Introduction

Chili (Caspsicum annum L.) is the second most popular solanaceous vegetable after tomato. It is a diploid (2n = 24) species and genetically self-pollinated and chasmogamous plant (Lemma, 1998). Chili is believed to be originated from South and Central America (Bahurupe et al., 2013). However, a rich genetic diversity of Capsicum exists due to varied geoclimatic conditions of Indian continent (Thul et al., 2009).

Chili, due to its pungent characteristic, has become a vital part of culinary cultures worldwide and has a long history of use for flavoring, coloring, and preserving food. Capsaicin (trans-8-methyl-N-vanillyl-6-nonenamide), the major pungent bioactivator in the chili, is a homovanillic acid derivative that has been used medicinally for centuries, but recently more extensively researches have been carried out for its analgesic (Simone et al., 1989 and Brederson et al., 2013), antioxidant (Galano and Martinez, 2012), anti-inflammatory (Kim et al., 2003) and anti-obesity (Kang et al., 2007) properties. The receptor for capsaicin is called the transient receptor potential vanilloid subtype 1 (TRPV1) which belongs to the transient receptor potential (TRP) family. This family is a heterogeneous group of non-selective cation channels (Sun et al., 2016). TRPV1 has prominent roles in oxidative stress, inflammation and pain sensation (Mózsik, 2014). Recent findings have shown that TRPV1 plays  a  very important role  in  the  progression  of  cardiac  hypertrophy,  and thus,  presents  a  possible  therapeutic target for the treatment of cardiac hypertrophy and heart failure (Buckley and Stokes, 2011). TRPV1 activation increases kidney function by enhancing glomerular filtration rate (Li and Wang, 2008), attenuates abnormal glucose homeostasis by increasing insulin secretion and glucagon-like peptide 1 levels (Gram et al., 2007; Wang et al., 2012), lowers blood pressure by promoting endothelium-dependent vasodilation (Yang et al., 2010), prevents obesity by effecting energy balance (Whiting et al., 2012). Furthermore, TRPV1 works as a regulator of growth factor signaling in the suppression of tumorigenesis (De Jong et al., 2014), and its anti-cancer effects are also confirmed (Ramos-Torres et al., 2015; Anandakumar et at., 2015). More than 80% of capsaicin is passively absorbed in the stomach and upper portion of the small intestine and is also circulated in the blood by albumin (Kawada et al., 1984), therefore, it may extensively activate local TRPV1 channels in different organs or tissues to initiate aforementioned physiological effects.

Chili is invariably used in almost every Bangladeshi cuisine. Introduced diverse chili germplasms are cultivated for centuries and adapted to various agro-ecological zones of Bangladesh (Hasanuzzaman and Golam, 2011). Low and medium pungent chili varieties cultivated on a field scale in Bangladesh mainly belong to Capsicum annuum L. (Farhad et al. 2010). Chili cultivars are grown based on its size, shape, appealing color, pungency and consumer's preference in various parts of Bangladesh (Hasan et al., 2014). However, despite its huge medicinal value and rich genetic variability, the average yield of chili is low (1.3 ton/ha) in Bangladesh compared to the neighboring countries like India (1.74 ton/ha) and China (6.82) (FAOSTAT, 2013). The prime constrain in achieving good production of chili in Bangladesh is the lack of high yielding chili varieties with multiple resistance against biotic and abiotic stresses. Therefore, it is desirable to develop improved varieties through systematic breeding programme.

