Assessment of Genetic Variability, Diversity and Trait Correlation Analysis in Common Bean (Phaseolus vulgaris L.) Genotypes

Common bean (Phaseolus vulgaris L.) is one of the most important food legume crops in the world (Broughton et al., 2003; Beebe, 2012). It is an important source of nutrients for more than 300 million people, representing 65% of the total protein consumed, 32% of energy and a major source of micronutrients e.g., iron (Fe), zinc (Zn), thiamin and folic acid (Welch et al., 2000; Broughton et al., 2003; Blair et al., 2010a; Petry et al., 2015). It is one of the main source of dietary protein for human consumption in the world including developed countries like Latin America, Brazil, Africa, the Middle East, China, Canada and Mexico (Ma and Bliss, 1978). Due to its high protein, minerals and vitamins contained in dry seed, it is also popularly called as the “poor men’s meat,” (Blair, 2013). In terms of global production, Latin America is the region of greatest producer of common bean representing about 50% of the world total volume, followed by Africa with 25%. Brazil, Mexico and the United States of America are the three largest producers in the Western Hemisphere. However, it is an extremely diverse crop in terms of cultivation methods, uses, the range of environments to which they have been adapted and morphological variability. They are found from sea level and up to 3000 m above sea level, are cultivated in monoculture, intercropping with maize or any other vegetable crop like potato or amaranths or in rotations that are adapted to many niches, both in terms of agronomic and consumer preference.
 
In India, it is highly remunerative pulse crop for small and marginal land holding farmers in the hill regions viz., Jammu and Kashmir, Uttarakhand, foothills of Himalayas and Sikkim. It is consumed as tender pods, shelled beans and dry beans. The local vernacular name for grain type common bean is rajmash or rajma (Hindi) in northern India. in the recent year this crop has becoming increasingly popular in northern plains as well as central India due to its high yielding potential, good adaptability to different niches with increased cultivating area expansion in Uttar Pradesh, Maharashtra, Andhra Pradesh and Tamil Nadu. The major genetic diversity of this crop exists in the foothills of Himalayan region, Jammu and Kashmir and Sikkim. In these region this crop noticed wide range of genetic diversity for seed size, colour and pod types, plant habits from bush to climbing or pole type bean, range of maturities, photoperiod sensitivity and neutrality, adaptation zones, wide range of diseases and stress resistances and different nutritional quality components (Rana et al., 2015). This genetic variability should be used by breeders to further enhance the crop improvement. Even though high levels of rajmash genetic diversity exists in the hilly tracts of Himalayan region, but very little information is available on number of rajma landraces exploited, their current situation and their contribution to the farming community. Due to limited research activity and inadequate use of local rajma germplasm in breeding programs could affect the rajma improvement in India.
 
However, under these circumstances assessment of genetic variation present in the prevailing rajma germplasm is paramount in common bean breeding. It would be useful for determining the morphological variation among the gene pool and also reveal patterns of genomic differentiation. This can also provide information on the population structure, allelic richness and diversity parameters of germplasm. This genetic variability provides opportunity for rajma breeders to develop new and improved cultivars with desirable characteristics, which include both farmer-preferred traits (high yield potential, determinate plant type with medium seed size etc.) and breeder-preferred traits (pest and disease resistance with cold and drought resistance, etc.) (Ashish Kumar et al., 2014: Bhandari et al., 2017).
 
Yield improvement is an important breeding objective of most crop improvement programs (Ghobary and Abd-Allah, 2010). Like other pulse crops, yield in common bean is a complex trait and many morphological and physiological traits constitute it. Seed yield is affected by genotype and environmental factors because of its quantitative properties (Park et al., 2000; Gonzalez et al., 2010; Perez-Vega et al., 2010). The relationships between yield and yield contributing traits on one hand and among themselves on the other hand could be measured by correlation coefficient (Bendangkumzuk and Chaturvedi, 2014). It is one of the prerequisites for crop improvement programmes. Hence, it is important to know the extent of existing genetic variations in the germplasm material.
 
Keeping these in view, the present study was undertaken to assess the magnitude of genetic variability parameters, genetic diversity and correlation coefficient among yield components present in Common bean genotypes.

Plant genetic materials
 
The plant genetic materials for investigation comprised of 60 Rajmash genotypes collected from National Gene Bank unit NBPGR, New Delhi and three check varieties such as Uday, Arun and Utkarsh (Table 1).
 

 
Field evaluation and data collection
 
Field evaluation of rajmash genotypes was conducted at the main research farm of ICAR-Indian Institute of Pulse Research, Kanpur, India during rabi 2015-16. Seeds were sown on raised bed with 60 cm row to row and 15 cm plants to plant spacing. The experiment was laid out in augmented block design (ABD) and all the recommended agronomic practices were followed for raising a good crop. The data were recorded from randomly selected 5 plants on nine morphological characters, namely, days to fifty per cent flowering, days to maturity, plant height, number of branches per plant, number of pods per plant, pod length (cm), seeds per pod, 100 seeds wt (g) and seed yield per plant (g).
 
