Estimation of genetic variation in yield, its contributing characters and capsaicin content of Capsicum chinense Jacq. (ghost pepper) germplasm from Northeast India

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Plant Biology

Introduction

The northeast region of India is a home to rich diversity of Capsicum domesticated species and among them C. chinense Jacq. (ghost pepper or bhut jolokia or Naga King chili) is immensely popular for its unique aroma and high pungency (Sarwa et al., 2013; Baruah et al., 2019). The region with unique ecological conditions and high humidity, acting as centre of speciation has given rise to the hottest pepper in the world (Guinness Book of World Records, 2007). The genus Capsicum (family Solanaceae), a new world crop is represented by thirty-five species (Barchenger, Naresh & Kumar, 2019). It was introduced to India at the end of the 17th century by Portuguese explorers and to Northeast India by Christian missionaries (Basu & De, 2003). Ghost pepper, a semi-perennial species, is a naturally occurring variety of Northeast India (Verma et al., 2013; Baruah & Lal, 2020). Besides its use as a flavoring agent, the capsaicin extracted from this plant species has many pharmacological applications (Meghvansi et al., 2010).

Ghost pepper shows wide variation in morphological characteristics (Purkayastha et al., 2012). According to literature reports, ghost pepper has higher capsaicin concentration than other Indian chili varieties (Baruah et al., 2014; Baruah et al., 2019), making it a potential crop for the extraction of oleoresin capsicum and capsaicin for commercial uses (Meghvansi et al., 2010; Baruah & Lal, 2020; Bhandari et al., 2021). Low capsaicin-yielding varieties are not suitable for commercial cultivation because of the bottleneck associated with a high cost of capsaicin extraction (Baruah & Lal, 2020). However, despite its advantages, no measures were taken for the improvement of this important crop as the production is not up to the demand to meet the quality requirements, such as high fruit yield, high capsaicin, etc. It may be due to the lack of superior varieties available in the public domain or the use of low-quality capsaicin content lines for cultivation. To overcome this, information on morphological characteristics, evaluation of a large number of germplasm, diversity study and creation of gene bank is pre-requisite, allowing the exploration of genetic variability efficiently (Santos-Pessoa et al., 2018; Karim et al., 2022). Heritability criteria determine the extent to which it is transmissible from parents to offspring and is mostly preferred when used in association with other parameters (Afiah, Mohammad & Saleem, 2000; Munda, Dutta & Lal, 2021; Begum et al., 2022). Path coefficient analysis helps in identifying useful traits associated with yield based on the direct and indirect effect of the traits on economical traits (Seyoum, Alamerew & Bantte, 2012; Pandey et al., 2022). Also, to select suitable advanced cultivars within a short period of time, the information on all the genetic parameters will be very much helpful for the breeder (Dutta et al., 2017; Karim et al., 2022). The variability present in the germplasm is used for effective selection of diverge parents, which in turn would be helpful to obtain hybrids with greater heterotic effect (Correa & Gonçalves, 2012). The degree of genetic variability can be measured using Mahalanobi’s D2 analysis, which is a powerful tool for determining the relative contribution of different traits (both inter and intra cluster level) on total divergence in self-pollinated plants (Hasan et al., 2015; Munda, Dutta & Lal, 2021). The knowledge of morphological as well as genetic diversity is very much essential for initiating any breeding programme which focuses on the development of superior varieties. Many reports were available in the public domain regarding the variability and diversity study of different Capsicum species. However, very few reports were accessible regarding this important plant with small population, which cannot be considered reliable as small sample size often produces skewed results (Baruah et al., 2019). So, there is a need for proper scientific study on genetic variability, heritability, genetic advance, and interrelationship among economically important traits along with their direct and indirect effect on fruit yield and capsaicin content through path studies. Therefore, a planned breeding experiment was conducted to identify the selection criteria to develop high-yielding and higher capsaicin content lines of this industrially important crop.

Materials & Methods

Planting material and experimental design

The experiment was carried out at CSIR-NEIST (North East Institute of Science and Technology) experimental farm, Jorhat, Assam, India (26°44′N, 94°9′E, 94 m a.s.l.). One hundred and twenty (120) genotypes with more than 1.2% (1,92,000 SHU) capsaicin content on dry weight basis were selected from an initial collection of 227 germplasm (Baruah et al., 2019), which were planted in randomized complete block design (RCBD) with three replications during three consecutive years i.e., kharif 2017, kharif 2018 and kharif 2019. Among them genotypes-RRL-BJ-102 and 18 were brown variants and RRL-BJ-20 and 25 were yellow variants while RRL-BJ-92 and 58 were round-shaped red variants (Fig. S1). For capsaicin estimation, fully ripe chilies per plot were harvested, followed by immediate drying to retain their quality, such as intact red colour, pungency, etc. A total of 16 plants from each genotype were planted in a plot size of 2.5 × 3 m, with 60 × 60 cm plant-plant and line-line spacing. As recommended fertilizer dose (NPK) of 120:80:60 kg/ha/year was applied in the experiment. All standard agronomical practices were followed to raise a good crop. Morpho-agronomic characterization was done based on IPGRI (International Plant Genetic Resources Institute, 1995) report on Capsicum species. For all the studied traits, morphological data were collected in triplicate during kharif 2017 and their average was calculated. The same process was followed during kharif 2018 and 2019. The data obtained from three years were then pooled, and their average value was taken for final statistical analysis. Meteorological data recorded during the study years was presented in Table S1.

