Genetic divergence and combining ability of mini tomato (Solanum lycopersicum var. cerasiforme) lines from different sources

ABSTRACT

The objectives were to evaluate genetic divergence in 59 Solanum lycopersicum var. cerasiforme lines correlating with the country of origin, and to estimate the combining ability of 13 selected lines based on high soluble solids contents (°Brix). The DNA of the 59 lines was amplified using nine inter-simple sequence repeat (ISSR) primers. Shannon’s diversity index (I = 0.47) and Nei’s genetic diversity (h = 0.30) indicated high genetic diversity. Genetic differentiation coefficient (G_ST) = 0.035 suggested low genetic differentiation between populations and analysis of molecular variance (AMOVA) showed that high variability was not confined to any single country of origin. Principal coordinates analysis (PCoA) clustered the genotypes into three main groups, and a dendrogram confirmed no apparent relationship between the origin and the genetic profiles of the lines. Genetic classification of the mini tomato lines based on their country of origin is not feasible using ISSR markers. The 78 hybrids obtained from the crosses among the 13 selected lines were evaluated in a complete diallel scheme over two seasons, considering the number of bunches, number of fruits per bunch, average fruit weight, fruit length and width, peduncular scar, °Brix, productive cycle, and yield. Average fruit weight and yield were the traits that most influenced the genetic diversity of these 13 parental lines. Based on general combining ability (GCA) we identified lines with good performance for each trait, while specific combining ability (SCA) revealed crosses that exhibited outstanding performance. Promising lines and hybrids were selected for mini tomato breeding program advancement.

°Brix; ISSR markers; genetic similarity; hybrids

Introduction

The effectiveness of a breeding program is directly related to the choice of parents and the subsequent selection of superior genotypes in populations generated by crosses, which exhibit genetic diversity and superior phenotypes (Allier et al., 2020). The genetic research herein relies on morphological markers, which were crucial to reinforcing theoretical principles. However, the combination of morphological and molecular evaluations provides more accurate results (Zhou et al., 2015).

Molecular markers are crucial to breeding programs, as they allow for the observation of both intrapopulation and interpopulation variation without environmental interference (Cavers et al., 2005). These markers enable monitoring of the genetic relationships of parents for selecting crosses, assessing genetic variability, and other applications (Nassau et al., 2023). Various types of molecular markers based on sequence size polymorphism are available, with one of the most commonly used being inter-simple sequence repeat (ISSR) (Mondini et al., 2009).

The tomato (Solanum lycopersicum L.) originated in Ecuador, Chile, and the Andes Mountains. Indigenous Mexicans domesticated and later spread it to other regions, including Europe (Kalogeropoulos et al., 2012). Until the 18^th century, it was cultivated primarily as an ornamental plant. It was then incorporated into cooking by the Italians and introduced to Brazil at the end of the 19^th century. Its tiny, easily transportable seeds facilitated tomato’s rapid global spread (Thomson et al., 2011). It has since become one of the most widely produced vegetables, attracting significant research interest (Kimura and Sinha, 2008).

This study aimed to assess the genetic divergence in 59 S. lycopersicum var. cerasiforme (Dunnal) A. Gray lines using ISSR molecular markers, correlate this divergence with their respective countries of origin, and estimate the combining ability of 13 selected lines with high soluble solids contents (°Brix) in a complete diallel scheme.

Materials and Methods

Fifty-nine mini tomato lines were selected from the Universidade Estadual do Centro-Oeste germplasm collection, originating from Brazil (24), France (7), Italy (17), the United States (6), Spain (2), England (1), South Africa (1), and Ukraine (1).

The genotypes were sown in styrofoam trays filled with commercial substrate. After reaching the three-leaf stage, the leaflets were collected and stored in liquid nitrogen for subsequent DNA extraction, following the method described by Sharma et al. (2008).

The extracted DNA was quantified using electrophoresis on a 0.9 % agarose gel stained with ethidium bromide, with known concentrations of phage λ DNA (100, 200, and 300 ng) used as standards. Ten ISSR primers developed by the University of British Columbia (UBC), Vancouver, Canada, were employed to amplify the DNA (Table 1).

Table 1
– List of the 10 inter-simple sequence repeat primers used for analyzing variability and genetic relationships among Solanum lycopersicum var. cerasiforme lines from different origins.

Primer	Sequence*	AT
		°C
UBC 807	(AG)8T	52
UBC 808	(AG)8C	50
UBC 809	(AG)8T	51
UBC 810	(GA)8T	52
UBC 834	(AG)8CTT	52
UBC 835	(AG)8YC	52
UBC 836	(AG)8YA	53
UBC 848	(CA)8AGG	55
UBC 864	(ATG)6	50
UBC 873	(GACA)4	54

• *Y = C or T; AT = Annealing temperature in °C.

