Character Association and DNA profiling in Groundnut

CONTENTS

1. INTRODUCTION

2. REVIEW OF LITERATURE

3. MATERIALS AND METHODS

4. EXPERIMENTAL RESULTS AND DISCUSSION

5. SUMMARY AND CONCLUSION

6. LITERATURE CITED

7. ABSTRACT (ENGLISH)

8. ABSTRACT (HINDI)

9. APPENDICES

LIST OF TABLES

3.1 Weather data during crop period

3.2 List of genotypes used in present study and their pedigree

3.3 Analysis of variance and expected mean squares

3.4 Analysis of covariance between two characters

3.5 Preparation of stock solutions for DNA extraction and electrophoresis

3.6 Preparation of extraction buffer for DNA extraction

3.7 Detail of RAPD primers used in molecular analysis of groundnut cultivars

3.8 PCR reaction mixture content

3.9 Different cycles of PCR amplification

4.1 Mean square for various characters in Groundnut

4.2 Variability parameters for various characters in Groundnut (Arachis hypogaea L.)

4.3 Genotypic and phenotypic correlation coefficients between dry pod yield per plant and other characters in groundnut (Arachis hypogaea L.)

4.4 Genotypic and phenotypic correlation coefficients between kernel yield per plant and other characters in groundnut (Arachis hypogaea L.)

4.5 Genotypic (above diagonal) and Phenotypic (below diagonal) correlation coefficients among different characters in Groundnut (Arachis hypogaea L.)

4.6 Direct (diagonal) and indirect effects of different correlated characters towards dry pod yield per plant in Groundnut (Arachis hypogaea L.)

4.7 Number and Name of Genotypes in different Clusters

4.8 Pattern of amplified product appeared on agarose gel of five Groundnut genotypes with different primers

4.9 Polymorphism information of RAPD primers used

4.10 Details of the random primers used for amplification of genomic DNA of groundnut

4.11 Jaccard similarity coefficient

LIST OF FIGURE

4.1 Dendrogram generated for 5 groundnut genotypes using UPGMA cluster analysis based on jaccard similarity coefficient

LIST OF APPENDICES

I Mean performance of genotypes for different characters in groundnut

II Estimation of seed oil content (Soxhlet’s ether extraction method-A.O.A.C., 1984)

ABBREVIATION

Abbildung in dieser Leseprobe nicht enthalten

1. INTRODUCTION

Groundnut (Arachis hypogaea L.), the 'king' of oilseeds is commonly known as “peanut” or “monkey nut” or ‘Wonder nut” or “poor man’s cashew nut”. It belongs to subfamily Papilionaceae of the family Fabaceae. It is a self-pollinating crop with basic chromosome number ten (2n = 4x = 40) (Stebbins, 1957; Stalker and Dalmacio, 1986) and genome size 2800 Mb/lC (Guo et al. 2009). Peanut is grown for its high amount of edible oil (45-50%) and a reasonable amount of digestible protein (25-30%). It is the richest plant source of thiamine and also rich in niacin, which is low in cereals. Peanut is also valuable source of vitamins E, K and B (Encyclopaedia of Agricultural Science, 1994: Robertson, 2003).

Groundnut kernels are consumed as raw, boiled, roasted or fried products and also used in a variety of culinary preparations like peanut candies, butter, peanut milk and chocolates (Desai et al. 1999). Cake left after extraction of the oil is an excellent feed for livestock. Vegetative parts of groundnut like leaf and stem are good source of nutritionally high quality fodder for farm animals.

Groundnut is believed to be originated from South America (Southern Bolivia/North West America region). Peanut is cultivated around the world in the tropical, sub-tropical and temperate climatic conditions between 40º South and 40º North of equator (Encyclopaedia of Agricultural Science, 1994). The crop is grown in more than 100 countries worldwide. The major groundnut producers are China, India, Nigeria, USA, Senegal, Myanmar, Indonesia and the Sudan (undivided). Groundnut is grown on nearly 20.88 million ha worldwide with a total production of 34.66 million tons and an average yield of 1660 kg /ha (FAOSTAT 2012). Developing countries account for over 97 per cent of world groundnut area and 95 per cent of total production.

India is the second largest groundnut producing nation in the world with an area of about 5.53 million ha with a production and productivity of 9.67 million tones and 1750 kg/ha, respectively (Annual report of DGR Junagadh, 2014). Currently, six states viz., Gujarat, Andhra Pradesh, Karnataka, Tamil Nadu, Maharashtra and Rajasthan accounts for more than 90 per cent of the groundnut area and production of the country.

In Rajasthan, it is mainly cultivated on an area of 4.66 lakh hectares with a production and productivity of 9.06 lakh tones and 1943 kg/ha, respectively (Annual report of DGR Junagadh, 2014). Under present scenario, the major area of groundnut in Rajasthan is represented by Chittorgarh, Bhilwara, Jaipur, Tonk, Sawai Madhopur, Dausa, Bikaner and Hanumangarh.

Yield is an important quantitative trait for any crop improvement programme. Genetic improvement for quantitative traits depends on the nature and amount of variability present in the genetic stock and the extent to which the desirable traits are heritable. Since, groundnut pods develop below the ground level hence, genotypes for yield cannot be screened or evaluated prior to harvest. Therefore, association studies are very important.

Inheritance of quantitative traits is largely affected by the environmental factors. Therefore, selection made in field is not likely to reliable. Here DNA polymorphism can provide an opportunity to measure genetic variability more precisely because DNA markers are not affected by the environment. DNA analysis technique can add in assessment of variability that can be linked to phenotypic traits. Genotypic selection at the DNA level can be exploited in marker assisted selection to identify desirable genotypes. The use of molecular marker technique which is independent of environmental factors offers significant advantage for identification of genotypes. They are rapid, relatively cheap and eliminate the need to grow plants up to maturity. Therefore, attempts were made for molecular profiling of selected genotypes under study.

Keeping in view the above facts, the present investigation was carried out to fulfil the following objectives in ground nut (Arachis hypogaea L.):

1. To estimate the nature and magnitude of variability present in groundnut genotypes with respect to yield and its component characters.
2. To estimate genotypic and phenotypic correlations among different economic characters.
3. To determine the direct and indirect influences of various yield attributing characters through path coefficient analysis.
4. DNA profiling of selected groundnut genotypes through RAPD markers.

