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Article

QTL Mapping of Tomato Fruit Weight-Related Traits Using Solanum pimpinellifolium Introgression Lines

by
Yuanhao Zhang
1,†,
Fei Ding
1,†,
Huiling Qui
1,
Yingjie Tian
1,
Fangling Jiang
1,
Rong Zhou
1,2,* and
Zhen Wu
1,*
1
College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
2
Department of Food Science, Aarhus University, Agro Food Park 48, 8200 Aarhus, Denmark
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(8), 1914; https://doi.org/10.3390/agronomy15081914
Submission received: 22 June 2025 / Revised: 6 August 2025 / Accepted: 6 August 2025 / Published: 8 August 2025
(This article belongs to the Special Issue Genetics and Breeding of Field Crops in the 21st Century)
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Abstract

As the primary harvested organ, fruit size and weight hold significant economic importance during tomato production. Therefore, elucidating the genetic mechanisms underlying fruit size and weight is of considerable agronomic value. In this study, the Solanum pimpinellifolium introgression lines were constructed with “LA2093” as the donor and “Jina” as the recipient, and a genetic linkage map was constructed. Preliminary QTL mapping was conducted using four fruit-related traits: single fruit weight, fruit diameter, fruit length, and fruit shape index. A total of 10 QTLs were identified, including one for single fruit weight (qFw-3), five for fruit diameter (qFtd-3-1, qFtd-3-2, qFtd-4, qFtd-7, and qFtd-12), two for fruit length (qFl-3 and qFl-11), and two for fruit shape index (qFsi-2 and qFsi-3). To explore the key regulatory genes of the single fruit weight QTL qFw-3 locus, it was further finely mapped between SSR3-14 and C03M65101. The SSR3-14 and C03M65101 interval contained 57 genes on chromosome 3 (64.68–65.10 Mb) in the reference genome. Among these, eight genes, including Solyc03g114830, Solyc03g114870, Solyc03g114880, Solyc03g114890, Solyc03g114900, Solyc03g114910, Solyc03g115200, and Solyc03g115380, were identified as candidate genes involved in regulating fruit weight. These studies provide a basis for future functional validation of key regulatory genes and offer valuable genetic resources for the improvement of fruit size and weight during tomato breeding.

1. Introduction

Tomato (Solanum lycopersicum L.) is one of the most economically important vegetable crops globally cultivated. Due to its high nutritional value and distinctive flavor, it is widely grown and consumed worldwide [1,2]. According to the statistics of the Food and Agriculture Organization of the United Nations (FAO), tomato production was about 190 million tons in 2023 [3]. Fruit size and weight are key economic traits which directly affect yield, marketability, mechanical harvest adaptability, and consumer demand [4]. As a result, fruit size and weight have become primary target traits during tomato breeding. However, the regulatory mechanisms underlying tomato fruit size and weight, which are determined by cell division and cell expansion, remain incompletely understood [5]. A thorough understanding of the genetic mechanisms underlying fruit size and weight is essential for precise manipulation of this trait, which will provide a theoretical foundation for variety improvement and enhancing production efficiency in tomato breeding programs [5]. Before the tomato fruit reaches maturity, it will undergo a series of cell divisions and amplification, which determines the size and weight of the fruit, and is affected by cell size, cell structure, meristem, plant hormones, number of ventricles of the fruit, and genes of cell wall structure [6]. Elucidating the genetic mechanisms that govern tomato fruit size and weight and achieving precise regulation of this trait will not only advance the theoretical foundations of varietal breeding but also provide a scientific basis for enhancing the efficiency of tomato production.
QTL mapping is a genetic approach that identifies the chromosomal positions of molecular markers linked to quantitative traits and analyzes their genetic effects. Currently, the key QTL loci have been localized in tomato responding to abiotic and biotic stress as well as development, such as salt tolerance (qST1, qST3, qST5, qST7, qST12, etc.), drought tolerance (qDT1, qDT4, qDT8, qDT9, qDT10, qDT12, etc.), disease resistance (qPh3, qBwr2, qBwr6, qBwr9, qBw11, etc.), earliness (qFT1.1, qFT3.2, qDTF5.1, etc.), and leaf morphology traits (qLM2, qPL3, qPL5, etc.) [7,8,9,10]. Substantial progress has also been made in QTL mapping for tomato fruit size and weight. To date, 28 QTLs associated with single fruit size have been identified, of which seven QTLs (fw1.1, fw2.2, fw2.3, fw3.1, fw3.2, fw4.1, and fw9.1) exhibit effect values exceeding 20%. Moreover, five QTLs (fw1.1, fw2.2, fw3.1, fw3.2, and fw11.3) have been consistently detected across different populations [11,12]. The FW2.2 gene was reported to be a significant QTL that regulates tomato fruit size, which negatively regulates cell division during fruit development, and it may modulate fruit size through callose deposition at plasmodesmata [13,14,15]. The fs8.1 locus is a major QTL controlling tomato fruit weight, and fs8.1 mutations can promote cell proliferation in the ovary wall, leading to altered fruit morphology and a distinctive oblong phenotype [16]. Fruit weight (fw11.3) and fasciated (fas) have been mapped to the same region on chromosome 11; fw11.3 and fas are not allelic and instead represent separate genes [17]. The fasciated (fas) and locule number (lc) loci functioned as major QTLs that regulated tomato fruit size by controlling locule number [18].
As a complex quantitative trait, fruit size and weight still present challenges in terms of genetic localization and gene discovery. On one hand, most existing studies have primarily focused on major-effect QTLs, while minor-effect QTLs and QTL-by-environment interactions have received insufficient attention. On the other hand, many QTL intervals remain relatively large, which often encompass a wide range of candidate genes and make subsequent fine mapping and functional validation more difficult. Introgression line populations play a vital role in preliminary QTL mapping, fine mapping, gene discovery, and trait improvement. However, the current understanding of QTLs and key genes regulating tomato fruit size and weight remains incomplete and requires further elucidation. In this study, we constructed a BC3F4 introgression line population with the wild tomato S. pimpinellifolium “LA2093” (donor) and the cultivated tomato “Jina” (receptor). QTL mapping was mainly carried out based on single fruit weight, fruit diameter, fruit length, and fruit shape index, and ten (numbers from 1 to 10 in letters) QTL loci were preliminarily mapped. Furthermore, fine-mapping of the single fruit weight QTL qFw-3 delimited a candidate region between markers SSR3-14 and C03M65101, within which eight putative candidate genes were identified. These studies provide valuable resources for the functional verification of key regulators of fruit. In particular, the study of candidate genes in the qFw-3 mapping interval promotes the genetic improvement and gene mapping of tomato fruit weight.

