Characterizing genetic diversity and population structure in onion (Allium cepa L.) genotypes using agro-morphological and molecular markers

doi:10.21203/rs.3.rs-2621775/v1

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Characterizing genetic diversity and population structure in onion (Allium cepa L.) genotypes using agro-morphological and molecular markers

https://doi.org/10.21203/rs.3.rs-2621775/v1

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Background

It is crucial to gain understanding of crop diversity at the genetic and phenotypic levels. For breeding onion with higher yield and quality together with greater resilience to biotic and abiotic stressors agro-morphological and molecular characterization of onion genotypes is of utmost importance.

Methods and results

In the present study, genetic diversity in 49 onion genotypes were assessed using 6 agro-morphological descriptors, 19 quantitative traits along with 23 ISSR markers. All the agro-morphological descriptors were found polymorphic with Bulb: basic colour of dry skin (1.44) exhibiting the highest diversity index. The multivariate analysis using Mahalanobis D² statistic grouped 49 genotypes into seven clusters with highest inter cluster distance between V and VII (364.35). A total of 78 fragments were produced from 13 polymorphic primers with a mean of 6 alleles per primer. The polymorphic information content (PIC) ranged from 0.42 (UBC 835) to 0.75 (UBC 825) with a mean of 0.61 per primer. With a mean of 0.36, the inter-genotype genetic distance ranged from 0.12 to 0.72. Based on cluster analysis using UPGMA algorithm, the genotypes were divided into two major clusters, whereas the cluster tree constructed using the described ISSR markers identified three major groups. The structure analysis divided the population into two main groups.

Conclusion

From the findings of present study, it can be stated that characterization at both molecular and morphological basis is of utmost importance to understand the genetic diversity in onion. Hybridization between distantly related genotypes can produce desirable transgressive segregants in future onion breeding programmes.

Onion

diversity

agro-morphological characterization

D2 analysis

molecular markers

ISSR markers

Onion (Allium cepa L.) is among the ancient vegetables known to human race for its flavour, aroma and taste. It is one of the most indispensable agricultural commodities of the Indian households. Onion (2n = 2x = 16), popularly regarded as “Queen of Kitchen”, belongs to the family Alliaceae and is a valuable bulbous vegetable crop cultivated all over the world [23]. Onion is a major source for carbohydrates, proteins, minerals (Ca, P, and Fe), vitamins (B and C) and dietary fibres [8]. Onion, with an approximate genome size of 16 GB [15], is a highly cross-pollinated in nature with a higher degree of genetic variability [12]. Besides being native to Central Asia [52], onion has exhibited a rich genetic diversity in Greece, North-West India, Pakistan, Turkey and the USSR.

Globally, onion is grown in an area of 4,300 thousand hectares with a production of 98,000 thousand metric tons and productivity of 22.79 tonnes/ha [13]. India ranks second in the world after China, contributing approximately 20 per cent of the global onion production. In India, the annual production of onion is 26,738 thousand metric tons produced from over 1,434 thousand hectares area with a productivity of 18.65 metric tons/ha [6]. However, the onion production in India is far less than Republic of Korea [51], which stands as number one producer across the globe. The potential export value of Indian onion is approximately $460.52 [7]. Due to the constraints in traditional breeding of onion including significant heterozygosity, self-incompatibility, severe inbreeding depression, a biennial crop cycle and a restricted genetic base, the genetic improvement of onion has been limited [27, 29, 31]. The key prerequisites for the significant improvement of any crop are the availability of sufficient genetic variability and rich genetic diversity [41]. Additionally, understanding genetic diversity aids in the effective management of germplasm and the selection of diverse parents to produce heterozygous offspring.

The simplest method of determining the genetic diversity which predominates in the genetic resources of a crop is to investigate phenotypic variance using morphological markers. Morphological markers represent genetic polymorphisms which are easily identified and manipulated [17]. However, morphological markers can sometime fail to distinguish between closely related individuals or lines as they are fewer in number and subject to environmental influences [42]. Another limitation of morphological assessment is requirement of large sets of phenotypic data, which can be tedious. Nonetheless, many vegetative characters show continuous variations because of environmental factors [20]. Hence, molecular markers are considered to be more reliable in assessment of genetic variability than morphological data since they are not influenced by environmental variables and are effective, rapid and able to test a large number of distinct genotypes at the DNA level [32]. Various molecular markers such as restriction fragment length polymorphism (RFLP) [9], random amplified polymorphic DNA (RAPD) [54–55], inter-simple sequence repeat (ISSR) [58], microsatellite or simple sequence repeat (SSR) [49], amplified fragment length polymorphism (AFLP) [53] and gene specific markers [24, 27–29] have been employed for molecular analysis of onion genotypes.