Dendrogram constructed based on Mahalanobi’s distance matrix and unweighted pair-group method to determine the genotypes clustering for morphological characters.

Figure 1: Dendrogram constructed based on Mahalanobi’s distance matrix and unweighted pair-group method to determine the genotypes clustering for morphological characters.

Traits studied

Data were recorded for 11 traits, viz-vegetative plant height (cm), number of main branch, leaf length (cm), leaf breadth (cm), fruit length (cm), fruit girth (cm), number of fruits per plant, fruit yield per plant (g), capsaicin content percent, days to 50% flowering and days to maturity for three consecutive years (Table S2). After harvesting the mature fruits were oven dried (45 °C for 3–4 days depending on fruit thickness) for extraction of capsaicin content. The estimation of capsaicin was done using a spectrophotometric method in triplicates (Thimmaiah, 1999), followed by their validation using uHPLC method. Two grams of dried chilli powder was dissolved in 4 mL of ethanol extract and kept in a water bath at 80 °C for 3 h, manually inverted after every hour. The samples were then kept in room temperature for cooling. The supernatant layer of each sample was filtered through Nylon 33 mm 0.45 µm filter (AxivaSchem. Pvt. Ltd., Sonipat, India). A uHPLC Ultimate 3000 (Thermo Fisher Scientific, Waltham, MA, USA) system equipped with Betasil C18 column (particle size 3 µm, dimension 150 × 4.6 mm) was used for analysis, maintaining column temperature at 60 °C, sampler temperature at 20 °C and sample volume: 5 µL. A binary mixture of water-acetonitrile at a 50:50 ratio was used as mobile phase and the flow rate was 1.5 mL/min. The procedure for capsaicin estimation described by Bhandari et al. (2021) was used in the study.

Statistical analysis

To confirm the homogeneity of the studied environments Levene’s test (1960) was performed using SPSS software (version 16.03) before pooling the data, which is given as-

HO: σ 1 2 = σ 2 2 = = σ k 2

Ha: σ i 2 σ j 2 for at least one pair.

For ‘Y’ variable with ‘N’ sample size having ‘k’ subgroups, Levene’s test is statistically defined as- W = N k k 1 i = 1 k N i Z ¯ i . Z ¯ . . 2 i = 1 k j = 1 N i Z i j Z ¯ i . 2

where ‘Ni’ is the sample size of ith subgroup.

Zij = |Yij − Yi.|, where Yi. is either the mean or median of the ith subgroup.

Statistical analyses were performed using INDOSTAT software version 8.2. The data were subjected to a standard statistical method of Analysis of variance (ANOVA) for RCBD (Panse & Sukhatme, 1978). Genotypic and phenotypic coefficients of variability (GCV, PCV) were calculated following the method proposed by Burton & De-Vane (1953); Broad sense heritability (Hbs) was computed following method suggested by Allard (1960); correlation coefficient by Fisher (1954); genetic advance as per the method given by Johnson, Robinson & Comstock (1955) and path coefficient analysis by Dewey & Lu (1959). XLSTAT software was used for clustering analysis and principal component analysis based on Mahalanobi (1936) distance matrix and unweighted pair-group method. The genetic parameters were calculated using the following equation as follows-

  1. Genotypic coefficients of variation (GCV) = G V X ¯ × 100

  2. Phenotypic coefficients of variation (PCV) = P V X ¯ × 100 ,

  3. Heritability in broad sense (Hbs) = G V P V × 100 , where, GV = Genotypic variance, PV = Phenotypic variance, X = mean of the character

  4. Genetic advance as percent mean (GA) = K × G V P V × 100 , where, K = standardized selection differential ( K = 2.06 at 5% selection intensity).

Results

Levene’s test of homogeneity of variance (1960) studied for three environments presented in Table 1, where the traits did not show any significant deviation ( P ≤ 0.005) over the environments and hence homogeneity of variance assumption is reasonably met for one-way ANOVA. Further ANOVA analysis (Table 2) performed for all the traits showed highly significant differences in the studied genotypes at P ≤ 0.005.