2. REVIEW OF LITERATURE

In the present investigation genetic variability, correlation and path coefficient studies carried out for yield and its component characters including molecular characterization of selected genotypes in groundnut (Arachis hypogaea L.). The literature pertaining to objectives of this investigation have been reviewed briefly under the following sub-heads:

2.1 Variability parameters

2.2 Correlation coefficients & Path coefficient

2.3 Molecular characterization

2.1 VARIABILITY PARAMETERS

The existence of genetic variability is prerequisite for any crop improvement programme; however, loss of locally adapted variable material has been rapid which, need to be maintained. The variability existing among homozygous genotypes/ population is generally considered as free variability, which can be exploited for genetic advancement through selection. This together with information on heritability and genetic advance would be rewarding in designing an effective breeding programme. The genetic variability is determined with the help of certain genetic parameters viz., genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) and heritability estimates.

Heritability is the heritable portion of phenotypic variance and it is a good index of extent of transmission of a character from parents to their off-springs. Heritability in broad sense is the ratio of genotypic variance to phenotypic variance. Its estimation is important because it determines the expressivity of genes being carried by a genotype. If the heritability of a character is high, the phenotypic value provides a fairly close measure of the genotypic value and thus breeder can base his selection on the phenotypic performance. There by the knowledge of heritability helps the plant breeder in pre-assessing the results of selection for a particular character. However, for predicting the effect of selection, heritability estimates along with genetic advance are more useful than the heritability estimates alone. The review of literature pertaining to variability parameters in groundnut is presented in the subsequent paragraphs.

Azad and Hamid (2000) observed very close estimation of GCV and PCV for all the characters under study except primary branches per plant. High values of GCV and PCV together with high heritability and genetic advance as per cent of mean were observed for plant height, pods number and kernel as well as pod yield.

Chishti et al. (2000) evaluated 16 early maturing genotypes of groundnut to estimate variability parameters. They observed significant variation for all the characters expect 100 kernel weight. The estimates of PCV were higher than those of GCV for all the characters except days taken to flowering as well as maturity; for these characters those were at par with high heritability estimates.

Naazar-Ali et al. (2000) studied genetic variability, heritability, genetic advance and correlation coefficients with 16 groundnut varieties, high values of GCV and PCV were observed for kernel weight, pod length and pod yield. High heritability coupled with high genetic advance as per cent of mean was observed for kernel weight and pod length revealing an importance of additive gene effect for these traits and selection pressure on these attributes would be effective for their improvement.

Prakash et al. (2000) studied variability parameters in 91 spreading groundnut cultivars and observed that genotypic coefficient of variation was the highest for pod yield per plant and it was the lowest for oil content. Heritability in broad sense was high for pod yield per plant, oil content and 100 kernel weight. High genetic advance as per cent of mean was observed for pod yield per plant, pods per plant and 100 kernels weight.

Venkatramana (2001) evaluated thirty groundnut genotypes including 20 spanish bunch and 10 virginia bunch for genetic variability parameters and reported that estimates of PCV were higher than corresponding GCV for all the characters under study. However, both PCV and GCV estimates were high for 100 kernel weight and kernel yield as well as oil yield. Whereas, heritability in broad sense was high for oil content, 100 kernel weight and sound mature kernel percentage. Moderate heritability coupled with high genetic advance as per cent of mean was observed for kernel yield and oil yield. Additive gene effect could be preponded for 100 kernel weight as it had high heritability estimates along with high genetic advance.

Venkataramana et al. (2001) studied genetic variability in 144 groundnut germplasm lines. High genotypic and phenotypic coefficients of variation were observed for plant height, oil percentage, 100 kernel weight and kernel yield per plant. They noticed high heritability coupled with high genetic advance as per cent of mean for plant height, pod yield per plant, 100 kernel weight and oil percentage. They suggested that characters like pod yield per plant, 100 kernel weight, plant height and oil percentage would be improved effectively through simple selection.

Dashora and Nagda (2002) evaluated 22 germplasm lines with one local check (TAG-24) to estimate variability parameters and revealed that dry pod yield, 100 kernel weight and kernel yield had high genetic advance, genetic gain and heritability estimates suggesting preponderance of additive gene effect. High heritability was accompanied with low genetic advance as per cent of mean for days to 50% flowering, days to maturity, shelling per cent, 100 kernel weight and oil content revealing preponderance of non-additive gene effect.

Nath and Alam (2002) evaluated 15 exotic groundnut genotypes procured from ICRISAT along with a local check (Dhaka-1)and genetic variability parameters were studied for yield and yield contributing characters. The estimates of PCV were in accordance with those of GCV for days to flowering, plant height, pods per plant, 100 pod weight, shelling per cent and harvest index. However, heritability estimates were higher for all the characters studied and GA as per cent of mean was also high for all the characters except days to flowering. Therefore, direct selection would be effective for improvement of all the characters except days to flowering.

Prasad et al. (2002) evaluated 30 spanish bunch groundnut genotypes to estimate the variability parameters, they reported that PCV and GCV estimates were high for harvest index; while, magnitude of these parameters was moderate for pod yield per plant, primary branches per plant, height of main axis, pods per plant and 100 kernel weight. High estimates of heritability and genetic advance as per cent of mean were observed for harvest index, pod yield per plant, height of main axis and pods per plant indicating prime role of additive gene effect for the inheritance of these characters.

Makhan Lal et al. (2003) studied genetic variation and selection response for twelve attributes using 67 groundnut lines and cultivars. They reported higher values of PCV than GCV for all the characters studied except days to maturity; however, estimates of GCV were low to moderate for all the characters. The heritability estimates were high along with high GA as per cent of mean for plant height and 100 pod weights.

Kumar and Rajamani (2004) observed highly significant differences among 12 genotypes for seed yield and other characters. For plant height, pod yield, 100 kernel weight and percentage of sound mature kernels GCV and PCV estimates were high whereas, those were moderate for shelling percentage. The values of PCV were higher than GCV indicating an influence of environment in expression of all the characters.

Parmeshwarappa et al. (2004) studied nature and magnitude of genetic variability in 44 released varieties of groundnut. The characters pod yield per plant, kernel yield per plant, shelling out-turn and sound mature kernels showed high values of genetic coefficient of variation. An extent of heritability was moderate for days to maturity and high for days to 50% flowering as well as 100 kernel weight. High heritability coupled with high genetic advance as per cent of mean was expressed by pod yield, kernel yield and shelling out-turn; hence, improvement in these traits could be brought by applying selection pressure on per se performance of genotypes.