To carry out any successful breeding programme, it is necessary to select suitable parental lines from available indigenous germplasms. The study of genetic divergence is a popular method in parent selection for researchers involved in breeding programs of several crops, leading to reduce the number of crosses (Guerra et al. 1999). The progenies derived from diverse parents are expected to show a broad spectrum of genetic variability and provide better scope to isolate superior recombinants. Therefore, genetically diverse genotypes should be used in a hybridization program to get superior recombinants. The multivariate analysis is the most widely used statistical tools to quantify the genetic divergence. Among the various methods developed to study the genetic divergence in the genotypes/accessions, the Mahalanobis D² (Mahalanobis, 1936) is the most reliable and widely used statistical tools to quantify the degree of genetic divergence by assessing the relative contribution of different characters to the total divergence. It is a very useful technique and has been used by several workers in case of self-pollinated crops (Bashar et al., 2016; Hasan et al., 2015; Huque et al., 2012; Natarajan et al., 1988; Shidhu et al., 1989). Relative contribution of different traits to the total divergence helps to select a particular trait or a combination of traits for intercrossing which avoids wastage to time and labor. Principal component analysis (PCA) is also a powerful technique which allows the visualization of natural grouping of the genotypes and is precise indicator of differences among genotypes (Huque et al., 2012). Ranking of parents based on their variance will be promising as it gives an idea about more stable genotypes for a particular location. More stable genotypes are believed to generate superior recombinants through crossing as there is less environmental influence on them.  The genotype-by trait (GT) biplot analysis, proposed by Yan and Rajcan (2002), is another powerful statistical tool for studying relationships among traits, evaluating cultivars based on multiple traits and for identifying those genotypes that are superior in certain traits. The GT analysis allows visual display of the genetic correlation among traits, thus, helps detect less important (redundant) traits, and identify those that are appropriate for indirect selection for a target trait.

Keeping these points in mind, the present investigation was undertaken to evaluate the genetic diversity among the chili genotypes collected from diverse location and to identify superior genotypes for future use.

1 Results and Discussion

1.1 Cluster analysis

On the basis of Mahalanobis D² values, the 30 genotypes were grouped into six divergent clusters (Table 1), indicating adequate genetic diversity for selecting superior and diverse parents which can be exploited for any breeding program. Cluster I was the largest group containing 9 genotypes, followed by cluster III, cluster IV and cluster III containing 7, 6 and 5 genotypes, respectively. Cluster VI had 2 genotypes while cluster V possessed single genotype. For convenient understanding of the clustering pattern, a three dimensional scattered diagram was formed based on PCA score I, II, and III obtaining from the principal component analysis which accounted for 96.6% toward to total variation (Figure 1). Scattered diagram also showed 6 distinctive clustering patterns. The clustering pattern comprising of the 30 genotypes from different sources indicated that there was no association between eco geographical distribution of genotypes and genetic divergence. Similar result was found by Indra et al. (2000), Sreelathakumary and Rajmony (2004), Farhad et al. (2010), Datta and Jana (2011), Datta and Das (2013) and Yatung et al. (2014).

1.2 Inter and intra-cluster distances

The average intra and inter cluster distances were presented in Table 1. The highest intra cluster divergence (54.519) for cluster VI was smaller than the lowest inter cluster divergence between cluster I and cluster IV (91.948), thus validating the clustering pattern formed in this study. Cluster VI showed maximum intra cluster distance (54.519) followed by cluster IV (30.323) and cluster II (38.535). Cluster V showed no intra cluster distance because of having solitary genotype. Minimum and maximum inter cluster D² values referred close relationship and maximum divergence between clusters, respectively. The maximum inter cluster distance was observed between cluster II and cluster V (532.214) followed by cluster IV and cluster V (470.108), and cluster II and cluster VI (407.136) (Table 1). Therefore, the genotypes falling in these clusters were genetically divergent and suitable for chili hybridization programme to gain maximum heterosis.

1.3 Trait contribution towards genetic divergence

The contribution of different traits in per cent towards genetic divergence was presented in Figure 2. Among the twelve characters studied in this present investigation, the maximum contribution towards divergence was found for fruit diameter (16%), plant height (14.5%), fruits/plant (12.5), days to 1^st flowering (12), fruit pedicel length (12%). Moderate contribution was found for fruit length (10.4%) and yield/plant (9.4%). Remaining characters had very less contribution toward genetic diversity. Janaki et al. (2015) and Thul et al. (2009) also observed that fruit diameter had the maximum contribution toward genetic divergence in chili. The maximum contribution was found in number of fruits per plant, yield per plant, fruit weight, fruit length and plant height towards divergence in chili by Hasan et al. (2015), Srinivas et al. (2015); Karad et al. (2002); Varalakshmi and Babu (1991); Roy and Sarma (1996), respectively. Therefore, the clusters having high values in these seven diverged characters could be considered as superior cluster and the presence of most promising genotypes in them could be extensively used for further breeding programmes to generate new recombinant chili lines.