DNA amplification reactions for each genotype were performed by Polymerase Chain Reaction (PCR) in a final volume of 12.5 μL, containing: 20 ng of DNA, 0.2 μM of primer, 200 μM of each dNTP, 1.5 mM of MgCl2, 1 U of Taq DNA Polymerase, and 1× PCR buffer. The thermocycler was programmed for initial denaturation at 94 °C for 5 min, followed by 35 cycles of 94 °C for 45 s, primer annealing temperature (Table 1) for 45 s, and 72 °C for 90 s. A final extension step at 72 °C for 7 min was also included.

The amplification products were separated on a 1.8 % agarose gel stained with ethidium bromide (0.5 μg mL^–1) at 110 V. DNA Ladder 100 bp was used as a molecular weight marker. The results were visualized under UV light and documented using a digital system.

The gel samples were genotyped for the presence (1) or absence (0) of the amplified loci, and only polymorphic loci were used for analysis. Nei’s genetic diversity (h), Shannon’s diversity index (I), and the genetic differentiation coefficient (G_ST) were calculated for all populations using POPGENE software (version 1.32). The analysis of molecular variance (AMOVA) was performed using GenAlEx software (version 6.501), with Brazilian genotypes grouped as one population and all others as another. For other analyses, genotypes were not grouped into populations.

A dendrogram showing genetic similarity among the 59 lines was constructed based on ISSR markers using the unweighted pair group method with arithmetic mean. Principal coordinates analysis (PCoA) was conducted using GenAlEx software to examine the spatial distribution of individuals and their contributions to genetic diversity.

Genetic group distribution between populations (i.e., set of lines) had similar values, indicating that these populations were determined through clustering based on the Bayesian model using the STRUCTURE program version 2.3.4. Genetic similarity among genotypes was estimated using the Jaccard similarity coefficient with NTSYS 2.2 software.

Considering that the °Brix is an essential characteristic to be considered for mini tomato breeding, crosses were made in 13 (BR13, BR14, BR20, BR22, FR29, FR32, ES37, IT38, BR44, IT55, BR59, BR64, and BR65) of the 59 experimental mini tomato lines, selected for their higher °Brix, in a complete diallel scheme, resulting in 78 hybrids (Table 2). In the second season, two commercial hybrids, T1 = 13722 and T2 = 24313, were evaluated as checks.

Table 2
– Origin of the 78 experimental mini tomato hybrids derived from their respective parent lines, evaluated in two seasons in Guarapuava, Paraná state, Brazil.

Treatments	Origin	Treatments	Origin	Treatments	Origin	Treatments	Origin
1	BR13 × BR14	21	BR14 × BR59	41	BR22 × BR64	61	ES37 × BR59
2	BR13 × BR20	22	BR14 × BR64	42	BR22 × BR65	62	ES37 × BR64
3	BR13 × BR22	23	BR14 × BR65	43	FR29 × FR32	63	ES37 × BR65
4	BR13 × FR29	24	BR20 × BR22	44	FR29 × ES37	64	IT38 × BR44
5	BR13 × FR32	25	BR20 × FR29	45	FR29 × IT38	65	IT38 × IT55
6	BR13 × ES37	26	BR20 × FR32	46	FR29 × BR44	66	IT38 × BR59
7	BR13 × IT38	27	BR20 × ES37	47	FR29 × IT55	67	IT38 × BR64
8	BR13 × BR44	28	BR20 × IT38	48	FR29 × BR59	68	IT38 × BR65
9	BR13 × IT55	29	BR20 × BR44	49	FR29 × BR64	69	BR44 × IT55
10	BR13 × BR59	30	BR20 × IT55	50	FR29 × BR65	70	BR44 × BR59
11	BR13 × BR64	31	BR20 × BR59	51	FR32 × ES37	71	BR44 × BR64
12	BR13 × BR65	32	BR20 × BR64	52	FR32 × IT38	72	BR44 × BR65
13	BR14 × BR20	33	BR20 × BR65	53	FR32 × BR44	73	IT55 × BR59
14	BR14 × BR22	34	BR22 × FR29	54	FR32 × IT55	74	IT55 × BR64
15	BR14 × FR29	35	BR22 × FR32	55	FR32 × BR59	75	IT55 × BR65
16	BR14 × FR32	36	BR22 × ES37	56	FR32 × BR64	76	BR59 × BR64
17	BR14 × ES37	37	BR22 × IT38	57	FR32 × BR65	77	BR59 × BR65
18	BR14 × IT38	38	BR22 × BR44	58	ES37 × IT38	78	BR64 × BR65
19	BR14 × BR44	39	BR22 × IT55	59	ES37 × BR44	T1	Hybrids 13722
20	BR14 × IT55	40	BR22 × BR59	60	ES37 × IT55	T2	Hybrids 24313

Hybrids 30 (BR20 × IT55) and 35 (BR22 × FR32) were excluded from evaluation as they did not produce viable seeds, probably due to poor physiological quality or perhaps cross-incompatibility, although S. lycopersicum var. cerasiforme genotypes are generally compatible with each other (Gramazio et al., 2020; Zeist et al., 2023). Hybrids 15 (BR14 × FR29), 16 (BR14 × FR32), and 26 (BR20 × FR32) were evaluated only in the first season due to seed germination failure in the second season. Data from missing treatments were estimated in the diallel analysis.

