of 3,072 novel SSR primer pairs were synthesized and tested for length polymorphism on a set of 22 parental genotypes of 13 mapping populations segregating for traits of interest. In total, we identified 842 polymorphic SSR markers that will have utility in pigeonpea improvement. Based on these markers, the first SSR-based genetic map comprising of 239 loci was developed for this previously uncharacterized genome. Utility of developed SSR markers was also demonstrated by identifying a set of 42 markers each for two hybrids (ICPH 2671 and ICPH 2438) for genetic purity assessment in commercial hybrid breeding programme.
In summary, while BAC libraries and BESs should be useful for genomics studies, BES-SSR markers, and the genetic map should be very useful for linking the genetic map with a future physical map as well as for molecular breeding in pigeonpea.
Pigeonpea [Cajanus cajan (L.) Millsp.], also known as tuar or arhar, is an economically important legume crop with an annual production of 3.65 Mt. Cultivation of pigeonpea occurs on ~5 million hectares, primarily in Asia and countries of eastern and southern Africa, and to a lesser extent in countries of Latin America and the Caribbean. As a member of the sub tribe Cajaninae, pigeonpea is contained in an early diverging lineage of tribe Phaseoleae, a monophyletic group of legumes that contains several of the world's most important food legumes including soybean, common bean, cowpea and mung bean. Similar to most other Phaseoleae species, pigeonpea contains 11 pairs of chromosomes (2n = 22) and has a moderately sized genome in the range of 0.853 pg or 858 Mbp .
India is the world's largest producer of pigeonpea and the presumed center of origin . Relative to most other crop legumes pigeonpea is highly drought tolerant, being able to retain productivity with less than 650 mm annual rainfall. Owing to its capacity for symbiotic nitrogen fixation, pigeonpea seeds have high levels of protein and they specifically enriched for amino acids that are often limiting in the human diet, including methionine, lysine, and tryptophan. In resource poor areas of the world, pigeonpea serves as an important forage and cover crop, while the stems provide wood for tool making and fuel, and thatch for roofing. These factors, especially the ability to withstand elevated temperatures and limited water availability, add to pigeonpea's importance as a crop in semi-arid tropical (SAT) regions of the world, especially in the SAT of India where approximately 77% of global production occurs. Despite its importance in the SAT regions, little concerted research effort has been directed at either improvement or technology transfer in this crop. Thus, the pigeonpea production has remained static  and a range of biotic and abiotic stresses continue reduce yields by 50% or greater . Among the most important limiting factors are Fusarium wilt, sterility mosaic disease, pod borer, soil salinity and water logging. Very recently, hybrid breeding technology based on the cytoplasmic-nuclear male-sterility (CMS) system has been implemented in the pigeonpea breeding programme at ICRISAT , and this technology holds great potential to increase pigeonpea productivity.
Various advances in plant biotechnology and especially genomics together with traditional plant breeding technologies have led to the development of new improved varieties in a number of crop species with greater tolerance/resistance and higher yield [6, 7]. In this context, molecular markers play a very important role as these are used for estimating diversity in germplasm, trait mapping, molecular breeding, genetic purity assessment of hybrid seeds, etc. Among a range of molecular markers starting with isozymes, RFLP (restriction fragment length polymorphism), RAPD (random amplified polymorphic DNA), AFLP (amplified fragment length polymorphism), SSR (simple sequence repeat), DArT (diversity array technology), and most recently SNP (single nucleotide polymorphism), that have become available during last two decades , SSR markers have emerged as the current markers of choice for plant genetics and breeding applications . While SNP markers have a promising future in plant breeding applications, and may augment or displace SSR based marker systems, SNP based markers and associated technologies are in their infancy in most crops, including pigeonpea, while SSR marker technologies are better established for wide spread use in molecular breeding.
In case of pigeonpea, at present, only a few hundred SSR markers are available [10–13], a situation that is further hampered by low levels of genetic diversity within cultivated germplasm demands development of SSR markers at large scale.
Traditionally, three approaches are used for identification and development of SSR markers: (i) construction of SSR-enriched library followed by sequencing of SSR positive clones , (ii) mining of EST (expressed sequence tag) transcript sequence generated by Sanger sequencing  or short transcript sequences generated by next generation sequencing technologies , (iii) mining the BAC (bacterial artificial chromosome)- end sequences (BESs) . So far, the first two approaches have been used for developing SSR markers in pigeonpea with some success despite the labour-intensive and time consuming nature of the SSR enrichment and very low polymorphism levels of SSRs identified from the mining of transcript sequences. The development of SSR markers from BESs circumvents the limitations of the first two approaches, as a large number of SSRs can be rapidly identified and such genomic SSRs tend to display higher level of polymorphism relative to transcript associated SSRs. In addition, BES-SSR markers serve a useful resource for integrating genetic and physical maps [16–18].
The present study was undertaken with following objectives: (i) construction of two BAC libraries and sequencing of BAC-ends, (ii) comprehensive analysis of BAC-end sequences (BESs) for gaining insights in pigeonpea genome, (iii) mining the BESs for development of large scale SSR markers, (iv) characterization of newly developed BES-SSR markers on a panel of parental genotypes, (v) development of the first SSR-based genetic map for pigeonpea, and (vi) identification of an informative set of SSR markers suitable for purity assessment of two leading hybrids, ICPH 2438 and ICPH 2671 to facilitate efficient hybrid seed production.
BAC-end sequence analysis
Two BAC libraries were developed from pigeonpea cultivar "Asha", based on partial digestion with HindIII and BamHI restriction enzymes. BAC clones were sequenced from both insert ends to yield 88,860 DNA sequences with an average read length of 620 bp.