For the present investigation, we have used ISSR (inter-simple sequence repeat) markers for the genetic diversity studies in onion. The present investigation has been outlined using following objectives: appraisal of the genetic diversity and variability in 49 accessions of onion based on morphological traits and using molecular markers.

2.1 Plant materials and field experiment

The present investigation was conducted at the Vegetable Research Farm, Department of Vegetable Science and Floriculture, College of Agriculture, CSK HPKV, Palampur (32^º6'N latitude and 76^º3'E longitude at an elevation of 1290.8 m above mean sea level) India, during rabi season 2020-21. The investigation was carried out using 49 onion genotypes (released varieties and breeding lines) including two checks viz., Palam Lohit and Him Palam Shweta (Supplementary Table 1). Genotypes were raised in a Randomized complete block design (RCBD) with three replications in a plot size of 1.5 m × 2.0 m (3 m²), maintaining row to row and plant to plant spacing of 15 × 10 cm. The seeds were sown in the nursery beds in rows 5 cm apart on 8th October, 2020 and eight-week-old healthy seedlings were transplanted on 18th December, 2020, and harvesting was carried out in the last week of April. Five representative plants were randomly selected from each genotype in each replication, and were tagged for identification at 90–100 days after transplanting. As per the guidelines prescribed for DUS test of onion by PPV&FRA, the data for six DUS traits of 49 onion genotypes was converted to the respective DUS test scores and further transformed to binary data. The observations of 17 morphological traits (plant height, leaves/plant, neck thickness of bulb, bolting (%), polar diameter, equatorial diameter, bulb shape index, bulb maturity, average bulb weight, A grade bulbs, B grade bulbs, C grade bulbs, D grade bulbs, double bulbs, biological yield, total bulb yield and marketable bulb yield) were also recorded from the average of these five plants. Grading of onion bulb was done according to the guidelines of ICAR-DOGR, Pune. All the plants of each genotype were scored for stemphylium blight [59] and thrips incidence [60].

2.2 DNA extraction and ISSR analysis

DNA isolation was done using CTAB DNA extraction method [10] from two-week-old leaf tissue of individual plants from 49 onion genotypes grown in nursery. To estimate the quantity and quality of isolated genomic DNA, spectrophotometry was performed by using nanodrop machine. The integrity of genomic DNA was judged on agarose gel (1%) [25]. In the present study, 23 ISSR primers were screened for the molecular analysis (Supplementary Table 2), out of which 13 primers showed high polymorphism.

For primer dilution, nuclease free water was used at a concentration of 5 to 10 pico mol. The PCR reaction was carried out using Master Mix Kit utilising a total 25 µL. PCR tubes containing reaction mixture were mixed thoroughly by using spinner and subjected to the thermocycler, followed by a loop of 35 cycles each consisting of denaturation at 94 ˚C for 1/2 minute, annealing temperature at 50 ˚C. The amplification reaction was carried out in a gradient thermocycler. An initial denaturation step at 94 ˚C for 3 minutes and extension at 72 ˚C for 1 minutes was programmed. The final extension was performed at 72 ˚C for 5 minutes. The PCR products were then stored at 4˚C for ∞. PCR products were separated on 3 per cent agrose gel along with 100 bp DNA ladder as a marker. The gel was visualised by staining with ethidium bromide, and banding patterns of the PCR product’s band were visualised under UV trans-illuminator and image captured for further analysis.

Primer selection. The ISSR primers were obtained from the previous publications by Sudha et al. [44] and Yadav et al. [61].

2.3 Statistical Analysis

DUS data analysis. NTSYS-pc (version 2.02) was used to construct the dendrogram from the binary data of six DUS descriptors. For each descriptor the diversity index (DI) was determined using formula: $\text{H}=-{\sum }_{i=1}^{R}Pi\text{ln}Pi$, where Pi is the proportion (n/N).

Morphological diversity analysis. The genetic divergence analysis was carried out using Mahalanobis D² statistic. The genotypes were grouped into different clusters by Tocher’s method suggested by Rao [40]; inter- and intra-cluster distances were also computed. WINDOSTAT 8.0 software developed by Indostat Services was used for conducting D² analysis. Mahalanobis D² analysis between two genotypes estimated on the basis of the ‘p’ characters is given by the equation:

$${D}^{2}=\sum _{i=1}^{p}\sum _{j=1}^{p}Wij(Xi1-Xi2)(Xj1-Xj2)$$

Where, w^ij= variance-covariance matrix, w^ij = reciprocal of (w_ij), (ij = 1,2….., p), X_i1 = sample mean for i^th character for first sample, X_i2 = sample mean for i^th character for second sample. Further, the grouping pattern of the 49 onion genotypes was estimated by principal component analysis (PCA) in software PAST 4.03 [5] using EIGEN procedure on the basis of correlation coefficient between two genotypes. The principal components with eigenvalues > 1.0 were selected as suggested by Jeffers [63].