2. Materials and Methods

2.1. Plant Materials and Cultivation

The S. pimpinellifolium introgression line population was created using “LA2093” as the donor and “Jina” as the receptor, comprising 310 lines. The qFw-3 subpopulation was derived from selfed progeny of five heterozygous individuals within the qFw-3 region identified in the BC3F4 population, comprising 41 lines. “LA2093”, as a representative wild tomato, produces small and round mature fruits (2~3 g). In contrast, “Jina” is an elite cultivated variety characterized by elliptical mature fruits (approximately 15 g). Both populations were cultivated during the 2023 and 2024 growing seasons at the Baima Experimental Base of Nanjing Agricultural University. Plants were established using a wide-narrow row paired planting pattern. The spacing between rows was 1.1 m and 0.6 m for wide and narrow rows, with 0.5 m between plants. Standard field management was applied throughout the experiment, including single-stem pruning and periodic adjustment of plant architecture.

2.2. Phenotypic Evaluation of Fruit Size and Weight-Related Traits

The fruit characteristics of the third inflorescence of tomato plants during the red ripening stage were statistically analyzed, including single fruit weight (g), fruit diameter (cm), fruit length (cm), and fruit shape index (fruit length ratio fruit diameter). The fruit weight, fruit length and fruit transverse diameter were investigated by an analytical balance and vernier caliper. At least three biological replicates were conducted for each phenotype.

2.3. DNA Extraction

Tomato leaf DNA was extracted using the rapid plant DNA extraction kit according to the instruction manual (Shanghai Puti Biotechnology Co., Ltd., Shanghai, China). Approximately 100 mg fresh leaf tissue was thoroughly ground in liquid nitrogen. Then, 400 µL buffer FA was added, vortexed for 1 min, and incubated at room temperature for 10 min. Afterwards, 130 µL of buffer FB was added, mixed, and vortexed for 1 min. The mixture was centrifuged at 14,000 rpm for 3 min, and the supernatant was transferred to a new 1.5 mL centrifuge tube and centrifuged again for 5 min. The supernatant was then transferred to a fresh tube. An equal volume (0.7×) of isopropanol was added and mixed. The solution was centrifuged at 13,000 rpm for 2 min. The supernatant was discarded, and the pellet was retained. The pellet was washed twice with 600 µL of 70% ethanol. After each wash, it was vortexed for 5 s and centrifuged at 13,000 rpm for 2 min. The supernatant was discarded. The pellet was air-dried with the tube cap open for 30 min at room temperature. Then, a sterile elution buffer was added to dissolve the DNA, followed by incubation in a 65 °C water bath for 30 min. DNA concentration and purity were measured using 1.0 µL of DNA solution. The DNA was diluted to 10~20 ng/µL for downstream analyses.