A series of parallel analyses were performed to annotate the features of singletons and clustered BESs. Similarity to transcribed sequences or known proteins was assessed by BLAST'N' and BLAST'X' of sequences against the TIGR plant transcript assemblies http://plantta.jcvi.org/ and the National Center for Biotechnology Information (NCBI) non-redundant protein database, respectively, using an E-value cut-off of <1.00E-20. Further evidence of protein coding regions, as well as standardized nomenclature, was obtained by queries against the Interpro and GeneOntology Molecular Function databases. Similarity to known plant repeat sequences was assessed by BLAST'N' and tBLAST'X' against a database of plant repeat sequences (http://www.jcvi.org).
BAC-end sequence (BES) characteristics
G + RE
G + TE
Total unique clusters a
Average cluster depth
Total unique sequence b
Total clusters with SSRs
Selected SSR-BESs d
Polymorphic SSRs c
Average number of alleles
5.0 ± 1.7
5.4 ± 1.7
5.8 ± 2.1
5.7 ± 2.0
Average PIC value
0.53 ± 0.20
0.58 ± 0.18
0.57 ± .19
0.57 ± 0.18
Identification of BES-SSRs
With the goal of increasing genetic marker repertoire in pigeonpea, BESs (clusters + singletons) were surveyed for the presence of SSRs by means of the MIcroSAtellite (MISA) search module . In total, 18,149 SSRs were identified, with mononucleotide (49% of total) and di-nucleotide (42% of total) repeats predominating. Excluding mono-nucleotide repeats, which were almost exclusively poly-A motifs, A/T-rich repeats accounted for 63% of all SSRs. The frequency of AT-rich repeats increased in rank order as motif length increased, from a low of 57% in di-nucleotide repeats to a high of 95% in penta-nucleotide repeats; this situation was absent only in the case of hexa-nucleotide repeats, where motifs with ≥50% GC content accounted for 53% of all repeats.
SSRs were either perfect SSRs (i.e., containing a single repeat motif such as 'TAA') or compound SSRs (i.e., composed of two or more SSRs separated by ≤100 bp). Perfect SSRs were further subdivided according to the length of SSR tracts : Class I SSRs (≥ 20 nucleotides in length) and Class II SSRs (≥ 10 but < 20 nucleotides in length). Class I SSRs were enriched for di-nucleotide (69.2%) and tri-nucleotide repeats (17.2%), while Class II repeats were enriched in mono-nucleotide repeats (56.7%), with a less frequent occurrence of di- (37.1%) and tri-nucleotide (6.3%) repeats.
Correlation between BAC end annotation and SSR occurrence
After excluding all mono-nucleotide repeat SSRs and SSRs with length <10 bp, the remaining 6,212 SSRs were selected for further analysis. These 6,212 SSRs were derived from 4,614 non-redundant BAC ends (singletons and clusters), 17 of which were annotated as organelle (15 chloroplast and 2 mitochondria).
For purposes of developing a uniform analysis of known pigeonpea SSRs, we obtained 457 SSRs submitted to NCBI GenBank by researchers at the University of Bonn (submitted by Odney et al.) and previously developed by our group (Varshney et al.). Both of these publicly available SSR sets were generated using PCR-based microsatellite enrichment strategies. As shown in the Table 1, the relative distribution of SSRs between genome fractions differs substantially for SSRs obtained by means of genome enrichment compared to random BAC end sequencing. In particular, genome-enrichment methodologies produced approximately three times the rate of retroelement-associated SSRs and an ~100-fold increase in the rate of SSRs derived from organelle or rRNA sequences, most of which were chloropast derived (data not shown).
Development of novel SSR genetic markers
Primer pairs were designed and synthesized for a total of 3,072 non-redundant BAC-end sequence SSRs (BES-SSRs). We refer to these SSR markers as CcM (Cajanus cajan Microsatellite) (Additional file 1: List of newly developed SSR markers isolated from BESs of pigeonpea).
List of genotypes used and their characters
Distribution of polymorphic markers into different repeat classes
Number of markers
Number of markers
Number of polymorphic
SSR polymorphism status on 13 mapping populations
Number of polymorphic
ICP 28 × ICPW 94
ICPB 2049 × ICPL 99050
ICP 332 × ICPL 7035
ICPL 332 × ICPL 20096
FW and SMD
ICPL 87119 × ICPL 87091
FW and SMD
ICPL 8863 × ICPL 20097
ICPL 88034 × ICPL 84023
ICPA 2043 × ICPR 2671
ICPA 2043 × ICPR 3467
ICPA 2039 × ICPR 2447
ICPA 2039 × ICPR 2438
ICPL 20102 × ICP 2376
TTB7 × ICP 7035
Construction of an SSR-based genetic map
An inter-specific F2 population derived from ICP 28 (C. cajan) × ICPW 94 (C. scaraboides) was selected for the construction of a reference genetic map. Consistent with a wide genetic cross, this pairwise comparison had the highest number of polymorphic SSRs (Table 4). The mapping population was genotyped with all polymorphic markers and marker segregation data were analyzed by the goodness of fit test for a 1:2:1 segregation ratio. Only 138 (36.50%) markers showed good agreement with the expected segregation ratio 1:2:1 (at the threshold of p = 0.05). Among the 240 markers with deviation from Mendelian ratios we observed instances of complete absence or very low occurence of one parental allele, and instances of excess heterozygosity.
