Genetic Variability and Diversity in Red Onion ( Allium cepa L.) Genotypes: Elucidating Morpho-Horticultural and Quality Perspectives

: Onion ( Allium cepa L.) is a prominent spice and vegetable crop farmed commercially worldwide. Variability is viewed as a key signal for any red onion enhancement effort. The current study was, therefore, carried out to learn about genetic variability and diversity among selected genotypes of 20 red onions at the C Block Farm, Bidhan Chandra Krishi Viswavidyalaya, India, during the winter ( rabi ) season of 2021–2022, in Randomized Complete Block Design (RCBD) with three replications. The characters, viz., average weight of bulbs (kg), neck thickness (cm), total soluble solids ( ◦ Brix), total sugar (%), and reducing sugar (%) demonstrated high heritability (97.38–99.97%) coupled with high genetic gain. Such traits were the least inﬂuenced by environmental effects, and additive gene action played a pivotal role in the manifestation of such characters. Traits with high heritability values (51.37–67.94%) demonstrate that the observed variability was under genetic control and


Introduction
With the rising world population, natural resources are severely threatened [1].This has rendered it difficult to feed the steadily expanding population [2].Onion is an invaluable crop that has become a key component for flavoring and making cuisines due to its pungency [3].The crop originated in ancient civilizations and was domesticated in Central Asia, where wild relatives still persist [3].Onion is a significant source of vitamins (B and C), minerals (Ca, P, and Fe), carbohydrates, protein, dietary fibre, and sulphur in the human diet.The organic sulphur compounds help reduce the level of cholesterol in human bodies and also break down blood clots, lower the risk of heart disease and stroke.In addition, onions are low in calories and rich in flavonols-antioxidant compounds like quercetin [4].Worldwide, onion accounts for about 18.78 per cent of the production process and 22.18 per cent of cultivated area [5].Moreover, 70% of total forex comes from fresh onion export [6].Despite such global acceptance, the crop faces sharp lacuna in its genetic improvement, owing to its semi-perishability, poor shelf-life, and proneness to Alternaria porri, causing purple blotching and Stemphylium vesicarium, causing Stemphylium blight diseases, which limits optimal productivity [7].Thus, it can not be determined whether a large amount of produced onions is consumed by people [8].Hence, germplasm evaluation is of utmost importance in terms of crop genetic and agronomic enhancement in the future and at present [9].The unavailability of high-yielding cultivars along with a lack of genetic variability, data classification, and proper evaluation widens the gap between the actual and expected yields of any crop [10].Therefore, it is crucial to broaden genetic variation by evaluating available germplasms in order to develop high-yielding onion varieties.Genetic variation lies in the foundation of plant breeding and is the 'must-have' for any improvement programme.The components of variability like heritability (h 2 ), genetic gain, phenotypic co-efficient of variation (PCV), and genotypic co-efficient of variation (GCV) are important biometric tools for judging heterogeneity in any population, which makes the selection and evaluation of onion germplasm easier for improvement [11,12].Furthermore, the perception of yield and its relationship to the other contributing traits is a crucial element in improving onion yield, particularly through selection.In this context, correlation studies are important.Correlation/association studies provide good apprehension in the contribution of yield towards each trait using variance and co-variance matrices [4].But association studies alone might not infer the extent and nature of contributions made by each independent trait towards yield, and thus path analysis (a biometrical analysis) helps in assigning weights to individual traits while conducting breeding programmes [13].
To date, breeders are placing their efforts in search of new genetic resources of onion for its efficient use in improvement programmes by utilizing a hybridization technique [14].For conducting hybridization, it is crucial to choose parents from different genetic backgrounds and it is only possible through genetic diversity analysis [15].This is due to the fact that more diversified germplasm would be more responsive to changing environmental circumstances.In addition, genetic diversity study provides new insights into gene sources for the improvement and development of both up-to-date and novel cultivars and understanding the array of different traits inherited within a species [16].It augments the chances of procuring transgressive segregants in F 2 and succeeding generations [17].Very little information is available, in general, in these areas of research related to onion, especially in red onion, throughout the world, and the study area in particular.Taking together the importance of genetic variability, correlation, and diversity studies, it is imperative to evaluate the genotypes and apprehend superior types for their use in future breeding programmes.The specific aims of this research were to precisely assess the performance of genotypes and variability in red onion, to determine the correlation among yields and yield attributing traits, and to select the best genotype for the study area.Accordingly, the present investigation, as a novel approach, was centred on red onion genotypes to elucidate morpho-horticultural and quality traits for future breeding programmes.

