Assessment of Genetic Variation in Indian Mustard (Brassica juncea L.) Using PCR Based Markers  

Nancy Gupta1 , Sajad Majeed Zargar1 , Moni Gupta2 , S.K. Gupta3
1. School of Biotechnology, SKUAST-J, Chatha, Jammu, J&K, 180009, India
2. Division of Biochemistry and Plant Physiology, SKUAST-J, Chatha, Jammu, J&K, 180009, India
3. Division of Plant Breeding and Genetics, SKUAST-J, Chatha, Jammu, J&K, 180009, India
Author    Correspondence author
Molecular Plant Breeding, 2014, Vol. 5, No. 3   doi: 10.5376/mpb.2014.05.0003
Received: 31 Mar., 2014    Accepted: 09 Apr., 2014    Published: 24 Apr., 2014
© 2014 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Gupta et al., Assessment of Genetic Variation in Indian Mustard (Brassica juncea L.) Using PCR Based Markers, Molecular Plant Breeding, Vol.5, No. 3 10-17 (doi: 10.5376/mpb.2014.05.0003)


Indian mustard (Brassica juncea L.) is an important oilseed crop with oil content ranging from 30 to 48 percent. However, presence of high erusic acids, glucosinolates and saturated fatty acids and also narrowed genetic base of existing varieties confines its use. The present investigation was undertaken to explore the diversity among Indian mustard genotypes (varieties) using molecular markers. We used RAPD and EST based SSRs as the markers for assessment of genetic variation among 23 genotypes of Brassica juncea L. cultivated in North India.  In order to be sure about the authenticity of primers especially RAPDs, four other genotypes of different Brassica species were also considered for this investigation. To determine the discriminatory power of the RAPD primers, polymorphism percentage, PIC, MI and Rp were calculated. Finally the cluster analysis was done to determine the genetic diversity among the 27 genotypes, which includes 4 genotypes of other Brassica species. The polymorphism percentage of more than 91% and 86.66% was observed using the fifteen reproducible RAPDs and three EST SSRs respectively. Moreover the PIC values observed corresponds to 0.303, with 4.44 marker index and 6.89 resolving power for RAPDs, however, SSRs showed the PIC value of 0.281, with 0.94 marker index and 0.269 resolving power. The parameters calculated to estimate the discriminatory power presented a significant correlation and their high values depict the potential of primers for distinguishing the genotypes. The cluster analysis based on UPGMA separated the genotypes in two major groups. To the best of our expectations, all the genotypes of Brassica juncea are grouped in one major cluster and genotypes of other Brassica species are grouped in different cluster. Based on these preliminary results, the diverse genotypes can be used as a genetic stock for improvement of this crop in future breeding programs. 