Table 1:
Levene’s test of homogeneity computed for three environments.
Characters Levene Statistic DF1 DF2 α value
Plant height (cm) 0.046 2 357 0.955
No of main branch 1.373 2 357 0.255
Leaf length (cm) 0.272 2 357 0.762
Leaf breadth (cm) 0.548 2 357 0.579
Days to 50% flowering 1.283 2 357 0.278
Capsaicin content (%) 0.145 2 357 0.865
Fruit length (cm) 0.259 2 357 0.772
Fruit girth (cm) 0.945 2 357 0.390
Fruit yield/plant (g) 0.017 2 357 0.984
No of fruits/plant 0.423 2 357 0.656
Days to maturity 1.852 2 357 0.158
DOI: 10.7717/peerj.15521/table-1

Notes:

DF

Degree of freedom

α

Significance at P ≤ 0.005 (0.5%)

Table 2:
Analysis of variance (ANOVA) and estimation of genetic parameters for 120 genotypes of C. chinense Jacq.
Characters ANOVA Genetic parameters Mean performance
Replication (DF = 2) Treatment (DF = 119) Error (DF = 238) GCV PCV H2 (Broad Sense) GA 5% General Mean CV% SE CD 5% Range
Plant height (cm) 1812.817 103.967* 23.119 8.609 11.735 53.824 13.011 60.299 7.974 3.926 7.739 42.777–75.477
No of main branch 7.284 1.214* 0.463 12.138 20.486 35.103 14.814 4.123 16.503 0.556 1.095 2.777–5.777
Leaf length (cm) 81.737 3.315* 0.527 11.117 13.917 63.810 18.294 8.672 8.372 0.593 1.168 5.547–10.603
Leaf breadth (cm) 65.284 2.011* 0.407 14.762 19.596 56.750 22.909 4.953 23.652 0.521 1.905 2.710–6.580
Days to 50% flowering 1533.333 86.141* 4.864 7.053 7.660 84.780 13.378 73.799 2.988 1.801 3.550 65.110–91.110
Capsaicin content (%) 2.100 0.874* 0.025 25.283 26.362 91.987 49.953 2.105 7.532 0.128 0.255 1.327–4.187
Fruit length (cm) 21.823 2.531* 0.066 13.869 14.417 92.543 27.485 6.536 3.937 0.210 0.414 4.590–8.83
Fruit girth (cm) 21.619 2.658* 0.076 13.734 14.331 91.840 27.113 6.755 15.259 0.226 1.673 4.087–8.733
Fruit yield/plant (g) 116631.064 62121.563* 3027.908 33.702 36.200 86.676 64.636 416.437 13.213 44.929 88.566 188.627–877.060
No of fruits/plant 2246.073 1116.781* 84.437 29.583 33.014 80.297 54.609 62.706 10.655 5.425 10.694 32.777–110.577
Days to maturity 1598.982 63.686* 5.244 2.588 2.915 78.789 4.732 170.566 1.339 1.870 3.675 157–180.333
DOI: 10.7717/peerj.15521/table-2

Notes:

P ≤ 0.005 (0.5%).

DF

Degree of freedom

GCV

Genotypic coefficient of variation

PCV

Phenotypic coefficient of variation

GA

Genetic advance

CV

Critical variance

SE

Standard error

CD

Critical difference

Genetic variability parameters evaluated for all the germplasm are presented in Table 2. The highest genotypic and phenotypic variation coefficients (GCV, PCV) were recorded for fruit yield per plant (33.702, 36.200). It was followed by number of fruits per plant (29.583, 33.014), capsaicin content (25.283, 26.362), leaf breadth (14.762, 19.596), fruit length (13.869, 14.417) and fruit girth (13.734, 14.331). The lowest GCV and PCV estimates were recorded for traits-days to maturity (2.588, 2.915), days to 50% flowering (7.053, 7.660) and plant height (8.609, 11.735). All the studied traits showed lower GCV estimates than PCV, except for days to 50% flowering and days to maturity, where least difference in magnitude was observed. The highest difference between GCV-PCV estimates was observed for traits-plant height, number of main branch, leaf length and breadth, fruit length and fruit girth, fruit yield per plant and number of fruits per plant. High heritability (Hbs) was observed for fruit length (92.543), capsaicin content (91.987), fruit girth (91.840), fruit yield per plant (86.676), days to 50% flowering (84.780) and number of fruits per plant (80.297). Moderate heritability was observed for traits days to maturity (78.789), leaf length (63.810), leaf breadth (56.750) and plant height (53.824) while low heritability was depicted in number of main branch (35.103). High genetic advance (%) was observed for fruit yield per plant (64.636), followed by number of fruits per plant (54.609), capsaicin content (49.953), fruit length (27.485) and girth (27.113) and leaf breadth (22.909). While, high heritability with high genetic advance, was observed for the traits-fruit yield per plant (64.636), number of fruits per plant (54.609), capsaicin content (49.953), fruit length (27.485) and fruit girth (27.113) and moderate heritability with high genetic advance for leaf breadth (22.909).