Molecular analysis. After PCR amplification of ISSR markers, reproducible amplicons were scored '1' as present or '0' as absent, each of phenotypes were assembled and used in statistical analysis. The dendrogram was constructed using the UPGMA algorithm in SAHN program of NTSYS–PC package (version 2.02) [43]. The Jaccard dissimilarity index was used to create a genetic dissimilarity matrix, and the DARwin programme (version 6.0) was used to generate an unweighted neighbour joining tree [38]. Neighbor-Joining (NJ) algorithm in DARwin v.6.0 was used to calculate pair-wise inter-genotype genetic distance. POPGEN computer software version 1.32 was used to calculate Na (number of alleles), Ne (number of effective alleles), I (Shannon’s diversity index), Ho (observed heterozygosity) and He (expected heterozygosity). The Polymorphic information content (PIC) were calculated [4] using formula $\text{P}\text{I}\text{C}=1-{\sum }_{i=1}^{k}P{i}^{2}$, where k is the total number of alleles and p_i is the frequency of the i^th allele in the set of genotypes investigated. MAF (major allele frequency) was calculated using Cervus v 3.0.3. Genetic structure of the 49 onion genotypes was determined by following Bayesian model-based program STRUCTURE (v.2.3.3) [39]. The analysis was done with k ranging from 1 to 10 using an admixture model with 10,000 burning periods and 10,000 replicates. The final peak of plotting LnPD values were determine using online web-based Structure Harvester program.

Principal Coordinates Analysis (PCoA) in GenALEx 6.5, was used to visualize the Analysis of Molecular Variance (AMOVA) and the genetic relationship patterns contained in the matrix [37].

Agro-morphological diversity and DUS characterization

All 49 onion genotypes' scores for the six morphological descriptors were recorded. Significant variation was seen for all the important traits across the 49 onion genotypes (Supplementary Fig. 1). The Shannon diversity index for six traits ranged from 0.56 (foliage: waxiness and foliage: intensity of green colour) to 1.44 (Bulb: Basic colour of dry skin) with an average of 0.90 and a range of 0.88 (Table 1). The similarity coefficient varied from 0.56 to 1.00, depicting that all genotypes had a wide range of genetic diversity. At 0.56 level of genetic similarity, the cluster analysis divided all the genotypes into two clusters A and B. These two clusters were further divided into sub clusters namely, A1 (31 genotypes), A2 (5 genotypes), B1 (11 genotypes) and B2 (2 genotypes). The grouping pattern of the genotypes under each cluster was mentioned in Fig. 1a.

Table 1

Diversity indices of six morphological descriptors in onion genotypes
Trait	Class or scale of descriptor	Frequency	Relative frequency (%)	Diversity index
Foliage: waxiness	Absent	12	24.49	0.56
Foliage: waxiness	Present	37	75.51
Foliage: Intensity of green colour	Light	12	24.49	0.56
	Medium	0	0.00
	Dark	37	75.51
Bulb: General shape	Elliptic	0	0.00	0.92
	Oval	4	8.16
	Globe	22	44.90
	Flat globe	0	0.00
	Flat	23	46.94
Bulb: Basic colour of dry skin	White	18	36.73	1.44
	Yellow	3	6.12
	Pink	16	32.65
	Light Red	6	12.24
	Dark Red	7	14.29
	Brown	0	0.00
	Purple	0	0.00
Bulb: Colour of epidermis	Whitish	18	36.73	1.25
	Yellowish	3	6.12
	Reddish	16	32.65
	Purplish	12	24.49
Uniformity in bulb size	High	34	69.39	0.69
	Medium	14	28.57
	Low	1	2.04
Mean				0.90
Maximum				1.44
Minimum				0.56
Range				0.88

Genetic divergence through Mahalanobis D ² analysis

The multivariate analysis (D²) following Tocher’s procedure depicted through dendrograms (Fig. 1b) grouped 49 onion genotypes into seven clusters, where cluster I comprised the maximum number of genotypes (35) followed by cluster II (6 genotypes), cluster III (4 genotypes) and clusters IV, V, VI and VII (1 genotype each). The intra-cluster distance varied from 43.86 (cluster I) to 53.49 (cluster III), whereas it was zero for the mono-genotypic clusters. The inter-cluster distance ranged from 75.06 (clusters I and IV) to 364.35 (clusters V and VII) (Supplementary Table 3). Clusters VI and VII were observed to be the most important with maximum cluster means for most of the valuable traits. Cluster VI showed maximum means for leaves/plant, polar diameter, B grade bulbs, biological yield and marketable bulb yield. Similarly, maximum means for plant height, bolting, A grade bulbs and per cent double bulbs was shown by cluster VII (Supplementary Table 4).

