QTL mapping of pericarp and fruit-related traits in melon (Cucumis melo L.) using SNP-derived CAPS markers

Scientia Horticulturae 265 (2020) 109243

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QTL mapping of pericarp and fruit-related traits in melon (Cucumis melo L.) using SNP-derived CAPS markers

T

Taifeng Zhanga,b, Zhuo Dinga,b, Jiajun Liua,b, Boyan Qiua,b, Peng Gaoa,b,* a

College of Horticulture and Landscape Architecture, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China Key Laboratory of Biology and Genetic Improvement of Horticulture Crops (Northeast Region), Ministry of Agriculture and Rural Affairs, Harbin, Heilongjiang, 150030, China

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Melon Quantitative trait loci Fruit quality trait Genetic map CAPS marker

As an important fruit vegetable crop, the quality of melon affects consumer preferences and the market value. Most quantitative trait loci (QTLs) related to fruit quality are controlled by polygenes and influenced by environmental factors. The objective of the present study was to identify QTLs for fruit quality traits in the F2 population derived from thick-skin line Elizabeth (M4-5) and thin-skin line M1-15 melons using single-nucleotide polymorphism (SNP)-derived cleaved amplified polymorphic sequence (CAPS) markers. We sequenced the melon lines M4-5 and M1-15 using next-generation sequencing (NGS) to identify CAPS loci across the assembled genome and constructed a linkage map with 195 CAPS markers to map the QTLs associated with fruit quality traits. Forty QTLs for fruit quality traits were identified with the new CAPS-based linkage map. Among these, 12 QTLs were associated with pericarp-related traits (exocarp type and thickness, pericarp thickness and firmness, and cracking) and 28 QTLs were associated with fruit-related traits (fruit length, width, and weight, and brix content). Our results suggest that the genetic components for fruit quality traits are complex and additive. The majority of QTLs for fruit quality traits were clustered around LG6 and LG9, among which two QTLs showed pleiotropic effects. The location of these QTLs with narrow genomic intervals could facilitate marker-assisted selection (MAS) application of the underlying genes in breeding programs. The findings of the present study provide the fundamentals for pyramiding the favored allele at multiple QTL locations into elite material using highly efficient molecular breeding tools.

1. Introduction Melon (Cucumis melo L.) (2n = 24) is one of the most widely grown and consumed vegetable crops worldwide and plays a significant role in the global horticulture industry. As the biggest producer of melon in the world, China has produced about 8 million tons on 350,000 ha annually since 2000, accounting for more than 50 % of the world production by weight (Sun et al., 2017). Although traditional breeding programs were focused on agronomic traits, fruit quality has recently become a primary goal. Fruit quality is a complex concept that encompasses diverse traits related to fruit appearance (size, fruit weight), nutritional (soluble brix content) and organoleptic traits, and post-harvest traits including resistance to storage and transportation. High yield and uniform fruit shape and size are high priorities for growers and the market in countries such as China and the United States (Zalapa et al., 2007). Consumer preferences are mostly determined by fruit traits such as fruit shape, netting density, and groove depth (Fernandez-Silva et al., 2010;

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Obando et al., 2008). High phenotypic and genetic variability for diverse traits including yield and fruit characteristics exist in melon. Since Pitrat (1991) developed the world's first melon linkage map using morphological markers (Pitrat, 1991), several melon genetic maps have been constructed (Oliver et al., 2001; Zalapa et al., 2007; Fukino et al., 2008). With the development and deployment of advanced “-omics” approaches including next-generation sequencing (NGS), high-resolution genetic maps (Henry, 2008; Kole and Abbott, 2010) have been constructed using single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS) data for melon (Deleu et al., 2009; Jordi et al., 2012; Amanullah et al., 2018; Pereira et al., 2018). Quantitative trait loci (QTL) analysis has been used to detect regions of the genome involved in fruit-related traits including fruit shape, size, weight, and brix content in several different mapped populations. Most fruit quality traits such as the diameter, length, size, weight, and flesh thickness are polygenic. Previous studies identified more than 74 QTLs

Corresponding author. E-mail address: [email protected] (P. Gao).

https://doi.org/10.1016/j.scienta.2020.109243 Received 28 November 2019; Received in revised form 16 January 2020; Accepted 28 January 2020 0304-4238/ © 2020 Elsevier B.V. All rights reserved.