Planting Material
The current study was conducted at the C Block Farm, Bidhan Chandra Krishi Viswavidyalaya, Kalyani, Nadia, West Bengal, India during the winter (rabi) season of 2021-2022 on 20 red onion genotypes.The experimental site (22 • 57 N latitude and 88 • 20 E longitudes) has a sub-tropical climate, in general, with temperatures in the range of 9.27-37.61• C, RH of 83-98%, and rainfall of 0.62-6.09during the experimental season and N, P, K contents were 208.57, 8.02, and 184.96 kg/ha −1 , respectively.The experiment was laid out in Randomized Complete Block Design (RCBD) with three replications.Healthy and uniform 50-day-old seedlings were uprooted carefully from nursery beds and treated with a solution of Bavistin @ 0.1% for 30 min, and then transplanted into the field with having a gross plot size of 2 m × 1.95 m and a spacing of 15 cm row to row and 10 cm plant to plant so as to accommodate 260 plants.Directly after transplanting, light watering was administered, and a total of six irrigations was provided till crop maturity.Recommended doses of nitrogen (120 kg/ha), phosphorus (60 kg/ha), potassium (80 kg/ha), and sulphur (30 kg/ha) were applied in the form of urea, DAP, MOP, and zinc sulphate, respectively.To ensure robust crop stand, conventional cultural practices were used.Pendimethalin (Stomp R) @ 6mL per litter of water was applied as a pre-emergence herbicide to prevent initial weed growth.As plant protection measures, planting infected sets and overcrowding were avoided.The experimental site was so chosen that it rotated crops to non-allium species for at least 3-4 years; all infected crop debris weredestroyed; and appropriate foliar fungicides were applied, taking care to apply thoroughly to waxy leaves, whenever necessary.Harvesting was performed when 75% of the top had started to fall but before the foliage was completely dry.

Analysis of Growth, Yield, and Biochemical Parameters
Observations were made in three replicates for the selected qualitative and quantitative morpho-horticultural and quality traits of onion genotypes taken under study.Qualitative traits, including foliage colour, leaf width, foliage behaviour, degree of leaf waxiness, shape of bulb, and bulb skin colour, were documented as per the standard descriptors on onion (Protection of Plant Varieties and Farmers' Right Authority, Government of India, 2009).Quantitative traits included plant height (cm), number of leaves per plant, polar diameter (cm), equatorial diameter (cm), neck thickness (cm), bolters (%), doubling percentage, days to maturity, and average bulb weight (g).Five plants were selected randomly from each replication of each genotype to record the data.For morpho-horticultural traits, including plant height, five random plants were selected at 125 days after transplanting.The ruler (measuring scale) was placed at the base of the plant and the height was measured up to the tip of fully opened leaves and averaged and expressed in centimetres.Number of leaves per plant was recorded from five randomly selected plants by counting and averaging completely developed, green, and photosynthetically functional leaves.The crop was harvested when 75% of the tops started to fall but before the foliage was completely dry.The bulbs were harvested by hand-pulling and with the help of a handheld hoe.The tops were removed one day after field curing, leaving only 2.5 cm of the top with the bulb.To measure polar (vertical) and equatorial (horizontal) diameters, five bulbs were selected randomly from five random plants and measured using vernier callipers and were conveyed in centimetres.The data for neck thickness were measured from below the joint of the leaf lamina of five randomly selected bulbs of each genotype, collected from each plot, and the average was then worked.Average bulb weight was measured by weighing five bulbs selected randomly replication-wise from each genotype using electronic weighing machine and averaged.The data for bolting percentage were recorded by counting the emergence of the number of seed stalk prior to the bulb formation and its percentage was calculated.The data for doubling percentage were calculated by dividing the number of bulbs having double bulbs per plot out of the plants having normal bulbs and then expressed in percentage.Five randomly chosen onion bulbs from each genotype were measured for their total soluble solid content using a digital hand refractometer.It was then averaged and expressed in percentage.Total sugar and reducing sugar content of five randomly selected onion bulbs were determined as per the method proposed by Dubois et al. [18].