Mothilal et al. (2004) reported that values of GCV and PCV were high for mature pods per plant and pod yield per plant and moderate to low for plant height, branches per plant, shelling out-turn, 100-pod weight, 100-kernel weight and sound mature kernels in groundnut. These characters also exhibited high magnitude of heritability. However, genetic advance as per cent of mean was high for pods per plant and it was moderate for branches per plant, plant height and 100-kernels weight, indicating that weightage should be given to these characters to improve yield potential of groundnut.

Wani et al. (2004) reported high value of genotypic coefficient of variation for mature pods per plant and harvest index. High heritability was observed for days to maturity, number of branches per plant, 100-kernel weight, days to first flower, 100-pod weight and shelling out-turn. High heritability coupled with high genetic advance as per cent of mean was observed for days to maturity, 100-pod weight and 100-kernel weight revealing that selection would be effective for improvement in these characters.

Golakia et al. (2005) evaluated 24 spanish bunch groundnut genotypes to study variability parameters. They noticed close correspondence between PCV and GCV estimates for all the eleven characters studied suggesting that characters studied were less influenced by environmental factors. They also estimated high GCV and PCV for all the characters, except shelling out-turn and oil content. High heritability coupled with high genetic advance as per cent of mean was observed for plant height, pods per plant, 100 kernel weight and kernels as well as pods yield per plant. High heritability along with low genetic advance as per cent of mean was observed for shelling out-turn. For oil content heritability was moderate. All these indicated that large portion of non-additive gene action was responsible for expressions of shelling out-turn and oil content.

Mahalaxmi et al. (2005) studied genetic variability parameters in 57 genotypes of groundnut. They reported higher value of PCV than corresponding value of GCV for all the characters under study. However, estimates of PCV and GCV were high for number of mature as well as immature pods, pod yield per plant and oil content; whereas, those were low for plant height, shelling percentage and 100 kernel weight. However, heritability estimates were high for all the characters except oil content. All the characters except days to first flowering, days to 50% flowering, plant height, number of primary branches and oil content registered high heritability along with high values of GA as per cent of mean.

John et al. (2006b) studied variability parameters in groundnut and reported that estimates of PCV were in accordance with estimates of GCV for plant height and number of primary as well as secondary branches; whereas, PCV estimates were high than those of GCV for number of mature as well as immature pods, pod length, pod width, pod weight, shelling out-turn and kernel yield. However, both the estimates were high for number of secondary branches and number of immature pods. The broad sense heritability was high for all characters except number of mature pods, pod length, pod weight and shelling out-turn, for these traits it was moderate; most of the characters studied had moderate to high estimates of GA as per cent of mean except pod width and shelling out-turn.

Kadam et al. (2007) studied 40 groundnut genotypes of different botanical groups to assess the amount of genetic variation, heritability and genetic advance with respect to pod yield and other agronomic characters. The genotypic coefficient of variation was high for kernel yield, pod yield, number of pods, number of branches, plant height and harvest index. High heritability coupled with high genetic advance was also observed for pod yield and kernel yield.

John et al. (2008) reported close correspondence between GCV and PCV values for days to maturity, pod yield per plant, shelling per cent and 100-kernel weight; whereas, value of PCV was higher than corresponding GCV for days to initial flowering, number of primary branches and kernel yield per plant. All the characters had high estimates of broad sense heritability, but GA as per cent of mean was high for shelling per cent and kernel yield per plant, hence improvement in these characters would be effective on the per se performance of the individual.

John et al. (2009) evaluated 60 genotypes of groundnut to study variability parameters for seventeen characters and they reported high GCV and PCV values for all the characters except for plant height, shelling percentage and 100 kernel weight. However, low GCV and PCV values were observed for days to initiation of flowering, days to 50% flowering and number of primary branches. In general estimates of PCV were high than those of GCV for respective character. The broad sense heritability estimates were high for all the characters and estimates of GA as per cent of mean were also high for all the characters except growth attributes, days to initiation of flowering as well as 50% flowering, plant height and number of primary branches per plant.

Korat et al. (2009) evaluated 80 diverse genotypes of bunch groundnut for variability parameters. The estimates of PCV and GCV were high for number of secondary branches per plant and number of aerial pegs per plant; whereas, for rest of the characters those were low to moderate. The broad sense heritability estimates were high for all the characters, but genetic advance as per cent of mean was high for pod yield per plant, number of primary branches, number of secondary branches per plant, plant height and 100 kernel weight indicating that these traits predominantly governed by additive gene action and responsive to selection for their further improvement.

Shoba et al. (2009) evaluated three F2 cross derivatives and their four parents to study their mean performance, genetic variability, heritability and genetic advance as percentage of mean for yield and contributing characters. Among the crosses, TMV2 x COG0437 had higher mean performance for all the characters followed by TMV2 x COG 438. Higher PCV and GCV values were also exhibited by this cross. The cross TMV2 x COG0437 had high heritability and high to moderate GAM for most of the characters followed by TMV2 x COG0438. Hence, based on mean and variability parameters, TMV2 x COG437 is adjudged as best cross combination for further selection programme to evolve a promising progeny.

Cholin et al. (2010) evaluated two spanish bunch groundnut genotypes for variability parameters and results revealed that magnitude of variation (PCV, GCV) was low to moderate. For the protein content (%), genetic advance as per cent of mean was moderate with high heritability indicating the role of additive gene action in controlling these traits and for oil content lower magnitude of variation with higher heritability and lower genetic advance was reported.

Dolma et al. (2010) evaluated 33 advanced breeding lines and genotypes of groundnut to study the variability parameters, correlations and path coefficients for 13 metric traits. Significant genotypic differences were observed for all the traits studied indicating the considerable amount of variation among genotypes for each character. The highest genotypic and phenotypic coefficient of variation was observed for late leaf spot (LLS) score at 80 DAS followed by LLS score at 90 DAS, kernel yield/plant, plant height, pod yield/plant and test weight. Similarly, high heritability coupled with high genetic advance was observed for these traits indicating the scope for their improvement through selection.

Shinde et al. (2010) evaluated 50 elite genotypes of virginia bunch groundnut for variability parameters and observed that GCV and PCV estimates were higher for pod yield per plant, number of immature pods per plant, number of mature pods per plant and biological yield per plant. High heritability was associated with high genetic advance for pod yield per plant and number of mature pods per plant. These characters were mainly under the influence of additive gene action and there is ample scope for improvement in these traits through simple selection.

