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Genetic analysis and QTL mapping of fruit length and diameter in a cucumber (Cucumber sativus L.) recombinant inbred line (RIL) population
Scientia Horticulturae 250 (2019) 214–222
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Genetic analysis and QTL mapping of fruit length and diameter in a cucumber (Cucumber sativus L.) recombinant inbred line (RIL) population
T
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Tingting Zhang, Xvzhen Li, Yuting Yang, Xiao Guo, Qin Feng, Xiangyu Dong, Shuxia Chen College of Horticulture, Northwest A&F University, Yangling, Shaanxi, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Cucumber Fruit length and diameter Fruit shape index QTL mapping SSR
Fruit length and diameter are very important appearance quality which affects preference of consumers and quality of fruit. To improve the genetic mechanisms of cucumber fruit shape and size, an F2:5 recombinant inbred line (RIL)-population was derived from a cross between Q16 inbred lines which had slender fruit and Q24 inbred lines which had round fruit to screen fruit shape QTLs locus. 85 RILs, 15 F1 and 15 of each parental line were grown in the spring of 2016 and 2017 respectively. The length and diameter of ovary, immature fruit and mature fruit were measured at blooming day, 12 days after pollination and 35 days after pollination respectively. The RIL population was screened basing on the data related fruit using 89 SSR markers and 1 InDel marker. Twentyone QTLs loci were totally identified for fruit shape traits from RIL population. Comparing the flanking markers of 21 QTL loci allowed to 5 consensus QTLs FS1.1, FS1.2, FS2.1, FS3.1 and FS6.1 loci, in which FS3.1 played a role in fruit elongation, FS6.1 was important for the increase of fruit diameter, and the else 3 QTLs acted in both fruit elongation and diameter. The results from this study may increase the understanding of the genetic basis control of fruit shape and size and provide support for marker-assisted selection in cucumber with ideal fruit shape.
1. Introduction Cucumber (Cucumis sativus L.) belonged to the cucurbitaceae family and originated from the southern Himalayas, which was considered originating on Asian (Wei et al., 2016). Cucumber was an important vegetable around the world and it was usually consumed in raw or in preserved pickles at the immature period. The appearance quality of fruit largely influenced the preference of consumers, especially the fruit length and fruit diameter of the cucumber fruits, and the fruit shape has great influence on the fruit weight even the yield (Ramamurthy and waters, 2015; Amanullah et al., 2018). Actually, as one of the most important criteria, the traits of fruit length and diameter were always very important reference for breeders. After a long-term natural domestication and artificial selection basing on the C. sativus var. hardwickii, there were diverse cultivars and showed an increase variation of fruit shape and size. Currently, the cucumber were classified into several market types such as the Northern China type, Southern China type, Japanese Long Green type, European greenhouse type, wide type and so on (Colle, 2015). Every commercial type had different fruit
shape and size, and consumers of different locations had special preference to different kind of fruit shape. For example, the fruit shape of Northern China ecotype which was about 30 cm long, 4–5 cm wide was popular to people in the north of China, and the fruit shape of Southern China ecotype which was about 40 cm long and 5–6 cm diameter was popular to people in the south of China, and the fruit shape of European greenhouse type which was about 10 cm long and 3 cm wide was popular worldwide (Jiang et al., 2015; Pan et al., 2016). In fact, the shape of fruit was decided in some extent by the shape of ovary. The ovary exhibited a distinctive shape before female flowers were blooming and cell division occurs (Sinnott, 1936), which influenced the development of fruit and the length and diameter of the fruit. Reports showed that there was a strong correlation between the shape of ovary and the shape of fruit later (Wei et al., 2016). The shape of fruit was highly heritable and was influenced largely by the environmental factors (Ramamurthy and Waters, 2015; Perin et al., 2002). Notably, the shape and size of pre-anthesis ovary were influenced by increasing of cell division. During the period of 7 days pre-antheses to bloom, cucumber ovary showed a significantly increasing on the cell number,
Abbreviations: RIL, recombinant inbred lines; SSR, simple sequence repeats; INDEL, insertion deletion; PCR, polymerase chain reaction; OL, ovary length; OD, ovary diameter; IFL, immature fruit length; IFD, immature fruit diameter; IFS, immature fruit shape index; MFL, mature fruit length; MFD, mature fruit diameter; MFD, mature fruit shape index; CIM, composite interval mapping; LOD, polymerase chain reaction ⁎ Corresponding author. E-mail address: [email protected] (S. Chen). https://doi.org/10.1016/j.scienta.2019.01.062 Received 9 October 2018; Received in revised form 10 January 2019; Accepted 30 January 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.