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for fruit size-related characteristics in six diverse populations (Monforte et al., 2004; Eduardo et al., 2007; Zalapa et al., 2007; Obando et al., 2008; Paris et al., 2008; Harel-Beja et al., 2010). Thirty one QTLs for fruit traits were found in an F2 mapping population including six QTLs for pericarp thickness (Ramamurthy and Waters, 2015). Wang et al. (2016) discovered 18 QTLs for six traits including fruit weight (FW), fruit length (FL), fruit width (FW), pericarp thickness (PT), netting density, and netting width in an F2 mapping population derived from a cross between TARI-08874 and ‘Bai-li-gua’. Twenty seven QTLs for three traits such as shape, size characteristics, and pulp content with stable effects were found in at least two F2 trials (Díaz et al., 2017). Recently, a genetic linkage map for melon quality traits was constructed using an F2:3 population derived from a cross between MR-1 and M4-7, showing 24 QTLs for FW, FL, FW, fruit firmness, flesh firmness, Brix, and fruit shape (Amanullah et al., 2018). Pereira et al. (2018) identified 33 QTLs associated with fruit-quality traits using GBS-based genetic mapping in a recombinant inbred line (RIL) population. However, the molecular genetic control for most fruit quality traits remains largely unknown. In the present study, we developed cleaved amplified polymorphic sequence (CAPS) markers for the identification of QTLs associated with fruit quality traits in melon using an F2 population derived from a cross between thick skin melon Elizabeth (M4-5) and thin skin melon M 1-15. The findings of this study provide genetic information that will contribute to marker-assisted breeding for cultivars with specific fruit morphology. 2. Materials and methods Fig. 2. Bimodal distribution of exocarp (A) and continuous distribution of pericarp thickness (B) in F2 population derived from M4-5 and M1-15.

2.1. Plant materials

of the exocarp and edible pericarp was measured using a hardness tester with an accuracy of 0.1 kg/cm2. Cracked fruit was directly observed and recorded. Single fruit weight was measured using an electronic balance with an accuracy of 0.01 kg. Seed cavity and fruit length and width were measured using a ruler with an accuracy of 1 mm. The ratio of the seed cavity to the fruit length was estimated. Brix content was determined using a hand-held sugar meter with an accuracy of 0.1 %.

The F2 mapping population was derived from F1 between M4-5 (P1, female) and M1-15 (P2, male). The female M4-5 line is a thick-skin type with fruit cracking, while the male M1-15 line is a thin-skin type with no fruit cracking. In 2018, F2 progenies (n = 300) along with the two parental lines (M4-5, n = 15; M1-15, n = 15) and their F1 hybrids (n = 15) were evaluated in a plastic greenhouse. Irrigation, weeding, and pest control were conducted using standard horticultural procedures for typical Harbin climatic conditions. External and longitudinal sections of fruits from the two parent lines are shown in Fig. 1.

2.3. Genomic DNA extraction 2.2. Evaluation of fruit morphological traits Young leaves from individual P1, P2, F1, and F2 plants without any damage were collected and rapidly frozen at −80 °C. Total genomic DNA was extracted using the modified Cetyl Trimethyl Ammonium Bromide (CTAB) method (Allen et al., 2006).

The pericarp was divided into two parts for measurement: edible pericarp (center pericarp and endocarp) and exocarp. Edible pericarp measurement: The mature fruit was cut longitudinally along the central axis with a knife. The maximum edible skin thickness of the left and right parts was measured in centimeters (cm) with vernier calipers and the average was calculated. Exocarp measurement: The exocarp was photographed for measurement using an S-3400 scanning electron microscope. The hardness

2.4. CAPS marker development The DNA samples of the two parents M4-5 and M1-15 were sequenced on an Illumina HiSeq 2000 high-throughput sequencing

Fig. 1. External and longitudinal sections of fruits from two parent lines. A. P1- M4-5. B. P2- M1-15. 2

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Table 1 Mean with standard deviation and range for fruit-quality traits in the parental lines and the F1 and F2 generations. Trait

Exocarp thickness (μm) Edible pericarp thickness (cm) Exocarp firmness (kg/cm2) Edible pericarp firmness (kg/cm2) Average fruit weight (kg) Fruit length (cm) Fruit width (cm) Fruit shape Seed cavity (cm) Cavity and fruit ratio Brix content (%)

Parents and F1 mean

F2

M4-5

M1-15

F1

Mean ± SD

Range

Kurtosis

Skewness

64 ± 1.78 3.34 ± 0.07 14.29 ± 0.34 4.44 ± 0.37 0.96 ± 0.55 11.6 ± 0.21 12.95 ± 0.29 0.90 ± 0.13 6.55 ± 0.17 0.51 ± 0.01 9.24 ± 0.43