Nandini et al. (2011) studied variability parameters through 196 F8 recombinant inbred lines for water use efficiency in groundnut during kharif season and reported higher PCV than GCV for all the characters. Pod yield per plant showed maximum GCV followed by kernel yield per plant, number of pods per plant, sound mature kernel percentage, specific leaf area, number of branches per plant, shelling percentage, plant height, SCMR and days to fifty percent flowering. A moderate to high degree of heritability and genetic advance was observed for pod yield per plant, kernel yield per plant, pods per plant, sound mature kernel percentage, plant height, number of branches per plant and SLA.

Zaman et al. (2011) evaluated 34 genotypes for estimation of genetic variability, genetic parameters and correlation coefficient among different yield components. Highly significant variations were observed among the genotypes for all the characters studied. The highest genetic coefficient of variation was observed for kernel yield per hectare. The highest heritability was observed in kernel yield per pant (95.08%) while high values of genetic advance were obtained in all the characters except days to maturity and days to 50% flowering. The number of mature nuts per plant had high positive direct effect on seed yield per hectare followed by nut size, shelling percentage, days to 50% flowering and days to maturity. Therefore, branches per plant, plant height, nuts per plant, nut size, karnel size, days to 50% flowering, shelling percentage and days to maturity were identified to be the important characters which could be used in selection for yield.

John et al. (2012) evaluated twenty eight F2 populations for genetic parameters of 23 characters of morphological, physiological, yield and yield attributes during spring 2009. High genotypic coefficient of variation was observed for the number of secondary branches per plant. High heritability and high GAM was recorded for the number of secondary branches per plant, high heritability and moderate GAM were observed for days to 50% flowering. The leaf area index, number of well-filled and mature pods per plant, dry haulms yield per plant and harvest index showed moderate heritability and high GAM. This indicates that these characters are under additive genetic control and selection for genetic improvement will be worthwhile and may rapidly contribute to pod-and kernel yields.

Madhura et al. (2012) conducted an experiment using groundnut minicoreset, comprised of 182 accessions representing hypogaea bunch (42), hypogaea runner (39), Spanish bunch (63) and fastigiata (38) obtained from NRCG, Junagadh with nine cultivars (GPBD-4, JL-24, Mutant-III, TGLPS-3, DSG-1, Gangapuri, ICGS-44, GAUG-10 and Kadiri-3) during Kharif 2005. High genetic advance was observed for test weight pod yield per plant, moderate for shelling per cent, sound mature kernel and oil content and for days to 50 per cent flowering and days to maturity it was low.

Upadhyaya et al. (2012) studied variability for nutritional traits in the mini core collection of peanut, 184 mini core accessions and four control cultivars were evaluated for agronomic traits. Significant genotypic and genotype x environment interactions were observed for all the nutritional and agronomic traits in the entire mini core collection.

Vishnuvardhan et al. (2013) evaluated 8 parent and 28 crosses for variability parameters and observed high GCV accompanied by high heritability and high GAM were obtained for number of secondary branches per plant, percentage of leaves affected by foliar diseases per plant and number of immature pods per plant. Rust severity, number of mature pods per plant and pod yield per plant recorded high GCV and moderate heritability and GAM. Moderate GCV, moderate to low heritability and GAM were registered for number of primary branches per plant, kernel weight per plant, shelling out-turn, late leaf spot and harvest index.

Patil et al. (2014) evaluated 58 spanish bunch groundnut genotypes for variability studies in 16 plant characters. Analysis of variance revealed significant differences among the genotypes for all the characters studied. Maximum broad sense heritability was recorded for days to 50% flowering followed by plant height and 100-kernels weight. The maximum genetic advance was found for seed dormancy followed by 100-kernels. In general, moderate to high heritability coupled with moderate to high genetic advance for days to 50% flowering, plant height, 100-pods weight, 100-kernels weight, shelling percent and harvest index, indicated the involvement of additive gene action and scope of improvement in these traits through selection.

Yadlapalli (2014) conducted an experiment at the Agricultural Research Station, Darsi, to genetic variability, genetic parameters and correlation coefficients among different yield components. Highly significant variations were observed among the genotypes for all the characters studied. The highest genetic coefficient of variation was observed for no. of pods/plant followed by pod yield, 100 seed weight, no. of branches per plant, plant height and days to 50% flowering. The highest heritability was observed in 100 seed weight (98.0%) followed by pod yield (96.0%), no. of pods/plant (94.0%), no. of branches/plant (89.0%), plant height (88.0%) and days to 50% flowering (79.0%). while high values of genetic advance were obtained for all the characters except plant height and days to 50% flowering. Selection for characters showing high heritability with high genetic advance, positive and high significant correlation and showing high direct effects will helpful in the improvement of yield in the groundnut

2.2 CORRELATION COEFFICIENTS & PATH COEFFICIENT

The knowledge of association between yield and its component characters is of immense value for breeder, because it forms a basis for selection. It is well known phenomenon that different components of yield very often exhibit considerable degree of association in both positive and negative directions among themselves and with yield as well. Therefore, understanding of correlation between characters would helpful to accumulate optimum combination of yield contributing characters in a single genotype.

The concept of correlation was given by Galton (1889), which was further elaborated by Fisher (1918) in order to initiate effective selection programme aimed at genetic improvement in economic yield of a crop. The degree of association between yield and component characters might vary with genetic make of the material under study. Hence, it is essential to measure the correlations at genotypic and phenotypic levels.

Some of the important research results obtained on correlation coefficients studies on various characters of groundnut are presented here.

Chishti et al. (2000) evaluated 16 spanish groundnut genotypes and reported that at genotypic level all the characters viz., days taken to flowering, number of pods per plant, shelling percentage, 100-kernel weight, number as well as weight of sound mature kernel and oil per cent showed positive association with pod yield; whereas, pod yield depicted negative and significant association with days taken to maturity. However, sound mature kernel percent by weight, shelling percentage, days taken to flowering and number of pods per plant showed high positive direct effects on pod yield, while sound mature kernel per cent by number, days to maturity contributed high negative effects on pod yield.

Jayalakshmi et al. (2000) observed significant and positive association between kernel yield and mature pods per plant, but significant and negative association between kernel yield and oil content was also reported.

Naazar-Ali et al. (2000) reported that pod yield was significantly and positively correlated with kernel weight and oil content. Positive and highly significant correlation between pod length and kernel weight indicated that selection for larger kernel size could result in heavier kernel, which had close positive correlation with yield.

Mathews et al. (2001) reported that pod yield per plant had significant and positive genotypic correlation with days to flowering, days to 75% maturity, kernel yield per plant, plant height, haulm yield and 100-kernel weight. Dry pod yield showed positive and significant direct effect for kernel yield per plant.