The relative per cent contribution of individual trait to the genetic divergence among studied onion genotypes presented in supplementary Table 5. The maximum contribution towards total genetic divergence was exhibited by bolting (42.09%) followed by D grade bulbs (17.18%) and double bulbs (17.09%). The contribution of bolting, bulb maturity, neck thickness and double bulbs was also reported by earlier workers [45–46, 30].

The most effective traits for differentiating amongst onion accessions were more precisely identified using a principal component analysis (PCA). Data obtained from PCA resulted in six principal components with eigenvalues > 1 (Supplementary Table 6), which cumulatively accounted for 81.84% of variation. The total variance was maximum in PC-I (33.54) which was mostly contributed by marketable bulb yield, followed by PC-II (15.53) and was mainly due to bulb shape index and PC-III (11.61) which was mainly attributed due to neck thickness. PC1 and PC2 were utilized to obtain 2D biplot diagram of dispersion for all the 49 accessions revealing the differences among the genotypes (Fig. 2).

Genetic divergence study at molecular level

For the present investigation, twenty-three ISSR primers were used in the molecular analysis of 49 onion genotypes. A total of 13 ISSR primers generated polymorphic bands and were selected for their further use in the analysis of the genetic diversity in onion accessions.

These 13 primers detected a total of 78 alleles which ranged from 4 (UBC 812 and UBC 835) to 11 (UBC 834) with an average of 6 alleles per locus, whereas the effective number of alleles per locus ranged from 2.13 (UBC 835) to 4.62 (UBC 825) with a mean of 3.19. The Shannon’s Information index (I) ranged from 0.83 (UBC 835) to 1.72 (UBC 834) with a mean value of 1.30. The polymorphic information content (PIC) ranged between 0.42 (UBC 835) and 0.79 (UBC 825) with an average value of 0.67. Observed heterozygosity ranged from 0.84 to 1.00 with a mean value of 0.98, while the expected heterozygosity varied between 0.53 and 0.79 with an average of 0.67. Major allele frequency (MAF) ranged from 0.33 (UBC 807) to 0.76 (UBC 827) with an average of 0.57 (Table 2). To assess the genetic diversity, genetic matrix consisting 78 informative alleles (Fig. 3) were subjected to cluster analysis using NTSYS-pc program.

Table 2

Details of 13 polymorphic ISSR primers used for the characterization of onion genotypes
S. No.	Primers	Na	Ne	MAF	I	PIC	Ho	He	Fragment size
1	UBC 807	9	4.50	0.33	1.68	0.74	1.00	0.78	250–2800
2	UBC 808	5	2.36	0.63	1.01	0.49	1.00	0.58	300–1800
3	UBC 810	6	4.44	0.31	1.56	0.73	0.98	0.78	300–2200
4	UBC 811	5	3.73	0.41	1.38	0.68	1.00	0.74	450–1800
5	UBC 812	4	2.31	0.63	0.95	0.47	1.00	0.57	300–1200
6	UBC 813	5	2.44	0.64	1.03	0.51	1.00	0.60	300–1500
7	UBC 818	5	2.88	0.73	1.25	0.60	1.00	0.66	850–1900
8	UBC 824	6	2.71	0.51	1.23	0.57	0.98	0.63	650–2800
9	UBC 825	7	4.62	0.43	1.66	0.75	1.00	0.79	800–1800
10	UBC 826	5	3.17	0.70	1.32	0.64	1.00	0.69	400–1500
11	UBC 827	6	2.60	0.76	1.24	0.58	0.84	0.62	470–1500
12	UBC 834	11	3.64	0.70	1.72	0.70	1.00	0.73	250–2200
13	UBC 835	4	2.13	0.68	0.83	0.42	1.00	0.53	280–1500
Mean		6	3.19	0.57	1.30	0.61	0.98	0.67
St. Dev		2	0.90		0.29		0.04	0.09