Statistical Analysis
The analysis of variance (ANOVA) was executed using OPSTAT software version 1.0.2.The degree of significance was tested using 5% and 1% probability.The mean data values have been subjected to analysis of variance as described by Gomez and Gomez [19] for Randomized Block Design.The completely randomized experimental design for phytochemical analysis and its components was followed.GCV and PCV (components of genetic variability) were calculated as per formulae introduced by Burton and De-Vane [20].The formula proposed by Allard was used to calculate heritability in a broad sense [21].
Heritability in a broad sense was estimated for various characters as per the formula: where, h 2 = heritability percentage in a broad sense; σ 2 g = genotypic variance; and σ 2 p = phenotypic variance.The expected genetic advance (GA) was computed using formula proposed by Allard [21], and the genetic gain was calculated using formula proposed by Johnson et al. [22].
The extent of genetic advance expected through selection for each character was calculated as per the formula suggested by Johnson et al. [22]: where, h 2 = heritability percentage in a broad sense; σ 2 g = genotypic variance; σ 2 p = phenotypic variance; K = selection differential (2.06 at 5% selection intensity); and σp = phenotypic standard deviation.
The genotypic and phenotypic correlations were calculated as per Al-Jibouri et al. [23], where total variability was divided into genotypes, replications, and errors.Using the Dewey and Lu [24] approach, path co-efficient analysis was performed to calculate the direct and indirect contribution of different traits with yield.For genetic divergence, SPAR 1.0 software tools were used to analyse the data and a statistical software named R-4.3.1 was used for creating the dendrogram and PCA.
Correlation coefficients, including genotypic and phenotypic, of the abovementioned ten characters were analyzed and are demonstrated in Table 5.In genotypic correlation, average bulb weight was significant and positively correlated with bolting percentage (0.502), followed by neck thickness (0.328), equatorial diameter (0.210), and polar diameter (0.112); whereas in phenotypic correlation, average bulb weight was positive and significantly correlated with bolting percentage (0.273), succeeded by neck thickness (0.248), equatorial diameter (0.194), and polar diameter (0.120).The analysed characters show that genotypic association/correlation was greater than the corresponding phenotypic correlation, indicating a greater contribution of genetic factors towards the development of the traits related.Path coefficient analysis was carried out to determine the relative relevance of each feature and its direct and indirect effects (Table 6).The results showed a high positive direct effect on bulb average weight by equatorial diameter (0.968) followed by polar diameter (0.687) and neck thickness (0.159).Furthermore, a residual effect of 0.521 was obtained, indicating that97.38 percent of the yield-related traits considered in the present study.

Discussion
Germplasm evaluation is of utmost importance in terms of crop genetic and agronomic enhancement in the future and at present.Remarkable variations were observed in terms of qualitative characters from foliage colour to bulb skin colour (Table 1) and quantitative characters from plant height to average weight of bulbs (Tables 2 and 3) in different onion genotypes taken under study.Differences in such qualitative traits are dependent highly on genetic differences, cultural practices, and ecological conditions where varieties/cultivars grow.A few accessions performed well, corroborating that they are well adapted in the study area.Onion is an important crop worldwide, representing a significant value [4].Onion breeders have recently made a major contribution to yields by reducing genetic variation in specific onion genotypes or cultivars.Exploiting genetic variability and diversity is crucial in enhancing onion production by cultivating highyielding genotypes [7].In India, there is an urgent need to develop improved onion varieties with quality and high-yield potentials.
Earlier investigators studied significant variations for a wide range of characters in onion.Breeders must maintain a diverse breeding population in order to study the amount of genetic variation present in it [6,25].Genetic variability lays the foundation of selection, as the higher the variation present in any population, the higher is the scope for the improvement of crop genotypes for any given trait [26].The degree of variation among the available germplasm and its ability to transmit a trait from one generation to the next are crucial for any endeavour'ssuccess [27].Components of genetic variation include the genotypic coefficient of variation (GCV), phenotypic coefficient of variation

