RAPD analysis
The simplicity of laboratory assay for RAPD markers makes them an attractive method for obtaining intraspecific distinctions. This technique is already used for cultivar identification and genetic variability analysis of arecanut by
Purushotham et al., (2008), Manimekalai et al., (2012) and
Bharath et al., (2015). In the present study RAPD primers were used for distinguishing the arecanut genotypes. Comparatively higher percentage of polymorphic bands were detected indicating RAPD fragments are moderately polymorphic and particularly informative in the estimation of the genetic relationship of arecanut genotypes.
The polymerase chain reaction of arecanut genomic DNA using 6 selected RAPD primers generated a total of 368 amplified bands (Table 2). The highest number of bands (70) was observed with primers OPAH-18 and OPAH-01 separately and the lowest number of bands (39) was observed with primer OPBA-20. The size of amplified fragments ranged between 150 bp-1500 bp. The highest number of polymorphic loci (5) was observed with OPAF-15 and lowest polymorphic locus (1) was observed with OPBA-20. Comparatively, moderate to higher level of polymorphic information content (0.66 to 0.90) value was shown by selected polymorphic primers. The highest PIC value (0.90) was observed for the primer OPAF-15 (Fig 1) whereas, the lowest PIC value (0.66) was observed for OPAH-01. It was observed that RAPD primers showed an average PIC value of >0.5, which confirms that the primers are highly informative. The average number of bands across genotypes was found maximum in primers OPAH-18 and OPAH-01 (6.36 each) while minimum in primer OPBA-20 (3.54). The highest genetic similarity coefficient of 0.78 was found between the genotypes Mohitnagar and Mangala. The genotypes Maidan Local and Sagar Local showed the lowest similarity coefficient (0.56). The molecular diversity was not in agreement with most of the morphological diversity as reported in
Colocasia esculenta by
Singh et al., (2012). Comparatively high to moderate amplitude of the genetic similarity coefficient established in the present study confirms the occurrence of considerable genetic variability among arecanut genotypes. However, variation was slightly higher than that reported for 25 cultivars of mango (range 0.69-0.89) as reported by
Rajwana et al., (2008). The dendrogram (Fig 2) was constructed from values of similarity coefficients generated from RAPD data. The genotypes were divided into three major genotypic groups at 0.597 similarity coefficient, containing 3 clusters respectively, based on the UPGMA cluster analysis. The genotype Cameron was placed in a distinct cluster while other clusters subdivided into sub-clusters. Cluster ‘a’ consists of 5 genotypes, where these genotypes separated from each other at 0.60 similarity coefficient forming a two sub clusters a
1 and a
2. Further, a
2 cluster was divided into 2 cluster forming a distinct cluster for Hirehalli Dwarf. Cluster ‘b’ comprised of two sub clusters b
1 and b
2 at similarity co-efficient of 0.672. The cluster b
1 was divided into sub cluster forming a distinct cluster for Sarwamangala. The genotypes Mohitnagar and Mangala were placed at closer distance.
ISSR analysis
ISSR technique provides a quick, reliable and highly informative system for DNA fingerprinting. ISSR markers are inherited in Mendelian mode and segregated as dominant markers. This technique has been widely used in the studies of cultivar identification, genetic mapping, gene tagging, genetic diversity, evolution and molecular ecology. This technique is already used for cultivar identification and genetic variability analysis of arecanut by
Manimekalai et al., (2012). In this study 6 ISSR primers were used for the analysis (Table 2).
The polymerase chain reaction of arecanut genomic DNA using 6 selected ISSR primers generated a total of 307 amplified bands (Table 2). The highest number of bands (69) was observed with primers UBC-321 and the lowest number of bands (38) was observed with primers UBC-351 (Fig 3) and UBC-84. The size of amplified fragments ranged between 120 bp-1500 bp. Comparatively higher level of polymorphic information content (0.70 to 0.88) value was shown by selected polymorphic primers. The highest PIC value (0.88) was observed for primers UBC-72 and UBC-84 whereas, the lowest PIC value (0.70) was observed for UBC-351. It was observed that ISSR DNA primers showed an average PIC value of >0.5 indicating that the primers are highly informative. The highest similarity co-efficient (0.74) was observed between Teerthahalli Local and Hirehalli Dwarf. The genotypes Maidan Local and Cameron showed the lowest similarity coefficient (0.55). Comparatively, high to moderate amplitude of the genetic similarity co-efficient established in the present study confirms the occurrence of considerable genetic variability among arecanut genotypes studied. The variation was similar to that reported in molecular characterisation of coconut (
Cocos nucifera L.) varieties (0.657 to 0.775) by
Rasam et al., (2016) and to that of
Manimekalai et al., (2012) (0.526-0.855). The dendrogram (Fig 4) was constructed from values of similarity co-efficients generated from ISSR data. The genotypes studied were divided into two major genotypic groups at a 0.587 similarity coefficient, based on the unweighted pair group method using arithmetic average cluster analysis. Cluster ‘a’ consists of 8 genotypes, where these genotypes separated from each other at 0.59 similarity coefficients forming a two sub clusters a
1 and a
2. Further, a
1 cluster was divided into 2 cluster at 0.62 placing Maidan Local and Sumangala, Sagar Local and Sreemangala. a
2 cluster was divided into 2 groups at 0.663 forming a distinct cluster for Sarnamangala. Cluster ‘b’ formed a distinct cluster for Cameron.