The genotypic and phenotypic correlation coefficient for different traits is presented in Table 3. For the studied traits, the genotypic correlation was found higher than the phenotypic correlation, indicating strong inherent relation. At both genotypic and phenotypic level (significant at P ≤ 0.01), fruit yield per plant showed a positive and highly significant correlation with number of fruits per plant (0.880, 0.825, respectively), plant height (0.639, 0.569, respectively), capsaicin content (0.475, 0.437, respectively), days to 50% flowering (0.460, 0.412, respectively), fruit length (0.454, 0.451, respectively), fruit girth (0.417, 0.408, respectively), number of main branch (0.383, 0.185, respectively), leaf breadth (0.358, 0.250, respectively) and leaf length (0.311, 0.225, respectively). Capsaicin content showed positive and significant correlation (significant at P ≤ 0.01) with days to 50% flowering (0.622, 0.555, respectively), fruit yield per plant (0.475, 0.437, respectively), days to maturity (0.529, 0.517, respectively), number of fruits per plant (0.356, 0.315, respectively), plant height (0.237, 0.239, respectively), fruit girth (0.292, 0.276, respectively) and length (0.253, 0.242, respectively). Number of fruits per plant also showed positive and highly significant genotypic and phenotypic correlation with plant height (0.368, 0.217, respectively), number of main branch (0.284, 0.155, respectively) and days to 50% flowering (0.312, 0.277, respectively).

Table 3:
Genotypic and phenotypic correlation matrix for 11 characteristics of 120 C. chinense Jacq. genotypes from pooled data.
Characters Plant height (cm) No of main branch Leaf length (cm) Leaf breadth (cm) Days to 50% flowering Capsaicin content (%) Fruit length (cm) Fruit girth (cm) Fruit yield/ plant (g) No of fruits/ plant Days to maturity
Plant height (cm) G 1.000
P 1.000
No of main branch G 0.258** 1.000
P −0.124* 1.000
Leaf length (cm) G 0.435** 0.239** 1.000
P 0.306** 0.090 1.000
Leaf breadth (cm) G 0.429** 0.156** 0.963** 1.000
P 0.299** 0.049 0.933** 1.000
Days to 50% flowering G 0.301** 0.257** 0.242** 0.228** 1.000
P 0.230** 0.141** 0.204** 0.187** 1.000
Capsaicin content (%) G 0.237** 0.162** 0.008 0.076 0.622** 1.000
P 0.239** 0.085 0.058 0.052 0.555** 1.000
Fruit length (cm) G 0.535** 0.216** 0.357** 0.337** 0.288** 0.253** 1.000
P 0.360** 0.130* 0.278** 0.254** 0271** 0.242** 1.000
Fruit girth (cm) G 0.485** 0.271** 0.540** 0.522** 0.324** 0.292** 0.826** 1.000
P 0.324** 0.163** 0.415** 0.385** 0.301** 0.276** 0.828** 1.000
Fruit yield/plant (g) G 0.639** 0.383** 0.311** 0.358** 0.460** 0.475** 0.454** 0.417** 1.000
P 0.569** 0.185** 0.225** 0.250** 0.412** 0.437** 0.451** 0.408** 1.000
No of fruits/plant G 0.368** 0.284** 0.092 0.162** 0.312** 0.356** 0.009 −0.030 0.880** 1.000
P 0.217** 0.155** 0.043 0.094 0.277** 0.315** 0.025 −0.008 0.825** 1.000
Days to maturity G 0.199** 0.213** −0.017 −0.041 0.555** 0.529** 0.114** 0.146** 0.160** 0.067 1.000
P 0.183** 0.094 0.023 0.014 0.571** 0.517** 0.113* 0.136** 0.152** 0.079 1.000
DOI: 10.7717/peerj.15521/table-3

Notes:

P ≤ 0.05 (5%).
P ≤ 0.01 (1%) respectively.

G

genotypic correlation

P

phenotypic correlation

Genotypic path study for fruit yield per plant was analyzed and presented in Table 4 where number of fruits per plant (0.880) showed maximum positive direct effect on fruit yield per plant, followed by fruit length (0.274) and fruit girth (0.200). Direct positive effects of other traits were very negligible with low residual effects (0.298), which indicates that the traits taken for the study have 70% accuracy for yield determination in ghost pepper. Path analysis for capsaicin content (Table 5) showed that highest positive direct effect on the trait was shown by fruit yield per plant (2.375), followed by days to 50% flowering (0.273), days to maturity (0.202) and plant height (0.115) (Table 4). Leaf length showed very weak direct association (0.033) and rest of the traits showed direct negative effect. The number of fruits per plant showed maximum negative direct effect (−1.802) towards capsaicin content, indicating that both these characters cannot be improved simultaneously. Indirect contribution to capsaicin content was shown by days to 50% flowering via fruit yield per plant (1.092) and days to maturity (0.118). Fruit length also showed indirect contribution towards capsaicin content via plant height (0.108). Similarly, fruit yield per plant showed indirect contribution via plant height (0.114) and days to 50% flowering (0.125). Plant height also contributed to capsaicin content indirectly via fruit yield per plant (1.338). A residual effect of 0.504 was obtained which indicated that the studied traits have 50% accuracy for capsaicin estimation in ghost pepper.