14.48 ± 2.48 1.46 ± 0.40 14.12 ± 027 6.06 ± 0.17 0.24 ± 0.13 10.11 ± 0.18 6.64 ± 0.12 1.53 ± 0.18 3.78 ± 0.69 0.57 ± 0.05 10.69 ± 0.26

48.14 ± 1.87 2.36 ± 0.11 15.92 ± 0.76 4.93 ± 0.23 0.60 ± 0.68 13.36 ± 0.57 9.59 ± 0.44 1.40 ± 0.25 5.16 ± 0.24 0.54 ± 0.07 7.74 ± 0.58

45.93 ± 16.50 2.37 ± 0.54 14.64 ± 2.18 5.15 ± 1.64 0.67 ± 0.31 13.47 ± 2.56 10.01 ± 1.75 1.35 ± 0.19 5.44 ± 1.07 0.54 ± 0.052 8.30 ± 2.20

6.28-84.63 1.20-4.50 8.50-26.00 1.00-10.60 0.17-1.97 7.00-20.80 6.50-16.00 0.89-1.98 3.40-9.50 0.40-0.76 4.00-14.00

−0.18 0.48 4.23 0.27 1.58 −0.15 −0.21 0.53 0.71 1.12 −0.28

−0.44 0.60 1.06 0.63 1.12 0.40 0.45 0.39 0.82 0.48 0.44

which 229 pairs (43.8 %) were polymorphemic. The PCR mixture for CAPS amplification contained 2 μL template DNA, 0.4 μL Taq endonuclease, 1 μL of each forward and reverse primer, 2 μL Taq buffer, 0.6 μL dNTPs, and 13 μL double distilled water in a total volume of 20 μL. PCR amplification was performed using the touchdown PCR protocol (Zhang et al., 2019). The PCR products were digested with four restriction enzymes (EcoRI, HindIII, BamHI, and PstI), respectively. The reaction mixture for the enzyme digestion contained 5 μL PCR product, 0.3 μL restriction enzymes (concentration 10 U · mL-1), 1.5 mol L-1 enzyme corresponding buffer, and 8.2 μL double distilled water in a total volume of 15 μL at a constant temperature of 37 °C or 65 °C in an incubator for 3 h, in accordance with the manufacturer’s instructions for the restriction enzyme. 2.6. Genetic linkage map construction The linkage map for the F2 population was constructed using IciMapping V4 software (Meng et al., 2015). QTL analysis was performed using the composite interval mapping model. One thousand permutations at the α = 0.05 level were used to estimate the logarithm of odds (LOD) threshold within the genome. LOD scores greater than 2.5 were used for QTL detection with a 1.0-cM genome-wide scan. The identified QTLs were coded using abbreviations for the traits, followed by the linkage group number and QTL number. 2.7. Statistical analysis Statistical analysis of the field phenotypic data was performed using the statistical software SPSS 19.0 and Microsoft Excel 2007.

Fig. 3. Frequency distribution of the exocarp (A) and pericarp firmness (B) in F2 population derived from M4-5 and M1-15.

3. Results platform (Beijing Genomics Institute, Beijing, China). The resequencing data from the two parents were aligned with the reference genome using a Burrows-Wheeler Aligner (Li and Durbin, 2009) to detect SNP loci using Samtools software. The sequencing data were subjected to a series of quality control procedures using Perl scripts written by the Northeast Agricultural University to exploit candidate CAPS loci (approximately 500 bp) carrying restriction site-specific SNPs using SNP2CAPS software for the development of SNP-derived CAPS markers.

3.1. Phenotyping of fruit morphological traits 3.1.1. Exocarp and cracking type M4-5 has a thick-skinned and cracked exocarp type, while M1-15 shows a thin-skinned rind and no-cracked exocarp (Fig. 1). In the F1 generation, the exocarp was thick-skinned and cracked, similar to that of M4-5. Only the two parental types of exocarp and cracking were observed in the F2 population, with a thin-skinned to thick-skinned exocarp ratio of 35:244 and a cracked to non-cracked ratio of 122:162. The results suggested that the exocarp type was a qualitative trait, whereas the cracking characteristic was affected by several environmental factors in addition to genetic factors.