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2.2. Field trials and phenotype of F5 population
and the cell size had remained constant, and the different cultivars owned diverse ovary shapes had a difference multiple of cell number increasing (Colle, 2015). After the female flowers were pollinated, the shape and size of cucumber fruit was decided both by the cell division and expansion (Gillaspy et al., 1993; Zhang et al., 2006; Ando et al., 2012). Cucumber fruit with different shape or size showed differences in cell number and size in the longitudinal versus transverse diameter (Jiang et al., 2015; Yang et al., 2013a,b). While the orientation and amount of the cell division influenced the fruit shape and size, the cell expansion played an important role for the rapidly increasing in fruit size. Then cell division speeded up and then slowed down until 8 days post pollination. The fruit cell number increased significantly after this stage, and the cell expansion followed with the cell division, and fruit size enlarged quickly (Fu et al., 2008; Boonkorkaew et al., 2008). Both the fruit length and diameter growth followed by the increase of cell number and size, but the diameter was somewhat behind the length development at the time of 4˜6 day post pollination (Yang et al., 2013a,b). Recently, people were so interesting with QTL loci of fruit shape that researchers began to study the loci through the method of QTL mapping. It was difficult to measure the fruit shape accurately. Generally, the fruit length and diameter were referred to the cucumber fruit shape and size, also the fruit shape index showed the ration of length to diameter for the fruit so it reflected the fruit shape at some extent and considered a trait reflected the shape of fruit despite it’s a complex trait (Pan et al., 2016). Studies showed that shape index had a strong correlation with fruit length and diameter of ovaries, immature fruit and mature fruit for cucumber (Colle et al., 2017; Weng et al., 2015), so the shape index was also used to describe the cucumber fruit shape in this study. There were numerous of QTL mapping studies performed to understand the genetic basis of fruit shape and size in cucumber recently. Kennard and Havey (1995) reported the QTL mapping of cucumber fruit length and diameter for the first time. Bo et al. (2015) mapped 5 QTLs for mature fruit length and 3 QTLs for mature fruit diameter based on the 124 RILs population developed from XIS cucumber × cultivated cucumber inbred line. Miao et al. (2011) mapped 3 QTLs for fruit length and 1 QTL for fruit diameter using 148 RILs population developed from 9110 Gt and 9930. Wei et al. (2014) identified 8 QTLs for immature fruit and mature fruit length, in which a major QTL loci fl3.2 in chromosome 3 explained 38.87% of phenotypic variation. These studies provided a sight for loci of fruit shape and size. However, most of these QTLs were different because of the different population used. In this study, we developed a SSR (simple sequence repeats)-based map that contained 90 molecular markers loci for cucumber using a recombinant inbred line (RIL) population from Q16 and Q24. We surveyed the length and diameter of ovary, immature fruit and mature fruit under 2 environments over 2 years in RIL population. QTL analysis revealed five consensuses QTL, FS1.1, FS1.2, FS2.1, FS3.1 and FS6.1 which were related to the shape of cucumber fruit.