Dendrogram of 49 onion cultivars was constructed using Jaccard’s genetic DICE similarity coefficients divided onion genotypes into two major clusters A and B (Fig. 4a). Cluster A comprised of 48 genotypes, whereas one genotype was placed in cluster B. Cluster A was further sub-divided into two sub clusters A1 and A2. The sub cluster A1 comprised of 21 genotypes viz., Bhima Shakti, L-913, PRO-9, VL Piaz 3, NHO-920, JRO-14-14, ON20-24, ON20-51, Bhima Shweta, PKV White, ON20-45, ON20-47, ON20-43, ON20-50, ON20-68, ON20-70, ON20-72, ON20-77, ON20-79, DOGR-HT-2, Him Palam Shweta, DOGR-HT-1, ON20-11, ON20-15, ON20-13, Brown Spanish, ON20-27, ON20-30, ON20-74, PRO-8, DOGR-1626 and DOGR-1625. The sub cluster A2 comprised of 16 genotypes viz., Balwan, Rashidpura, Sandeep, Sona, DOGR-1639, CITH-0-1, ON20-41, CITH-0-2, DOGR-HT-3, DOGR-HT-4, ON20-90, Palam Lohit, W-340-EL-8, W-504-M-4, Agrifound White and W-355. Cluster B contained only one genotype viz., ON20-49.

The genetic identity of 49 onion genotypes was further confirmed by subjecting the marker data to construct the neighbor-joining tree (Fig. 4b) using the DARwin software version 6.0.21 [38]. Branch robustness was tested using 1000 bootstraps. The cluster tree generated with the described ISSR markers assorted all the genotypes into three major groups. Cluster I comprised of 17 genotypes viz., ON20-41, CITH-0-1, CITH-0-2, Palam Lohit, ON20-90, DOGR-HT-4, W-504-M-4, W-340-EL-8, W-355, Agrifound White, DOGR-1639, Sona, DOGR-HT-3, ON20-30, Rashidpura, Balwan, Sandeep while, cluster II comprised of 29 genotypes which further divided into four sub-clusters viz., cluster IIA (L-913, Bhima Shakti, JRO-14-14, VL Piaz-3, PRO-9, NHO-920), cluster IIB (ON20-70, ON20-68, ON20-77, ON20-72, DOGR-HT-2, ON20-79, Him Palam Shweta, DOGR-HT-1), cluster IIC (ON20-15, ON20-11, ON20-13, ON20-49, DOGR-1626, PRO-8, DOGR-1625) and cluster IID (ON20-50, ON20-43, ON20-47, ON20-45, Bhima Shweta, ON20-51, ON20-24, PKV White) and cluster III comprised of three genotypes viz., ON20-27, Brown Spanish, ON20-74. With a mean of 0.36, the dissimilarity values in terms of genetic distance (GD) ranged from 0.12 to 0.72 (Supplementary Table 7). The highest GD (0.72) was found among ON20-11 and ON20-49, whereas the lowest GD (0.12) was observed between Rashidpura and Bhima Shweta, and DOGR-HT-1 and DOGR-HT-3.

Molecular analysis of variance (AMOVA) was calculated by using GenALEx 6.5 [37], to analyse the molecular variation among onion genotypes. AMOVA showed high proportion of variability within population i.e., 95 per cent of total variation whereas, 5 per cent genetic variation among population (Supplementary Table 8). The genetic relationship as depicted by cluster analysis was further re-confirmed using Principal Coordinate Analysis (PCoA). PCoA assembled 49 onion genotypes into five population (Fig. 5). Using ISSR markers, percentage of variation is explained by the three axes. PC1 contributes 15.64 per cent variation whereas, PC2 contributes 8.11 per cent variation and PC3 contributes 7.39 per cent variation. Further, very little introgression between gene pools was observed in the dimension 1 versus 2 comparison.

Population structure analysis revealed that with increase in the model parameter K value, the LnP(D) (log-likelihood) also increased (Supplementary Fig. 2a) upto K = 7 and further declined at K = 8 till K = 9 and then showed a small peak at K = 10. The Evanno test revealed that the peak value of ΔK was obtained at K = 2 (Supplementary Fig. 2b). Structure analysis suggested that the population was divided of two populations viz., POP 1 and POP 2 (Fig. 6). With the help of structure bar plot, POP1 showed maximum of 100 per cent and minimum of 55 per cent membership and POP2 showed maximum of 100 per cent and minimum of 65 per cent membership. Majority of the genotypes in POP1 were from Maharashtra followed by Jammu and Kashmir and Rajasthan. Similarly, POP2 comprised of genotypes from Maharashtra followed by Himachal Pradesh, Uttarakhand, Punjab, Haryana, Gujarat and Rajasthan. Both sub population comprised onion genotypes having different maturity group, bulb yield and bulb colour. These results showed that the classification based on the genetic structure was not consistent with their geographic origin, maturity, bulb yield and bulb colour.