Indian mustard; RAPD markers; Polymorphism; Genetic diversity; PIC

Brassica juncea commonly known as Indian mustard is an amphidiploid species that originated through the interspecific hybridization of Brassica rapa and Brassica nigra (U., 1935). It is utilized worldwide as an oilseed, a condiment, vegetable, green manure, forage and fodder and cultivated primarily in tropical and sub-tropical countries (Gangapur et al., 2010). As for production of mustard is concerned, India is far behind, which may be due to lack of proficient varieties. In order to enhance the production by developing new cultivars, the knowledge about the genetic wealth of available germplasm is must. Genetic diversity information forms the foundation of any breeding program and is of great importance to the sustainability of plant populations. An understanding of organized germplasm and relationship among genotypes provides an opportunity to develop improved crops by more efficient sampling of genotypes (Afiah et al., 2007).
DNA based molecular marker techniques are being used enormously for estimating the magnitude of diversity. Diversity analysis studies in various crops have extrapolated the potential of RAPD and SSR markers (Gupta et al., 1999; Khan et al., 2011; Gangapur et al., 2010; Yousuf et al., 2013; Turi et al., 2012; Huang et al., 2011). RAPD-like assays can search large genomic portions due to their abundant distribution in the genome and there by presenting a more accurate picture of genetic variation within the crop plants in an efficient and inexpensive ways (Souframanien and Gopalakrishna, 2004). RAPD markers have been successfully used to evaluate genetic diversity in Brassica (Demeke et al., 1992; Jain et al., 1994; Thormann et al., 1994; Bhatia et al., 1995; Dulson et al., 1998; Zu and Wu, 1998; Divaret et al., 1999), common wheat (Liu et al., 1999; Sivolap et al., 1997), maize (Zhang et al., 1998; Bernado et al., 1997), barley (Hamza et al., 2004) and sesame (Salazar et al., 2007). The number of markers used, their abundance in the genome and the degree of precision with which the results are analyzed determine the accuracy to distinguish the genotypes (Schut et al., 1997). In the present investigation, we analyzed 23 B. juncea L. varieties cultivated in Northern states of India using PCR based markers to examine the efficiency of these techniques viz-a-viz genetic diversity.
A number of studies on genetic variation among Indian mustard genotypes have been done; where mostly RAPDs have been used (Ali et al., 2007; Ahmad et al., 2009). Since the information on EST-SSRs in public domain is limited (Hopkins et al., 2007). Hence in the present study an attempt was made to determine genetic diversity among B. juncea genotypes cultivated in Northern states of India using RAPDs (Figure 1 A). Moreover, we tested few EST-SSRs, for polymorphism selected from Hopkins et al. (2007) (Figure 1 B). Here we observed that, polymorphism shown by EST-SSRs was much less compared to RAPDs. So the detailed study of only reproducible RAPDs was done. The total number of bands, polymorphic bands and monomorphic bands were counted from the RAPD profiles of 15 reproducible primers. The Polymorphism percentage, PIC, marker index and resolving power for each RAPD was calculated to depict their discriminatory power as represented in Table 1. A total of 260 bands were produced with an average of 17.33 per primer, among which 236 bands were polymorphic having an average of 15.73 per primer. The number of amplified fragments varied between 13 for OPE-01 to 23 for OPA-10 and the amplicon size varied from 150 bp to 2750 bp. The highest number of polymorphic bands viz. 21 bands for OPA10 primer followed by 19 for OPC-08 and 18 for OPE-03, whereas OPE01 produced the least polymorphic bands corresponding to 11. Twenty one unique bands were recognized out of 236 polymorphic bands. High number of unique bands corresponding to six from OPA-10 and three from OPA-07. However, two bands for OPC-02 and OPA-11 and one unique band each from OPA-03, OPA-05, OPB-10, OPC-08, OPD-07, OPD-18, OPE-01 and OPE-02 were observed. Polymorphic information content with an average of 0.30, ranged from 0.20 (for OPE-01) to 0.39 (for OPC-02 and OPE-03). Higher PIC was observed for OPC-08, OPA-09, OPB-10, OPA-02 and OPA-10 where as EST-SSRs showed comparatively very less values ranging from 0.173 to 0.29. Highest resolving power was obtained with the primers OPE-03 (10.71), OPA-10 (9.92) and OPC-08 (9.98) where as least were observed for OPE-01 (3.15) and OPA-07 (3.88). On an average, 4.44 and 6.89 values were obtained for marker index and resolving power respectively. We observed a significant positive correlation between PIC, resolving power and marker index. Based on all these parameters, RAPDs (OPE-03, OPC-08, OPA-10) were considered best for assessing diversity among B. juncea genotypes. However, OPE-01 was least efficient. Primer OPC-02 showed a high value for all the parameters but it was not able to reproducibly resolve all the genotypes. Further, cluster analysis was done using STATISTICA program, based on UPGMA. The genotypes were grouped in 2 major clusters having linkage distance of 10.8 U. The cluster I as represented in Figure 2, includes four genotypes which are grouped in two sub-clusters having linkage distance of 9.2U. The second major cluster is grouped in 2 sub-clusters at linkage distance of 8.8 U which are further sub divided in many sub-sub clusters. All B. juncea genotypes were clustered together in one group which further showed diversity among themselves as represented in Figure 2.