Identification of informative SSR markers for hybrid purity assessment
In pigeonpea, there is a need for genetic markers to assess hybrid seed purity. Among the genotypes surveyed for SSR polymorphism (Table 4), four genotypes (ICPA 2039, ICPR 2438, ICPA 2043 and ICPR 2671) have been used for the development of two hybrids: ICPH 2438 (ICPA 2039 × ICPR 2438) and ICPH 2671 (ICPA 2043 × ICPR 2671) [5, 21]. For each hybrid, 42 polymorphic markers were selected that distinguished the parental lines and which gave high quality amplification in prior analyses. To assess the reliability of these SSR markers, 183 seeds of ICPH 2438 and 174 seeds of ICPH 2671 were obtained from the ICRISAT germplasm and analyzed together with seeds of parental lines. Based on this analysis, both ICPH 2438 and ICPH 2671 seed stocks had high rates of purity (96.3% and 94.8%, respectively). However, the frequency with which tested hybrids showed banding patterns typical of both parental alleles was dependent upon the markers under analysis. Accordingly the marker wise hybrid purity index varied between markers, ranging from 31.88% (CcM0724) to 99.42% (CcM0752) for ICPH 2671 and from 71.26% (CcM0133) to 100% (CcM2241) for ICPH 2438. A total of 30 markers for ICPH 2671 and 35 markers for ICPH 2438 could detect purity between 90 - 100% (Additional file 3: Purity index of polymorphic SSR markers on pigeonpea hybrid ICPH 2671 individuals and Additional file 4: Purity index of polymorphic SSR markers on pigeonpea hybrid ICPH 2438 individuals). The frequency of heterozygosity for the hybrid in ICPH 2438 ranged from a minimum of 53.1% (23/42) to a maximum of 100% (42/42). In case of ICPH 2671 heterozygosity for a hybrid ranged from minimum 53.1% (23/42) to a maximum of 95.24% (40/42).
Details on marker groups (MGs) for multiplex assays for assessing purity of two hybrids
The narrow genetic base of pigeonpea has hindered the wide use of molecular marker technology for crop improvement . In the present study, two BAC libraries were developed with an estimated ~11× genome coverage of pigeonpea. Sequencing of 50,000 BAC clones from both insert ends provided 88,860 BESs. Removal of cytoplasmic orgeneller BESs and cluster analysis facilitated the maximum possible recovery of nuclear genomic sequences comprising 41,329 singletons and 10,601 non-redundant contigs. With an objective to understand the constitution of SSR containing BAC clones, BESs were run through an annotation pipeline. Major proportion of the sequences remained non-annotated which may be considered as 'novel' C. cajan sequences. The overall repetitive fraction, resulting from BES analysis was found to be intermediate (22.15%) when compared with the percentage of repetitive elements in BESs of other legumes such as Trifolium (8.5%), soybean (33.5%), and common bean (49.3%) . BES annotation analysis has shown a considerable variability in the amount of repetitive fraction in different crop species such as tomato (49.3%) , papaya (16%) , banana (36%)  and citrus (25%) . This variation in the amount of repetitive elements in BESs is an indicative feature of presence of repetitive elements in the genome of a species. A varying level of annotations in different species may also be responsible for difference in repetitive elements. Proportion of annotated genic fraction was found more or less similar as observed in the BESs analysis of other crop species such as Phaseolus (29.3%) , apple (10.9%) , banana (11%) , Brassica (11%)  and papaya (19.%) .
BESs have been very useful to develop SSR markers in several plant species including legumes like soybean , common bean  and Medicago . In terms of SSRs abundance, overall density of 1 SSR per 5.64 kb seems to be in good congruency with the earlier reports in plant genomes . Similar results showing SSR frequencies of 1 SSR per 4 to 10 kb were achieved in different plant species like Medicago, soybean, Lotus, Arabidopsis and rice . This discrepancy observed in different studies may be accounted to (i) amount of sequence data analyzed, (ii) criteria for SSR identification, and (iii) different sources of derived sequences. It is also important to note that after excluding non-annotated BESs, majority (70.21%) of SSRs belong to be associated with genes. These observations are in agreement of the comprehensive study in plant genomes where SSRs were found associated mainly with genes .
In terms of distribution of SSRs, unlike the common occurrence of 'CG' motif in monocot species, 'CG' motifs were the least abundant in pigeonpea genome, as previously observed in other legume species (Medicago, Lotus and soybean). Such low abundance of "CG" di-nucleotide repeats may be attributed to their tendency of forming secondary structures (hairpins), leading to a selective pressure against 'CG' accumulation in genomes .
While converting identified SSRs into genetic markers, though 3,072 SSR primer pairs were synthesized; of these 2,964 (96.48%) primers yielded scorable amplicons. This rate of successful amplification is quite higher than earlier reported in pigeonpea [10–13]. All the repeat classes showed more than 98% amplification except di-nucleotide repeats which had comparatively lower rate of amplification (95.98%).
All the successfully amplified primer pairs were screened for polymorphism on a set of 22 diverse pigeonpea genotypes representing parents of 13 mapping populations segregating for various traits. These mapping populations represented the best cross combinations based on diversity revealed through morphological attributes and available marker data . The overall frequency of length polymorphism was found to be 28.40% which is lower than reported in earlier studies i.e. 50% , 81.3%  and 95% . This can be attributed to use of only one wild species genotype in this study unlike earlier studies. Occurrence of a very low level of DNA polymorphism among pigeonpea cultivars is not unexpected as several studies have documented such results [33–35].