For the evaluation of morphological/agronomic characteristics of the diallel hybrids, experiments were conducted over two seasons: Feb to July 2022 and Aug 2022 to Feb 2023, in a greenhouse in Guarapuava, Paraná state (25°23’02” S, 51°29’43” W, 1100 m altitude), Cfb climate according to the Köppen classification (Costa and Andrade, 2017).

The experiments were carried out in both seasons using a randomized block design with three replications. Each experimental unit consisted of one pot with a single plant. The pots were arranged in double rows, spaced 1.20 m apart with 0.60 m between pots.

Hybrids were sown in 128-cell styrofoam trays with commercial substrate. Daily drip irrigation was applied until seedlings were ready for transplanting, approximately one month after sowing. Eighteen-liter pots were filled with a 3/4 soil and 1/4 sand mixture, and the base fertilizer was adjusted. Plants were tutored, manually pruned, and managed for pests and diseases, as per Lins Junior (2019).

In both seasons, the number of bunches per plant and fruits per bunch were recorded at a height of 2 m. Five ripe fruits per plant were sampled to assess the average fruit weight (g), length, width, and diameter of the peduncular scar (mm, measured with a pachymeter), soluble solids content (°Brix, using a digital refractometer), productive cycle (days from first to last harvest), and total yield (total fruit weight per plant, in grams) in the second season.

Genetic divergence in the 13 parental lines was estimated using the generalized Mahalanobis distance, and the relative contributions of six fruit characteristics (length, width, °Brix, peduncle scar, average weight, and yield) to divergence were calculated, according to Singh (1981). The data were analyzed using Griffing’s joint diallel analysis with GENES statistical software (version Windows).

Results

The h for a set of the Brazilian lines was the same as for the lines from other countries (h = 0.30). Similarly, the I for the Brazilian population (I = 0.47) was comparable to that of the set of lines comprising genotypes from other origins (I = 0.46). The genetic differentiation coefficient was very low (G_ST = 0.035).

The AMOVA revealed that 97 % of the variation occurred within populations, while only 3 % was observed between populations.

The dendrogram illustrates that there is no clear relationship between the lines’ origin and genetic makeup. However, it does show that some lines from Brazil, France, Italy, and the United States form clusters with higher genetic similarity (Figure 1).

Figure 1
– Dendrogram of the 59 tomato lines from the Universidade Estadual do Centro-Oeste germplasm collection, generated using data from ten inter-simple sequence repeat primers.

Principal coordinates analysis clustered the genotypes into three main groups, predominantly consisting of genotypes from Brazil, the United States, and France (Figure 2). Additionally, European-origin genotypes were the closest in the PCoA analysis (Figure 2). The first, second, and third axes of the PCoA accounted for 8.63, 7.42, and 7.13 % of the genetic variation observed, respectively. Together, these three axes explained 23.18 % of the total genetic variation. This low percentage suggests that genetic divergence cannot be clearly associated with the origin of the lines (Figure 2).

Figure 2
– Principal coordinates analysis (PCoA) based on data obtained with ten inter-simple sequence repeat primers from the 59 Solanum lycopersicum var. cerasiforme lines in the Universidade Estadual do Centro-Oeste germplasm collection. Circles indicate the formation of groups in the PCoA.

The results from G_ST and PCoA indicate that genetic classification of the mini tomato lines based on their country of origin is not feasible.

The diallel crosses selected 13 mini tomato lines from different origins (eight from Brazil, two from Italy, two from France, and one from Spain) with the highest °Brix. Of the 78 possible hybrids, seven exhibited a similarity greater than 5.0. The BR20 × BR22 cross showed the highest similarity (0.61), while the FR29 × FR32 cross exhibited the lowest similarity (0.15) (Table 3).

Table 3
– Average similarity among the mini tomato lines used in the diallel crosses, based on inter-simple sequence repeat markers.

Lines	BR65	BR13	BR14	BR20	BR22	BR44	BR59	BR64	ES37	FR29	FR32	IT38
BR65
BR13	0.40
BR14	0.34	0.59
BR20	0.35	0.56	0.44
BR22	0.39	0.52	0.52	0.61
BR44	0.34	0.29	0.28	0.28	0.33
BR59	0.43	0.44	0.38	0.46	0.48	0.35
BR64	0.57	0.36	0.27	0.28	0.35	0.27	0.39
ES37	0.35	0.43	0.40	0.41	0.54	0.28	0.49	0.29
FR29	0.27	0.30	0.28	0.35	0.36	0.24	0.29	0.20	0.27
FR32	0.44	0.35	0.35	0.37	0.34	0.34	0.41	0.32	0.37	0.15
IT38	0.34	0.39	0.39	0.44	0.47	0.34	0.42	0.32	0.39	0.27	0.46
IT55	0.43	0.43	0.36	0.39	0.36	0.30	0.31	0.33	0.34	0.26	0.31	0.37

The similarity estimates between the parental lines of hybrids 30 (BR20 × IT55) and 35 (BR22 × FR32) were 0.39 and 0.34 respectively (Table 3). This shows they are contrasting parents, which can contribute to cross-incompatibility since they did not produce viable seeds.