Figure 1 A: RAPD profile for OPA-11 primer; B: EST-SSR profile for JU 9 primer

Table 1 Calculated parameters for RAPD and EST-SSR primers

Figure 2 Cluster analysis based on molecular data

Characterization and evaluation of available genetic resources is the first and foremost requirement to ameliorate any crop. In the present investigation RAPD markers proved to be more efficient as compared to EST-SSRs. Since limited number of EST-SSRs were used and they were evaluated on agarose gel that may be the reason of getting less polymorphism, further, variation among genic portion is least considered to non-genic that may be attributing to least polymorphism. Primers OPE-03, OPC-08, OPB-10 and OPA-10 have shown remarkable potential to discriminate genotypes on the basis of amplified products generated, calculated parameters and high polymorphism percentage. The polymorphism percentage obtained in this study is comparable to 87% as reported (Bhat et al., 1999) in case of sesame and lower than 100% as reported (Salazar et al., 2006). Abdelmigid et al. (2012) reported a percentage of polymorphism to be 87% with 13.4 polymorphic fragments in B. napus comparable to that of 91.2 polymorphism percentage and 15.73 bands per primer reported in this study. 97.66% polymorphism was reported for 16 primers by Ahmad et al., 2009. In other studies, percentage of polymorphic primers in mustard reported (Ali et al., 2007) was in the range of 21.54 to 59.36%; exactly similar polymorphism percentage i.e. 21.54 to 59.36% was found by Khan et al., (2011). Twenty one unique bands were obtained for twelve out of fifteen primers in some of the varieties. These unique bands have high potential value since these can be converted to sequence tagged site markers (STS) and sequence characterized amplified regions (SCARs). Moreover, exclusive bands can be proved valuable to discriminate these cultivars at the molecular level without using field data (Fernandez et al., 2002). Thus, it is suggested that twelve primers used in the present work showing unique bands can be used for obtaining genotype specific profiles.
PIC calculated for these primers is 0.303 which is comparable to 0.37 PIC value reported by Salazar et al. (2006) in sesame. However, Russel et al. (1997) obtained a very high value of PIC i.e. 0.5, greater than PIC, reported in the present investigation. On an average, marker index and resolving power i.e. 4.44 and 6.89 respectively obtained in the present study were greater than that of 2.79 MI and 4.26 Rp values obtained in case of sesame (Salazar et al., 2006). A strong correlation has been observed in this study between marker index, resolving power and polymorphic information content which contradicts non- significant relationships observed from the findings of Salazar et al. (2006).
Higher numbers of polymorphic bands obtained confirm a wide range of genetic diversity among existing species. Cluster I comprise of four genotypes belonging to different species chosen for authenticity of this work. RSPT-1 (B. campestris variety) is relatively divergent from rest of three varieties. Two B. napus varieties i.e., RSPN-28 and DGS-1 are closely related in the sub cluster, however, relatively distant from CCS-08 (B. oleracea variety). The dendogram presented in Figure 2, clearly indicated that two closely related B. napus varieties are intermediate between B. campestris and B. oleracea varieties. This could be imputed to the fact that B. napus (AACC) is an amphidiploid species originated through the spontaneous hybridization of B. campestris (AA) and B. oleracea (CC) comprising the full chromosome compliments of its two progenitors. Cluster II consist of all the 23 B. juncea genotypes having two main clusters, one comprised of only four varieties and other constituted of 17 varieties. In cluster II, two varieties i.e. RB-55 and CS-56 are found to be the most divergent and varieties like RH-30 and RH-0406, RB-50 and BR-24, RH-0749 and RH-0119 are closely associated.
The analysis with RAPD markers disclosed wide variation within mustard and proved to be suitable for use with Brassica species. However, molecular approach is more likely to generate an unbiased picture of diversity than an agro-morphological one. Molecular characterization should be seen as equilibrating the traditional approach because most desirable traits are the result of interaction among expressed genes. That is why, morphological studies are still critical for discrimination of cultivars. The present findings further strengthened previous reports that the RAPD markers can be used effectively to estimate genetic differences among genotypes. Genetic variation existing among selected genotypes of B. juncea can further be utilized in strengthening Brassica breeding programs. The present study can provide further assistance in developing and planning breeding strategies by understanding relationship among species taken into consideration.
Materials and Methods
Plant Material
Twenty seven genotypes of Brassica species (23 Brassica juncea genotypes, 2 Brassica napus genotypes and one each of Brassica campestris and Brassica oleracea) were considered in the present study (detailed in Table 2). The seed material was procured from different institutes of Northern India.