2.5. Validation of CAPS markers by PCR The PCR products were cleaved by restriction endonucleases to identify the polymorphisms of the CAPS markers between the two parental lines and their F1. Primer Premier 6.0 software was utilized to design and select 523 pairs of primers, with the appropriate CAPS candidate sequences. Polymorphisms of 523 pairs of CAPS primers were verified using the DNA of the parental lines and F1 generation, among

3.1.2. Exocarp and pericarp thickness The exocarp and edible pericarp thickness of the two parental lines differed. The exocarp thickness of M4-5 and M1-15 was 64 ± 1.78 μm and 14.48 ± 2.48 μm, respectively, and the pericarp thickness was 3

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Fig. 5. Frequency distribution of SC (A) and CFR (B) in F2 population derived from M4-5 and M1-15.

Fig. 6. Frequency distribution of BRC in F2 population derived from M4-5 and M1-15.

The results suggested a degree of dominance for thick exocarp and edible pericarp. Among the F2 generation, the exocarp thickness exhibited a bimodal distribution (Fig. 2A), but pericarp thickness displayed a continuous distribution (Fig. 2B). Transgressive segregation was observed for both traits, indicating that exocarp and edible pericarp thickness quantitative traits are governed by polygenes.

3.1.3. Exocarp and pericarp firmness As shown in Table 1, the exocarp firmness was very similar for the two parents but exceeded the values for both parents in the F1 generation. In the F2 population, exocarp firmness ranged from 8.50 (minimum) to 26.00 (maximum) kg/cm2, exhibiting a continuous distribution (Fig. 3A). The pericarp firmness was 4.44 ± 0.37 kg/cm2 for M4-5 and 6.06 ± 0.17 kg/cm2 for M 1-15. The pericarp firmness for F1 was 4.93 ± 0.23 kg/cm2, between that of the two parents. In the F2

Fig. 4. Frequency distribution of FWT (A), FL (B), FW (C), and FSI (D) in F2 population derived from M4-5 and M1-15.

3.34 ± 0.07 cm and 1.46 ± 0.40 cm respectively. In the F1 generation, the exocarp and pericarp thickness was 48.14 ± 1.87 μm and 2.36 ± 0.11 cm, close to the high end of the scale for the parental lines.

4

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Table 2 Correlation coefficients between fruit traits. Traits

FW

FL

FWT

FSI

ET

EPT

SC

EF

EPF

BRC

CFR

FW FL FWT FSI ET EPT SC EF EPF BRC FCR

1 0.794** 0.911** −0.52 0.106 0.811** 0.708** 0.214** 0.141* 0.235** −0.210**

– 1 0.707** 0.473** 0.48 0.609** 0.552** 0.164** 0.164** 0.333** −0.177**

– – 1 −0.277** 0.124* 0.790** 0.859** 0.262** 0.116* 0.333** −0.086

– – – 1 −0.73 −0.152* −0.311** −0.96 0.76 −0.183** −0.129*

– – – – 1 0.065 0.124* 0.47 0.22 0.109 0.023

– – – – – 1 0.461** 0.201** 0.135* 0.244** −0.489**

– – – – – – 1 0.185** 0.079 0.276** −0.429**

– – – – – – – 1 0.141* 0.348** −0.092

– – – – – – – – 1 0.033 −0.061

– – – – – – – – – 1 −0.064

– – – – – – – – – – 1

minimal effect of exocarp thickness on the overall fruit consumption rate and yield. The exocarp thickness was not significantly associated with the exocarp firmness.

Table 3 The parameters of the genetic linkage map. Chr

No. Marker

Genetic distance of linkage groups, cM

Average distance of linkage groups cM

1 2 3 4 5 6 7 8 9 10 11 12 Total

19 18 18 23 17 13 14 16 15 12 14 16 195

149.53 187.34 106.09 158.79 116.03 165.33 125.97 156.29 181.13 105.16 125.28 125.61 1702.55