2.4. Genotypic Analysis of S. pimpinellifolium Introgression Lines

The S. pimpinellifolium introgression line population genetic map construction and genotype analysis, including 310 lines. Based on our previous screening experiments, 110 Indel molecular markers and 13 SSR polymorphic molecular markers were used for genotype analysis (Table S1) [19,20,21]. A genetic linkage map was constructed using QTL IciMapping 4.2 (http://www.isbreeding.net, accessed on 20 April 2024) under the threshold of LOD ≥ 3.0, and the significance level was set at 0.05 [22,23].
Polymerase chain reaction (PCR) amplification was conducted under the following thermal cycling conditions: initial denaturation at 95 °C for 5 min; 35 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 45 s; and a final extension at 72 °C for 10 min. The 10 µL PCR reaction system contained 0.4 µL forward and reverse primer (10μM), 5 µL Taq Master Mix (0.05 units/µL bioGold taq plus DNA polymerase, 4 mM MgCl2, and 0.4 mM dNTPs) (Nanjing SinoMol Biotechnology Co. Ltd., Nanjing, China), 1 µL genomic DNA template, and 3.2 µL ddH2O. PCR products were separated using 8% polyacrylamide gel electrophoresis (PAGE) (Shanghai Macklin Biochemical Co., Ltd., Shanghai, China). The PAGE solution was prepared using the following components: 49 mL H2O, 7 mL 10× TBE, 14 mL 40% PAGE, 70 µL tetramethylethylenediamine (TEMED) (Shanghai Macklin Biochemical Co., Ltd., Shanghai, China), and 500 µL 10% ammonium persulfate (APS) (Shanghai Macklin Biochemical Co., Ltd., Shanghai, China). After thorough mixing, the gel was poured into the casting tray and the comb inserted. Once solidified, the gel was placed into the electrophoresis chamber filled with 1× TBE buffer. Afterwards, 2.5 µL PCR product was loaded into each well, and electrophoresis was performed at 200 V for 70 min. According to the results of polyacrylamide gel electrophoresis, when the size of the bands was the same as recipient “Jina” and donor “LA2093”, it was recorded as “a” and “b”, respectively, with the heterozygote being recorded as “h”.

2.5. Preliminary QTL Mapping of Fruit Size and Weight-Related Traits

The fruit weight, fruit length, fruit transverse diameter, and fruit shape index of 220 lines from the BC3F4 population were statistically analyzed (Table S2). Microsoft® Excel® 2021and SPSS 22 were used to analyze the data of maximum value, minimum value, average value, skewness and kurtosis (Table S2). The normal distribution test was performed by Shapiro–Wilk [24]. QTL mapping was performed on the genotype data, statistical data on fruit size and weight-related traits, and the genetic linkage map of the 310 lines BC3F4 population by QTL Ici-Mapping 4.2 software. QTL mapping was performed by Inclusive Composite Interval Mapping (ICIM) in Ici-Mapping 4.2. The key parameters are as follows: PIN = 0.001, step size = 1 cm (Li Morgan), LOD ≥ 3 was selected as the threshold to determine QTL existence, and the significance level was set at 0.05 [22,23].

2.6. Fine Mapping of Single Fruit Weight qFw-3 Interval

The fruit weight of the qFw-3 subpopulation of 41 lines (Table 4) was statistically investigated and tested by Shapiro–Wilk, which conformed to the normal distribution and was used for fine mapping. Based on sequence information from the qFw-3 interval in the tomato reference genome SL2.4, SSR Hunter 1.3 software (http://en.bio-soft.net/, accessed on 18 June 2024) was applied to search for SSR loci within the preliminary mapping region. Parameters were set to detect dinucleotide repeats with a minimum of five repetitions. Primers for SSR markers were designed using Primer 5 software [25]. Polymorphisms were assessed using 8% PAGE, and 41 subpopulations were genotyped using 14 polymorphic SSR markers (Table S3).
Matplotlib 3.7.1 is used for data visualization, but it does not have a special statistical test function. We use scipy.stats 1.11.1 and statsmodels 0.14.0 software packages of Python 3.12 for correlation analysis. In association analysis, one-way analysis of variance (ANOVA) was used to test the differences between molecular markers and fruit weight traits. To control the first type of error caused by multiple comparisons, Benjamini–Hochberg FDR was used to correct the response. The criterion of significance was p-value < 0.05. The region containing the most significantly associated markers was further narrowed and defined as the target QTL interval. Gene functional annotation within this refined interval was examined by referring to the plant genome database (https://phytozome-next.jgi.doe.gov/, accessed on 5 September 2024), and candidate genes were predicted based on annotation and the literature evidence (Table S4).

2.7. Statistical Analysis

A statistical significance test was performed using the one-way analysis of variance (ANOVA) and Student’s t-test, with a significant difference in p-value < 0.05.

3. Results

3.1. Construction and Analysis of the Genetic Linkage Map for the S. pimpinellifolium Introgression Lines

Based on the distribution of 123 polymorphic molecular markers (110 InDel markers and 13 SSR markers) (Table S1), a genetic linkage map was constructed using QTL IciMapping 4.2 under the threshold of LOD ≥ 3.0 [23]. The resulting map spanned a total genetic distance of 534.22 cM, covering all 12 chromosomes of tomato, with an average inter-marker distance of 4.34 cM (Figure 1 and Table 1).
The number of markers and total genetic distances varied across different chromosomes (Table 1). Chromosomes 1, 3, 6, 9, and 12 harbored more markers, whereas chromosomes 5 and 7 had fewer. Chromosomes 1, 3, and 9 exhibited longer genetic distances, while chromosomes 2, 5, 7, and 8 had shorter spans. Specifically, chromosome 1 contained the highest number of markers (12) with the longest genetic length (80.45 cM). In contrast, chromosome 7 had the fewest markers (6), and chromosome 5 showed the shortest total genetic length (13.97 cM) (Table 1).