Table 4:
Genotypic path coefficient table showing direct and indirect effects of different traits on fruit yield/plant (g).
Characters Plant height (cm) No of main branch Leaf length (cm) Leaf breadth (cm) Days to 50% flowering Capsaicin content (%) Fruit length (cm) Fruit girth (cm) No. of fruits/ plant Days to maturity Fruit yield/ plant (g)
Plant height (cm) 0.039 0.001 0.028 −0.013 0.004 0.008 0.146 0.097 0.324 0.006 0.639*
No of main branch −0.010 0.005 0.015 −0.005 0.003 0.004 0.059 0.054 0.250 0.007 0.383*
Leaf length (cm) −0.017 0.001 0.064 −0.029 0.003 0.002 0.098 0.108 0.081 −0.001 0.311*
Leaf breadth (cm) −0.017 0.001 0.062 −0.030 0.003 0.002 0.092 0.104 0.142 −0.001 0.358*
Days to 50% flowering −0.012 0.001 0.016 −0.007 0.013 0.013 0.079 0.065 0.274 0.017 0.460*
Capsaicin content (%) −0.013 0.001 0.005 −0.002 0.007 0.025 0.069 0.058 0.314 0.011 0.475*
Fruit length (cm) −0.021 0.010 0.023 −0.010 0.004 0.006 0.274 0.165 0.008 0.004 0.454*
Fruit girth (cm) −0.019 0.001 0.035 −0.016 0.004 0.007 0.226 0.200 −0.026 0.005 0.417*
No of fruits/plant −0.014 0.001 0.006 −0.005 0.004 0.009 0.003 −0.006 0.880 0.002 0.880*
Days to maturity −0.008 0.001 −0.001 0.001 0.007 0.009 0.031 0.029 0.059 0.031 0.160*
DOI: 10.7717/peerj.15521/table-4

Notes:

Residual effects: 0.298.

Indicates significance at 1% level.

Bold values indicate direct effects.

Table 5:
Genotypic path coefficient table showing direct and indirect effect of different traits on capsaicin content (%).
Characters Plant height (cm) No of main branch Leaf length (cm) Leaf breadth (cm) Days to 50% flowering Fruit length (cm) Fruit girth (cm) Fruit yield/ plant (g) No of fruits/ plant Days to maturity Capsaicin content (%)
Plant height(cm) 0.115 −0.033 0.050 −0.170 0.082 −0.415 −0.059 1.338 −0.664 0.007 0.237*
No of main branch 0.052 −0.129 0.027 −0.062 0.070 −0.168 −0.033 0.909 −0.511 0.007 0.162*
Leaf length(cm) 0.088 −0.031 0.033 −0.381 0.066 −0.277 −0.066 0.738 −0.166 −0.001 0.008
Leaf breadth(cm) 0.110 −0.020 0.087 −0.396 0.062 −0.262 −0.064 0.851 −0.292 −0.001 0.076
Days to 50% flowering 0.061 −0.033 0.028 −0.090 0.273 −0.224 −0.040 1.092 −0.562 0.118 0.622*
Fruit length(cm) 0.108 −0.028 0.041 −0.133 0.079 −0.776 −0.101 1.077 −0.017 0.004 0.253*
Fruit girth (cm) 0.098 −0.035 0.062 −0.207 0.088 −0.641 −0.123 0.990 0.054 0.005 0.292*
Fruit yield/plant (g) 0.114 −0.049 0.036 −0.142 0.125 −0.352 −0.051 2.375 −1.586 0.005 0.475*
No of fruits/plant 0.074 −0.037 0.010 −0.064 0.085 −0.007 0.004 2.090 −1.802 0.002 0.356*
Days to maturity 0.040 −0.027 −0.002 0.016 0.151 −0.089 −0.018 0.381 −0.120 0.202 0.529*
DOI: 10.7717/peerj.15521/table-5

Notes:

Residual value: 0.50402.

P ≤ 0.01 (1%) level.

Bold values indicate direct effects.