Statistical analysis
 
Mean values recorded for the quantitative traits were analysed (ANOVA) following standard statistical techniques (SAS Enterprise Guide 4.2 version). To assess the magnitude of genetic variability, diversity available in common bean germplasm, correlation coefficients for the quantitative traits studied were determined using INDOSTAT software 8.1 version.

The estimation of genetic diversity in gene pools conserved in the gene banks is important for deciphering the nature and magnitude of variability and genetic relationship between traits for the efficient management and use of germplasm (Stoilova et al., 2013; Blair et al., 2010; Szilagyi et al., 2011). The germplasm evaluation process has been found to be useful for preliminary characterisation and discrimination of accessions to understand the level of genetic diversity existing in gene pool (Foschiani et al., 2009; Atilla et al., 2010; Szilagyi et al., 2011). Keeping above facts in view, the present investigation was carried out to evaluate the genetic variability, association among yield contributing traits and genetic diversity in common bean germplasm. The germplasm noticed a wide range of genetic variability for all traits studied among 63 accessions including three check varieties with the experimental results are discussed below.
 
Analysis of variance for nine quantitative traits
 
The analysis of variance revealed significant differences among the genotypes for all characters studied, indicating a high degree of variability in the material (Table 2). The mean sum of squares for all traits for different sources of variation. The block effect (unadjusted) showed only days to 50% flowering was significant while all other traits showed non-significant. On the other hand, the treatment effects (adjusted) were noticed significant for all traits expect days to 50% flowering and number of branches per plant. Likewise, the treatment effects (unadjusted) were noticed non-significant for all the traits studied. Similarly, the effects due to checks showed significant for all traits except days to 50% flowering and pod length and varieties were significant. However, the adjusted block effects were non-significant for all traits except days to 50% flowering, pods per plant, seed per pod and seed yield per plant indicating homogeneity of the evaluation blocks. Similarly, the mean square due to checks v/s Augmented (genotypes) was significant for all the traits except days to 50% flowering, indicating thereby that the test entries were significantly different from the checks except for days to 50% flowering. Similar findings were observed by Sajad et al., (2014); Iram Saba et al., (2017). In addition to above, the paired t test was estimated to compares the means of two paired groups, to understand the whether there was a difference in mean of traits after two year of germplasm evaluation under open filed condition. The results showed that genotypes were highly significantly different for the mean value of days to fifty percent flowering, days to maturity, number of pods per plant and seed yield per plant (g) (Table 6) and these genotypes can be utilized for breeding programme for improvement of specific traits.
 

 

 
Mean, range, variance, coefficient of variance, heritability and genetic advance          
 
Knowledge of genetic variability, heritability and genetic advance of important economic traits and their genotypic and phenotypic correlation coefficient among themselves, plays an important role in the breeding programme of any crop. The success of a breeding programme depends on the genetic variability present in the population. Therefore, partitioning of the phenotypic variation into genetic and environmental variation is necessary. In our investigation, we have estimated simple variance parameters and genetic variation components such as phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability and genetic advance. The statistical analysis of data on quantitative traits showed a wide range of variability among the genotypes studied (Table 3). The mean numbers of DF were 45.98, but it ranged from 32 for genotype HUR 35 to 66 for ET 8494L. DM had a mean value is 103.17, EC14920 and Arun took longest time of 112 days for maturity as compared IC84607 and HUR35 which matures in 65 days. Likewise, PH has a mean value of 36.97, the highest plant height was recorded in GPR203 which is 118 cm as compared to lowest plant height in GPR4190 which is 20.30 cm. The NBP noticed mean value of 3.31, while higher NBP (4.20) is observed for EC565673B, EC14920, IC311676, EC500407, EC400414 and BLF101. In contrast to this, the lower NBP (2.0) were recorded for IC25537 and EC500232. Similarly, Highest NPP (37.0) were recorded in EC400414, while NPP lowest (8.20) were recorded in HURG0478 and average value of NPP is 16.52. The average value PL is 11.74, higher PL (18.23 cm) is recorded in EC400361 and lowest PL (6.25 cm) is recorded in in ET84030. In addition, NSP has mean value of 3.66, genotype EC150250 were recorded highest NSP (5.64) and lowest NSP (2.76) were recorded in EC41702. In the same manner, highest HSW noticed in PL227468 (84.93 gm) while lowest HSW (14.14) were recorded in EC150250 and average HSW of genotypes were recorded as 48.42 gm. It is observed that variation found in size was significantly wider (14.14-84.93 g/HSW) in the genotypes. These results were in consistent with findings of different researcher who have reported wide variation in seed per pod, shape and size in bean germplasm (Cabral et al., 2010; Lioi et al., 2012). Further, SYP were noticed mean value of 21.38 gm, highest SYP were recorded in EC400398 (50.63 gm) while lowest SYP were observed in ET8490 (19.32). These results were in agreement with studies conducted by Singh et al., (1991) and Bitocchi et al., (2012).
 