Average fruit weight (40.71 %) and yield (24.54 %) contributed the most to the divergence in the lines, accounting for a total of 65.25 %.

The Mahalanobis genetic distance in the 13 parental lines revealed differences in the evaluations of the mentioned characteristics, resulting in varying degrees of genetic distance. The greatest distance was observed between the BR20 and BR44 lines (406.61), while the smallest distance was found between the BR59 and BR64 lines (6.63).

The BR44 line exhibits the greatest genetic distance from the other lines of the diallel, making it the most divergent. This line recorded the highest genetic distance values compared to the other eight lines. Additionally, the BR13 line showed the most significant genetic distance from the three other lines.

In Griffing’s joint diallel analysis, significant effects were observed for the general combining ability (GCA) of the 13 lines and the specific combining ability (SCA) of the hybrids for several traits: number of fruits per cluster, number of clusters per plant, average fruit weight, °Brix, and productive cycle (Table 4).

Table 4
– Summary of the joint diallel analysis for the number of fruits per cluster, number of clusters per plant, average fruit weight (g), soluble solids content (°Brix), and productive cycle (days) of the 78 mini tomato hybrids, evaluated during the 2022 and 2022/23 growing seasons in Guarapuava, Paraná state, Brazil.

	GL	QM
		Number of fruits per cluster	Number of Clusters	Average fruit weight	°Brix	Productive cycle
				g		days
Gen	77	102.74**	11.8**	156.21**	16.78**	1495.27**
GCA	12	214.17**	28.36**	661.25**	21.99**	4107.56**
SCA	65	82.17**	8.75**	62.97**	15.82**	1013.01**
Environment	1	2017.89	113.13	1.79	164.29	8070.59
Gen × Season	77	21.19*	1.17	12.64	1.15**	204.46**
GCA × Season	12	26.89*	1.40	12.96	1.44*	163.28
SCA × Season	65	20.14*	1.13	12.58	1.09**	212.07**
Error	288	14.65	0.93	10.54	0.71	110.61
Overall Average		13.03	4.65	11.34	5.92	51.21

• QM = mean square; GL = degrees of freedom; GCA = general combining ability; SCA = specific combining ability; Gen = Genotype. **, *Significant at 1 and 5 % by F test, respectively.

The interaction between GCA and seasons did not significantly affect the number of bunches and average fruit weight (Table 4). Consequently, these traits’ GCA estimates (ĝ_i) are reported as averages comprising both seasons. For the remaining traits, significant interactions were noted, and ĝ_i were separately obtained for each season.

As regards the number of fruits per bunch, the ES37 line had the highest GCA estimate (ĝ_i = 3.53) in the first season. In the second season, the BR59 line exhibited the highest estimate (ĝ_i = 3.55) (Figure 3A and B).

Figure 3
– A) Estimates of the general combining ability (GCA) (ĝi) for the number of fruits per cluster in the first season; B) ĝi for the number of fruits per cluster in the second season; C) ĝi for the average number of clusters per plant; D) ĝi for the average fruit weight; E) ĝi for soluble solids content (°Brix) in the first season; F) ĝi for °Brix in the second season; G) ĝi for the productive cycle in the first season; and H) ĝi for the productive cycle in the second season. The vertical line indicates 1.5 times the standard deviation of the GCA of the 13 experimental mini tomato lines evaluated in the 2022 and 2022/23 growing seasons in Guarapuava, Paraná state, Brazil.

As for the number of bunches per plant (Figure 3C), the BR65 line demonstrated the highest average estimate of GCA over both seasons. For average fruit weight, the BR44 line had a significant estimate of GCA (Figure 3D).

The °Brix and productive cycle exhibited significant interactions between GCA and seasons, with significance defined as 1.5 times the standard deviation (Table 4). For °Brix, the BR64 line showed a ĝ_i = 0.787 °Brix in the first season, while the BR65 line had a ĝ_i = 0.742 °Brix in the second season (Figure 3E and F). For the productive cycle, the ES37 line had a significant ĝ_i in both seasons (Figure 3G and H), reflecting the influence of additive gene effects to prolong the cycle in the crosses it participated in (Rakha and Sabry, 2019). The ES37 line also stood out for the ĝ_i of the number of fruits per cluster in the first season (Figure 3A).

Significant interactions between SCA and the seasons were observed for the number of fruits per bunch, °Brix, and productive cycle. In contrast, this interaction was not significant for the number of bunches and average fruit weight (Table 4).