3.2. QTL Mapping of Tomato Fruit Size and Weight-Related Traits

3.2.1. Phenotypic Analysis of Four Fruit-Related Traits in the S. pimpinellifolium Introgression Lines

Significant differences were observed between the receptor “Jina” and the donor S. pimpinellifolium “LA2093” for all four fruit-related traits, including single fruit weight, fruit length, fruit diameter, and fruit shape index (Table 2). In the BC3F4 population, the coefficients of variation (CV) for the four traits ranged from 0.09 to 0.25, indicating moderate phenotypic variability within the population. Among them, fruit diameter exhibited the lowest CV (0.13), indicating relatively low variation, whereas single fruit weight had the highest CV (0.70).
Kurtosis values that indicated the peak of data distributions were all less than two in absolute value, with a maximum of 0.88. This suggested that trait distributions were relatively smooth and conformed to normality. Skewness values, which reflected distribution symmetry, ranged from −0.5 to 0.5 for all four traits, indicating approximately symmetrical distributions with minimal skewness. In addition, the fruit weight and fruit shape index detected by Shapiro–Wilk conformed to the normal distribution, and the fruit length and fruit transverse diameter conformed to the skewed normal distribution. These results confirm that the phenotypic data for all traits followed a normal distribution, consistent with the characteristics of quantitative traits and thus suitable for QTL mapping (Figure 2).

3.2.2. Preliminary QTL Mapping of Four Fruit Size and Weight-Related Traits in the S. pimpinellifolium Introgression Lines

A total of 10 QTLs distributed across chromosomes 2, 3, 4, 7, 11, and 12 associated with four fruit-related traits were identified in the BC3F4 population, comprising 310 lines (Figure 3). These included one QTL for single fruit weight, two QTLs for fruit length, five QTLs for fruit diameter, and two QTLs for fruit shape index (Figure 3).
For single fruit weight, one QTL (qFw-3) was detected on chromosome 3 with a LOD score of 4.40 (Table 3). The additive effect of this QTL was positive, indicating that the favorable allele was contributed by the receptor. This QTL explained over 15% of the phenotypic variance, which was thus classified as a major-effect QTL. For fruit diameter, five QTLs located on chromosomes 3, 4, 7, and 12 were identified, including qFtd-3-1, qFtd-3-2, qFtd-4, qFtd-7, and qFtd-12. Their LOD scores were 3.92, 3.77, 4.25, 3.28, and 3.56, respectively (Table 3). Among them, only qFtd-3-1 exhibited a negative additive effect, suggesting that the beneficial allele originated from the donor. The other four QTLs showed positive additive effects, indicating that the favorable alleles were inherited from the receptor. Notably, qFtd-7 exhibited the highest phenotypic contribution among them, explaining 4.48% of the variance (Table 3). For fruit length, two QTLs (qFl-3 and qFl-11) located on chromosomes 3 and 11 with LOD scores of 7.20 and 3.78 were identified. The additive effect of qFl-3 was positive, indicating that the allele from the receptor reduced the trait value. In contrast, qFl-11 showed a negative additive effect, indicating that the favorable allele originated from the donor. These QTLs explained 13.24% and 4.56% of the phenotypic variation, respectively (Table 3). For the fruit shape index, two QTLs were identified: qFsi-2 and qFsi-3, located on chromosomes 2 and 3, respectively. Their LOD scores were 4.97 and 6.07. Both QTLs showed positive additive effects, indicating that favorable alleles were derived from the receptor. Among these, qFsi-3 explained 15.45% of the phenotypic variation and was therefore considered a major-effect QTL. In comparison, qFsi-2 explained 9.40% of the variation (Table 3).

3.3. Phenotypic and Genotypic Analysis and Fine Mapping of the qFw-3 Locus for Single Fruit Weight

3.3.1. Screening of Polymorphic SSR Markers Within the qFw-3 Interval

Based on the mapping results with the tomato reference genome 2.4 as a template, 52 SSR markers were designed within the genomic interval flanked by markers SSR111 and C03M65101. After testing with PAGE using DNA from both parental lines, 14 SSR polymorphic markers were identified, which were sequentially named SSR3-1 to SSR3-14 (Table S3).