Discussion
Germplasm evaluation is of utmost importance in terms of crop genetic and agronomic enhancement in the future and at present.Remarkable variations were observed in terms of qualitative characters from foliage colour to bulb skin colour (Table 1) and quantitative characters from plant height to average weight of bulbs (Tables 2 and 3) in different onion genotypes taken under study.Differences in such qualitative traits are dependent highly on genetic differences, cultural practices, and ecological conditions where varieties/cultivars grow.A few accessions performed well, corroborating that they are well adapted in the study area.Onion is an important crop worldwide, representing a significant value [4].Onion breeders have recently made a major contribution to yields by reducing genetic variation in specific onion genotypes or cultivars.Exploiting genetic variability and diversity is crucial in enhancing onion production by cultivating high-yielding genotypes [7].In India, there is an urgent need to develop improved onion varieties with quality and high-yield potentials.
Earlier investigators studied significant variations for a wide range of characters in onion.Breeders must maintain a diverse breeding population in order to study the amount of genetic variation present in it [6,25].Genetic variability lays the foundation of selection, as the higher the variation present in any population, the higher is the scope for the improvement of crop genotypes for any given trait [26].The degree of variation among the available germplasm and its ability to transmit a trait from one generation to the next are crucial for any endeavour's success [27].Components of genetic variation include the genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), heritability (h 2 ), and genetic gain.The utilization of genetic gain, heritability (h 2 ), and genetic variability assists in genotype selection.A higher magnitude of h 2 was present in the majority of genotypes, indicating that these features are highly heritable (Table 4).Understanding PCV and GCV is ideal for assessing the amount of genetic variation existing in a population.GCV helps us to measure a range of genetic variation present in a population and presents us a way to collate variation present among traits under study.In the present investigation, PCV values were found to be higher than corresponding GCV values, suggesting a significant influence of the growing environment [6].In the current study, the average weight of bulb (kg), total sugar (%), reducing sugar (%), bolting (%), and doubling (%) exhibited higher GCV and PCV values.Such results demonstrated naturally occurring variations among the genotypes examined.This enables an enormous range of opportunities for selection for developing feasible onion genotypes, allowing them to adopt changing environmental conditions alongside maintaining high-yielding nature.Other traits taken under study exhibited low to moderate magnitudes of GCV and PCV.Such findings were corroborated with the findings of Bal et al. [6], Bal et al. [9], Hosamani et al. [28], Porta et al. [29], Khosa and Dutt [30], and Ijeomah et al. [31].Heritability (h 2 ) further helps in gauging to what degree or extent a trait is acquired/inherited [32].Genetics describes the presence of high h 2 very well, which indicates the presence of variation in a trait between genotypes and with low heritability, specifying that the difference is not genetic.A higher heritability alone is not an absolute requirement for selection, but genetic gain enhances the likelihood of successful selection [33].Therefore, an accurate assessment of h2 is required for an effective improvement programme to improve quantitative attributes [25].In the current study, the average weight of bulbs (97.38%), total sugar % (99.97%), and reducing sugar % (99.93%) recorded higher heritability estimates.Such traits can be used to exploit genetic variability and can be improved by the process of selection [34].Bal et al. [6] perceived higher h 2 values for almost all characters except the trait double bolter (%), which strongly validated our results.Another biometric tool, genetic advance, helps to decide on selection and scope for selection [33].Characters with a higher genetic advance and h 2 are far more accurate at predicting genetic advancement through selection [35].Medium genetic advance along with high h 2 would be ideal for conducting single-plant selection for the improvement of genotypes.It is thus imperative to conduct further hybridization to create preferred variations if both the components are low in traits [36].A higher genetic advance along with h 2 for an average weight of 10 bulbs, total soluble solids ( • Brix), and neck thickness (cm) confirms that an effective selection of these attributes would result from the presence of additive gene action [6].In additive gene action, genes behave uniformly in the phenotype and do not control one another.The higher the number of genes, the stronger the phenotype [36].It is thus essential to choose genotypes with high h 2 and genetic advances for attributes relevant to yields.[37].A high heritability and genetic advance as a % mean was detected for the average weight of bulbs (kg), total soluble solids ( • Brix), neck thickness (cm), total sugar (%), reducing sugar (%), and bolting (%) in the present study, indicating that superior genotypes can be developed by effectively selecting these characters.Our findings were corroborated with the findings of Bal et al. [6], Bal et al. [9], Hosamani et al., [28], Porta et al., [29], Khosa and Datt [30], and Ijeomah et al. [31].In addition, traits revealing a higher h 2 coupled with low to moderate genetic gain are controlled by environmental effects [38].These characters/attributes may be taken advantage of by the epistatic and dominant components of heterosis.