85 F5 populations, 15 parental lines and 30 F1 individuals were grown in a plastic tunnel at the Horticulture Experimental Station (34° 16′ N, 108° 4′ E) of the College of Horticulture, Northwest A&F University, Yangling, Shaanxi Province, China at spring of 2016 and 2017 year respectively, which were named F5-2016S and F5-2017S. 5 plants were planted for every F5 RIL family. The 2–3 female flowers of each individual at about 14–17th node which were blooming were marked as 0 day and recorded as ovary, The fruit which were 12 days old was named immature fruits and the fruits which were 35 days old were named mature fruits according to Li and Zhu (2005). The length and diameter of immature fruits and mature fruits were recorded at 12 day and 35 day after blooming respectively. The ovary length (OL), ovary diameter (OD) and immature fruit diameter (IFD) were measured using digital caliper very gently. The immature fruit length (IFL), mature fruit length (MFL) and mature fruit diameter (MFD) were measured using a tap. The immature fruit shape index (IFS) and mature fruit shape index (MFS) were calculated followed the method by Li and Zhu (2005). 2–3 fruits from each F5 RIL individual were measured. Subsequent data (e.g., mean, coefficient of variation, range) were conducted in Microsoft Excel 2013. The correlation analysis was conducted among different traits, basing on the mean of individual plants measurement for RILs.
2.3. SSR markers analysis and linkage map development To screen the polymorphism markers between the parental lines, a total of 1505 cucumber or melon SSR primer pairs (Ren et al., 2009; Yang et al., 2012) distributed throughout the 7 chromosomes and 1 INDEL primer pair from the our lab were used. All the polymorphic markers between Q16 and Q24 were used to genotype the RIL population. The genomic DNA of the individuals was extracted using the CTAB method (Murray and Thompson, 1980). PCR amplification and gel electrophoreses were followed Li et al. (2011). The genetic linkage map was constructed based on the genotyping date from the RIL population and using the QTL IciMapping 4.0 software (http://www.isbreeding.net/software/?type=detail&id=18). Segregation distortion of each marker was tested using chi-squared (χ2) at an expected 1:1 segregation ratio.
2.4. QTLs mapping QTL analysis was performed by composite interval mapping (CIM) using R/qtl software package (http://www.rqtl.org/) (Broman et al., 2003; Weng et al., 2015). Using the data collected from individuals of RILs in two seasons, a whole genome scan was performed to detect the QTL of cucumber fruit shape traits procedures following Pan et al. (2017). To declare the presence of QTL, genome-wide LOD threshold was determined using 1000 permutations. In order to detect QTLs, a 1.5-LOD-support interval was calculated and defined by the left and right markers, and the interaction between QTLs in the same trait was examined by using the ‘effect plot’ function in R/qtl to conduct the interaction plots of QTL pairs. The name of the QTL was based on its chromosome location and traits names. For example, ol1.1 and ifl2.1 refer to the first QTL for the length of ovary and immature fruits on cucumber chromosome 1 and 2, respectively. A consensus QTL was judged by the following criteria: (1) if the QTL locus was detected both in the two environments; (2) if the QTL locus shared the same or overlapping 1.5-LOD-support intervals; (3) as a major-effect QTL with R 2 > 10% was detected at least one environment.
2. Materials and methods 2.1. Plant materials Two parental lines Q16 and Q24 were used to develop segregating populations in this study.Q16 was an inbred lines which was a typical Northern China ecotype with slim fruit shape and a length of above 35 cm, a diameter of about 3–4 cm at immature fruit stage, and a length of 40 cm at mature fruit stage. Q24 was a landrace which had round shape with 10 cm diameter both in immature and mature fruit period (Fig. 1). In this study, a single F1 plant from the cross between Q16 and Q24 was self-pollinated to produce an F2 population, and an 85 F5 recombinant inbred lines (RILs) were developed through single seed descent which were used during the 2016 and 2017 field trials. 215
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Fig. 1. Fruit morphology of Q16 and Q24. B are ovaries of Q16 (left) and Q24 (right). A and E are immature fruit shape of Q16 and Q24, respectively. C is an mature fruit of Q16 and D is an mature fruit of Q24.