3.3.2. Phenotypic and Genotypic Analysis of Plants in the qFw-3 Subpopulation

Based on the preliminary mapping of the qFw-3 region, five heterozygous lines were selected from the BC3F4 population: 14-6-1, 14-7-6, 6-1-5, 6-8-2, and 5-4-6. The genotypes at the flanking markers of these lines were bh, hb, ah, ab, and bh, respectively, which were applied to generate a subpopulation for fine mapping of qFw-3.
In the subpopulation, both genotypic and phenotypic segregation were observed among the progeny of each line. For single fruit weight, the progeny of lines 5-4-6, 14-6-1, and 14-7-6 showed clear trait segregation. For example, in the 5-4-6 line, individual 5-4-6-8 had a maximum fruit weight of 23.42 g, while 5-4-6-2 had a fruit weight of only 11.71 g. Similarly, progenies 14-6-1-8 and 14-6-1-5 had a fruit weight of around 18 g, whereas the parent line 14-6-1 had a fruit weight of only 6.32 g. The results showed a significant phenotypic variation (Table 4).
Table 4. The single fruit weight statistics of the qFw-3 subpopulation.
Table 4. The single fruit weight statistics of the qFw-3 subpopulation.
NumberAverage Weight/(g)NumberAverage Weight/(g)NumberAverage Weight/(g)NumberAverage Weight/(g)NumberAverage Weight/(g)
5-4-6-823.42 a6-1-5-318.79 a6-8-1-812.36 a14-6-1-515.60 a14-7-6-814.96 a
5-4-6-717.94 b6-1-5-118.32 a6-8-1-511.59 ab14-6-1-818.00 ab14-7-6-1012.36 ab
5-4-6-517.48 b6-1-5-715.18 b6-8-1-211.33 ab14-6-1-216.11 bc14-7-6-312.23 ab
5-4-6-117.00 b6-1-5-812.09 bc6-8-1-710.87 ab14-6-1-414.69 cd14-7-6-910.59 b
5-4-6-415.12 c6-1-5-513.89 bc6-8-1-410.20 ab14-6-1-713.23 d14-7-6-510.52 b
5-4-6-613.97 cd6-1-5-412.49 cd6-8-1-69.59 ab14-6-1-69.21 e14-7-6-210.28 b
5-4-6-312.95 de6-1-5-612.09 cd6-8-1-39.52 ab14-6-1-36.32 f14-7-6-410.22 b
5-4-6-211.71 e6-1-5-211.28 d6-8-1-19.28 b//14-7-6-79.50 b
////////14-7-6-69.29 b
////////14-7-6-19.25 b
Note: Statistical significance test was performed using a one-way analysis of variance (ANOVA). Different lowercase letters indicated differences at the p < 0.05 level.
In addition, the genotypes of 41 individuals from the subpopulation were analyzed using the 14 SSR markers (SSR3-1 to SSR3-14) developed within the qFw-3 interval, along with the flanking marker C03M65101. The results showed that introgressed regions were present in all five lines, although the length of the introgressed regions varied. Furthermore, recombination events were detected in four of the five lines (excluding 6-8-2), indicating that segment segregation had occurred (Figure 4).

3.3.3. Fine Mapping of qFw-3 and Prediction of Candidate Genes for Single Fruit Weight in Tomato

To further delimit the QTL interval associated with single fruit weight, a single marker association analysis was conducted to evaluate the statistical correlation between each molecular marker and the fruit weight phenotype. The results showed that the flanking marker C03M65101, along with SSR3-14, SSR3-2, and SSR3-11, exhibited significant associations with single fruit weight at a p < 0.05 level (Figure 5). Among these, markers C03M65101 and SSR3-14 showed the strongest correlation with the trait. Therefore, the refined qFw-3 QTL interval was narrowed to the region between SSR3-14 and C03M65101, with a physical location of 64.68 Mb–65.10 Mb.
Based on gene annotation information from the tomato reference genome SL2.4 and the relevant published literature, 8 out of the 57 genes within the fine-mapped interval were identified as potential candidate genes involved in regulating single fruit weight (Figure 6). Among these, Solyc03g114830 (SlFUL2) belongs to the MADS box family, which plays a key role during the fruit expansion stage. The genes Solyc03g114870, Solyc03g114880, Solyc03g114890, Solyc03g114900, and Solyc03g114910 all encode members of the COBRA protein family. Solyc03g115380 encodes a UDP-glucose 6-dehydrogenase, while Solyc03g115200 encodes a β-glucosidase (Table S4).

