Research Article

Assessment of trait efficiency and selection of parents in brinjal (Solanum melongena L.)  

Abul Bashar1 , Rokib Hasan1 , Md. Kamal Hossain1 , Nazmul Alam1 , Nguyen Vu Hong An2 , A K M Mahmudul Huque2
1 Plant Breeding & Crop Improvement Laboratory, Department of Botany, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
2 Plant Molecular Genetics Laboratory, Division of Life Sciences, Korea University, Seoul 136-701, Republic of Korea
Author    Correspondence author
Plant Gene and Trait, 2015, Vol. 6, No. 7   doi: 10.5376/pgt.2015.06.0007
Received: 19 Oct., 2015    Accepted: 07 Dec., 2015    Published: 19 Dec., 2015
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This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 Bashar A., Hasan R., Alam N., Hossain M.K., Nguyen Vu Hong An, Mahmudul Huque A.K.M., 2015, Assessment of trait efficiency and selection of parents in brinjal (Solanum melongena L.), Plant Gene and Trait, 6(7): 1-18 (doi: 10.5376/pgt.2015.06.0007)

Abstract

Twenty one brinjal genotypes with a number of quantitative traits were assessed under biometric analysis for evaluating efficient agro-economic traits and selecting promising parents. No. of fruits in inflorescence/plant, fruits/plant, yield/plant (g), fresh weight/fruit (g), harvesting period and no. of fruits in solitary/plant can consider as favorable attributes for improvement through selection because of their higher GCV, PCV, heritability and genetic advance as percent of mean. Emphasis can be given to characters with high positive direct effect on yield like no. of fruits in inflorescence/plant, fresh weight/fruit (g) and no. of secondary branches/plant. Improvement would be more efficient with selection index I134 based on three characters (yield/plant (g) + no. of fruits in inflorescence/plant + no. of fruits in solitary/plant) which showed genetic gain 3.39 and relative efficiency 258.14% significantly higher to other indexes based on four or more characters. Biplot analysis using selection score and decisive trait index indicated that Sada Begun, Debjhuri Hajari, Kajla, and BARI-9 can be the promising parents for future breeding programme.

Keywords
Intensity; Brinjal (Solanum melongena L.); genetic parameter; correlation coefficient and path coefficient; selection index

Introduction
Solanum melongena L. (2n = 24) commonly known as brinjal or eggplant or aubergine of Solanaceae family, a self-pollinated annual perennial vegetable extensively cultivated both in temperate and tropical zones of the world (Rahman et al., 2014). It belongs to 3 main botanical varieties under the species melongena: esculentum, serpentinum and depressum (Singh et al., 2014). Demand of brinjal as a fruit vegetable is increasing gradually all over the world because of its low price and high nutritive value (Lakshmi et al., 2013). At present, annual production of brinjal in Bangladesh is around 3.28 metric tons per acre(BBS, 2013). Whereas, higher production per acre was observed for China 14.61 mt/acre, India 7.53 mt/acre, Iran 12.9 mt/acre, Egypt 11.27 mt/acre and Turkey 12.58 mt/acre (Faostat, 2013).
 
For improvement of yield potential, some Asian countries have conducted extensive research on genetically modified insect resistant Bt brinjal containing Cry1Ac gene from the soil bacterium Bacillus thuringiensis. But genetic modification may have adverse effect on human health, soil eco-system and environment. So, public perception is always against genetically modified brinjal. Considering the ever increasing consumer demand and recent concern in consumption of genetically modified brinjal, it is imperative to improve brinjal through conventional breeding programme. And for successful breeding programme, the information about variability is a prerequisite as the manipulation of functional breeding mostly depends on the existence, nature and extent of genetic variability among concerned genotypes for different quantitative traits (Arunkumar et al., 2014). Bangladesh possesses a wide local and commercial collection of brinjal cultivars having considerable morphological and genetic variability, so, varietal improvement through systemic breeding programme would be much efficient in Bangladesh.
 
The phenotypic variability among a collection of germplasms gives an indication of potential genotypic variability; however, the quantitative characters are greatly influenced by the environment. Therefore, selection of the important traits for a sound breeding programme should be based on the extent of variability along with heritability and genetic advance. Selection competence would be more operative when we determine relationship between various plant character and yield through correlation coefficient analysis (Rekha and Celine, 2013). Path coefficient analysis separates genotypic association among variables into direct and indirect effect of component characters towards yield (Nayak and Nagre, 2013). Selection index commonly use in conventional breeding as an important selection criterion in order to reduce improvement complexity of trait relationship and to gain economic yield (Fotokian and Agahi, 2014).
 