8.31 11.02 6.24 7.22 7.25 13.78 9.69 10.42 12.94 9.56 9.64 8.37 8.78

3.3. Construction of genetic linkage map In total, 195 polymorphic CAPS markers were used to construct a genetic linkage map consisting of 12 linkage groups spanning a total genome length of 1702.55 cM with an average distance of 8.78 cM between flanking markers (Table 3). 3.4. QTLs detected for fruit traits Using the CAPS-based linkage map, a total of 40 QTLs were identified for the fruit traits (Table 4, Fig. 7). Two QTLs (TOP9.1, TOP11.1) were detected for the exocarp type (Fig. 7). The major QTL TOP9.1 (LOD = 48.1402) was flanked by the markers M9-15 and M9-43 at an interval of 1.86 cM, explaining 72.91 % of phenotypic variation. The minor QTL TOP11.1 was flank by the markers M11-7 and M11-25 at an interval of 2.89 cM, accounting for 12.85 % of phenotypic variation. Three QTLs (FCR4.1, FCR6.1, FCR9.1) associated with cracking were located in LG4, LG6, and LG9 (Fig. 7), respectively. Among them, FCR6.1 showed a positive additive effect on cracking, accounting for 8.48 % of phenotypic variation. FCR4.1 with an interval of 1.87 cM and FCR9.1 showed negative additive effects on cracking, explaining 4.81 % and 6.53 % of phenotypic variation, respectively. Two (ET7.1, ET11.1) and four (EPT3.1, EPT3.2, EPT5.1, EPT6.1) minor QTLs were identified for exocarp and pericarp thickness (Fig. 7), respectively. ET7.1, which has an additive effect, spanned the markers M7-24 and M7-17 at a distance of 16.33 cM and explained 5.59 % of the phenotypic variation. ET11.1 was positioned between markers M11-7 and M11-25 with an interval of 1.11 cM and accounted for 4.49 % of the phenotypic variation. QTLs EPT3.1 and EPT3.2 were located in LG3, whereas EPT5.1 and EPT6.1 were positioned in LG5 and LG6, respectively, explaining 3.64%–6.97% of the phenotypic variation. One QTL (EPFI3.1) was associated with pericarp firmness. Three QTLs, FW5.1, FW6.1 and FW11.1, were identified for FWT (Fig. 7), among which FW5.1 on LG5 spanned markers M5-37 and M5-22 at a distance of 0.89 cM and explained 6.85 % of the phenotypic variation. The FWT was increased by the QTL FW6.1 on LG6 but reduced by the QTL FW11.1 on LG11. Five QTLs for FL (FL3.1, FL5.1, FL6.1, FL9.1, FL11.1) were identified (Table 4, Fig. 7), among which FL5.1 spanned LG5 at an interval of 3.99 cM, and positively accounted for 10.35 % of the phenotypic variation. Four minor QTLs, FL3.1, FL5.1, FL6.1, FL9.1, and FL11.1, reduced the FL and accounted for 3.50%–4.65% of the phenotypic variation. Two QTLs (FW5.1 and FW6.1) were identified for FW, explaining 7.14 % and 9.40 % of the phenotypic variation, respectively. Among them, FW5.1 was flanked by the markers M5-48 and M5-49 at an interval of 2.37 cM, whereas FW6.1 spanned LG6 at an interval of 20.91 cM.

population, the pericarp firmness ranged from 1.00 kg/cm2 - 10.60 kg/ cm2, presenting a continuous distribution (Fig. 3B). 3.1.4. Fruit length (FL), width (FW), weight (FWT), and shape index (FSI) The FL, FW, FWT, and FSI of the two parental lines differed (Table 1). For M4-5 and M1-15, the mean FLs were 11.6 ± 0.21 cm and 10.11 ± 0.18 cm, the mean FWs were 12.95 ± 0.29 cm and 6.64 ± 0.12 cm, the mean FWTs were 0.96 ± 0.55 kg and 0.24 ± 0.13 kg, and the mean FSIs were 0.90 ± 0.13 and 1.53 ± 0.18 (Table 1). The FWT ranged from 0.17 kg to 1.97 kg in the F2 population, exhibiting a continuous distribution shifted toward the lower FWT scale (Fig. 4A). The FL, FW, and FSI were segregated in the F2 population with transgression, exhibiting continuous distributions (Fig. 4B–D). 3.1.5. Seed cavity (SC) and ratio of cavity to fruit (CFR) For M4-5 and M1-15, the SCs were 6.55 ± 0.17 cm and 3.78 ± 0.69 cm, and the CFRs were 0.51 ± 0.01 and 0.57 ± 0.05 (Table 1). The SC and CFR displayed continuous distributions (Fig. 5A and B) with transgressive segregation. 3.1.6. Brix content (BRC) The BRC was 9.24 ± 0.43 % for M4-5 and 10.69 ± 0.26 % for M115 (Table 1). In the F2 population, the BRC ranged from 4.00 % to 14.00 %, exhibiting a continuous distribution with transgressive segregation (Fig. 6). 3.2. Correlation analysis between fruit morphological traits The correlations between fruit traits are shown in Table 2. The FWT was significantly correlated with the FL, FW, pericarp thickness, SC, and CFR, suggesting a significant impact of these traits on yield. No association of the exocarp with other traits was observed, indicating a 5