As expected degree of marker polymorphism was lower in intra-specific populations than in inter-specific mapping population (ICP 28 × ICPW 94). The frequency of marker polymorphism increased dramatically with SSR locus longer than 200 bp. PIC values for SSR markers were also analyzed in relation to repeat length and unit type. In terms of repeat length, Class I SSRs were more polymorphic as compared to the Class II SSRs which may be accounted to the hyper-variable nature of Class I SSRs  Among different type of repeat unit classes, tetra-nucleotide repeats, in general, showed the higher average PIC value (0.64) followed by di-nucleotide repeats (0.57). It was also observed that among tri-nucleotide repeat class, the 'TAA' repeat motifs, displayed higher polymorphism (average PIC value = 0.59). Similarly, 'TA' repeat motifs in di-nucleotide repeat class had a higher average PIC value (0.59) compared to the others. Similar trends were also observed in other legumes such as chickpea ,  and  where the SSR markers with repeat motifs 'TAA' or 'TA' exhibited extensive abundance and polymorphism as well. Higher average PIC value of compound SSRs (0.58) can be attributed to the fact that the markers with compound SSRs have more than one SSR motif, which increases their chance to be polymorphic .
This study provides a list of polymorphic markers for different mapping populations that segregate for a number of important traits like Fusarium wilt (FW), sterlity mosaic disease (SMD), fertility restorer (Rf) etc. that are important for pigeonpea improvement . Genotyping of these mapping populations with identified polymorphic markers together with phenotyping data should provide the markers associated with QTLs (quantitative trait loci)/gene(s) for trait of interest that can be used for enhancing the breeding efficiency through marker-assisted selection.
To develop a reference genetic map, an inter-specific cross was used so that a larger number of segregating loci can be integrated into the genetic map. Usually SSR markers are co-dominant and follow Mendelian inheritance . However deviation from the expected segregation ratio for SSR markers is not an uncommon feature in inter-specific crosses and especially F2 population. Significant distortion observed in the marker data may be attributed to several possible reasons such as the abortion of male or female gametes or the selective exclusion of a particular gametic genotype from fertilization, owing to incompatibility, incongruity, certation, or zygote selection . Percentage distortion observed in the present study is comparable with previously reported studies performed on inter-specific crosses .
In the present study, the genetic map derived from an inter-specific cross ICP 28 × ICPW 94 included eleven discrete linkage groups corresponding to the basic chromosome number of the genus (x = 11). Initial construction of a skeletal map with un-skewed markers and followed by integration of distorted markers helped in minimizing the possibility for spurious assignments of markers . The final map comprised of 239 marker loci with a total map length of 930.90 cM having average spacing of 3.8 cM between two marker loci. This is the first report on the construction of SSR-based genetic map in pigeonpea. Therefore this map should serve as a 'reference map' for other future genetic maps of pigeonpea. Moreover as the SSR markers are derived from the BAC-end sequences, these markers and the map should be very useful resource for linking the genetic map with a 'future' physical map of pigeonpea .
Developed set of large number of SSR markers should be very useful for applied aspects of genetics and breeding in pigeonpea, especially when the cultivated gene pool has a narrow genetic diversity. In case of pigeonpea, CMS- hybrid technology is becoming popular to tackle the low crop productivity . For assessing the genetic purity of hybrids, in general, grow out test (GOT) based on morphological criteria is used. However, GOT is limited by the accuracy, time and labour cost . In this context, for each of two hybrids (ICPH 2671 and ICPH 2438), a set of 42 markers has been identified that can be used for purity assessment of hybrid seeds. SSR markers have been found very effective for determining hybrid purity in many species like rice , maize  and cotton . In fact in case of ICPH 2438 hybrid, two diagnostic SSR markers were identified for purity assessment in an earlier study also . Although some studies report suitability of even one marker for hybrid purity assessment test [43, 47, 48]. This study increases the diagnostic markers in large number for ICPH 2438 and also identifies a set of diagnostic markers for another pigeonpea hybrid ICPH 2671. Moreover identification of different marker groups, especially the group of common markers (CcM0257, CcM1559, CcM1825 and CcM1895) for both hybrids, for undertaking multiplex assays provides an added value to enhance their utility for hybrid purity assessment.
In summary this study reports a large-scale development of SSR markers and construction of SSR based genetic map in pigeonpea for the first time. In addition, a large number of informative SSR markers that can be used in multiplexes for assessing the seed purity of two hybrids. It is anticipated that SSR markers and the genetic map reported in this study should provide a reference resource for construction and comparison of genetic maps for new mapping populations, finger printing and cultivar identification, assessment of genetic diversity and gene flow among Cajanus species. New genetic maps, to be devloped based on polymorphic markers identified in this study, will facilitate trait mapping and marker assisted selection. Furthermore, genomic SSR markers identified from BESs and integrated into genetic maps provide a valuable resource for anchoring future physical map or whole genome sequence to the genetic map.
Plant material and DNA extraction
Two pigeonpea genotypes namely ICP 28 and ICPL 87119 ("Asha") were employed for checking the amplification of SSR loci with newly designed primer pairs. To identify informative set of SSR markers, a set of 22 genotypes was utilized for screening the polymorphism (Table 2). These genotypes represent parents of 13 mapping populations which are segregating for various agronomical important traits.
A F2 population of 79 individuals derived from an inter-specific cross of ICP 28 (Cajanus cajan accession) and ICPW 94 (Cajanus scarabaeoides accession) was used for development of a genetic map.
For assessment of genetic purity of hybrids ICPH 2438 and ICPH 2671, a set of 183 and 174 seeds of two cytoplasmic-nuclear male-sterility (CMS) based hybrids (obtained from ICRISAT) were used respectively. Total genomic DNA from leaf tissue was isolated and purified according to protocol provided by Cuc and colleagues .