Therefore, in the present study, an attempt has been made to assess the trait efficiency in brinjal by geneticvariability, character association and selection index, and select some promising genotypes through biplot analysis.
 
1 Results and Discussion
Analysis of variance showed significant variability at 1% and 5% level of significance for all the traits selected for present investigation (Table 1). Expectedly, phenotypic coefficient of variation (PCV) was higher than the genotypic coefficient of variation (GCV) in all the characters under investigation (Table 1), probably due to genotype environment interaction or other environmental factors which influencing phenotypic expression of these traits. Similar observation and recommendation in eggplant were done by Roychowdhury et al. (2011).
 
  
Table 1 Mean sum of square values of analysis of variance (ANOVA) and estimation of genetic parameter for 15 quantitative traits in brinjal
 
In the present study the maximum GCV and PVC was observed for no. of fruits in inflorescence/plant followed by fruits/plant, yield/plant (g), fresh weight/fruit (g), no of fruits in solitary/plant, fruit circumference (cm) and harvesting period (Table 1). So these high variability characters can be considered while selecting parents for further breeding program. Dhaka and Soni (2014) observed high GCV and PCV for average fruit weight and suggested that selection of brinjal would be effective for these high variability traits. Among all characters low environmental influence was observed in harvesting period followed by Days to first fruiting, Fruit length (cm), Days to first flowering and fruit circumference (cm) due to low GCV PCV difference. Low GCV PCV difference for these characters indicated that genetic construction is responsible for their phenotypic expression rather than external environmental factors.
 
Selection efficiency based on phenotype performance would be worthwhile when we determine GCV PCV as well as heritability in broad sense. In the present study highest heritability was recorded for harvesting period followed by fresh weight/fruit (g), fruit circumference (cm), fruit length (cm), fruits/plant, no. of fruits in inflorescence/plant and yield/plant (g) indicated that phenotypic performance rarely influenced by environmental factors and these characters can be determined for improvement program. High heritability for most of the characters also observed by Kumar et al. (2013a) and concluded that there was more number of additive genes for these characters where variability is mostly due to genotypic causes.
 
In selection program heritability in broad sense is not adequate for signifying genotypic potentiality because of restriction by genotype environment interaction. As heritability in broad sense includes both additive and epistatic gene effects prediction of genetic progress would be possible when it is accompanied with genetic advance as percent of mean (Burton, 1952; Johnson et al., 1955). High heritability coupled with high genetic advance as percent of mean was observed for no. of fruits in inflorescence/plant, fresh weight/fruit (g), fruits/plant, yield/plant (g) whose revealed preponderance of additive gene action and simple selection would be operative for these traits. Similar observation were reported by Mili et al. (2014) for seed yield/plant, single fruit weight, fruits/plant, total fruit yield/plot and fruit diameter.
 
High heritability coupled with moderate genetic advance as percent of mean was observed for harvesting period, fruit circumference (cm), fruit length (cm) indicated that characters were controlled by additive gene and would also be effective for improvement as reported by Solaimana et al. (2015) and Karak et al. (2012). Character representing moderate heritability and low genetic advance indicating predominant role of non-additive gene action and progeny testing needs to exploit of this character (Mili et al., 2014).
 
Correlation coefficient analysis determines the component characters for improvement of yield based on their degree of association. In the present study strong positive correlation with yield both genotypic and phenotypic level were observed for fruits/plant followed by fresh weight/fruit (g), fruit circumference (cm), no. of fruits in inflorescence/plant and no. of secondary branches/plant (Table 2). Kumar and Arumugam (2013) suggested that selection should be based on number of fruit per plant, fruit circumference and fruit weight because of their significant positive association with yield both genotypic and phenotypic level.
 