For the number of fruits per bunch, the BR14 × BR20 cross had the highest SCA estimate (ŝ_ij) in the first season (ŝ_ij = 6.00), followed by the FR32 × BR44 (ŝ_ij = 5.91), FR29 × ES37 (ŝ_ij = 5.35), and BR22 × BR65 (ŝ_ij = 4.93) crosses (Figure 4A). In the second season, the highest ŝ_ij were observed for the FR32 × IT55 (ŝ_ij = 7.932), BR14 × BR20 (ŝ_ij = 7.913), and BR20 × BR59 (ŝ_ij = 6.790) crosses (Figure 4B).

Figure 4
– A) Estimates of the specific combining ability (SCA) (ŝij ) for the number of fruits per cluster in the first season; B) ŝij for the number of fruits per cluster in the second season; C) ŝij for the average number of clusters per plant; D) ŝij for average fruit weight; E) ŝij for soluble solids content (°Brix) in the first season; F) ŝij for °Brix in the second season; G) ŝij for the productive cycle in the first season; and H) ŝij for the productive cycle in the second season. The vertical line indicates 1.5 times the standard deviation of the SCA for the 78 experimental mini tomato hybrids evaluated in the 2022 and 2022/23 growing seasons in Guarapuava, Paraná state, Brazil.

For the number of bunches per plant, none of the crosses achieved an ŝ_ij exceeding the significance threshold (Figure 4C). However, the FR32 × BR44 (ŝ_ij = 1.56) and BR13 × FR32 (ŝ_ij = 1.45) crosses had positive ŝ_ij. For average fruit weight, significant ŝ_ij were observed for the BR14 × BR20 (ŝ_ij = 4.99 g), BR13 × BR44 (ŝ_ij = 4.98 g), and FR32 × BR59 (ŝ_ij = 4.497 g) crosses (Figure 4D).

As regards °Brix, the FR32 × BR65 cross had the highest significant ŝ_ij in the first season (ŝ_ij = 2.4 °Brix) (Figure 4E). In the second season, the crosses with significant estimates were FR32 × IT55 (ŝ_ij = 2.6 °Brix), BR14 × BR65 (ŝ_ij = 2.6 °Brix), and BR20 × BR65 (ŝ_ij = 2.6 °Brix) (Figure 4F).

For the productive cycle, significant ŝ_ij were recorded for several hybrids. In the first season, the BR14 × BR20 cross showed a significant estimate of 31.7 days, followed by FR32 × BR44 with 18.0 days and IT55 × BR64 with 17.8 days (Figure 4G). In the second season, significant ŝ_ij were found for the FR32 × BR65 cross with 25.9 days and the BR14 × BR20 cross with 21.8 days (Figure 4H).

Among the lines evaluated for estimates of fruit yield, the BR22 line exhibited the highest GCA with ĝ_i = 306.51 g per plant (Figure 5A). Five crosses demonstrated significant SCA: BR22 × IT38 (ŝ_ij = 316.24 g per plant), ES37 × BR59 (ŝ_ij = 248.40 g per plant), BR13 × BR14 (ŝ_ij = 196.23 g per plant), FR29 × FR32 (ŝ_ij = 170.66 g per plant), and BR44 × BR65 (ŝ_ij = 169.08 g per plant) (Figure 5B).

Figure 5
– A) Estimates of the general combining ability (GCA) (ĝᵢ) for fruit yield. The vertical line indicates 1.5 times the standard deviation of the GCA for the 13 experimental mini tomato lines evaluated in the 2022/23 growing season in Guarapuava, Paraná state, Brazil. B) Estimates of the specific combining ability (SCA) (ŝᵢⱼ) for fruit yield. The dashed vertical line indicates 1.5 times the standard deviation of the SCA for the 78 experimental mini tomato hybrids evaluated in the 2022/23 growing season in Paraná state, Brazil.

Considering the interaction between SCA and season, the FR32 × IT55 cross exhibited the highest heterosis effect for the number of fruits, °Brix, and productive cycle in the second season. This cross demonstrated significant interaction effects, distinguishing it from all evaluated crosses.

Although the BR14 and FR32 lines were involved in crosses with significant ŝ_ij, they exhibited negative ĝ_i across all traits. Consequently, these lines are not recommended for continued mini tomato breeding program use.

In contrast, the BR65 line is recommended for further use in the breeding program. It displayed high GCA values for the number of bunches and °Brix and was involved in crosses that demonstrated significant SCA for °Brix. Given that °Brix is a critical trait for mini tomatoes, the BR65 line’s performance highlights its potential value for breeding.

Discussion

The I measure diversity within a population and reflects the population’s genetic variability (Holcomb et al., 1977). In this study, the comparison between Brazilian and non-Brazilian populations (i.e. a set of lines) had similar values, indicating that these populations exhibit high genetic variability, which is notable given that these are genotypes that have undergone breeding (Aguirre et al., 2017).

Nei’s genetic diversity for Brazilian accessions of cherry tomatoes was notably high (h = 0.40), followed by Mexico (h = 0.32) and Ecuador (h = 0.20), as reported by Aguirre et al. (2017). According to the authors, this high value can be attributed to the relatively low number of accessions used in their study and because genotypes have undergone breeding.