BAC-end sequence (BES) data
Two BAC libraries were constructed by using HindIII and BamHI restriction enzymes. The HindIII library was composed of 34,560 clones with an estimated average insert size of 120,000 bp, while the BamHI library was composed of 34,560 clones with an estimated average insert size of 115,000 bp. These clones collectively represented ~11× coverage of the pigeonpea genome. A total of 50,000 BAC clones were attempted for end-sequencing. BAC clones were inoculated into Luria Broth (LB) media containing appropriate antibiotic (chloramphenicol or kanamycin) and incubated in a shaking incubator. BAC-DNA was purified by alkaline lysis solutions. Big dye terminator chemistry was used to end sequence the BAC clones. Post reaction removal of excess dye was performed using a Sephadex G50 mini-column filter plate method. Sequences were analyzed with an automated sequencer. Base calling and sequence trimming were performed with PHRED software . The PHRED output was converted into FASTA format and vector sequences were masked. Terminal vector sequences were then trimmed and BESs shorter than 100 bp were discarded and the remaining 88,860 BESs were then used for mining of SSRs.
Mining of SSRs
BESs were used for mining the SSRs using Perl based MIcroSAtellite (MISA) http://pgrc.ipk-gatersleben.de/misa search module which is capable of identifying perfect as well as compound SSRs. All BESs with a minimum size of 100 bp were arranged in a single text file in FASTA format and this file was used as an input for MISA. The criteria used for the identification of true SSRs included minimum ten repeats for mono (N)-, six repeats for di (NN)- and five repeats for tri (NNN)-, tetra (NNNN)-, penta (NNNNN)- and hexa (NNNNNN)- nucleotide repeat units. Two SSRs separated by maximum 100 nucleotide bases were considered as part of a compound SSR. Sequence complementarity was considered while classifying identified SSRs under different classes.
For generating the genetic markers, redundancy in the identified SSRs from BESs was taken into account. Cluster analysis was done on the BESs to identify non-redundant sequences. In general, one SSR containing BES was selected from each cluster for designing the primer pairs.
Designing of primer pairs for identified SSRs was done by using standalone Primer3 http://frodo.wi.mit.edu/ program using MISA generated Primer3 input file . The criteria used for designing primer pairs included annealing tempeature (Tm) range of 57°C - 60°C with an average of 59°C, amplicon size 100 - 280 bp, primer length 20 ± 5 bp and GC% 50 ± 5. M13 dye labeled primer pairs were synthesized for the selected SSRs.
Amplification and separation of SSR loci
Polymerase chain reactions (PCRs) for amplification of SSR loci were performed in a 5 μl reaction volume [0.5 μl of 10× PCR buffer, 1.0 μl of 15 mM MgCl2, 0.25 μl of 2 mM dNTPs, 0.50 μl of 2 pM/μl primer anchored with M13-tail (MWG-Biotech AG, Bangalore, India), 0.1 U of Taq polymerase (Bioline, London, UK), and 1.0 μl (5 ng/μl) of template DNA] in 96-well micro titre plate (ABgene, Rockford, IL, USA) using thermal cycler GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA, USA). A touch down PCR programme was used to amplify the DNA fragments: initial denaturation was for 5 min at 95°C followed by 5 cycles of denaturation for 20 sec at 94°C, annealing for 20 sec at 60°C (the annealing temperature for each cycle being reduced by 1°C per cycle) and extension for 30 sec at 72°C. Subsequently, 35 cycles of denaturation at 94°C for 20 sec followed by annealing for 20 sec at 56°C and extension for 30 sec at 72°C and 20 min of final extension at 72°C. PCR products were checked for amplification on 1.2% agarose gel. Separation of amplified products on capillary electrophoresis using GeneMapper software version 4.0 (Applied Biosystems, Foster City, CA, USA) was undertaken.
Polymorphism information content (PIC)
where k is the total number of alleles detected for a given marker locus and Pi is the frequency of the ith allele in the set of genotypes investigated.
Segregation data obtained for polymorphic SSR markers on the F2 population were used for linkage mapping. Due to segregation distortion for some SSR loci, initially a framework genetic map was prepared with normally segregating markers at logarithm of odds (LOD) of 5 with a minimum recombination threshold of 37.5 using MAPMAKER/EXP 3.0 . Initially 'Group' command was used to group markers in various linkage groups. Then 'Compare' and 'Try' commands were used to locate the SSR markers within each linkage group. The ordered marker sequences were confirmed by the 'Ripple' command and finally the linkage groups were generated by 'Map' command. Kosambi mapping function was used to convert recombination frequency into map distances . The whole data set was then analyzed with the help of JoinMap 3.0 software . Linkage groups were established at LOD ≥ 3 with other parameters like recombination threshold of 0.40, ripple value of 1 and jump threshold of 5. The framework map order was fixed as 'anchor' using 'fixed order' command and all the remaining markers including the distorted ones were integrated because with JoinMap, the risk of errors in the placement of distorted markers to a linkage group are minimized . Final linkage maps were drawn with the help of Mapchart version 2.2 .
Hybrid purity assessment
Bacterial artificial chromosome
Simple sequence repeats
Polymerase chain reactions
Polymorphism information content
Quantitative trait loci
Authors are thankful to Indo-US Agricultural Knowledge Initiative (Indo-US AKI) of Indian Council of Agricultural Research (ICAR), National Science Foundation (NSF), USA and Generation Challenge Programme of CGIAR for supporting this research. Thanks are also due to Mr Abdul Gafoor, Mr S Ramesh and Ms K Himabindu for their excellent technical support.