4. Discussion

Preliminary QTL mapping typically relies on populations of adequate size and a genome-wide molecular marker map. Compared with conventional populations such as F2, introgression line populations have the advantage of identifying minor-effect QTLs, which can be utilized over multiple generations. For example, Chaïb et al. successfully mapped QTLs associated with fruit sensory traits (e.g., sweetness and acidity) using tomato introgression lines, which were often difficult to detect using traditional segregating populations [26]. Similarly, Yang et al. identified multiple QTLs associated with fruit firmness using introgression lines, where the QTL Crf12a and Crf-R-7b were the strongest and weakest QTL, respectively [27]. In this study, a total of 178 polymorphic molecular markers were initially identified among the parental lines. However, due to the fact that the gradual infiltration population underwent three rounds of backcrossing and four generations of self-crossing, certain chromosomal segments became homozygous and did not segregate in the progeny population. As a result, these markers exhibited a genetic distance of zero. To avoid redundancy, only one representative marker was selected from each group of markers with identical genetic distances for the construction and localization of the genetic linkage map. Ultimately, 123 polymorphic molecular markers were selected, which collectively covered the entire tomato genome. We performed QTL mapping for tomato fruit size and weight traits (single fruit weight, fruit diameter, fruit length, and fruit shape index) using S. pimpinellifolium introgression lines. Initial analysis identified 10 QTLs associated with fruit morphology, where five, two, two, and one were for fruit diameter, fruit length, fruit shape index, and single fruit weight, respectively. The QTLs explained 4.03–15.74% of phenotypic variation with LOD scores ranging from 3.28 to 7.20. However, the initial QTL intervals remained relatively broad, containing numerous genes, suggesting the necessity for higher resolution mapping with additional markers to refine these regions.
Preliminary QTL mapping can further expand the shortened interval of the F2 population using traditional methods, which can facilitate key regulatory genes screening, especially when it is combined with transcriptome sequencing, genome-wide association studies (GWAS) and genome functional annotation. For example, according to the fine mapping QTL-fl3.1 that regulates eggplant length, the candidate gene Smechr0302217 (SmeFL) was identified by RNA-Seq and qPCR [28]. Qin et al. reduced the interval of the QTL qKnps-4A (from 6.8 Mb to 2.19 Mb) controlling kernel number per spike in wheat, and subsequently identified two candidate genes (TraesKN4A01HG38570 and TraesKN4A01HG38590) through transcriptome analysis and genome annotation [29]. Similarly, Yang et al. fine-mapped the major effect QTL qHYF_B06 responsible for peanut yield to an 890 kb region and identified two candidate genes (Arahy.129FS0 and Arahy.3R9A5K) using a similar integrative strategy [30]. In this study, the refined qFw-3 interval (approximately 400 kb), which is associated with tomato fruit weight, contained 57 annotated genes, where eight genes were identified as potential candidates regulating single fruit weight.
Among them, Solyc03g114830 (SlFUL2) was reported to regulate fruit development. Overexpression of SlFUL2 regulates auxin transport to promote fruit tip formation, and inhibits cell division to cause peel thinning [31,32]. Interestingly, our results showed that the single fruit repositioning interval was SSR3-14-C03M65101, which was colocated with BER3.2 and Fw3.2/SLKIUH as reported in previous studies [33]. fw3.2 is a fruit mass QTL located on chromosome 3 of tomato, which encodes a P450 protein SlKLUH of the CYP78A subfamily and makes fruit larger by increasing the number of cells in peel and septal tissue [34]. Zhou et al. found that SmCYP78A5 and SmCYP78A10 were highly expressed in the ovary, which is involved in the development of eggplant fruit [35]. NnCYP90B1 was highly overexpressed in lotus rhizome and involved in rhizome development [36]. In addition, ectopic overexpression of Solanum tuberosum resulted in early tuberization and increased tuber yield [36]. FW3.2/SlKLUH is an ortholog of cytochrome P450 KLUH in Arabidopsis thaliana, which is involved in regulating fruit size development [37].
Solyc03g114870, Solyc03g114880, Solyc03g114890, Solyc03g114900, and Solyc03g114910, encoding COBRA-like proteins and being involved in secondary cell wall biosynthesis and fruit development, were identified as key candidate regulators of fruit size [33]. The sjs1597 locus was also positioned near markers associated with fruit shape QTLs, where we identified two candidate genes encoding a kinesin and a COBRA-like protein (Solyc03g114380 and Solyc03g114910) [38]. During fruit expansion, increased cellular water uptake elevated turgor pressure, which subsequently induced cell wall loosening and remodeling. This process facilitated rapid cell enlargement, thereby driving fruit growth. The study identified Solyc03g114890 and Solyc03g114900 as key regulators of cell wall dynamics, both of which were significantly upregulated under water stress [39]. Solyc03g114870, Solyc03g114880, Solyc03g114890, Solyc03g114900, and Solyc03g114910 were associated with cell wall expansion, a key regulatory process in fruit enlargement and development. Solyc03g115380 encodes a glucose 6-dehydrogenase, which was found to be specifically expressed in the pericarp of fruits and was mainly involved in the synthesis of pectin in the cell walls [40]. Solyc03g115200 encodes an endo-1,3-beta-glucosidase that catalyzes the hydrolysis of β-1,3-glucan bonds, which participate in plant cell wall biosynthesis, remodeling, and defense responses. During plant growth and organ maturation, this gene product regulates cell wall plasticity through hydrolysis of cellulose and hemicellulose, thereby facilitating cell elongation and tissue expansion. Furthermore, as a glucosidase, it activated glycoside-conjugated enzymes or signaling molecules, releasing bioactive components that contributed to cell wall restructure and stress signaling pathways [41,42]. In summary, this study found eight critical genes as the key genes regulating fruit weight that have not been explicitly studied, which laid the foundation for mining critical genes regulating fruit weight and accelerating molecular breeding and variety improvement. However, their biological functions and molecular regulatory mechanisms need to be further deciphered.