SSR analysis
Simple sequence repeats have become the genetic markers of choice in many plant species because they are PCR-based, highly reproducible, polymorphic and abundant in plant genomes
(Powell et al., 1996). They are generally considered as the marker of choice for DNA fingerprinting purposes in perennial trees due to their high levels of polymorphism, high degree of reliability and reproducibility and codominant mode of inheritance. In plants these markers have been used widely for cultivar identification and genetic mapping
(Cipriani et al., 1999 and
Guilford et al., 1997). Hu et al., (2009) isolated and characterized polymorphic microsatellite loci from
Areca catechu (Arecaceae), these SSR primer pairs were used for microsatellite analysis in the present study. Simple sequence repeats were already used for genetic variability analysis of arecanut by
Kiran Kumar et al., (2020),
Nagaraja et al., (2019; 2016a);
Bharath et al., (2012).
Comparatively moderate to higher level of polymorphic information content (0.58 to 0.89) value was shown by selected polymorphic primers (Table 3). The highest PIC value (0.89) was observed for primer AC23 (Fig 5) whereas, the lowest PIC value (0.58) was observed for AC08. It was observed the SSR primers showed an average PIC value of >0.5, which indicates that the primers are highly informative. The highest similarity co-efficient (0.91) was observed between Mohitnagar and Mangala. The genotypes Maidan Local and Cameron showed the lowest similarity coefficient (0.52). Comparatively, high to moderate amplitude of the genetic similarity coefficient established in the present study confirms the occurrence of considerable genetic variability among arecanut genotypes. The similarity coefficient was ranged between 0.52 to 0.91. The variation was slightly higher than that reported in molecular characterisation of coconut (
Cocos nucifera L.) varieties (0.037 to 0.304) by
Rasam et al., (2016) and lower to that of
Liu et al., (2008) (0.061-0.896). The dendrogram (Fig 6) was constructed from values of similarity coefficients generated from SSR data. The genotypes were divided into two major genotypic groups at 0.52 similarity coefficient, based on the unweighted pair group method using arithmetic average cluster analysis. Cluster ‘a’ consists of 5 genotypes, where these genotypes separated from each other at 0.60 similarity coefficient forming a two sub clusters a
1 and a
2. a
1 cluster was comprised of two genotypes Maidan local and Sarnamangala. a
2 cluster was divided into 2 groups at 0.886 forming a distinct cluster for SAS-1. Cluster ‘b’ was divided into two sub clusters at 0.57 co-efficient. Sub cluster b
1comprised of four varieties and b
2 sub cluster consisted of two genotypes.
Combined analysis of RAPD, ISSR and SSR markers data
A dendrogram was constructed using the values of similarity coefficients generated from RAPD, ISSR and SSR data together. As per the dendrogram (Fig 7), the genotypes were divided into two major clusters a and b at 0.55 similarity coefficient, containing 8 and 3 genotypes respectively, based on unweighted pair group method using arithmetic average cluster analysis. At similarity coefficient of 0.595 cluster a was divided placing 8 genotypes in three sub clusters. One sub cluster was comprised of 4 genotypes, other consisted of 3 genotypes and the Maidan Local genotype alone was placed in separate sub cluster. The genotypes Mohitnagar and Mangala were placed very closely at similarity coefficient of 0.76. Cluster b was divided into 2 groups placing Cameron at distinct cluster at similarity coefficient of 0.648. The genotypes Hirehalli Dwarf and Theerthahalli Local were placed in another group.
The molecular markers analysis showed a high degree of variation among the arecanut genotypes studied. The present study revealed that the molecular markers can be successfully utilized for inferring genetic relationship and diversity in arecanut genotypes. The higher similarity was observed between the genotypes Mohitnagar and Mangala. It was also observed that the genotypes Theerthahalli Local and Hirehalli Dwarf also showed higher similarity. These genotypes were grouped nearer to each other in all the dendrograms of the markers indicaing that they are genetically closer. The genotypes Maidan Local and Cameron were placed in very distinct clusters in all the dendrogram of the markers, showing that they are having very distinct characters. The dendrograms prepared using SSR data and combined data of the all the three markers used showed very similar results. Hence, Using the SSR marker for further studies will be more useful since it is more reliable and repeatable.