- Greilhuber J, Obermayer R: Genome size variation in Cajanus cajan (Fabaceae): a reconsideration. Plant Syst Evol. 1998, 212: 135-141. 10.1007/BF00985225.View ArticleGoogle Scholar
- van der Maesen LJG: Pigeonpea: origin, history, evolution and taxonomy. Pigeonpea. Edited by: Nene YL, Hall SD, Sheila VK. Wallingford: CAB International; 1990:15-46.Google Scholar
- Reddy LJ, Faris DG: A cytoplasmic male sterile line in pigeonpea. International Pigeonpea Newslett. 1981, 1: 16-17.Google Scholar
- Marley PS, Hillocks RJ: Effect of root-knot nematodes (Meloidogyne spp.) on Fusarium wilt in pigeonpea (Cajanus cajan). Field Crop Res. 1996, 46: 15-20. 10.1016/0378-4290(95)00083-6.View ArticleGoogle Scholar
- Saxena KB, Sultana R, Mallikarjuna N, Saxena RK, Kumar RV, Sawargaonkar SL, Varshney RK: Male-sterility systems in pigeonpea and their role in enhancing yield. Plant Breed. 2010, 129: 125-134. 10.1111/j.1439-0523.2009.01752.x.View ArticleGoogle Scholar
- Varshney RK, Hoisington DA, Tyagi AK: Advences in cereal genomics and applications in crop breeding. Trends Biotechnol. 2006, 24: 490-499. 10.1016/j.tibtech.2006.08.006.PubMedView ArticleGoogle Scholar
- Varshney RK, Thudi M, May GD, Jackson SA: Legume genomics and breeding. Plant Breed Rev. 2010, 33: 257-304.Google Scholar
- Jones N, Ougham H, Thomas H, Pasakinskiene I: Markers and mapping revisited: finding your gene. New Phytol. 2009, 183: 935-966. 10.1111/j.1469-8137.2009.02933.x.PubMedView ArticleGoogle Scholar
- Gupta PK, Varshney RK: The development and use of microsatellite markers for genetic analysis and plant breeding with emphasis on bread wheat. Euphytica. 2000, 113: 163-185. 10.1023/A:1003910819967.View ArticleGoogle Scholar
- Burns MJ, Edwards KJ, Newbury HJ, Ford-Lloyd BR, Baggot CD: Development of simple sequence repeat (SSR) markers for the assessment of gene flow and genetic diversity in pigeonpea (Cajanus cajan). Mol Ecol Notes. 2001, 1: 283-285. 10.1046/j.1471-8278.2001.00109.x.View ArticleGoogle Scholar
- Odeny DA, Jayashree B, Ferguson M, Hoisington D, Cry LJ, Gebhardt C: Development, characterization and utilization of microsatellite markers in pigeonpea. Plant Breed. 2007, 126: 130-136. 10.1111/j.1439-0523.2007.01324.x.View ArticleGoogle Scholar
- Odeny DA, Jayashree B, Gebhardt C, Crouch J: New microsatellite markers for pigeonpea (Cajanus cajan (L.) Millsp.). BMC Research Notes. 2009, 2: 35-10.1186/1756-0500-2-35.PubMedPubMed CentralView ArticleGoogle Scholar
- Saxena RK, Prathima C, Saxena KB, Hoisington DA, Singh NK, Varshney RK: Novel SSR markers for polymorphism detection in pigeonpea (Cajanus spp.). Plant Breed. 2010, 129: 142-148. 10.1111/j.1439-0523.2009.01680.x.View ArticleGoogle Scholar
- Varshney RK, Graner A, Sorrells ME: Genic microsatellite markers in plants: features and applications. Trends Biotechnol. 2005, 23: 48-55. 10.1016/j.tibtech.2004.11.005.PubMedView ArticleGoogle Scholar
- Varshney RK, Nayak SN, May GD, Jackson SA: Next generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol. 2009, 27: 522-530. 10.1016/j.tibtech.2009.05.006.PubMedView ArticleGoogle Scholar
- Mun JH, Kim DJ, Choi HK, Gish J, Debelle F, Mudge J, Denny R, Endre G, Saurat O, Dudez AM, Kiss GB, Roe B, Young ND, Cook D: Distribution of microsatellites in the genome of Medicago truncatula: a resource of genetic markers that integrate genetic and physical maps. Genetics. 2006, 172: 2541-2555. 10.1534/genetics.105.054791.PubMedPubMed CentralView ArticleGoogle Scholar
- Shultz JL, Samreen K, Rabia B, Jawaad AA, Lightfoot DA: The development of BAC-end sequence-based microsatellite markers and placement in the physical and genetic maps of soybean. Theor Appl Genet. 2007, 114: 1081-1090. 10.1007/s00122-007-0501-9.PubMedView ArticleGoogle Scholar
- Schlueter JA, Lin JY, Schlueter SD, Vasylenko SIF, Deshpande S, Yi J, O'Bleness M, Roe BA, Nelson RT, Scheffler BE, Jackson SA, Shoemaker RC: Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing. BMC Genomics. 2007, 8: 330-10.1186/1471-2164-8-330.PubMedPubMed CentralView ArticleGoogle Scholar
- Varshney RK, Thiel T, Stein N, Langridge P, Graner A: In silico analysis on frequency and distribution of microsatellites in ESTs of some cereal species. Cell Mol Biol Lett. 2002, 7: 537-546.PubMedGoogle Scholar
- Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S: Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 2001, 11: 1441-1452. 10.1101/gr.184001.PubMedPubMed CentralView ArticleGoogle Scholar