5. Conclusions

In this study, a BC3F4 introgression line population with “LA2093” and “Jina” as parents was constructed. It covered 94.41% of the genome of the currant tomato, which provided excellent materials for the application of subsequent populations. Using the constructed BC3F4 generation introgression line population, QTL mapping of fruit size and weight was performed, including 5 fruit transverse diameter sites, 2 fruit longitudinal sites, 2 fruit shape index sites, and 1 single fruit weight site, which laid a foundation for the subsequent fine mapping of related sites. Eight candidate genes were mined by fine mapping of the single fruit weight QTL qFw-3, which laid a foundation for subsequent functional verification of related genes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15081914/s1, Table S1: Primer list of 123 polymorphic molecular markers; Table S2: Statistics of fruit weight, fruit length, fruit transverse diameter, and fruit shape index; Table S3: Primer sequences of 14 SSR molecular markers within the qFw-3 interval; and Table S4: List and functional description of 8 candidate genes in the qFw-3 interval.

Author Contributions

Conceptualization, Z.W. and R.Z.; laboratory work, Y.Z., F.D., Y.T., and H.Q.; data collection, Y.Z., F.D., Y.T., and H.Q.; writing—original draft preparation, Y.Z., F.D., and R.Z.; writing—review and editing, Z.W., F.J., and R.Z. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge the financial support from Jiangsu Seed Industry Revitalization Project [JBGS(2021)015], Fundamental Research Funds for the Central Universities (KJYQ2025027), and the National Natural Science Foundation of China (Grant No. U190310207).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data were contained within the article.

Acknowledgments

We acknowledged the support from Yankai Li during the experiment and the high-performance computing platform of the Bioinformatics Center at Nanjing Agricultural University for their assistance.

Conflicts of Interest

The authors declared there were no conflicts of interest.