Venkatramana (2001) evaluated 30 groundnut genotypes and found that genotypic correlation coefficients were, in general, marginally higher than the phenotypic correlation coefficients for all the 5 characters i.e. 100-kernel weight, SMK per cent, kernel yield, oil yield and oil content. Oil content was significantly and positively correlated with 100-kernel weight, sound mature kernel per cent, kernel yield and oil yield.

Dashora and Nagda (2002) reported that dry pod yield exhibited significant and positive association with shelling percentage and kernel yield. Path analysis revealed that shelling percentage and kernel yield were major components of dry pod yield.

Izge et al. (2004) evaluated 16 groundnut genotypes to determine the correlation between pod yield and important yield traits and to determine their interrelationship through path analysis. The groundnut genotypes used represent the combinations of low and high levels of traits that are identified as important yield determinants, i.e. number of mature pods per plant, 100-seed weight and shelling percentage. The study suggests that the number of mature pods per plant and 100-seed weight are traits for possible consideration for selection as regards to pod yield in groundnut.

Kavani et al. (2004) evaluated 15 genotypes of groundnut and reported that pod yield expressed significant and positive association with pods per plant, kernel yield per plant, 100-kernel weight and biomass yield. Kernel yield per plant had significant and positive association with pods per plant, 100-kernel weight and biomass yield per plant. Strong association between biomass and pod yield per plant indicated possibilities for simultaneous improvement in both the traits.

Nagda and Joshi (2004) evaluated 52 genotypes of groundnut and observed significant and positive association between pod yield per plant and harvest index. Harvest index expressed high positive direct effect towards pod yield per plant. While 100-kernel weight influenced indirectly via harvest index, suggested that harvest index and 100-kernel weight should be considered as important traits in selection programme.

Suneetha et al. (2004) studied 23 diverse genotypes for their character association and reported significant and positive correlation of pod yield per plant with mature pods per plant and harvest index. The character combinations of days to 50% flowering with days to maturity and 100-pod weight with 100-kernel weight showed significant and positive correlations between themselves. Days to 50% flowering and plant height expressed negative direct contribution. They also concluded that days to 50% flowering, plant height and mature pods per plant should be considered as selection criteria for improving pod yield in groundnut.

Golakia et al. (2005) observed strong association of pod yield per plant in both groups with mature pods per plant, kernel yield per plant, developed pods per plant, biomass yield per plant and harvest index, indicating that simultaneous selection for these characters might bring an improvement in pod yield.

Gomes and Lopes (2005) studied eight cultivars to estimate the genetic parameters of agronomic traits of groundnut and the genotypic correlation coefficients between seed yield and the primary components of the yield apportioned into direct and indirect effects. The highest estimates of the coefficient of genotypic determination were obtained for weight of 100 seeds, number of pods/plot, number of seeds/pod and pod yield. The splitting of the genotypic correlations into seed yield and the primary components, in direct and indirect effects, showed that the seed yield was positively influenced by the number of pods and weight of 100 seeds and negatively by the number of pod. Thus, the number of pods had the maximum direct influence on the seed yield.

Kotzamanidis et al. (2006) observed that pod yield per plant had significant and positive correlation with seed length, 100-pod weight, 100-seed weight, pod length, pod width and seed width. However, significant and positive correlation was found between 100-seed weight and 100-pod weight.

Patil et al. (2006) evaluated 17 groundnut genotypes at 6 locations to study correlation and path analyses for yield and yield components. Pod yield per plant showed a highly significant and positive association at the genotypic and phenotypic levels at 3 locations (Dharwad, Sankeshwar and Nippani) with shelling percentage and sound mature kernel percentage. Correlation and path analyses revealed that the number of pods per plant, shelling percentage and sound mature kernel percentage were important yield-contributing traits irrespective of the environment. Thus, these traits should be considered during selection for the improvement of pod yield per plant in groundnut.

Sumathi et al. (2007) evaluated 48 diverse genotypes of groundnut to analyse and determine that pod and kernel characters having greater interrelationship with pod yield. The pod yield per plant had significant positive association with kernel yield, sound mature kernel weight and 100-seed weight both at genotypic and phenotypic levels. The shelling percentage and oil content had negative association with pod yield per plant both at genotypic and phenotypic levels. In general, the genotypic correlations in most characters were higher than the phenotypic correlation coefficients thereby suggesting strong inherent association between genotypic and phenotypic levels. The inter correlations of kernel yield with sound mature kernel weight, 100-seed weight were also positive and significant at both genotypic and phenotypic levels.

Mane et al. (2008) performed correlation analyses to assess the relationship among different characters in summer bunch groundnut and reported that pod yield per plant exhibited significant and positive correlation with per cent sound mature kernel, number of pegs per plant, number of pods per plant and shelling percentage. However, it showed negative and non-significant correlation with hundred kernel weight and days to 50 per cent flowering.

John et al. (2009) reported that pod and kernel yields per plant showed significant and positive association with days to 50% flowering, plant height, number of secondary branches per plant, number of mature pods per plant, SMK weight, sound mature kernel number as well as weight and 100-kernel weight. So these characters were considered as selection indices for the improvement of kernel and pod yields per plant.

Awatade et al. (2010) carried out correlation analysis to assess the relationship among different characters in groundnut and reported that the phenotypic correlation coefficient was slightly higher than phenotypic correlation coefficient. The characters viz., number of pods per plant, number of primary branches per plant, number of kernels per plant and kernel yield per plant showed significant and positive correlation with dry pod yield per plant.

Dhaliwal et al. (2010) studied direct and indirect effects by path analysis for dry pod yield and its components in groundnut. Dry pod yield had significant positive association with days to flowering, days to maturity, haulm yield per plant and kernel yield per plant. At genotypic level too these traits had high positive correlation with dry pod yield. Path analysis indicated high positive direct contribution of kernel yield per plant. Days to flowering, days to maturity and haulm yield per plant made indirect contribution to dry pod yield via kernel yield per plant. It was concluded that these traits must be given importance during selection in segregating generation for improvement of dry pod yield in groundnut.

Khanpara et al. (2010) carried out character association and path coefficient analysis in groundnut for pod yield. The correlation of pod yield per plant was associated significantly and positively with number of mature pods per plant, 100 kernel weight and number of primary branches per plant, but which was negative with days to 50 per cent flowering and days to maturity. Number of mature pods per plant manifested maximum direct effect towards dry pod yield per plant followed by days to maturity, biological yield per plant and other characters had high indirect effects through number of mature pods per plant.