Dendrogram constructed for genetic divergence study using Mahalanobi’s D2 analysis are shown in Fig. 1. Based on the degree of divergence the genotypes were grouped into 20 clusters. Cluster I consists of highest number of genotypes (60), followed by cluster II (16) and 3 (9), while nine genotypes came out as distinct entity. For most of the genotypes, the grouping is in accordance with their morphological characteristics, viz-RRL-BJ-102 and 18 (brown variants), RRL-BJ-20 and 25 (yellow variants), RRL-BJ-92 and 58 (round red variants) (Fig. S1). According to Mahalanobi’s distance matrix average intra-cluster divergence ranged from 0 to 112.11 (Table 6) with maximum distance observed within cluster I (60 genotypes) and minimum for cluster V, XII, XIV, VIII, XV, XVIII, XVII, XIX and XX (0.00). This indicates the presence of wide genetic diversity among the genotypes of the cluster, while the genotypes in solitary clusters can serve as potent parents owing to their diverge traits, which separates them from genotypes in other clusters. Inter-cluster divergence ranged from 11.48–691.79, with highest distance (691.79) seen between cluster I and XIX, followed by cluster XV and XIX (689.64), cluster VII and XIX (677.99) and cluster XVI and XIX (663.10). Minimum intercluster distance (11.48) was observed between cluster III and XIII.

Table 6:
Average intra and inter-cluster distances values (Mahalanobi’s D2) for 20 clusters in C. chinense Jacq. genotypes using pooled data.
I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI XVII XVIII XIX XX
I 112.11 137.733 150.048 112.892 262.486 222.660 288.027 128.520 84.072 38.201 247.272 159.776 150.988 301.766 299.643 273.028 217.098 206.807 691.791 14.274
II 64.580 14.868 26.561 124.843 86.389 151.094 15.437 59.832 117.918 110.157 24.499 14.779 164.776 162.378 136.287 80.777 71.136 527.584 127.390
III 68.606 39.148 112.799 73.709 138.587 24.959 72.000 130.215 98.100 16.552 11.482 151.984 150.204 123.744 69.443 59.471 539.959 139.390
IV 108.224 150.030 110.727 175.537 20.153 37.674 93.891 134.505 47.688 39.201 189.620 186.970 160.678 104.566 94.310 502.696 103.176
V 0.00 42.973 29.692 134.692 181.773 240.323 19.704 103.844 111.962 42.100 38.761 19.314 48.375 58.815 652.408 252.017
VI 12.747 65.889 94.620 141.453 199.736 29.926 66.584 74.417 79.466 78.169 50.838 16.849 21.915 612.724 212.403
VII 12.805 160.040 207.049 265.322 42.298 129.685 138.424 17.188 15.963 17.295 72.600 82.454 677.993 277.758
VIII 0.00 48.745 107.044 119.928 36.457 27.572 173.729 171.745 145.044 89.646 79.832 518.525 118.484
IX 42.504 58.801 167.243 82.820 72.773 220.683 218.398 191.599 135.772 125.932 472.868 75.593
X 50.967 225.696 140.731 131.061 278.974 276.723 249.815 194.113 184.137 416.258 38.263
XI 2.130 88.215 97.228 57.388 53.453 30.188 33.511 42.797 637.014 237.055
XII 0.00 14.389 143.826 141.035 115.399 60.486 50.850 549.211 149.548
XIII 42.646 152.134 149.275 123.479 67.732 58.216 540.700 140.629
XIV 0.00 15.198 30.450 87.152 96.971 390.137 291.217
XV 0.00 28.217 83.112 93.454 689.641 289.384
XVI 13.924 57.353 67.146 663.101 262.774
XVII 0.00 12.338 606.995 207.216
XVIII 0.00 596.651 196.945
XIX 0.00 400.766
XX 0.00
DOI: 10.7717/peerj.15521/table-6

Notes:

Diagonal elements—intracluster values, non-diagonal elements—intercluster values.

The percentage contribution of each trait towards total divergence was studied and presented in Table 7, where fruit yield per plant showed highest contribution towards genetic divergence (17.498%) followed by fruit length (11.390%), plant height (11.836%), days to 50% flowering (10.921%), fruit girth (9.297%), leaf length (8.672%), capsaicin content (8.554%) and leaf breadth (6.705%). While the traits number of main branch and days to maturity showed minimum contribution towards total divergence.

Table 7:
Principal components values and percentage contribution of 11 different traits towards total divergence.
Traits F1 F2 F3 F4 F5 Percentage contribution
Plant height (cm) 0.671 0.108 0.067 −0.166 −0.350 11.836
No of main branch 0.360 −0.069 0.027 0.124 0.883 3.415
Leaf length (cm) 0.574 0.620 0.173 0.358 −0.050 8.672
Leaf breadth (cm) 0.505 0.588 0.260 0.464 −0.078 6.705
Days to 50% flowering 0.645 −0.349 −0.313 0.347 −0.063 10.921
Capsaicin content (%) 0.570 −0.449 −0.223 −0.071 −0.148 8.554
Fruit length (cm) 0.658 0.310 −0.298 −0.456 0.048 11.390
Fruit girth (cm) 0.595 0.419 −0.373 −0.373 0.131 9.297
Fruit yield/plant (g) 0.816 −0.296 0.396 −0.207 0.034 17.498
No of fruits/plant 0.547 −0.513 0.634 −0.059 −0.009 7.864
Days to maturity 0.383 −0.395 −0.545 0.436 −0.058 3.846
Eigen value 3.805 1.849 1.334 1.096 0.961
Variability explained (%) 34.587 16.806 12.125 9.962 8.734
Cumulative (%) 34.587 51.393 63.518 73.480 82.214
DOI: 10.7717/peerj.15521/table-7