 

Table 2 Genotypic and phenotypic correlation coefficient between yield and yield contributing traits in brinjal

 
No. of fruits in solitary/plant, no. of primary branches/plant and fruit length (cm) had significant positive correlation to yield in both genotypic and phenotypic level. So these characters would also be effective in selection programme. On the other hand negative correlation were found for plant height (cm), stem diameter (cm), days to first flowering, days to first fruiting, harvesting period, fruit stalk length (cm) with yield/plant both genotypic and phenotypic level in different extant. No. of primary branches/plant and no. of secondary branches/plant, days to first flowering and days to first fruiting, fruit length (cm) and fruit stalk length (cm), fruit circumference (cm) and fresh weight/fruit (g), no. of fruits in inflorescence/plant and fruits/plant also showed strong positive association among them. Significant positive association also observed among different pairs of traits both genotypic and phenotypic level indicated that simultaneous selection of these traits would be effective for improvement of yield as same as concluded by Shinde et al. (2012).
 
The correlation coefficient between yield and a particular trait is the ultimate result of direct effect of that attribute and indirect effect over other attributes. Direct selection based on correlation coefficient may be misleading, at that case path coefficient analysis assess the actual impact of causal variable on yield through direct and indirect effects as presented in Table 3. Highest positive direct effect on yield was observed by no. of fruits in inflorescence/plant and fresh weight/fruit (g) in both genotypic and phenotypic level. So direct selection based on these two traits would be effective to increase yield of brinjal as same as reported by Shende et al. (2014) for length of fruit, number of fruits per cluster, plant height, days to last picking, average weight of fruit and number of fruits per plant.
 
 
Table 3 Genotypic and phenotypic path coefficient analysis of different quantitative traits on yield showing direct (bold) and indirect effects
  
No. of secondary branches/plant had high positive direct effect on yield at genotypic level and positive direct effect on yield at phenotypic level. No of fruits in solitary/plant showed significant positive direct effect on yield both genotypic and phenotypic level. These are the moderate contributing characters towards yield and selection based on these characters could also be effective for developing high yielding brinjal varieties. Similar conclusion were reported by Kumar et al. (2013b) for Plant height, number of branches per plant, fruit length, fruit pedicel length, number of fruits per plant, average fruit weight and little leaf incidence because of positive direct effect on yield. Fruits/plant would also be effective to increase yield because of its strong positive correlation with yield/plant and high indirect effect on yield/plant via the no. of fruits in inflorescence/plant. Similar observation in brinjal was reported by Ullah et al. (2014).
 
At genotypic level residual effect was recorded 0.363 75 and at phenotypic level residual effect was recorded 0.318 39 (Figure 1). This indicated that character considered in the present study responsible for 64% variation in genotypic level and 69% variation in phenotypic level. Residual effects both genotypic and phenotypic level indicated that some other possible characters need to include in the analysis to get entire variation in fruit yield as same as concluded by Kumar and Arumugam (2013).
 
 
Figure 1 Genotypic path diagram of different quantitative traits on yield in brinjal
 
The character yield/plant (g), fresh weight/fruit (g), no. of fruits in inflorescence/plant, no. of fruits in solitary/plant and no. of secondary branches/plant were considered to estimate selection index (Table 4) based on genetic variability, significant genotypic correlation with yield and high direct positive effect towards yield/plant as same suggested by Chattopadhyay et al. (2011).
 
 
Table 4 Selection function, expected genetic gain and relative efficiency (RE%) of different selection indices of brinjal
  
It was observed that selection for individual character index I3 (no. of fruits in inflorescence/plant) showed highest expected genetic gain (2.19) with highest relative efficiencies (166.79%) over straight selection for yield. It indicated that this trait is enough to determine genotypic value for yield and can be utilized as important aspect in the improvement of these brinjal genotypes. Relative efficiencies of fresh weight/fruit (g) (93.20%) and no. of fruits in solitary/plant (97.55%) are close to the efficiency of yield/plant (100%). Higher relative efficiencies over straight selection for grain yield in rice was observed by Habib et al. (2007) for individual characters like filled grains/panicle, plant height and days to maturity.
 
When two characters were included in the selection index, maximum relative efficiency (231.89%) with highest expected genetic gain (3.04) over selection for yield was obtained for the index I13 (yield/plant (g) + no. of fruits in inflorescence/plant). Purohit and Majumder (2009) observed maximum relative efficiency in rice for two characters namely grain yield and number of effective tillers. But in a conventional approach, breeders consider at least three characters function. Considering three characters, maximum relative efficiency (258.14%) was obtained for index I134 in combination of yield/plant (g), no. of fruits in inflorescence/plant, no. of fruits in solitary/plant with highest expected genetic gain (3.39). So the selection index I134, can be identified as profitable effectiveness over direct selection for improvement of the brinjal.
 