1.4 Cluster mean analysis

Character wise mean were calculated for all the genotypes spread over six clusters (Table 2). Cluster VI showed highest mean value for fruit diameter (14.04) and fruits per plant (219). Days to first flowering, days to 50% flowering and days to fruit maturity were highest in cluster II. Cluster V showed highest mean value for yield per plant (617.13) and fruit length (8.15). Cluster I had highest mean value for plant height and primary branches per plant. Cluster IV did not show highest value for any character.  Therefore, considering the cluster mean values along with intercluster distances, it would be logical to say that the  intercross between genotypes belonging to more diverse clusters like cluster II and V, cluster I and VI and cluster II and VI will be effective to create wide spectrum of variability and ultimately to produce transgressive segregants for chili.

1.5 Rank distribution of genotypes within cluster

It would be highly fruitful to select one potential homozygous genotype from each cluster rather than two or more and test these genotypes by a diallel analysis (Singh and Chudhary, 1885). Therefore, ranking of genotypes within each cluster based on variance was performed to choose suitable homozygous parents from each cluster. Similar method was practiced by Hasanuzzaman and Golam (2011) in chili. Table 3 showed the value of phenotypic variability. Genotypes having lowest phenotypic variability were ranked lowest and genotypes containing highest phenotypic variability were given highest rank. Based on the values from Table 3, rank distribution of genotypes within cluster has been generated (Table 4). Six relatively stable parents were selected from six different clusters based on lowest ranking value within each cluster (Table 4). G1 was most stable among nine genotypes from cluster I as it showed lowest ranking value (44). Therefore, G1 was selected from cluster I. Next, G30 was selected from Cluster II due to its lowest ranking value. Similarly, G13, G11 and G27 were selected from cluster III, IV and VI, respectively. Finally, G25 was the single genotype in cluster V. Therefore, these six genotypes can be selected as parent for 6X6 diallel cross. Table 5 showed the mean performance of six selected genotypes. Among six genotypes no one showed highest mean value for all characters.

1.6 Genotype by trait (GT) biplot

A Genotype by trait (GT) biplot was constructed by plotting PCA1 scores against PCA2 scores for six parents and twelve traits to display the genetic variability among parents selected from six different clusters along with their suitable characters in one frame (Figure 3). In the GT biplot, a vector is drawn from the biplot origin to each marker of the traits to facilitate visualization of the relationships between and among the traits. The length of the vector measures the magnitude of its effects. The correlation coefficient between any two traits is approximated by the cosine of the angle between their vectors. Acute angles show a positive correlation, obtuse angles show a negative correlation and right angles no correlation (Yan and Rajcan 2002). Therefore, by visualizing the GT biplot, indirect selection of appropriate traits (vectors showing acute angles to the target trait vector) and less important (redundant) traits (vectors showing obtuse angles or right angles to the target trait vector) can be done easily.

In our study, the GT biplot captured 69.17% of the total variation. This relatively high percentage variation reflects the accuracy of inter-relationships among the measured traits. From GT biplot (Figure 3), we can visualize genotype 1 from cluster I was the best genotype for number of primary branches only. Next, genotype 30 from cluster II was best for traits like fruit diameter (mm) and plant height (cm). Genotypes 13 from cluster III was suitable for number of secondary branches. However, Genotype 11 from cluster IV and genotype 25 from cluster V were suitable for fruit pedicle length (cm) and, fruit length (cm), fruit weight (gm) and yield/plant (gm), respectively. And lastly, genotype 27 from cluster was suitable for fruit/plant only.