Table 2 Genotypes used for diversity analysis along with their pedigree

Genomic DNA Isolation and Quantification

The genomic DNA was isolated from 7-8 cm young and actively growing fresh leaves using Doyle and Doyle (1990) method with slight modifications. Leaf material was grinded to fine powder in liquid N2, transferred to 1ml of pre-warmed (at 65ºC) extraction buffer and incubated for 35 minutes. An equal volume of Chloroform: Isoamylalcohol (24:1) was added to the tube, tilted for 10 minutes and centrifuged for 15 minutes at 8,000 rpm. Supernatant was collected to which, 0.6 volume of ice-cold isopropanol was added and stored at -200C for 3-4 hours. Centrifugation was done at 10,000 rpm for 10 minutes at 40C and pellets were purified with 0.01M ammonium acetate (200µl -300µl). The pellet was washed twice with 70% chilled ethanol, air dried, dissolved in 300µl TE (10 mM Tris-Cl, 1 mM EDTA pH 8.0) buffer, purified with 3µl of RNase (10mg/ml) and finally stored at -200C for further use. The amount and quality of DNA was confirmed using Nanodrop (mySPEC, Wilmington, USA). Finally the DNA was diluted to 25ng/µl concentration for PCR amplification.
RAPD and SSR Assay
A set of 15 arbitrary random 10-mer primers and 3 EST-SSR primers (detailed in Table 3) were used in the present investigation. The RAPD primers were diluted to 5 pmol concentration and final concentration of 25 pmol was used per reaction whereas the final concentration of SSR primers were set to 15 pmol for carrying out PCR reaction. DNA amplification was carried out in PCR tubes containing 25 µl reaction mixtures. Reaction mixture contained 2.5 µl of template DNA (25 ng/µl), 2.5 µl of 10× PCR Buffer, MgCl2 (2 mM), 0.2mM of each dNTPs (dTTPs, dGTPs, dCTPs, dATPs), primer (5pmol) concentration with 1 U Taq polymerase per reaction. For SSR primers, the primer concentration was 15pmol. PCR tubes containing master mix and DNA template were thoroughly mixed and subjected to the thermal profile under following conditions: 4 min at 94°C, 35 cycles (1 min at 94°C, 1 min at 36°C, 2 min at 72°C) and 10 min at 72°C for RAPD markers. In case of SSR markers, annealing temperature was calculated based on primer sequence.The same reaction mixture without genomic DNA was run for each reaction to serve as a negative control. PCR amplification was carried out in a 96 well Universal Gradient Thermal Cycler (PEQLAB, Deutschland and Osterrtich, United kingdom).

Table 3 Sequence information of the RAPD and EST-SSR primers

PCR Banding Profile
The amplification products were then subjected to electrophoretic separation using horizontal agarose gel electrophoresis. 1.5% and 3.0% agarose gels were prepared for resolving RAPD and SSR amplified PCR products respectively. The gel was visually examined under UV and documented using Biometra Gel documentation system.
Data Analysis
PCR bands were detected in the gel and their sizes were estimated using 100bp standard marker.The banding patterns of all genotypes against each primer were compared. Variable bands were used to score for polymorphism and binomial data matrix was generated which was further used for calculating total number of bands, number of polymorphic bands, and monomorphic bands for each primer. In order to check the informativeness and discriminatory power of RAPD primers used in this study, different parameters like polymorphism percentage, polymorphic information content, resolving power and marker index were calculated. Polymorphism percentage was calculated by dividing the number of polymorphic bands by the total number of scored bands and multiplied with 100. Average PIC indicates the ability of utilized markers to differentiate the genotypes. Maximum Polymorphic information content in case of dominant markers such as RAPD is 0.5 (De Rick et al., 2001). It is calculated as proposed by Roldan-Ruiz et al. (2000), as PIC = 2fi (1-fi), where fi is the frequency of bands present and (1-fi) = frequency of bands absent. Resolving power was calculated as proposed by Prevost and Wilkinson (1999). Rp= ΣIb, where Rp is the resolving power and Ib is the band informativeness. Ib is calculated by using the formula, Ib=1-[2×‌‌‌‌‌‌‌I0.5-pI], where p is the proportion of genotypes containing the band (Salazar et al., 2007). Marker Index for all the 15 primers was calculated according to Powell et al., (1996), which was further used by Milbourne et al., 1997. Marker Index=DI×EMR, where DI is the Diversity index that is equivalent to PIC and EMR is the effective multiplex ratio and EMR=fraction of polymorphic loci×number of polymorphic loci. Finally the cluster analysis was done, to determine genetic diversity. Binary data matrix was then used to construct a dendogram based on UPGMA (unweighted pair group method of arithmetic averages) using STATISTICA software, version 7.
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