High genetic differentiation was indicated by F_ST value of 0.34 in cherry tomatoes (Aguirre et al., 2017) and F_ST = 0.17 in Solanum pimpinellifolium L. (Nuez et al., 2004). In contrast, low F_ST values of 0.052 and 0.023 for S. lycopersicum var. cerasiforme and S. pimpinellifolium, respectively, reflected low genetic differentiation between the accessions (Nakazato et al., 2008). In the present study, a G_ST coefficient of 0.035 was obtained for the genotypes of S. lycopersicum var. cerasiforme, indicating low genetic differentiation between the genotypes from Brazil and those from other origins. This result demonstrates that while there is minimal genetic differentiation, both populations exhibit high genetic diversity.

Similar findings of AMOVA were reported by Aguirre et al. (2017), who also noted that the majority of variation (89 %) was within groups. This was attributed to genetic diversity arising from hybridization between S. lycopersicum var. cerasiforme and other tomato cultivars introduced to the same region, indicating greater genetic variation within the groups.

Principal coordinates analysis revealed that the sum of the first three coordinates explained 67.23 % of the total variation in ten grape-tomato hybrids using amplified fragment length polymorphism markers, in the study by Eisele et al. (2022). The difference in variation may be attributed to the number of accessions studied. Origin explains only a small portion of the total variation between lines, making it challenging to relate genotype origin to genetic relationships.

This outcome may be attributed to the widespread dispersal of tomato seeds globally due to their small size, which facilitates their transport even without proper registration. These findings align with those of Nuez et al. (2004), who also failed to correlate wild tomato accessions’ geographical and genetic distances. This lack of correlation can be explained by human influence and certain species’ ability to adapt and thrive in similar habitats despite their geographical origins.

The similarity values between lines evaluated in this study (0.15 to 0.61) supports the PCoA analysis, which revealed greater variability in the lines studied. This finding is noteworthy because successful breeding programs necessitate the identification of divergent genitors with superior traits and the maintenance of diversity within the population (Eisele et al., 2022).

Despite the absence of clear separation based on origin, the 59 lines evaluated exhibit high genetic variability and significant genetic differentiation when assessed pairwise, confirming the presence of genetic differentiation when assessed pairwise within the S. lycopersicum var. cerasiforme (Maciel et al., 2018). Both genetic dissimilarity and similarity data are crucial to breeding programs, as these parameters are used to select appropriate genitors for new crosses (Aguirre et al., 2017).

Lines within the same group are less divergent from each other, indicating higher genetic similarity (Eisele et al., 2022). Conversely, lines in different groups demonstrate genetic diversity in the accessions studied. This finding aligns with the results of Finzi et al. (2019), who categorized 32 accessions into six groups, underscoring the substantial genetic diversity in tomato crops.

The complete diallel scheme generates and evaluates all possible crosses between parent lines, offering insights into additive and non-additive genetic effects on various traits (Mather and Jinks, 1982). This suggests that each parent line contributes favorably to specific traits, reflecting additive gene effects (Rakha and Sabry, 2019), while the hybrids exhibit non-additive gene effects.

Lines with positive GCA for fruit number should be prioritized for selection, as this trait is directly linked to yield and can enhance overall production (Rakha and Sabry, 2019). However, to develop high-quality hybrids that meet market demands, other important characteristics for mini tomatoes, such as °Brix, must also be considered. Even though environmental conditions influence them, they are genetically controlled (Marodin et al., 2016).

Tomato lines with significant GCA values typically maximize genetic diversity in their progeny for specific traits (Vekariya et al., 2019). SCA refers to the deviation in a cross’s performance from the expected performance based on the GCA of the parental lines, revealing the heterosis resulting from the cross (Pandiarana et al., 2015).

This study confirms that the best SCA results for hybrids are not always achieved by crossing parent lines with the highest GCA values (Vekariya et al., 2019).

In the present study, SCA values for °Brix ranged from –4.7 to 2.4 °Brix in the first season and from –6.0 to 2.6 °Brix in the second. These variations highlight the contribution of the SCA to the results obtained (Pandiarana et al., 2015).

It is crucial to consider the productive cycle jointly with yield components such as the number of fruits, number of bunches, and average fruit weight. A longer productive cycle is only advantageous if it correlates with improved yield; otherwise, it may lead to increased costs and reduced efficiency (Mahapatra et al., 2013).

Fruit yield was assessed only in the second season, revealing significant general and SCA effects. Selection based on GCA is usually more effective for since production is mainly influenced by additive genetic effects (Kaushik and Dhaliwal, 2018).

The mini tomato lines exhibited substantial genetic divergence; however, it was not feasible to classify them based on their country of origin. The primary traits contributing to this divergence were average fruit weight (g) and fruit yield (g per plant). In the diallel analysis, the lines ES37, BR44, BR59, and BR65 demonstrated significant performance and are recommended for continued use in the mini tomato breeding program. The findings enable the selection of promising hybrids for further development, particularly hybrids 1 (BR13 × BR14), 13 (BR14 × BR20), 23 (BR14 × BR65), 33 (BR20 × BR65), and 37 (BR22 × IT38). While crosses with the least genetic similarity, such as FR29 × FR32, exhibited high combining ability for yield, this relationship did not consistently apply across all evaluated traits.