In case of traits like, genotype showing high performance should be considered as unsuitable for hybridization as earliness is preferred in any breeding strategies. Therefore, if genotype 30, genotype 13 and genotype 11 are selected as parents, days to 50% flowering, days to first flowering and days to fruit maturity would be indirectly selected as negative traits, respectively.

In our previous report (Hasan et al., 2016), we generated a biplot by considering only fruit weight (gm), fruits/plant, yield/plant (gm) and relative genetic score, and found that genotype 25 (surjomukhi) was the ideal genotype G29, G27 and G19. In this study, we considered all the 12 traits and found that genotype 25 from cluster V is the superior one for the traits like fruit length (cm), fruit weight (gm) and yield per plant (gm). Therefore, if we target to improve the traits like yield/plant (gm), fruit weight (gm) and fruit length (cm), for instance, for future hybridization programme, we should select genotype 25 as a suitable parent. And a crossing between genotype 25 from cluster V and genotype 27 from cluster VI will be fruitful for indirect selection of fruit/plant along with traits yield/plant (gm), fruit weight (gm) and fruit length (cm) as main target traits.

2 Conclusion

This study has revealed the existence of considerable genetic variation among the tested chili genotypes that indicates the presence of excellent opportunity to bring about improvement. Rank distribution of genotypes within cluster based on variance has indicated six promising parents from six clusters which can be used for 6X6 diallel analysis. GT biplot has represented six potential parents from six different clusters with their suitable characters in one frame which helps to select indirectly other appropriate traits and discard less important traits. Genotype 25 (Surjomukhi) is the superior genotype from cluster V and suitable for the improvement of characters yield/plant, fruit weight and fruit length. As yield/plant is the most desirable trait for high yielding variety production, therefore, genotype 25 (Surjomukhi) is by far the best parent for among the 30 genotypes for future hybridization programme.

3 Materials and Methods

3.1 Experimental materials

Thirty chili (Capsicum annuum L.) genotypes were included in this research work collected from Md. Matin Akand’s breeding programme of Regional Spices Research Centre, BARI, Joydebpur, Gazipur (Table 6). A map was generated using QGIS software to show geographical distributions of the genotypes across the Bangladesh (Figure 4).

3.2 Field experiment

The research work was carried out at the Regional Spices Research Centre of Bangladesh Agricultural Research Institute (BARI) Joydebpur, Gazipur, during the period from December 2013 to May 2014. A Randomized Complete Block Design (RCBD) with two replications was applied in this experiment where the genotypes were randomly allotted in each block. Each replication contained 30 genotypes having 50cm x 50cm spacing. The unit plot size was 2m length and 1m breadth. Block to block distance was 50cm. Sowing of the seeds on the tray was done at a depth of one centimeter for easy emergence. Thirty seven days old healthy seedlings were transplanted in the experimental plots. The recommended manure and fertilizer doses were applied. Gap filling and necessary intercultural operations were done during the crop period for proper growth and development of the plants. Each plot was covered with water hyacinth in the dry month to conserve the soil moisture.

3.3 Experimental data

The observation were recorded on 5 randomly selected plants per replication for each accession on 12 quantitative characters i.e., plant height (cm), no. of primary branches/plant, no. of secondary branches/plant, days to first flowering, days to 50% flowering, days to fruit maturity, fruit diameter (mm), fruit length (cm), fruit pedicle length (cm), fruit weight (gm), fruits/plant, yield/plant (cm).

3.4 Statistical analysis

R-3.1.1 statistical program was used in all statistical analysis. The "Cluster" package (MAECHLER et al., 2016) from comprehensive R archive network (CRAN) was used to calculate the clustering pattern according to Mahalanobis D² statistics. Character contribution towards genetic divergence was calculated using "Biotools" package from CRAN (DA SILVA, 2016). Genotype by trait biplot was constructed using “GGEBiplotGUI” package from CRAN (FRUTOS et al., 2013).

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