A general understanding of yield as well as the relationships between the contributing qualities is essential for improving yield, particularly through selection.As a result, correlation studies can determine the yield's contribution to each feature by employing variance and covariance matrices [4].It includes two important components-phenotypic and genotypic correlation coefficients.The nature of the association between yield and each of its component characters, as well as the associations between those component characters, can be learned from such components [39].In our study, the characters' bolting percentage, neck thickness, equatorial diameter, and polar diameter revealed a highly positive correlation with the average weight of bulbs.This validates the fact that the average weight of bulb would be increased by an increase in components like bolting percentage, neck thickness, equatorial diameter, and polar diameter.In fact, our study shows that genotypic correlation coefficients were of a higher magnitude than that of phenotypic correlation coefficients, showing a larger importance of genetic factors towards the development of the traits related (Table 5).Additionally, it is challenging to determine the kind and level of contributions made by each independent character through correlation analyses.Path coefficient analysis, which assigns weights to various factors while undergoing a crop improvement initiative, serves as the ideal alternative.In our study, the maximum positive direct effect on the average weight of bulbs was registered by equatorial diameter, followed by polar diameter and neck thickness (Table 6).This was the prime reason for the positive association with the average weight of bulbs.Such characters can be considered as important criteria in terms of onion improvement.Other characters taken in our study revealed negative direct effects on the average weight of bulbs.Golani et al. [39] stressed the significance of path coefficient analysis and provided instances from their studies of direct and indirect impacts when undertaking selection in onion.
Based on D 2 analysis, the genotypes were grouped into four clusters.Maximal genotypes had entered into cluster I (n = 11), succeeded by cluster IV (n = 6) and cluster II (n = 2).Cluster III had only one genotype (Table 7).The clustering pattern made it clear that genotypes collected across diverse geographic areas were clustered into different clusters, demonstrating the fact that genetic diversity is unrelated to the origin of an individual [7].This has also happened due to recurrent germplasm exchange between various geographic regions.The maximum inter-cluster distance was found between cluster III and cluster I, demonstrating that the genotypes of these two clusters can be hybridized to produce superior recombinants or transgressive segregants [7].The minimum inter-cluster was recorded between cluster II and cluster IV (Table 8), indicating a genetic similarity of the genotypes grouped in these two clusters.Our results are in line with those of Nikhil and Jadhav [40] who grouped 20 onion genotypes in four clusters based on plant height (cm), number of leaves per plant, neck thickness (cm), days to maturity, polar diameter (cm), equatorial diameter (cm), and the average weight of bulbs (g).Bal et al. [7] took 23 genotypes in their study and grouped them into eight clusters based on growth, quality, and yield parameters.The present study indicated the excellent opportunity to bring about improvement through wide hybridization by crossing genotypes in different clusters.
In the current investigation, PCA was employed to explain trait variation among various genotypes (Tables 9 and 10).Principal Component Analysis (PCA) is a statistical tool used to reduce the dimensionality of datasets and aid in improved interpretation with no information loss [10].Both PCA biplot and scree plot analysis are important in assessing divergence patterns and trait-genotype similarity [41].Figure 2 depicts scree plot between component numbers.The graph displayed eigenvalues on y-axis and component numbers on x axis.It exhibited a typical curve with downward slope.The point at which the slope of the curve becomes unambiguously flattened (a classic 'elbow' pattern), merely expressing the number of components, is where the analysis should begin.In the current scree plot, PC1, PC2, and PC3 were preserved in explanatory analysis for use in PCA, while the rest of the components were ignored.Out of 11 principal components, the first three displayed eigenvalues greater than 1 and above with a 78.32% total variability.The first principal component (PC1) had an eigenvalue of 3.253 with 29.573% of variability.PC2 had an eigenvalue of 2.817 with 25.613% variability and PC3 had an eigenvalue of 2.546 with 23.145% variability (Table 9).The characters which contributed positively to the first three principal components could be given due consideration while selecting the best genotypes without losing yield potential.From the PCA biplot analysis in Figure 3, the characters' polar diameter, equatorial diameter, neck thickness, bolting (%), total soluble solids, and average weight of bulbs were highly correlated because of their position in the same area.Furthermore, all the traits in each component showed a positive contribution towards the average weight of bulbs; therefore, onion breeders have to carry out positive selection for those traits which show a positive contribution towards the average weight of bulbs.Our results are in line with the of findings of Bal et al. [10] in onion; Olfati et al. [42] in cucumber; and Hayder et al. [8] in potato.