Acknowledgments

We are grateful to express gratitude to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support provided through the grant and extend heartfelt thanks to everyone who contributed to this work.

References

• Aguirre NC, López W, Orozco-Cárdenas M, Coronado YM, Vellejo-Cabreba F. 2017. Use of microsatellites for evaluation of genetic diversity in cherry tomato. Bragantia 76: 220-228. https://doi.org/10.1590/1678-4499.116
  » https://doi.org/10.1590/1678-4499.116
• Allier A, Teyssèdre S, Lehermeier C, Moreau L, Charcosset A. 2020. Optimized breeding strategies to harness genetic resources with different performance levels. BMC Genomics 21: 349. https://doi.org/10.1186/s12864-020-6756-0
  » https://doi.org/10.1186/s12864-020-6756-0
• Cavers S, Degen B, Lemes MR, Margis R, Salgueiro F, Lowe AJ. 2005. Optimal sampling strategy for estimation of spatial genetic structure in tree populations. Heredity 95: 281-289. https://doi.org/10.1038/sj.hdy.6800709
  » https://doi.org/10.1038/sj.hdy.6800709
• Costa C, Andrade AR. 2009. Pluviometric precipitation dynamics in the city of Guarapuava-PR-Brazil: local and regional conditionings. Revista Brasileira de Climatologia 21: 205-224 (in Portuguese, with abstract in English). https://doi.org/10.5380/abclima.v21i0.51625
  » https://doi.org/10.5380/abclima.v21i0.51625
• Eisele TG, Constantino LV, Giacomin RM, Zeffa DM, Suzuki CHJ, Gonçalves LSA. 2022. Genotyping and phenotyping of grape tomato hybrids aiming at possible genitors for breeding program. Horticultura Brasileira 40: 352-359. https://doi.org/10.1590/s0102-0536-20220401
  » https://doi.org/10.1590/s0102-0536-20220401
• Finzi RR, Marquez GR, Maciel GM, Momesso MP, Pereira LM, Silveira AJ. 2019. Soluble solids due to the truss position in mini tomato hybrids from dwarf lines. Revista Agrarian 12: 33-39. https://doi.org/10.30612/agrarian.v12i43.8948
  » https://doi.org/10.30612/agrarian.v12i43.8948
• Gramazio P, Pereira-Dias L, Vilanova S, Prohens J, Soler S, Esteras J, et al. 2020. Morphoagronomic characterization and whole-genome resequencing of eight highly diverse wild and weedy S. pimpinellifolium and S. lycopersicum var. cerasiforme accessions used for the first interspecific tomato MAGIC population. Horticulture Research 7: 174. https://doi.org/10.1038/s41438-020-00395-w
  » https://doi.org/10.1038/s41438-020-00395-w
• Holcomb J, Tolbert DM, Jain SK. 1977. A diversity analysis of genetic resources in rice. Euphytica 26: 441-450. https://doi.org/10.1007/BF00027006
  » https://doi.org/10.1007/BF00027006
• Kalogeropoulos N, Chiou A, Pyrichou V, Peristeraki A, Karathanos VT. 2012. Bioactive phytochemicals in industrial tomatoes and their processing by products. LWT- Food Science and Technology 49: 213-216. https://doi.org/10.1016/j.lwt.2011.12.036
  » https://doi.org/10.1016/j.lwt.2011.12.036
• Kaushik P, Dhaliwal MS. 2018. Diallel analysis for morphological and biochemical traits in tomato cultivated under the influence of tomato leaf curl virus. Agronomy 8: 153. https://doi.org/10.3390/agronomy8080153
  » https://doi.org/10.3390/agronomy8080153
• Kimura S, Sinha N. 2008. Tomato ( Solanum lycopersicum ): A Model Fruit-Beating Crop. Cold Spring Harbor Laboratory, Cold Spring Harbor, Huntington, NY, USA.
• Lins Junior JC. 2019. Integrated pest management in tomato crops: a strategy for reducing the use of pesticides. Revista Extensão em Foco 7: 6-22 (in Portuguese, with abstract in English).
• Maciel GM, Finzi RR, Carvalho FJ, Marquez GR, Clemente AA. 2018. Agronomic performance and genetic dissimilarity among cherry tomato genotypes. Horticultura Brasileira 36: 167-172. https://doi.org/10.1590/S0102-053620180203
  » https://doi.org/10.1590/S0102-053620180203
• Mahapatra AS, Singh AK, Vani VM, Mishra R, Kumar H, Rajkumar BV. 2013. Inter-relationship for various components and path coefficient analysis in tomato ( Lycopersicon esculentum Mill .). International Journal of Current Microbiology and Applied Sciences 2: 147-152.
• Marodin JC, Resende JTV, Morales RGF, Faria MV, Trevisan AR, Figueiredo AST, et al. 2016. Tomato post-harvest durability and physicochemical quality depending on silicon sources and doses. Horticultura Brasileira 34: 361-366. https://doi.org/10.1590/S0102-05362016003009