Raut et al. (2010) investigated F2 generation for six crosses of groundnut, to study correlation coefficients among eleven yield and yield contributing traits with their path effects towards pod yield. The correlation coefficients of pod yield per plant were found positive and highly significant with kernel yield per plant, number of mature pods per plant and shelling out-turn. On the basis of correlations and direct, indirect effects, kernel yield per plant, number of mature pods per plant and shelling out turn were proved to be the outstanding characters influencing pod yield in groundnut and need to be given importance in selection to achieve higher pod yield.

Shinde et al. (2010) reported that the correlation of pod yield per plant was associated significantly and positively with number of mature pods per plant, 100-kernel weight and number of primary branches per plant, but which was negative with days to 50% flowering and days to maturity. Number of mature pods per plant manifested maximum direct effect towards the pod yield per plant followed by days to maturity, biological yield per plant and 100 kernel weight and other characters had high indirect effects through number of mature pods per plant.

Sonone et al. (2010) worked out character association with direct and indirect effects for forty genotypes of groundnut for fifteen characters. The correlation studies revealed that the genotypic correlation coefficients were slightly higher than the phenotypic correlation coefficients for most of the characters. The magnitude of genotypic and phenotypic correlation coefficients between dry pod yield per plant and kernel yield per plant was highest and positive followed by dry pod yield and number of pods per plant and number of kernels per plant Positive correlation between dry pod yield per plant and days to first flowering, days to 50 per cent flowering, days to maturity, number of primary branches per plant and 100 seed weight was also noticed. While, negative correlation with oil content, pod length and plant height was observed.

John et al. (2011) carried out correlation analysis to assess the relationship among different characters in F2 population of groundnut and reported that SCMR had significant negative association with specific leaf area. Positive significant association of transpiration rate with photosynthetic rate and pod yield per plant, dry haulm yield per plant with harvest index. The high direct effect of pods per plant was appeared to be the main factor for its strong positive correlation with pod yield.

Vekariya et al. (2011) evaluated fifty diverse genotypes of bunch groundnut during Kharif 2009 for genetic parameter viz., correlation and path analysis. The magnitudes of genotypic correlation coefficients were higher as compared to the corresponding phenotypic correlation coefficients. The pod yield per plant had highly significant and positive correlations at phenotypic levels with number of mature pods per plant, 100-pod weight, 100-kernel weight,kernel yield per plant, biological yield per plant and harvest index.

Babariya and Dobariya (2012) estimated correlation coefficients for pod yield per plant and its components by using 100 genotypes of Spanish bunch groundnut. The pod yield per plant was significantly and positively correlated with days to maturity, plant height, number of pods per plant, kernel yield per plant, number of mature pods per plant, 100-kernel weight, biological yield per plant and harvest index. Thus, these characters were identified as the most important yield components and due emphasis should be placed on these characters while selecting for high yielding genotypes in Spanish bunch groundnut.

Nandini et al. (2012) evaluated 196 F8 recombinant inbred line population to study the correlation and path association for ten growth and physiological traits related to water use efficiency in groundnut. The studies on Phenotypic and genotypic correlation coefficients revealed that pod yield per plant had strong positive correlation with pods per plant, kernel yield per plant, sound mature kernel percentage indicating that improvement in these characters will lead to improvement in yield.

Shoba et al. (2012) estimated correlation coefficients among nine yield and yield attributing characters with their path effects towards kernel yield in F3 generation for three crosses of groundnut, on the basis of correlations and direct and indirect effects, number of pods per plant, pod yield per plant, hundred kernel weight and shelling percentage were proved to be the outstanding characters influencing kernel yield in groundnut and need to be given importance in selection to achieve higher kernel yield.

Mukhtar et al. (2013) conducted irrigated trial to study the performance of three groundnut (Arachis hypogaea L.) varieties as affected by basin size and plant population. Plant height exhibited the highest positive (p<=0.05) effect, followed by total dry matter and number of branches in the three years and when combined. Path coefficient analysis revealed that among the growth characters selected, plant height made the highest positive contribution of 34.77% to pod yield of groundnut, followed by total dry matter with a positive contribution of 17.46%, suggesting plant height was the most critical growth parameter for determining yield of groundnut under irrigation.

Rao et al. (2013) evaluated the association between yield and yield components under drought condition and reported that dry pod yield exhibited significant positive association with pods per plant, 100-kernel weight and SPAD chlorophyll meter reading (SCMR). The direct effect was high and positive for pods per plant, SPAD chlorophyll meter reading (SCMR) and 100-kernel weight.

Vange et al. (2014) evaluated nine improved varieties of groundnut and one locally cultivated variety for their breeding potentials in the Guinea Savannah agro-ecological zone. Correlation studies revealed that grain yield correlated positively with all except the phenological traits. The path analysis implicated biological yield, failed pegs/plant, number of leaves/plant, and basal stem diameter as having substantial influence on grain yield in groundnut. Thus, selection of breeding lines based on the biological yield, failed pegs, number of leaves/plant and basal stem diameter could give a better scope for maximum grain yield in groundnut.

2.3 DNA PROFILING THROUGH RAPD

Until recent advances in molecular genetics, breeders have been improving both qualitative and quantitative inherited traits by conventional breeding methods based on phenotypic evaluation and selection, which are resource and time consuming.

The RAPD markers (Williams et al. 1990) have been increasingly employed for population studies and for analysis of molecular diversity (Hogbin et al. 1998; Fischer et al. 2000). RAPD technique has the advantage of assessing a greater number of potential polymorphic loci distributed randomly in the genome than allozymes. In addition, when compared to other DNA-based markers, the procedure is technically simple, economic and also does not require any prior knowledge of the target DNA sequence in the genome. However, most RAPD loci show dominant segregation and are assumed to possess only two alleles per locus, which may bias some population genetic parameters.

The standard RAPD technology utilized short synthetic oligonucleotides (10 bases long) of random sequences as a primer to amplify nanogram amounts of total genomic DNA under low annealing temperature by PCR. During annealing at appropriate temperature in the thermal cycler, oligonucleotide primers of random sequence bind several priming sites on the complementary sequences in the template genomic DNA and produced discrete DNA products. The profile of amplified DNA primarily depends on nucleotide sequence homology between the template DNA and oligonucleotide primer at the end of each amplified product. Welsh and McClelland (1990) independently developed a similar methodology using primers about 15 nucleotides long and different amplification and electrophoresis conditions than RAPD and called it arbitrarily primed polymerase chain reaction (AP-PCR) technique.