- Saxena RK, Saxena KB, Varshney RK: Application of SSR markers for molecular characterization of hybrid parents and purity assessment of ICPH 2438 hybrid of pigeonpea [Cajanus cajan (L.) Millspaugh]. Mol Breed. 2010, 26: 371-380. 10.1007/s11032-010-9459-4.View ArticleGoogle Scholar
- Saxena KB: Genetic improvement of pigeonpea-a review. Trop Plant Biol. 2008, 1: 159-178. 10.1007/s12042-008-9014-1.View ArticleGoogle Scholar
- Schlueter JA, Goicoechea JL, Collura K, Gill N, Lin JY, Yu Y, Vallejos E, Munoz M, Blair MW, Tohme J, Tomkins J, McClean P, Wing R, Jackson SA: BAC-end sequence analysis and a draft physical map of the common bean (Phaseolus vulgaris L.) genome. Trop Plant Biol. 2008, 1: 40-48. 10.1007/s12042-007-9003-9.View ArticleGoogle Scholar
- Budiman MA, Mao L, Wood TC, Wing RA: A deep coverage tomato BAC library and prospects toward development of an STC framework for genome sequencing. Genome Res. 2000, 10: 129-136.PubMedPubMed CentralGoogle Scholar
- Lai CW, Yu Q, Hou S, Skelton RL, Jones MR, Lewis KL, Murray J, Eustice M, Guan P, Agbayani R, Moore PH, Ming R, Presting GG: Analysis of papaya BAC end sequences reveals first insights into the organization of a fruit tree genome. Mol Genet Genomics. 2006, 276: 1-12. 10.1007/s00438-006-0122-z.PubMedView ArticleGoogle Scholar
- Cheung F, Town CD: A BAC end view of the Musa acuminata genome. BMC Plant Biol. 2007, 7: 29-10.1186/1471-2229-7-29.PubMedPubMed CentralView ArticleGoogle Scholar
- Terol JM, Naranjo A, Ollitrault P, Talon M: Development of genomic resources for Citrus clementina: characterization of three deep-coverage BAC libraries and analysis of 46,000 BAC end sequences. BMC Genomics. 2008, 9: 423-10.1186/1471-2164-9-423.PubMedPubMed CentralView ArticleGoogle Scholar
- Han Y, Korban SS: An overview of the apple genome through BAC end sequence analysis. Plant Mol Biol. 2008, 67: 581-588. 10.1007/s11103-008-9321-9.PubMedView ArticleGoogle Scholar
- Hong CP, Piao ZY, Kang TW, Batley J, Yang TJ, Hur YK, Bhak J, Park BS, Edwards D, Lim YP: Genomic distribution of simple sequence repeats in Brassica rapa. Mol Cells. 2007, 23: 3 49-356.Google Scholar
- Cardle L, Ramsay L, Milbourne D, Macaulay M, Marshall D, Waugh R: Characterization of physically clustered simple sequence repeats in plants. Genetics. 2000, 156: 847-854.PubMedPubMed CentralGoogle Scholar
- Morgante M, Hanafey M, Powell W: Microsatellites are preferentially associated with nonrepetitive DNA in plant genomes. Nat Genet. 2002, 30: 194-200. 10.1038/ng822.PubMedView ArticleGoogle Scholar
- Eustice M, Yu Q, Lai CW, Hou S, Thimmapuram J, Liu L, Alam M, Moore PH, Presting GG, Ming R: Development and application of microsatellite markers for genomic analysis of papaya. Tree Genet Genomes. 2008, 4: 333-341. 10.1007/s11295-007-0112-2.View ArticleGoogle Scholar
- Saxena RK, Saxena KB, Kumar RV, Hoisington DA, Varshney RK: Simple sequence repeat-based diversity in elite pigeonpea genotypes for developing mapping populations to map resistance to Fusarium wilt and sterility mosaic disease. Plant Breed. 2010, 129: 135-141. 10.1111/j.1439-0523.2009.01698.x.View ArticleGoogle Scholar
- Sivaramakrishnan S, Seetha K, Rao AN, Singh L: RFLP analysis of cytoplasmic male sterile lines in Pigeonpea (Cajanus cajan L. Millsp.). Euphytica. 1997, 126: 293-299.Google Scholar
- Yang S, Pang W, Harper J, Carling J, Wenzl P, Huttner E, Zong X, Kilian A: Low level of genetic diversity in cultivated pigeonpea compared to its wild relatives is revealed by diversity arrays technology (DArT). Theor Appl Genet. 2006, 113: 585-595. 10.1007/s00122-006-0317-z.PubMedView ArticleGoogle Scholar
- Nayak SN, Zhu H, Varghese N, Datta S, Choi H, Horres R, Jungling R, Singh J, Kavi Kishor PB, Sivaramakrishnan S, Hoisington DA, Kahl G, Winter P, Cook DR, Varshney RK: Integration of novel SSR and gene-based SNP marker loci in the chickpea genetic map and establishment of new anchor points with Medicago truncatula genome. Theor Appl Genet. 2010, 120: 1415-1441. 10.1007/s00122-010-1265-1.PubMedPubMed CentralView ArticleGoogle Scholar
- Cordoba JM, Chavarro C, Schlueter JA, Jackson SA, Blair MW: Integration of physical and genetic maps of common bean through BAC-derived microsatellite markers. BMC Genomics. 2010, 11: 436-10.1186/1471-2164-11-436.PubMedPubMed CentralView ArticleGoogle Scholar