Comparison of genetic diversity among various population through molecular markers

The genetic diversity on the basis of various groups among different populations is given in Table 3. The genetic diversity of the two groups (I and II) generated by NTSYS software, revealed that among the two groups, highest genetic diversity on the basis of polymorphism information content (PIC = 0.60), effective number of alleles (Ne = 3.15) and Shannon’s Information index (I = 1.28), while group II had lower genetic diversity based on values of PIC (0.34), Ne (2.00) and I (0.69). Similarly, among the three groups (I, II and III) generated by DARwin, the highest genetic diversity was exhibited by group II based on the values of PIC (0.59), Ne (3.10) and I (1.26). For different maturity populations, the highest genetic diversity was shown by intermediate maturity group based on PIC (0.61), Ne (3.06) and I (1.21). Grouping of genotypes on the basis of bulb yield revealed that the high yielding group had the maximum genetic diversity with PIC (0.60), Ne (3.18) and I (1.28). On the basis of bulb colour the genotypes were grouped into five distinct groups among which pink coloured group showed maximum genetic diversity with high values of PIC (0.62), Ne (3.23) and I (1.33). The onion genotypes were collected from eight states of India (Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Punjab, Haryana, Rajasthan, Gujarat and Maharashtra) having diverse geographical conditions. The genotypes derived from Maharashtra showed relatively high genetic diversity based on PIC (0.61), Ne (3.20) and I (1.31), whereas genotype from Gujarat had second highest genetic diversity on the basis of PIC (0.61), Ne (2.00) and I (0.69), while all other genotypes derived from the rest of the states showed low genetic diversity.

Table 3

Group wise genetic diversity statistics for 13 ISSR markers across 49 onion genotypes
Group/Population	Na	Ne	I	PIC	Origin and number of genotypes by regions	Total number of genotypes
Grouping by NTSYS
I	5.62	3.15	1.28	0.60	Jammu and Kashmir (2), Himachal Pradesh (3), Punjab (2), Haryana (1), Rajasthan (2), Gujarat (1), Maharashtra (37)	48
II	2.00	2.00	0.69	0.34	Maharashtra (1)	1
Grouping by Darwin
I	4.15	2.65	1.08	0.53	Jammu and Kashmir (2), Himachal (1), Haryana (1), Rajasthan (1), Maharashtra (12)	17
II	5.31	3.10	1.26	0.59	Himachal Pradesh (1), Punjab (2), Rajasthan (1), Gujarat (1), Maharashtra (24)	29
III	2.92	2.62	0.98	0.51	Himachal Pradesh (1), Maharashtra (2)	3
Maturity Group
Intermediate maturity	4.77	3.06	1.21	0.61	Jammu and Kashmir (1), Himachal Pradesh (2), Punjab (2), Haryana (1), Rajasthan (2), Gujarat (1), Maharashtra (11)	21
Late maturity	5.69	3.21	1.31	0.58	Jammu and Kashmir (1), Himachal Pradesh (1), Maharashtra (26)	28
Bulb yield group
High	5.38	3.18	1.28	0.60	Jammu and Kashmir (1), Himachal Pradesh (2), Punjab (1), Haryana (1), Rajasthan (1), Maharashtra (21)	27
Intermediate	5.23	3.05	1.24	0.59	Jammu and Kashmir (1), Punjab (1), Rajasthan (1), Gujarat (1), Maharashtra (14)	18
Low	3.38	2.86	1.07	0.54	Himachal Pradesh (1), Maharashtra (3)	4
Grouping By Bulb colour
Pink	5.31	3.23	1.33	0.62	Rajasthan (1), Maharashtra (14)	15
Light red	3.62	2.92	1.10	0.55	Himachal Pradesh (1), Gujarat (1), Maharashtra (4)	6
Dark red	3.76	2.89	1.12	0.56	Jammu and Kashmir (1), Himachal Pradesh (1), Punjab (2), Haryana (1), Rajasthan (1), Maharashtra (1)	7
Yellow	3.23	2.82	1.05	0.53	Jammu and Kashmir (1), Maharashtra (2)	3
White	4.46	2.95	1.18	0.58	Himachal Pradesh (1), Maharashtra (17)	18
Region wise group
Jammu and Kashmir	2.31	2.17	0.78	0.44	-	2
Himachal Pradesh	3.08	2.75	1.02	0.52	-	3
Uttarakhand	1.92	1.92	0.63	0.34		1
Punjab	2.54	2.46	0.88	0.47	-	2
Haryana	2.00	2.00	0.69	0.38	-	1
Rajasthan	2.62	2.46	0.91	0.48	-	1
Gujarat	2.00	2.00	0.69	0.61	-	1
Maharashtra	5.92	3.20	1.31	0.61	-	38

Genotypic and phenotypic variability are crucial for plant breeding, this refers to variations in the genome that can vary from the rearrangement of a single base pair to an entire chromosome [19]. These variations in how the genome interacts with the environment show observable changes in plant diversity [21]. The plant breeder can increase the genetic base of the local cultivar by understanding genetic diversity. As a result, estimating the genetic diversity among genotypes has emerged as a key strategy for detecting genetically superior and diverse parents along with desired traits.