Selection would be more effective if we consider four characters where index I1234 showed maximum efficiencies (277.54%) over straight selection with expected genetic gain (3.64). Character fresh weight/fruit (g), no. of fruits in inflorescence/plant and no. of fruits in solitary/plant also showed strong positive correlation with yield at both genotypic and phenotypic level. So selection index I1234 would be more effective for improvement of these genotypes as similar reported by Sameer Kumar et al. (2012) in rabi Sorghum. In the presented functions, selection index I12345 showed highest relative efficiency over direct selection was 295.87% with genetic gain 3.88 when all the characters were included to construct the selection index. But selection based on five characters would be incompetent because of inclusion low efficient character like no of secondary branches/plant which ultimately reduces relative efficiency of combination character for yield.
 
Selection index I134 as a desirable and economically favorable index was considered to evaluate selection score for 21 brinjal genotypes (Table 5). In the present study highest selection score was observed for Debjhuri Hajari followed by Kajla, Sada Begun, BARI-9 and regarded as elite genotypes because of their well response for yield and other yield enhancing traits. Selection of parents for high yielding ability in rice was observed by Purohit and Majumder (2009) based on best selection score.
 
 
Table 5 Selection score of brinjal genotypes based on best selection index
 
Selection score of all genotypes along with the mean value of best index (I134) traits yield/plant (g), no. of fruits in inflorescence/plant, no. of fruits in solitary/plant were considered under biplot analysis to obtain superior genotypes. Genotypes located at the center of concentric circle and close to the indicator of superiority (blue arrow line) are Sada Begun followed by Debjhuri Hajari, Kajla, BARI-9 regarded as superior genotypes (Figure 2). Thus it can be concluded that there is an effective reflection of trait index on genotypic yield and these genotypes would be effective parent’s in advance profitable breeding program.
 
 
Figure 2 Selection of best genotype using biplot analysis. (NFI.P = No. of fruits in inflorescence/plant, NFS.P = No. of fruits in solitary/plant, YP = Yield/plant SS = Selection score, 1-14 = Genotype serial of table 5)
 
To conclude, simultaneous selection procedure through different genetic parameters and relationship with direct effect revealed that yield/plant (g), fresh weight/fruit (g), no. of fruits in inflorescence/plant, no. of fruits in solitary/plant, no. of secondary branches/plant, harvesting period and fruit circumference (cm) are the more effective traits to get better yield and quality. Improvement of brinjal would be more worthwhile when we would focus on the index I134 based on three characters namely yield/plant (g), no. of fruits in inflorescence/plant, no. of fruits in solitary/plant.
 
2 Materials and Methods
2.1 Plant materials
Twenty one brinjal genotypes were collected from plant genetic resource center of Bangladesh agricultural research institute, different localities and certified seed companies.
 
2.2 Field experiment
The experiment was conducted in botanical garden of Jahangirnagar University, Savar Dhaka, Bangladesh during November, 2013 to June, 2014 in randomized complete block deign (RCBD) with three replications. Seedling developments as well as all the cultural practices were done as recommended commercially. Observations were recorded randomly from five plants of each genotype in each replication for different quantitative traits namely plant height (cm), no. of primary branches/plant, no. of secondary branches/plant, stem diameter (cm), days to first flowering, days to first fruiting, harvesting period, no. of fruits in inflorescence/plant, no. of fruits in solitary/plant, fruit length (cm), fruit stalk length (cm), fruit circumference (cm), fresh weight/fruit (g), fruits/plant and yield/plant (g).
 
2.3 Data analysis
Variance analysis for each character was carried out separately following to Singh and Chaudhary (1985) with the mean data of all replications. Genotypic and phenotypic co-efficient of variations were calculated according to formula given by Burton (1952) and Singh and Chaudhary (1985). Broad sense of heritability as well as genetic advance in percent of mean were estimated following formula suggested by Johnson et al. (1955) and Hanson et al. (1956). Correlation coefficients were computed according to method suggested by Johnson et al. (1955). Path coefficients were calculated as suggested by Dewey and Lu (1959) to estimate the direct and indirect effects of the characters on fruit yield. Selection indices were constructed using methods developed by Smith (1936) based on the discriminant function of Fisher (1936). Selection indices and their relative efficiencies in terms of expected genetic advance in yield were calculated according the method stated by Singh and Chaudhary (1985). All of the statistical analysis was done using Indostat and R software.

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