- Varshney RK, Penmetsa RV, Dutta S, Kulwal PL, Saxena RK, Datta S, Sharma TR, Rosen B, Carrasquilla-Garcia N, Farmer AD, Dubey A, Saxena KB, Gao J, Fakrudin B, Singh MN, Singh BP, Wanjari KB, Yuan M, Srivastava RK, Kilian A, Upadhyaya HD, Mallikarjuna N, Town CD, Bruening GE, He G, May GD, McCombie R, Jackson SA, Singh NK, Cook DR: Pigeonpea genomics initiative (PGI): an international effort to improve crop productivity of pigeonpea (Cajanus cajan L.). Mol Breed. 2010, 26: 393-408. 10.1007/s11032-009-9327-2.PubMedPubMed CentralView ArticleGoogle Scholar
- Beckmann JS, Soller M: Toward a unified approach to genetic mapping of eukaryotes based on sequence tagged microsatellite sites. Nat Biotechnol. 1990, 8: 930-932. 10.1038/nbt1090-930.View ArticleGoogle Scholar
- Kreike CM, Stiekema WJ: Reduced recombination and distorted segregation in a Solanum tuberosum (2x) × S. spegazzinii (2x) hybrid. Genome. 1997, 40: 180-187. 10.1139/g97-026.PubMedView ArticleGoogle Scholar
- Kianian SF, Quiros CF: Generation of a Brassica oleracea composite RFLP map: linkage arrangements among various populations and evolutionary implications. Theor Appl Genet. 1992, 84: 544-554. 10.1007/BF00224150.PubMedGoogle Scholar
- Elangovan M, Rai R, Dholakia BB, Lagu MD, Tiwari R, Gupta RK, Rao VS, Roder MS, Gupta VS: Molecular genetic mapping of quantitative trait loci associated with loaf volume in hexaploid wheat (Triticum aestivum). J Cereal Sci. 2008, 47: 587-598. 10.1016/j.jcs.2007.07.003.View ArticleGoogle Scholar
- Yashitola J, Thirumurugan T, Sundaram RM, Naseerullah MK, Ramesha MS, Sarma NP, Stone RV: Assessment of purity of rice hybrids using microsatellite and STS markers. Crop Sci. 2002, 42: 1369-1373. 10.2135/cropsci2002.1369.View ArticleGoogle Scholar
- Sundaram RM, Naveenkumar B, Biradar SK, Balachandran SM, Mishra B, IlyasAhmed M, Viraktamath BC, Ramesha MS, Sharma NP: Identification of informative SSR markers capable of distinguishing hybrid rice parental lines and their utilization in seed purity assessment. Euphytica. 2008, 163: 215-224. 10.1007/s10681-007-9630-0.View ArticleGoogle Scholar
- Asif M, Rahman MU, Zafar Y: Genotyping analysis of six maize (Zea mays L.) hybrid using DNA fingerprinting technology. Pakistan J Bot. 2006, 38: 1425-1430.Google Scholar
- Ali MA, Seyal MT, Awan SI, Niaz S, Ali S, Abbas A: Hybrid authentication in upland cotton through RAPD analysis. Aust J Crop Sci. 2008, 2: 141-149.Google Scholar
- Mishra GP, Singh RK, Mohapatra T, Singh AK, Prabhu KV, Zaman FU, Sharma RK: Molecular mapping of gene for fertility restoration of wild abortive (WA) cytoplasmic male sterility using a basmati rice restorer line. J Plant Biochem Biot. 2003, 12: 37-42.View ArticleGoogle Scholar
- Nandakumar N, Singh AK, Sharma RK, Mohapatra T, Prabhu KV, Zaman FU: Molecular fingerprinting of hybrids and assessment of genetic purity of hybrid seeds in rice using microsatellite markers. Euphytica. 2004, 136: 257-264. 10.1023/B:EUPH.0000032706.92360.c6.View ArticleGoogle Scholar
- Cuc LM, Mace ES, Crouch JH, Quang VD, Long TD, Varshney RK: Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea). BMC Plant Biol. 2008, 8: 55-10.1186/1471-2229-8-55.PubMedPubMed CentralView ArticleGoogle Scholar
- Ewing B, Green P: Base-calling of automated sequencer traces using Phred. II. Error probabilities. Genome Res. 1998, 8: 186-194.PubMedView ArticleGoogle Scholar
- Anderson JA, Churchill GA, Sutrique JE, Tanksley SD, Sorrells ME: Optimizing parental selection for genetic linkage maps. Genome. 1993, 36: 181-186. 10.1139/g93-024.PubMedView ArticleGoogle Scholar
- Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L: MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics. 1987, 1: 174-181. 10.1016/0888-7543(87)90010-3.PubMedView ArticleGoogle Scholar
- Kosambi DD: The estimation of map distance from recombination values. Ann Eugen. 1944, 12: 172-175.View ArticleGoogle Scholar
- Van Ooijen JW, Voorrips RE: JoinMap 3.0, software for the calculation of genetic linkage maps. Plant Research International Wageningen, The Netherlands. 2001.Google Scholar
- Dettori MT, Quarta R, Verde I: A peach linkage map integrating RFLPs, SSRs, RAPDs and morphological markers. Genome. 2001, 44: 783-790. 10.1139/gen-44-5-783.PubMedView ArticleGoogle Scholar
- Voorrips RE: MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002, 93: 77-78. 10.1093/jhered/93.1.77.PubMedView ArticleGoogle Scholar