The simplest method for determining the genetic diversity that predominates in the available genetic resources of the crop is to investigate the phenotypic variation using morphological markers. Genetic polymorphisms represented by morphological markers are simple to detect and alter [17]. Morphological markers can often fail to distinguish between closely related individuals or lines since they are limited in number and subject to environmental influences [42]. Therefore, a combined strategy based on conventional and molecular research may provide a better knowledge of variation pattern among the genetic resource that can be utilised to exploit the expansion of the genetic base for significant features.

The present investigation was formulated to characterize the genetic diversity of 49 onion accessions collected from different Indian states based on agro-morphological and molecular levels. Maximum variation on the basis of DUS characters was found for Bulb: Basic colour of dry skin (1.44) which had the highest Shannon diversity index. The genotypes were grouped into two clusters at 0.56 level of genetic similarity. Morphological descriptors may be employed to effectively identify and exploit the genotypes up to maximum potential which can be further used in hybridization programme to create superior varieties. Following the D² analysis the genotypes were grouped into seven distinct clusters which indicated high level of genetic variability in the studied germplasm. Among these, cluster IV, V, VI and VII were monogenotypic. This genetic diversity among the genotypes may have been due to the restricted gene flow following geographic barriers, or exhaustive natural and human selection which resulted in the formation of various single clusters. Different clustering patterns in onion genotypes were also reported by earlier workers [36, 62, 3, 45, 16, 47, 2]. The highest inter-cluster distance (364.35) was exhibited between the clusters V and VII, indicating that these genetically diverse clusters can provide relatively better parental lines that can be utilized in future breeding programmes for obtaining a wide spectrum of variation. The crossing using these diverse genotypes would be predicted to display maximal heterosis and may provide favourable recombinants in segregating generations. Among the above-mentioned genotype of cluster V, ‘Brown Spanish’ was found to be highly resistant for thrips infestation and moderately resistant for stemphylium blight (field screening). The genotype of cluster VII, ‘JRO-14-14’ was highly resistant for thrips infestation and moderately resistant for stemphylium blight. Hence, these genotypes can be utilized for mapping studies for stemphylium blight and thrips infestation in onion genotypes. Earlier workers [16, 47, 30] also found that parents from diverse clusters may be utilized in hybridization programme to obtain desirable transgressive segregants. The minimum inter-cluster distance was found between clusters I and VI (75.06), suggesting that the genotypes in these clusters were closely related.

One of the most effective tools for collecting information about an entire population of germplasm is principal component analysis (PCA), which subclassifies the whole population of germplasm based on its genetic variability. The genotypes were grouped using PCA in accordance with the quantitative measurements that revealed variances in those with comparable parentage. DOGR-HT-1, JRO-14-14, and CITH-0-2 genotypes in the PCA plot stood out from all other genotypes, which may be a result of their unique ancestry and extensive variance in morphological traits.

Due to the limitation of the morphological characterization in estimation of genetic diversity as their evaluation is subjective, ISSR markers were employed to assess the genetic diversity and to further discriminate the genotypes from different regions of the country. Therefore, molecular markers have been used for the estimation of genetic diversity in onion genotypes.

ISSR markers produced a total of 78 alleles among 49 genotypes of onion with an average of 6 alleles per locus. The approximate fragment size ranged from 250–2800 bp. The polymorphic information content (PIC) had an average value of 0.61. The average PIC value in the present study (0.61) was almost similar [43]. The average values of Ne (effective number of alleles), I (Shannon’s Information index), MAF (Major Allele Frequency), Ho (observed heterozygosity) and He (expected heterozygosity) were 3.19, 1.30, 0.57, 0.98 and 0.67, respectively. To determine the informativeness of each and every marker PIC values were calculated which represents the allele number and their distribution. A higher degree of genetic similarity between the genotypes of the crop under analysis is implied by a lower PIC score, and vice versa. For PIC values > 0.5, these markers are seen to be highly diverse, if PIC values fall in the range of 0.25 to 0.50, these markers belong to middle diverse and lower than 0.25, markers are of low diversity. The most informative ISSR marker was UBC 825, UBC 807 and UBC 810, with a PIC value of 0.75, 0.74 and 0.73, respectively. While the minimum PIC was recorded for UBC 835 (PIC = 0.42). When compared with the PIC values reported by other researchers, higher results were found in this study. For the analysis of the genetic diversity polymorphic markers were used but the observed heterozygosity was high 1.00 for almost all the markers expect for UBC 810 (0.98) and UBC 827 (0.84). The high heterozygosity in onion is attributable to its highly cross-pollinated nature and protandry.