Principal Components Analysis (PCA) was studied to determine the largest contributor of variation at different differentiation axis. PCA study revealed that about 73.48% of the variability is explained by the four principal components (F1, F2, F3 and F4) which have eigen value greater than 1. The first component added to 34.59% of the total variability, contributed by traits with high positive values viz-fruit yield per plant, followed by plant height, fruit length, days to 50% flowering, fruit girth, leaf length, capsaicin content, number of fruits per plant and leaf breadth (Fig. 2). The variability in this component is mostly associated with fruit characteristics. About 16.81% of the variability is added by the second component with strong positive contribution from traits-leaf length, leaf breadth, fruit girth and fruit length. The third and fourth components added to 12.13% and 9.96% of the total variability with strong contribution from the traits number of fruits per plant and fruit yield per plant for 3rd component and leaf breadth, and the traits days to maturity, leaf length and days to 50% flowering for 4th component, respectively. The biplot of the axes (PC1 and PC2) comprising about 51.39% of the variability showed that high PC1 and low PC2 is required for selection of genotypes with high fruit yield and capsaicin content. PC 1 showed positive correlation with all the studied traits while PC2 showed positive correlation with traits leaf length, leaf breadth, fruit length and girth and plant height.

PCA biplot of C. chinense Jacq. genotypes based on PC1 and PC2 to determine the maximum contributor of variation at both axes.

Figure 2: PCA biplot of C. chinense Jacq. genotypes based on PC1 and PC2 to determine the maximum contributor of variation at both axes.

Discussion

Levene’s test (Levene, 1960) is a hypothetical test performed before ANOVA to test whether the studied environments have same or different variances. If the researched environments have distinct variants, then further research will be ineffective since this will result in large discrepancies across all of the settings. In the present study, Levene’s test of homogeneity of variance was reasonably met for ANOVA analysis to be performed. ANOVA study conducted for three years pooled data showed significant differences among the studied genotypes. It indicates that the genotypes have significant variation for the studied traits, which will be helpful in the selection of various traits for crop improvement.

The presence of genetic variability is the basis of all improvement programmes. High variation indicates the presence of higher variability for the traits which provides greater scope for improvement through pure line selection (Nahak et al., 2018). In the present study high variability parameters (i.e., GCV, PCV) were obtained for fruit yield per plant, number of fruits per plant, capsaicin content, leaf breadth, fruit length and fruit girth. The present result is supported by the previous work of Ibrahim, Ganiger & Yenjerappa (2001), Manju & Sreelathakumary (2002), Nagaraju et al. (2018) and Negi & Sharma (2019). GCV and PCV estimates are done to determine the environmental effect on various traits. In the study it was observed that except for days to 50% flowering and days to maturity all the traits showed lower GCV estimates than PCV, indicating the great environmental influence on the expression of these traits and so selection should be made carefully considering the environmental changes, as suggested by Lal, Gupta & Dubey (2017). Singh & Kumar (2005) suggested that for more efficient selection process, heritability should be studied along with variability. According to Singh (2001) heritability of a trait is high when it is 80% or above, moderate when it ranged between 40–80%, and low when less than 40%. Based on these criteria seven traits showed high heritability, four traits showed moderate heritability, while low heritability was observed only for the trait number of main branch. Genetic advance is the enhancement in base population that can be possibly made from selection of a trait. Lal et al. (2013) suggested that high heritability along with high genetic advance is most preferred as these traits were controlled by additive gene action. Accordingly, five traits viz-fruit yield per plant, number of fruits per plant, capsaicin content, fruit length and fruit girth showed high heritability with high genetic advance and hence selection will be fruitful for these traits in early generations in ghost pepper. The present results were in agreement with the findings of Sharma, Semwal & Uniyal (2010), Meena et al. (2016) and Nagaraju et al. (2018) in C. annum. High heritability with high genetic advance was also observed for fruit length and girth in ghost pepper (Mena et al., 2019). Moderate heritability with low genetic advance observed for days to maturity (4.73%) indicated the occurrence of non-additive gene action, thereby making it difficult for improvement through direct selection. In such cases, improvement can be achieved using other breeding methods like-mutation, hybridization etc.