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Table 4 Genetic effects of QTLs on fruit morphological traits. Trait

QTL

LG

Adjacent markers

Position

Marker rang

LOD value

Additive

R2 (%)

Type of exocarp

TOP9.1 TOP11.1 ET7.1 ET11.1 EPT3.1 EPT3.2 EPT5.1 EPT6.1 EPFI3.1 FCR4.1 FCR6.1 FCR9.1 FW5.1 FW6.1 FW11.1 FL3.1 FL5.1 FL6.1 FL9.1 FL11.1 FWID5.1 FWID6.1 FSI1.1 FSI2.1 FSI4.1 FSI4.2 FSI7.1 FSI9.1 FSI10.1 SC4.1 SC5.1 SC6.1 BRC2.1 BRC6.1 BRC7.1 BRC7.2 BRC9.1 CFR4.1 CFR8.1 CFR9.1

9 11 7 11 3 3 5 6 3 4 6 9 5 6 11 3 5 6 9 11 5 6 1 2 4 4 7 9 10 4 5 6 2 6 7 7 9 4 8 9

M9-43 / M9-40 M11-7 / M11-25 M7-24 / M7-17 M11-7 / M11-25 M3-44 / M3-7 M3-18 / M3-41 M5-17 / M5-52 M6-8 / M6-36 M3-26 / M3-43 M4-37 / M4-13 M6-6 / M6-8 M9-26 / M9-28 M5-37 / M5-22 M6-39 / M6-22 M11-2 / M11-22 M3-17 / M3-18 M5-17 / M5-52 M6-23 / M6-18 M9-33 / M9-37 M11-2 / M11-22 M5-48 / M5-49 M6-39 / M6-22 M1-33 / M1-32 M2-35 / M2-12 M4-41 / M4-40 M4-33 / M4-32 M7-39 / M7-24 M9-33 / M9-37 M10-3 / M10-5 M4-45 / M4-37 M5-22 /M5-36 M6-39 / M6-22 M2-43 / M2-25 M6-23 / M6-18 M7-5 / M7-7 M7-39 / M7-24 M9-43 / M9-40 M4-41 / M4-40 M8-21 / M8-4 M9-43 / M9-40

121 88 65 90 14 85 86 85 4 67 83 3 60 101 125 71 88 110 47 125 69 101 114 138 42 84 63 40 29 65 61 102 108 111 45 62 159 37 31 157

91.73-152.13 87.81-91.08 64.80-81.53 87.81-91.08 13.91-19.06 84-88.96 74-98.01 86.71-92.31 0-4.27 65.44-70.43 76.85-86.71 0-20.51 58.98-60.33 98.01-104.07 115.35-125.28 70.96-84 74.00-98.01 109.61-120.93 39.58-50.21 115.35-125.28 65.99-69.64 98.01-104.07 108.07-125.45 138.16-140.91 36.56-47.56 83.76-92.38 61.94-64.80 39.58-50.21 21.30-41.41 62.32-65.44 60.33-61.95 98.01-104.07 108.74-110.55 109.61-120.93 40.42-46.50 61.94-64.80 159.08-164.46 36.56-47.56 29.01-41.01 152.13-159.08

48.1402 8.2863 3.6212 2.8792 2.5505 4.3229 2.9091 3.5564 2.6119 3.1731 5.0107 3.9124 4.781 3.9274 2.6932 3.3538 4.9543 3.0296 2.6683 2.5213 4.849 5.8713 3.1382 3.8015 2.7123 3.2696 13.9618 5.037 3.4369 3.321 3.9442 5.1223 2.8176 2.8073 2.6752 2.8363 3.991 2.6164 3.1105 2.5397

−0.4205 0.1579 5.3923 4.8729 0.1048 0.0917 0.1598 0.1771 −0.4767 −0.151948 0.1971-0.1615 0.1771 0.1056 0.1041 −0.084 −0.7414 1.0068 0.6951 −0.6705 −0.6726 0.6184 0.7330 0.0366 −0.0572 −0.0334 −0.0313 −0.1177 −0.0629 −0.0619 0.2894 0.3619 0.4035 0.645 0-0.6274 0.5011 0.4484 −0.7071 0.0144 −0.0126 −0.0027