Cluster analysis is the most important method for breeders to know the relationships and genetic diversity of parents for efficient hybridization program [1, 57]. In this study, the 49 onion genotypes were kept into two major groups by NTSYS-pc software by using ISSR markers (Fig. 5). The genetic identity was further confirmed by using the DARwin software to construct the neighbor-joining tree. The cluster tree constructed using the described ISSR markers identified three major groups. The assessment of genetic distance is crucial for the efficient use of diverse genotypes. The pair wise genetic dissimilarity was found highest between one pair, i.e., 0.72 (ON20-11 and ON20-49) with this high divergence, these pair can be used for the breeding programs to further develop new segregants. Whereas, lowest genetic dissimilarity was found between two pairs (0.12), indicating that these genotypes may share a common ancestory. Such pair of genotypes are not recommended to be used in breeding programmes. The average genetic distance recorded in this study (0.36) is lower than the earlier report by SaiSudha et al. [44] they reported the range of genetic distance from 0.55 to 0.87. The variation between the two software viz., NTSYS-pc v.2.02 and DARwin v.6.0.21, could be attributed to differences in the methods of clustering used by the two systems i.e., computing similarity and dissimilarity coefficients, respectively.

Based on the clustering, no similarity was found between the genetic and geographical diversity of the genotypes, since the genotypes of various geographical regions were clustered in different groups. Earlier workers [56, 34–35, 14, 44, 22] also reported similar findings. The results indicated that the clustering based on the morphological and molecular markers was different which may be due to environmental effects on the morphological characters.

To assess molecular variation among onion genotypes, molecular analysis of variance (AMOVA) was computed. AMOVA depicted high proportion of variability within population i.e., 95 per cent of total variation whereas, 5 per cent genetic variation among population. Rivera et al. [41] in onion also observed 7.13 per cent variation among population and 92.87 per cent variation within population. Lyngkhoi et al. [26] in onion reported 7.77 per cent variability among population and 92.23 per cent variability within population. PCoA is a multivariate approach for grouping based on similarity coefficients or variance-covariance values that provides more valuable information about major groups, whereas cluster analysis provides a higher level of resolution among closely related populations. PCoA grouped 49 onion genotypes into five population which is consistent with the results obtained by SaiSudha et al. [44] for nine onion genotypes.

Population structure analysis revealed two sub-populations in the 49 onion genotypes by using the model-based method STRUCTURE. The similar results were found by Lynkhoi et al. [26] for 96 onion accessions. Additionally, it was found that most onion genotypes did not exhibit a clear categorization pattern in terms of their geographic origin from structure analysis. Some dissimilarities were observed between cluster and structure analysis, as cluster analysis assigned a specific branch position for each genotype, whereas structure analysis divided the individuals into sub-populations [18]. Hence the grouping based on clustering and STRUCTURE was not identical.

For the classification of onion cultivars, early maturity, marketable bulb yield and bulb colour are considered to be the most important traits. However in our study, we found that the clustering was not as per these traits which indicated that some introgression was present in the gene pool of onion genotypes. Therefore, in onion breeding programmes, it is important to make extensive use of genotypes with varying maturity period, marketable bulb yields, and bulb colours to get suitable hybridization combinations.

In the present investigation, diversity analysis of onion genotypes was carried out to assess the variability in the present onion germplasm. The studied agro-morphological and molecular markers exhibited a good genetic variability among the 49 onion genotypes, conforming their effectiveness in determining the diversity among onion germplasm. ISSR markers grouped the genotypes into two major categories by cluster analysis and grouped into two sub-populations by structure analysis, which suggested that sufficient genetic variability was present in the genotypes. Thus, from the present investigation it can be concluded that genotypes viz., CITH-0-2, PRO-8, ON20-30, DOGR-HT-2 and ON20-74 were found to be the most diverse and can be directly used after multi-location testing as they possess inherent ability to produce high yields and have superior quality traits or can also be used in breeding programmes to exploit heterosis or to isolate transgressive segregants with desirable traits. The genotype pair viz., ON20-11 and ON20-49 exhibited the highest genetic dissimilarity based on ISSR analysis, thus, these can be used in QTL mapping to accelerate the onion molecular breeding programs. Thus, a combined approach of agro-morphological and molecular evaluation can be beneficial for the selection of diverse parental lines which can be exploited in future breeding programmes of onion.

Conflict of interest The authors declare that they have no conflict of interest.

Ethical approval This article does not contain any studies with animals performed by any of the authors.

Data availability statement All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Characterizing genetic diversity and population structure in onion (Allium cepa L.) genotypes using agro-morphological and molecular markers

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1. Introduction

2. Materials And Methods

2.1 Plant materials and field experiment

2.2 DNA extraction and ISSR analysis

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