Correlation study for fruit yield per plant showed positive and highly significant correlation with nine traits, which is consistent with the reports of Mena et al. (2018), Devi et al. (2018) and Ozukum et al. (2019) in ghost pepper. The correlation study for capsaicin content was also consistent with the reports of Datta & Jana (2010) and Vaishnavi, Khanm & Bhoomika (2017) in different Capsicum species. Genotypic path study for fruit yield per plant has been studied because the results at phenotypic level may not provide appropriate results of direct and indirect relation of the component traits (Negi & Sharma, 2019). Direct positive effect of number of fruits on fruit yield of ghost pepper was also previously reported by Devi et al. (2018). Further, our results were similar to the findings of Sharma, Semwal & Uniyal (2010), Bijalwan & Mishra (2016) and Vaishnavi, Khanm & Bhoomika (2017) in different Capsicum species. Therefore, number of fruits per plant should be taken as important selection criterion for the ghost pepper improvement programme. Path study for capsaicin content showed direct effect from days to 50% flowering, which was similar to the reports of Mini & Vahab (2002) and Misra et al. (2010) in C. annum.

Mahalanobi’s D2 statistics (Mahalanobi, 1936), which is a most ideal tool for determining genetic divergence, was used in the present study where many clusters were formed irrespective of their place of collection. This formation of many individual clusters based on morphological characterization indicates the presence of sufficient genetic variability among them. In C. annum, Ajjappalavara (2009), Kumari et al. (2010) and Datta & Jana (2011) also observed formation of large number of clusters, indicating the presence of wide variability in the studied material. According to Patel et al. (2017) genotypes from highly divergent clusters should be used for the development of high-yielding varieties. Based on these criteria genotypes in clusters I and XIX would be more fruitful for producing segregates with great heterotic effects as they produced maximum distance between them. Further inter-cluster range observed in the present study was much higher than the results obtained by Yatung et al. (2014) in C. annum.

The study of different traits contribution towards genetic divergence is important with regards to selection and choice of parents for hybridization programme and is performed on the basis of D2 values. The trait like fruit yield per plant can be taken into consideration during selection of parents for hybridization as it showed highest contribution towards genetic divergence. It was followed by the traits fruit length, plant height, days to 50% flowering, fruit girth, leaf length, capsaicin content and leaf breadth. The maximum contribution by similar traits towards total divergence was also reported earlier by Hasan et al. (2015) in C. annum. PCA analysis was done to determine the largest contributor of variation at different differentiation axis (Sharma, 1988). According to Pandey & Bhatore (2018) higher the percentage contribution of traits towards divergence, the more effective it will be in recovering transgressive segregates using multiple cross over. Afuape, Okocha & Njoku (2011) suggested that for an effective breeding, only the components having eigen value greater than one should be taken for determining traits that can produce phenotypic difference. The PCA results in the present study showed majority of the variability was explained by the first four principal components, which was similar to the reports of Sarmah, Sarma & Gogoi (2018) for different chili varieties of Assam. In a recent study on C. annum, it was observed that the percentage contribution by the first two principal component was 28% and 19% respectively (Karim et al., 2022). In this respect, the current study showed high percentage contribution from the first two components, together accounting for 51.39%. Study by Azeez & Morakinyo (2011) showed that for selecting genotypes with high fruit yield and capsaicin content high PC1 components should be taken into consideration, which implies that the traits contributing more in the first principal component should be given preference. Accordingly, the traits-fruit yield per plant, plant height, fruit length, days to 50% flowering, fruit girth, leaf length, capsaicin content, number of fruits per plant showed positive contribution in the first principal component. Based on three-year detailed study, the genotypes-RRL-BJ-102, 120, 118, 70, 60, 66, 5, 18, 88, 65, and 106 are considered superior for capsaicin content and fruit yield and can be opted for large-scale evaluation.

Conclusions

The two most significant characteristics of Capsicum chinense Jacq. crop in terms of economics are pungency and yield. Recent years witnessed a significant decline in popularity of this important crop due to the use of inferior planting material and lack of elite lines. It is therefore necessary to develop suitable elite lines with promising characteristics to meet the quality requirements. The current study aimed to identify the essential factors for selecting superior ghost pepper lines. Priority should be given to traits like high fruit yield per plant, fruit length, fruit girth, days to 50% flowering, days to maturity and plant height as these will either directly or indirectly influence varietal selection when developing a breeding programme for the development of ghost pepper. All these traits showed positive correlation, high heritability and genetic advance which are considered as important criteria for selection of elite lines. And based on percent contribution towards total divergence and PCA data, eleven genotypes (RRL-BJ-102, 120, 118, 70, 60, 66, 5, 18, 88, 65, and 106) were selected with high yield and capsaicin content. The lines can be opted for large-scale cultivation before going for variety recommendation and will be distributed for farmers’trial. To the best of our knowledge, this is the first detailed study on variability estimation and genetic divergence with three-year evaluation and incorporating large number of genotypes.

Supplemental Information

C. chinense variants

DOI: 10.7717/peerj.15521/supp-1

Raw data for morphological and quality data of 120 germplasm of Capsicum chinense used in the study

DOI: 10.7717/peerj.15521/supp-2

Mean values of three years pooled data for 11 traits of C. chinense

DOI: 10.7717/peerj.15521/supp-3
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