72.9145 12.8525 5.5926 4.4930 3.6743 6.9701 6.4395 5.7889 4.1761 4.8069 8.4836 6.5258 6.8534 6.0778 3.7775 4.6475 10.3497 4.2532 4.142 3.4974 7.1447 9.4046 4.3349 4.3904 3.6708 3.6062 17.9684 5.9039 5.3471 4.6808 5.4903 7.8495 4.1983 4.3069 4.02 3.8933 5.7898 4.0442 4.871 4.349

Exocarp thickness Edible pericarp thickness

Edible Pericarp firmness Fruit cracking

Fruit Weight

Fruit Length

Fruit Width Fruit shape index

Seed cavity

Brix content

Cavity and Fruit ratio

One QTL for the exocarp type and thickness was co-located on the linkage group LG11 and was flanked by the markers M11-7 and M1125. A few studies have investigated QTLs that affect the pericarp (Zhang et al., 2013; Ramamurthy and Waters, 2015). Zhang et al. (2013) revealed that the genes controlling pulp thickness (pericarp thickness) are mainly concentrated in the linkage groups LG6 and LG14. Ramamurthy and Waters (2015) reported a total of six QTLs for flash thickness in the linkage groups LG1, LG5, LG8, LG10, and LG12. In the present study, two and four minor QTLs were identified for exocarp and pericarp thickness, respectively; however, none of them were consistent with previous results (Zhang et al., 2013; Ramamurthy and Waters, 2015). The reason for the inconsistent results may be related to the mapping melon materials, which were obtained from different groups with substantial differences in the genetic background. A QTL for edible pericarp firmness (EPFI3.1) identified in this study was located in the same chromosomal region as that reported by Amanullah et al. (2018). To the best of our knowledge, we are the first to report the QTLs for fruit cracking on chromosomes 4, 6, and 9. Further studies are needed to validate these QTLs in different mapping populations for MAS in the future. The genetic components of QTLs that govern fruit traits were statistically characterized in the present study. The majority of QTLs showed significant additive effects on fruit traits, indicating that the inheritance of fruit traits was mainly additive. Comparing the QTLs to previously reported fruit trait-related QTLs resulted in the identification of nine novel QTLs (FL3.1, FL9.1, FSI4.2, FSI1.1, FSI10.1, SC6.1, BRC7.1, CFR4.1, CFR9.1) in this study for FL, FSI, SC, BRC, and CFR.

Seven QTLs (FSI11.1, FSI12.1, FSI4.1, FSI4.2, FSI7.1, FSI9.1, FSI10.1) were detected for FSI (Table 4, Fig. 7), explaining 41.55 % of the phenotypic variation. Among them, the major QTL FS7.1 on chromosome 7 spanned an interval of 0.74 cM and accounted for 17.96 % of the phenotypic variation. The minor QTL FS2.1 was located on LG2 and closely linked to marker M2-12 at a distance of only 0.16 cM. Three QTLs (SC4.1, SC5.1, SC6.1) were detected for SC (Table 4, Fig. 7), among which the major QTL SC6.1 was co-located with QTL FW6.1 on LG6 and was flanked by markers M6-39 and M6-22, explaining 7.85 % of the phenotypic variation. The QTL SC5.1 spanned an interval of 0.28 cM on chromosome 5. Three QTLs were associated with CFR, among which CFR4.1 increased SFR, whereas the other three QTLs reduced CFR. Five QTLs (BRC2.1, BRC6.1, BRC7.1, BRC7.2, BRC9.1) were associated with BRC (Table 4, Fig. 7), accounting for 3.89%–5.79% of the phenotypic variation. Among these, BRC6.1 and BRC9.1 were associated with BRC reduction, whereas the other three QTLs were related to BRC increase. 4. Discussion The external appearance of melon is an important trait related to fruit quality, since it is one of the main determinants of consumer choice in the market. The exocarp is categorized into suberized and non-suberized types. The mapping of QTLs for the exocarp type is unique to this study. The frequency distribution of the exocarp thickness in the F2 population corresponded exactly to the two exocarp types. 6

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Fig. 7. Genetic map harboring QTLs for fruit traits.