 
It is also observed that the estimates of phenotypic coefficient of variation (PCV) were higher than those of genotypic coefficient of variation (GCV) for all the traits studied. It is indicating that environmental factors influencing the characters (Table 3). The highest PCV and GCV were recorded for PH, NBP, NPP, NSP, HSW (g) and SYP (g). Indicating, presence of ample variation for these traits in the present material. These results were in accordance with the findings of Singh et al., (1994); Nimbalkar et al., (2002). Higher GCV coupled with heritability and genetic advance as % mean was recorded for PH, NPP, HSW and SYP as compared to rest of the traits. These results specify that; GCV alone will not be sufficient for the determination of the magnitude of heritable variation. GCV together with heritability estimates will give a better picture of the expected genetic gain from selection. Hence, selection of genotypes for the breeding programme for these traits was highly effective. Our results were in conformity with the findings of Syed et al., (2012) and Asati and Singh (2008). In addition to this, high heritability coupled with high genetic advance as % mean was recorded in DF, PH, NPP, PL, HSW and SYP. This indicated that these traits might be under the influence of additive gene interactions and the use of simple selection methods may bring significant improvement for these traits. Our results were in agreement with the findings of Kumar (2008); Ahmad and Kamaluddin, (2013); Rai et al., (2010); Sharma et al., (2012).
 
Correlation studies
 
To utilize various quantitative characters in a breeding program, interrelationship between the characters are of immense value. The genotypic correlation coefficients between seed yield and its components are presented in Table 4. The seed yield per plant exhibited significant positive correlation with NBP, NPP, NSP and HSW. This suggested that the direct selection of these traits would likely be effective in increasing seed yield. Similar findings were observed by other researchers such as Asati and Singh (2008) and Pandey et al., (2013). The significant positive correlation of NPP with NSP, PL and NBP showed that the selection of any of these traits may favour improvement in other traits also whereas negative correlation with HSW may adversely affect the gain. Likewise, HSW and PL having significant positive correlation with NSP. This indicated that indirect selection of these traits was highly effective for crop improvement programmes. Similar results were reported by Ahmed and Kamaluddin (2013); Mudasir et al., (2012); Sofi et al., (2014).
 

 
Cluster analysis
 
Every crop breeding programme has been aimed at the improvement of yield, adaptation, resistance to biotic and abiotic stresses and end-use quality. However, breeding objectives have changed over the years beyond yield improvement (Yong, 2015). New cultivars need to be developed with the capacity to achieve high yields in reduced chemical-input systems and with the genetic diversity needed to maintain yield stability under fluctuating climatic conditions (Heinemann et al., 2014). Thus, estimation of genetic diversity is a platform for stratified sampling of breeding population and to identify the desired genotypes for hybridization and use of genetically diverse parents is known to provide an opportunity for bringing together gene constellation yielding desirable transgressive segregants in advanced generations. Through Euclidian Clustering method, 63 genotypes were confined to two major clusters. The clustering pattern gave a different picture with cluster I containing 37 genotypes and cluster II consist 26 genotypes. Within cluster I two sub cluster were formed.  It might be due to genotypes relatively dissimilar within the same genepool. Similarly, in case cluster II multiple sub cluster were formed (Fig 1). Our results were in conformity with the findings of Gangadhara et al., (2014); Boros et al., (2014); Ankit et al., (2017) and Rana et al., (2015) According to the cluster means, cluster II showed better performance in the case of pod length, number of pods per plant, number of seeds per pod and 100 seed weight. This indicated genetic distance and closeness among accessions due to different genetic constitutions. Hence, the genotype of this cluster could be used as parent in future hybridization programme for higher seed yield. Despite this, based on the genetic diversity study, traits specific germplasm accessions were identified from inter cluster group such as early maturity (EC400419, GPR4189, EC565673A), no. of primary branches (EC400414, EC564797, EC-150250), pod length (EC565673A, PL227468, GPR4189), no. of seeds per pod (HUR53, EC500232, BLF-101), resistance to BCMV (EC150250, GPR203, EC400414), upright branching (BLF101, EC500232) and bold seed type (EC-400414) (Table 5). These selected germplasms serve as useful genetic resource for plant breeder for the development of common bean varieties for high yield and BCMV disease resistance. Accordingly, the mean value of selected promising genotypes and checks was indicated in Table 7.
 

 

 

From the present investigation, it is concluded that the genotypes used in the present study showed wide range variation for quantitative traits like the number of pods per plant, pod length, seeds per pod, 100 seed weight and seed yield per plant. The genotype such as EC400414, EC400398, ET8415 and EC540173 were superior over check varieties and higher seed yield potential due to their high seed yield per plant, pods per plant and seeds per pod. Besides this, we have also identified some of the trait-specific genotypes in our study. The promising genotypes could be used for common bean breeding programme in the improvement of specific traits.

We sincerely thank the Director, ICAR-NBPGR, New Delhi and team for providing germplasm accession to conduct the research and I extend my sincere thanks to the Director, ICAR-IIPR, Kanpur for facilitating us to conduct our research.