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Agronomy 15 01914 g001
Figure 1. Genetic map of S. pimpinellifolium introgression lines contained 123 polymorphic molecular markers, comprising 310 lines.
Figure 1. Genetic map of S. pimpinellifolium introgression lines contained 123 polymorphic molecular markers, comprising 310 lines.
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Agronomy 15 01914 g002
Figure 2. Frequency distribution of four fruit size and weight-related traits in S. pimpinellifolium introgression lines. (A) Single fruit weight, (B) fruit shape index, (C) fruit diameter, and (D) fruit length.
Figure 2. Frequency distribution of four fruit size and weight-related traits in S. pimpinellifolium introgression lines. (A) Single fruit weight, (B) fruit shape index, (C) fruit diameter, and (D) fruit length.
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Agronomy 15 01914 g003
Figure 3. QTL mapping for fruit weight, fruit length, fruit width, and fruit shape index. Single fruit weight QTL (qFw-3), fruit length QTL (qFl-3 and qFl-11), fruit diameter QTL (qFtd-3-1, qFtd-3-2, qFtd-4, qFtd-7, and qFtd-12), and fruit shape index QTL (qFsi-2 and qFsi-33) of the population, comprising 220 lines.
Figure 3. QTL mapping for fruit weight, fruit length, fruit width, and fruit shape index. Single fruit weight QTL (qFw-3), fruit length QTL (qFl-3 and qFl-11), fruit diameter QTL (qFtd-3-1, qFtd-3-2, qFtd-4, qFtd-7, and qFtd-12), and fruit shape index QTL (qFsi-2 and qFsi-33) of the population, comprising 220 lines.
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Agronomy 15 01914 g004
Figure 4. Distribution of infiltration fragments in the qFw-3 subpopulation of tomato single fruit weights. Genotypic analysis of the qFw-3 subpopulation, A and B indicated a molecular marker consistent with “Jina” and “LA2093”, respectively, while H indicated the heterozygous type. The 15 molecular markers within the qFw-3 subpopulation interval (SSR3-1 to 14, and C03M65101).
Figure 4. Distribution of infiltration fragments in the qFw-3 subpopulation of tomato single fruit weights. Genotypic analysis of the qFw-3 subpopulation, A and B indicated a molecular marker consistent with “Jina” and “LA2093”, respectively, while H indicated the heterozygous type. The 15 molecular markers within the qFw-3 subpopulation interval (SSR3-1 to 14, and C03M65101).
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Figure 5. Correlation analysis of tomato single fruit weight with 14 molecular markers. The horizontal coordinate is the molecular marker, and the vertical coordinate is −log10(p-adjust) in the figure. The threshold is p-adjust = 0.05, which corresponds to −log10(p-adjust) = 1.3 in the figure.
Figure 5. Correlation analysis of tomato single fruit weight with 14 molecular markers. The horizontal coordinate is the molecular marker, and the vertical coordinate is −log10(p-adjust) in the figure. The threshold is p-adjust = 0.05, which corresponds to −log10(p-adjust) = 1.3 in the figure.
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Figure 6. Fine Mapping of the qFw-3 diagrammatic map. (A) The preliminary location of qFw-3 was SSR111-C03M65101. (B) 5-4-6-1, 5-4-6-2, 5-4-6-3, 5-4-6-4, and 5-4-6-5 recombinant plants of 41 subpopulation plants. The physical location diagram of 8 candidate genes. According to the results of polyacrylamide gel electrophoresis, when the size of the bands was the same as recipient “Jina” and donor “LA2093”, it was recorded as “A” and “B”, respectively, with the heterozygote being recorded as “H”.
Figure 6. Fine Mapping of the qFw-3 diagrammatic map. (A) The preliminary location of qFw-3 was SSR111-C03M65101. (B) 5-4-6-1, 5-4-6-2, 5-4-6-3, 5-4-6-4, and 5-4-6-5 recombinant plants of 41 subpopulation plants. The physical location diagram of 8 candidate genes. According to the results of polyacrylamide gel electrophoresis, when the size of the bands was the same as recipient “Jina” and donor “LA2093”, it was recorded as “A” and “B”, respectively, with the heterozygote being recorded as “H”.
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Table 1. Distribution of molecular markers on the genetic map.
Table 1. Distribution of molecular markers on the genetic map.
ChromosomeNumber
of Markers		Length (cM)Average Distance Between Two Markers (cM)
11280.456.70
21029.642.96
31361.254.71
41043.584.36
5813.971.75
61144.994.09
7622.883.81
81021.312.13
91161.645.60
10952.315.81
111046.994.67
121355.214.25
Total123534.224.34
Table 2. Analysis of four fruit size and weight-related traits in S. pimpinellifolium introgression lines.
Table 2. Analysis of four fruit size and weight-related traits in S. pimpinellifolium introgression lines.
TraitFemale MeanMale
Mean		Parent
t-Test		BC3F4 Population
MeanSDCVSkewnessKurtosisRange
Fruit weight/g20.202.24**15.273.790.25−0.17−0.305.12–25.10
Fruit diameter/cm40.3015.50**33.754.860.14−0.530.6415.50–47.80
Fruit length/cm33.3317.00**27.232.500.09−0.390.4719.40–34.0
Fruit shape index1.290.91**1.230.160.13−0.060.060.87–1.72
Note: Parent t-test is Student’s t-test between parents, significant difference ** p < 0.05; SD—standard deviation; CV—coefficient of variation.
Table 3. QTL analysis of four fruit traits in S. pimpinellifolium introgression lines.
Table 3. QTL analysis of four fruit traits in S. pimpinellifolium introgression lines.
Trait CategoryQTL LociChrMaker IntervalLod
Score		Additive EffectVariance Explained
Fruit weightqFw-33SSR111~C03M651014.402.2815.74
Fruit transverse diameterqFtd-3-13C03M00629~C03M123813.91−1.504.08
qFtd-3-23SSR111~C03M651013.770.844.04
qFtd-44C04M64497~C04M663964.250.663.41
qFtd-77sli800~C07M589663.802.4684.48
qFtd-1212C12M62194~C12M640383.561.484.13
Fruit
longitudinal		qFl-33SSR111~C03M651017.203.3913.24
qFl-1111SSRD120~sli18003.78−1.964.56
Fruit
shape index		qFsi-22C02M52141~C02M534544.970.139.40
qFsi-33SSR111~C03M651016.070.0915.45
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Zhang, Y.; Ding, F.; Qui, H.; Tian, Y.; Jiang, F.; Zhou, R.; Wu, Z. QTL Mapping of Tomato Fruit Weight-Related Traits Using Solanum pimpinellifolium Introgression Lines. Agronomy 2025, 15, 1914. https://doi.org/10.3390/agronomy15081914

AMA Style

Zhang Y, Ding F, Qui H, Tian Y, Jiang F, Zhou R, Wu Z. QTL Mapping of Tomato Fruit Weight-Related Traits Using Solanum pimpinellifolium Introgression Lines. Agronomy. 2025; 15(8):1914. https://doi.org/10.3390/agronomy15081914

Chicago/Turabian Style

Zhang, Yuanhao, Fei Ding, Huiling Qui, Yingjie Tian, Fangling Jiang, Rong Zhou, and Zhen Wu. 2025. "QTL Mapping of Tomato Fruit Weight-Related Traits Using Solanum pimpinellifolium Introgression Lines" Agronomy 15, no. 8: 1914. https://doi.org/10.3390/agronomy15081914

APA Style

Zhang, Y., Ding, F., Qui, H., Tian, Y., Jiang, F., Zhou, R., & Wu, Z. (2025). QTL Mapping of Tomato Fruit Weight-Related Traits Using Solanum pimpinellifolium Introgression Lines. Agronomy, 15(8), 1914. https://doi.org/10.3390/agronomy15081914

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