Lanham et al. (1992) studied the detection of polymorphic loci in Arachis germplasm using RAPDs. From a total of 60 decamer oligonucleotide primers, 49 polymorphic loci were identified between A. hypogaea type and a synthetic amphidiploids (B x C)2 created from A . batizocoi and A. chacoense cross

Bhagwat et al. (1997) used RAPD analysis for radiation induced mutants of groundnut. It showed distinct morphological and biochemical characteristics. The analysis revealed characteristic band differences among the 12 mutants and their parents. The polymorphic bands were dominant in the F1 generation and segregated in a Mendelian fashion in F2.

Subramanian et al. (2000) selected 70 genotypes representing variability for several morphological, physiological and other characters. They studied polymorphism employing random amplified polymorphic DNA (RAPD) assay with 48 oligonucleotide primers. In all 48 oligonucleotide primers only 7 (14.6%) yielded polymorphic amplification products. These 7 primers produced 408 bands, of which 27 were polymorphic. Detection of polymorphism in cultivated groundnut opens up the possibility of development of its molecular map by judicious selection of genotypes that show DNA polymorphism. This approach will be useful for developing marker assisted selection tools for genetic enhancement of groundnut for desirable traits.

Amadou et al. (2001) assessed genetic diversity in 25 bambara groundnut (Vigna subterranea L.) germplasm. Fifty random decamer primers were screened to assess their ability to detect polymorphism in bambara; 17 of them were selected for further study. The 17 primers produced a total of 63 bands, 25 of which (approximately 40%) were monomorphic, while the remaining 60% showed at least one polymorphic band. The lowest value of Jaccard’s similarity coefficient observed was 0.63 ZM80-699 (Zambia) and FB85-3 (Nigeria), while the highest similarity coefficient (0.97) was found between ZM-2452B and ZM-2452C, both originating in Zambia.

Raina et al. (2001) used twenty-one random and 29 SSR primers to assess genetic variation and interrelationships among subspecies and botanical varieties of cultivated peanut, Arachis hypogaea (2n = 4x = 40). In contrast with the previous generalization that peanut accessions lack genetic variation, both random and SSR primers revealed 42.7 and 54.4% polymorphism, respectively, among 220 and 124 genetic loci amplified from 13 accessions. Moreover, the dendrograms based on RAPD, ISSR and RAPD + ISSR data precisely organized the five botanical varieties of the two subspecies into five clusters. One SSR primer was identified that could distinguish all the accessions analysed within a variety.

Bhagwat et al. (2001) revealed that random amplified polymorphic DNA (RAPD) analysis with a single random primer, OPX-10 (5'-CCCTAGACTG-3') expressed distinct polymorphism among closely related groundnut (Arachis hypogaea) varieties. The primer appeared very specific to groundnut as it did not reveal polymorphism in other crops. Considering the narrow genetic base of groundnut in general and that of the varieties analysed, all having been derived from a single variety (Spanish Improved), OPX-10 primer appears to have amplified a fast evolving region of the genome.

Dwivedi et al. (2001) selected twenty-six accessions and eight primers for random amplified polymorphic DNA assay to determine the genetic diversity. The genetic similarity (Sij) ranged from 59.0 per cent to 98.8 per cent with an average of 86.2 per cent. Both multidimensional scaling and UPGMA dendrogram revealed the existence of five distinct clusters. However, this classification could not be related to known biological information about the accessions falling into different clusters. Some accessions with diverse DNA profile (ICG 1448, 7101, 1471, ICGV 99006 and 99014) were identified for mapping and genetic enhancement in groundnut.

Massawe et al. (2003) evaluated genetic diversity in 12 landraces of bambara groundnut (Vigna subterranea), using RAPD markers. RAPDs revealed high levels of polymorphism among landraces. The percentage polymorphism ranged from 63.2 per cent to 88.2 per cent with the 16 RAPD primers evaluated. The construction of genetic relationships using cluster analysis groups the 12 landraces in two clusters.

Garcia et al. (2005) studied the RAPD-based linkage map of peanut based on a backcross population between the two diploid species Arachis stenosperma and A. cardenasii. Total 428 decamer primers were screened, from which 156 primers were selected based on the size and intensity of the RAPD polymorphisms amplified. One hundred sixty-seven RAPD loci were mapped to 11 linkage groups, covering a total genetic length of 800 cM. Clusters of 2 to18 markers were observed in most linkage groups. Twenty seven per cent of the markers showed segregation distortion and mapped to four regions. Six RAPD markers were used to establish correspondence between maps and to compare recombination frequencies between common markers. A generalized reduction in the recombination fraction was observed in the backcross map compared to the F2 map. All common markers mapped to the same linkage groups and mostly in the same order in both maps.

Mallikarjuna et al. (2005) studied genetic diversity among Arachis species using RAPDs. Thirty-two accessions of wild species of Arachis belonging to twenty-five species and grouped under six sections were used in this study for genetic relationship using RAPDs. Twenty-nine primers were used to study. All the primers showed polymorphic bands and the number of bands varied from five to thirty-three. Similarity values (Sij) for 464 pair wise comparisons among 32 accessions ranged from a minimum of 0% to a maximum of 49%, with an average of 15%.

Mondal et al. (2005) investigated the RAPD polymorphism among groundnut genotypes differing in disease reaction to late leaf spot and rust. Fifty primers were screened, out of which 11 primers exhibited polymorphism among the 19 genotypes. The extent of polymorphism ranged from 12.5% to 76.9% with an average of 37.5%. Genetic distance among the genotypes ranged from 1.41 to 6.40.

Nelson et al. (2006) studied assessment of genetic diversity and sectional boundaries in tetraploid peanuts (Arachis hypogaea L.) using RAPD methods. Forty 10-base oligonucleotide primers were initially evaluated; 16 were polymorphic and utilized for analyses and 156 loci were identified for a mean of 9.75 loci per primer. Ninety-two percent of the loci were polymorphic. Forty-three RAPD markers were observed exclusively in A. glabrata, none in A. hypogaea, one in A. monticola, and 15 in A. pseudovillosa. Four populations of A. glabrata showed high levels of genetic diversity and were genetically different from A. pseudovillosa even though they occur in the same section. These data provided the first evidence of high genetic diversity within wild, perennial, tetraploid peanuts and for possible multiple origins of tetraploids in the section rhizomatosae.