  » https://doi.org/10.1590/S0102-05362016003009
• Mather K, Jinks JL. 1982. Biometrical Genetics, the Study of Continuous Variation. 3ed. Chapman and Hall, London, UK.
• Mondini L, Noorani A, Pagnotta MA. 2009. Assessing plant genetic diversity by molecular tools. Diversity 1: 19-35. https://doi.org/10.3390/d1010019
  » https://doi.org/10.3390/d1010019
• Nakazato T, Bogonovich M, Moyle LC. 2008. Environmental factors predict adaptive phenotypic differentiation within and between two wild Andean tomatoes. Evolution 62: 774-792. https://doi.org/10.1111/j.1558-5646.2008.00332.x
  » https://doi.org/10.1111/j.1558-5646.2008.00332.x
• Nassau BRRM, Mascarenhas PSC, Guimarães AG, Feitosa FM, Ferreira HM, Castro BMC, et al. 2023. Inheritance of seedlessness and the molecular characterization of the INO gene in Annonaceae. Brazilian Journal of Biology 83: 5. https://doi.org/10.1590/1519-6984.246455
  » https://doi.org/10.1590/1519-6984.246455
• Nuez F, Prohens J, Blanca JM. 2004. Relationships, origin, and diversity of Galápagos tomatoes: implications for the conservation of natural populations. American Journal of Botany 91: 86-99. https://doi.org/10.3732/ajb.91.1.86
  » https://doi.org/10.3732/ajb.91.1.86
• Pandiarana N, Chattopadhyay A, Seth T, Shende VD, Dutta S, Hazra P. 2015. Heterobeltiosis, potence ratio and genetic control of processing quality and disease severity traits in tomato. New Zealand Journal of Crop and Horticultural Science 43: 282-293. https://doi.org/10.1080/01140671.2015.1083039
  » https://doi.org/10.1080/01140671.2015.1083039
• Rakha MK, Sabry SA. 2019. Heterosis, nature of gene action for yield and its components in tomato ( Lycopersicon esculentum Mill.). Middle East Journal of Agriculture Research 8: 1040-1053. https://doi.org/10.36632/mejar/2019.8.4.7
  » https://doi.org/10.36632/mejar/2019.8.4.7
• Sharma K, Mishra AK, Misra RS. 2008. A simple and efficient method for extraction of genomic DNA from tropical tuber crops. African Journal of Biotechnology 7: 1018-1022.
• Singh D. 1981. The relative importance of characters affecting genetic divergence. The Indian Journal of Genetics & Plant Breeding 41: 237-245.
• Thomson FJ, Moles AT, Auld TD, Kingsford RT. 2011. Seed dispersal distance is more strongly correlated with plant height than with seed mass. Journal of Ecology 99: 1299-1307. https://doi.org/10.1111/j.1365-2745.2011.01867.x
  » https://doi.org/10.1111/j.1365-2745.2011.01867.x
• Vekariya TA, Kulkarni GU, Vekaria DM, Dedaniya AP, Memon JT. 2019. Combining ability analysis for yield and its components in tomato ( Solanum lycopersicum L.). Acta Scientific Agriculture 3: 185-191. https://doi.org/10.31080/ASAG.2019.03.0541
  » https://doi.org/10.31080/ASAG.2019.03.0541
• Zeist AR, Resende JTV, Silva PR, Maluf WR, Silva Junior AD, Lima Filho RB, et al. 2023. Self-pollination, intra- and interspecific crosses in tomatoes. Scientia Agricola 80: e20220016. https://doi.org/10.1590/1678-992X-2022-0016
  » https://doi.org/10.1590/1678-992X-2022-0016
• Zhou R, Wu Z, Cao X, Jiang FL. 2015. Genetic diversity of cultivated and wild tomatoes revealed by morphological traits and SSR markers. Genetics and Molecular Research 14: 13868-13879. http://dx.doi.org/10.4238/2015.October.29.7
  » http://dx.doi.org/10.4238/2015.October.29.7

• Data availability statement:
  I want to confirm that this manuscript is an original work that has not been previously published and is not under consideration for publication elsewhere, in either print or electronic format, in its final form. The contents underlying the research text are included in the manuscript.
• Declaration of use of AI Technologies:
  IA technology was not used.

Edited by

• Edited by:
  Leonardo Oliveira Medici

Data availability

I want to confirm that this manuscript is an original work that has not been previously published and is not under consideration for publication elsewhere, in either print or electronic format, in its final form. The contents underlying the research text are included in the manuscript.

Publication Dates

• Publication in this collection
  27 June 2025
• Date of issue
  2025

History

• Received
  08 Oct 2025
• Accepted
  08 Jan 2025