These findings were in accordance with previous results for melon (Ramamurthy and Waters, 2015) and other crops including watermelon, eggplant, pepper, tomato, cabbage, corn, and Hibiscus cannabinus (Chaim et al., 2001; Cheng et al., 2016; Qiao et al., 2012; SalibaColombani et al., 2001; Prothro et al., 2012; Katherine et al., 2012; Ren et al., 2014; Zhao et al., 2011; Chen et al., 2011). Ramamurthy and Waters (2015) reported that QTLs associated with melon fruit traits (FL, FW, FSI, FWT, and pulp thickness) were enriched in the linkage group LG8. The QTLs for fruit traits were concentrated on the linkage groups LG6 and LG9 in the present study, among which QTLs for fruit shape, yield, pericarp thickness, and fruit cracking were clustered around LG6,

Additionally, 19 QTLs were located in the same chromosomal regions reported in previous studies (Table 5) (Monforte et al., 2004; Paris et al., 2008; Harel-Beja et al., 2010; Ramamurthy and Waters, 2015; Wang et al., 2016; Díaz et al., 2017; Amanullah et al., 2018; Pereira et al., 2018). The most important result in the present study was the detection of QTLs with pleiotropic effects on the SC and FW fruit traits and the exocarp type and thickness, consistent with the results of correlation analysis between fruit traits. In the present study, the major QTL SC6.1 was co-located with the QTL FW6.1 on LG6 and one QTL for the exocarp type and thickness was co-located on the linkage group LG11. 7

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Backbone” Project of Northeast Agricultural University (No. 16XG06), the “Young Talents” Project of Northeast Agricultural University (No. 14QC09), and the College Youth Innovation Talent Training Project of Heilongjiang Province (No. UNPYSCT-2016136)

Table 5 Putative QTLs homologous to those reported in previous studies. Trait

QTL

LG

References

Edible Pericarp firmness Fruit Weight

EPFI3.1

3

Amanullah et al. (2018)

FW5.1

5

FW6.1 FW11.1 FL5.1 FL6.1 FL11.1 FWID5.1

6 11 5 6 11 5

FWID6.1 FSI2.1 FSI4.1 FSI7.1 FSI9.1 SC4.1 SC5.1 BRC2.1 BRC6.1 BRC7.2 BRC9.1 CFR8.1

6 2 4 7 9 4 5 2 6 7 9 8

Wang et al. (2016); Pereira et al. (2018); Amanullah et al. (2018) Díaz et al. (2017) Wang et al. (2016) Pereira et al. (2018) Pereira et al. (2018) Pereira et al. (2018) Wang et al. (2016); Amanullah et al. (2018) Harel-Beja et al. (2010) Pereira et al. (2018); Paris et al. (2008) Ramamurthy and Waters (2015) Paris et al. (2008) Monforte et al. (2004) Ramamurthy and Waters (2015) Paris et al. (2008) Harel-Beja et al. (2010) Paris et al. (2008) Paris et al. (2008) Amanullah et al. (2018) Paris et al. (2008)

Fruit Length

Fruit Width

Fruit shape index

Seed cavity Brix content

Cavity and Fruit ratio

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whereas QTLs for fruit shape, yield, and exocarp type were concentrated on LG9. The pleiotropic effects of QTLs on fruit traits may be regulated by genetic multigenic effects and pleiotropic genetic mechanisms (Poole et al., 1941; Zhang et al., 1994), suggesting genetic control of the fruit traits, at least in part, by dependent loci. Therefore, these findings provide a theoretical foundation for simultaneously pyramiding the favored allele at multiple QTL locations for fruit traits in melon molecular breeding. 5. Conclusions In the present study, we sequenced the melon lines M4-5 and M1-15 with NGS to identify CAPS loci across the assembled genome and constructed a linkage map with 195 CAPS markers to map QTLs associated with fruit morphological traits. In total, 40 QTLs for fruit morphological traits were detected using the new CAPS-based linkage map. Our results suggest that the genetic components for fruit morphological traits are complex and additive. The majority of QTLs for fruit traits were clustered around LG6 and LG9, among which two QTLs showed pleiotropic effects. The location of these QTLs with narrow genomic intervals could facilitate MAS application of the underlying genes in breeding programs. CRediT authorship contribution statement Taifeng Zhang: Data curation, Formal analysis, Investigation, Methodology, Writing - original draft. Zhuo Ding: Data curation. Jiajun Liu: Investigation, Validation. Boyan Qiu: Investigation. Peng Gao: Project administration, Conceptualization, Funding acquisition, Project administration, Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Authors highly acknowledged the funding granted by the National Nature Science Foundation of China (No. 31672177), the “Academic 8

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