Azzam et al. (2007) studied the molecular markers associated with resistance to pod rot diseases and aflatoxin contamination by RAPD in which ten peanut mutants and their parent variety were evaluated using 13 arbitrary primers; ten of which successfully amplified DNA fragments of all genotypes. Number of bands ranged from 6 to 14 across all genotypes. The level of polymorphism ranged from 57.1% to 83.3% while the genetic similarity ranged from 0.58 to 0.93.

Gagliardi et al. (2007) studied genetic stability among in vitro plants of Arachis retusa using RAPD markers for germplasm preservation. Total 10 primers were screened and five primers were selected which showed the highest number of RAPD loci and reproducibility. Ninety genomic regions (loci) generated from RAPD analyses were evaluated. All amplified fragments detected in plants derived from the two explants types were monomorphic. The results indicated that the recovered shoots are genetically stable at the assessed genomic regions.

Lang et al. (2007) demonstrated the utility of RAPD markers to analyse genetic divergence of groundnut genotypes in the South Vietnam. They selected 29 groundnut cultivars and were amplified with 5 random decamer primers by PCR. The distinctive RAPD patterns generated from these cultivars could be used as genomic fingerprint to establish the identity of a given genotype. 29 groundnuts were clearly separated in distinct sub clusters in a phyllogram obtained by UPGMA of genetic distances.

Lang and Hang (2007) demonstrated utility of RAPDs to analyze genetic divergence in peanut genotypes. Nucleic acid extracts from 29 Arachis hypogaea L. cultivars were amplified using five random decamers by PCR. The distinctive RAPD patterns generated from these cultivars could be used as genomic fingerprint to establish the identity of a given genotype.

Mondal et al. (2007) studied F2 mapping population comprising 117 individuals, which were developed from a cross between the rust resistant parent VG 9514 and rust susceptible parent TAG 24. They identified 11 (out of 160) RAPD primers that exhibited polymorphism between these two parents. Using a modified bulk segregate analysis, primer J7 (5′CCTCTCGACA3′) produced a single coupling phase marker (J7 1350) and a repulsion phase marker (J7 1300) linked to rust resistance. Screening of the entire F2 population using primer J7 revealed that both J7 1300 (P = 0.00075) and J7 1350 (P < 0.00001) were significantly associated with the rust resistance.

Vasanthi et al. (2008) analyzed genetic diversity among 12 genotypes using RAPD markers consisting of 6 released cultivars (Tirupati 4, Narayani, Tirupati 3, Kalahasti, Prasuna and Abhaya), 2 pre-release cultivars (TCGS 888 and 913) and 4 advanced breeding lines (TCGS 653, 750,645 and TG 47) with known pedigrees to know the extent of relationship and to correlate it with pedigree information. Seven RAPD primers detected 48 polymorphic bands. Jaccards similarity coefficients ranged from 32.6 per cent (Tirupati 4 and TCGS 645) to 92.9 per cent (Kalahasti and Narayani). Tirupati 4 and Tirupati 3 exhibited least similarity with other genotypes. The extent of similarity did not correlate with pedigree information.

Kumari et al. (2009) studied 21 mutants belonging to different botanical types of groundnut and used to assess molecular diversity using RAPD analysis. All twenty-seven random primers showed polymorphic bands. The polymorphism per primer ranged from 9.09 to 71.42 per cent with an average of 30.16 per cent. Although the cluster analysis grouped genotypes into five different clusters, most of the genotypes (17 of 21) were grouped in a single cluster, indicating narrow genetic diversity among the genotypes.

Varshakumari et al. (2009) studied the molecular characterization of induced mutants in groundnut using random amplified polymorphic DNA markers. All twenty-seven random primers showed polymorphic bands. The polymorphism per primer ranged from 9.09 to 71.42 per cent with an average of 30.16 per cent. High genetic similarity values (Sij) of 0.88 to 0.98 were obtained for the genotypes, indicating limited genetic diversity. The cluster analysis grouped genotypes into five different clusters; most of the genotypes (17 of 21) were grouped in a single cluster, indicating narrow genetic diversity among the genotypes.

Gowda et al. (2010) studied the mutational origin of genetic diversity in groundnut (Arachis hypogaea L.) where 271 fragments were amplified by 21 decamer primers in 30 genotypes, 104 were polymorphic (38.38%). On an average, 13 bands per primer were amplified and 4.95 bands per primer were polymorphic. The polymorphism per primer ranged from 7.69% to 75%. The PIC values for primers ranged from 0.06 to 0.42 with an average of 0.23. The dendogram revealed three distinct clusters at similarity coefficient (Sij) of 0.87, 0.92 and 0.94, respectively.

Sharaf et al. (2011) tested genetic similarity of four mutants of groundnut and their control using random amplified polymorphic DNA (RAPD) approach. Although natural polymorphism among peanut cultivars was very low, RAPD patterns showed high polymorphism percentage of DNA fragments (37.13%).

Rungnoi et al. (2012) assessed a set of 363 Bambara groundnut (Vigna subterranea L. Verdc.) accessions from five geographical regions and unknown origin with 65 loci generated from inter simple sequence repeat (ISSR) and random amplified polymorphic DNA (RAPD) markers to provide more information on genetic diversity and origin. The results support the views that West Africa (including Cameroon and Nigeria) is the center of diversity of Bambara groundnut.

Mukakalisa et al. (2013) analyzed genetic diversity among 13 landraces of Bambara groundnut commonly found in Namibia. Random amplification of polymorphic DNA (RAPD) markers that showed results for other Bambara groundnut species were used for the study. Thirteen random primers were screened to assess their ability to detect polymorphism in Bambara; 7 of these primers were selected for the study. A similarity matrix and dendrogram were produced. The analysis showed that there were high similarities among the landraces, which showed inbreeding of the crop.

Vyas et al. (2014) studied genetic diversity among fifteen genotypes of groundnut by random amplified polymorphic DNA (RAPD) analysis using 15 primers. Out of 13 primers amplified, 11 primers showed variable degree of polymorphism ranged from 25 per cent (S-31) to 100 per cent (OPD-02), whereas two primers viz., OPA-02 and S-67 showed monomorphism. Thirteen RAPD primers amplified 54 fragments, out of which 28 were polymorphic (51.85%). Cluster analysis classified fifteen genotypes of groundnut into six main groups. The pair wise similarity values ranged from 76 to 94 per cent and showed that genotypes UG-109 and UG-110 were the closest with highest similarity value (94%). RAPD marker can be used effectively for characterization of groundnut.

[...]