Conclusions
The primary goal of this study was to discover significant genetic variability and diversity for different attributes in onion genotypes.Significant genetic variability was found for the characters studied.High heritability and genetic gain demonstrated that selection might substantially improve traits to enhance yield in onion.Our findings also revealed that additive gene action had an impact on the genetic manifestation of the characteristics.The selection of such traits would be effective for further onion improvement.The current study classified 20 onion genotypes into distinct groups and showed that most attributes varied widely among onion genotypes, offering us more potential for genetic gain through hybridization or selection.Our results also revealed that PCA and D 2 analysis can be used to classify onion genotypes on the basis of quantitative traits.Therefore, on the basis of yield, yield attributing parameters and quality, the genotype (RAVT-1) RVB-21-20 may be regarded as the most promising one followed by (RAVT-1) RVB-21-12 and (RIET) RVA-21-15.We strongly suggest conducting further research on these genotypes to validate their result for further improvement.Further initiatives should be devoted to develop novel genomic resources for onion improvement.

Figure 2 .
Figure 2. Scree plot of 11 variables of onion genotypes.It shows that the first three variables could be retained for PCA, while the others can be disregarded.

Figure 2 .
Figure 2. Scree plot of 11 variables of onion genotypes.It shows that the first three variables could be retained for PCA, while the others can be disregarded.

Author Contributions:
Conceptualization, A.A. and A.B.S.; Data curation, A.B.S.; Formal analysis, A.A. and S.B.; Funding acquisition, M.S., I.A., N.M.A. and M.S.; Investigation, A.A. and A.B.S.; Methodology, A.B.S. and U.T.; Project administration, A.B.S.; Resources, A.A., T.K.U., M.S. and U.T.; Software, M.S.K. and M.S.; Supervision, A.B.S.; Validation, A.B.S.; Writing-original draft, S.B.; Writing-review and editing, S.B., A.B.S. and M.S.All authors have read and agreed to the published version of the manuscript.Funding:The authors express their gratitude to the Bidhan Chandra Krishi Viswavidyalaya (Agricultural University) for necessary research infrastructure and the Deanship of Scientific Research at King Khalid University for funding this work through the Large Research Group Project under grant number RGP. 2/316/44.

Table 1 .
Range of variations for qualitative traitsofredonion genotypes.

Table 2 .
Quantitative characters recorded in different onion genotypes.

Table 4 .
Mean, range, and estimates of genetic variability of 11 characters of onion genotypes.

Table 5 .
Phenotypic and genotypic correlation among 11 characters of onion genotypes.

Table 8 .
Average intra-and inter-cluster D 2 values of 4 clusters for 20 onion genotypes viaWard's method.

Table 8 .
Average intra-and inter-cluster D 2 values of 4 clusters for 20 onion genotypes via Ward's method.

Table 9 .
Eigen -value and contribution of the principal component axes towards total genetic variation in onion genotypes.

Table 10 .
Contribution of different traits of onion towards major principal components.

Table 9 .
Eigen-value and contribution of the principal component axes towards total genetic variation in onion genotypes.

Table 10 .
Contribution of different traits of onion towards major principal components.