3. Result
mature fruit shape in RILs shown in Fig.S1. The box-plots showed expression of these traits were stable in the two experiments. Therefore, these eight traits in both two experiments were used to the subsequent analysis. The Spearman’s rank correlation coefficients were calculated of all traits in RILs from two experiments. The correlation coefficients between the traits of fruit shape in 2016 spring and 2017 spring were showed in Table 2. The correlation was significantly or very significantly positive among the same trait in different stages even in the different year, such as the correlation among the fruit length of ovary and that of immature fruit, mature fruit was 0.315 and 0.347 respectively in the year of 2016, and the correlation among the fruit length ovary and immature fruit, mature fruit was 0.722 and 0.526 respectively in the year of 2017. All of the correlation of them reached to significant level (P < 0.05) or very significant level (P < 0.01). The correlation among the ovary diameter and immature fruit diameter, mature fruit diameter also reached to significant level. The fruit length and fruit diameter had a negative correlation in different fruit development stages, indicating that the elongating of the fruit would influence the thickening growth.
3.1. Phenotypic evaluations of fruit shape in RILs Two parental lines Q16 and Q24 had considerable differences in fruit shape and size including fruit longitudinal diameter, transverse diameter and fruit shape index both in the ovary fruit stage, immature fruit stage and mature fruit stage. Images of ovary, immature and mature fruits of parents were shown in Fig. 1. Q16 was above 35 cm longitudinal and the diameter was 3–4 cm in the immature fruit phase, the fruit of Q24 showed round shape which longitudinal diameter and the transverse diameter were about 10 cm (Fig. 1A and E). Similarly, both in the ovary phase and mature fruit phase showed Q16 as a slender fruit shape, while Q24 was spherical (Fig. 1B–D). We collected phenotypic data of 8 traits (OL, OD, IFL, IFD, IFS, MFL, MFS, and MFS) from RIL populations in two experiments (F5-2016S, F5-2017S) except for OL and OD in F5-2016S. The phenotypic performance and variation of the 8 fruit shape traits in parental lines, F1 and RILs population were presented in Table 1. The fruit length of F1 from Q16 × Q24 was about 34 cm long, and the diameter was 6 cm in the middle fruit size of parental lines. As with the F1 fruit shape, RILs had an average fruit long and diameter between the parental lines (Table 1). A frequency distribution of OL, OD, IFL, IFD, IFS, MFL, MFD and MFS among the RILs from two experiments were illustrated in Fig. 2. It showed that the fruit length, diameter and shape index in RILs were a normal or near-normal distribution. It suggested that these traits were quantitative one and suitable for QTL analysis. Three box-plots depicted the frequency distribution of the ovary shape, immature fruit shape and
3.2. Linkage map A total of 1505 SSR markers were screened between Q16 and Q24 and 165 primers (10.96%) showed polymorphic and clear bands. All 165 pairs primers were used for screening of polymorphic primers in the RILs, and finally 119 pairs of available polymorphic SSR primers were obtained after removing the primers with indistinguishable or unclear bands. Eventually there were 90 markers being mapped on the
Table 1 Phenotypic means and range of fruit shape traits of Q16, Q24 and their derived populations. Trais
OL OD IFL IFD IFSI MFL MFD MFS
Q16
Q24
F1
F5-2016S
F5-2017S
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Range
Mean ± SD
Range
45.52 ± 6.78 5.36 ± 0.38 32.75 ± 2.74 3.11 ± 0.60 10.53 ± 1.77 38.63 ± 10.40 5.66 ± 1.41 7.35 ± 2.98
12.37 ± 1.12 7.96 ± 0.62 9.57 ± 1.50 8.19 ± 1.30 1.14 ± 0.09 10.27 ± 0.60 9.03 ± 1.67 1.17 ± 0.04
34.30 ± 5.34 6.38 ± 0.69 24.29 ± 3.12 4.65 ± 0.72 5.23 ± 1.97 27.01 ± 8.98 6.23 ± 1.27 4.48 ± 1.68
n/a n/a 21.72 ± 9.65 5.42 ± 1.32 4.63 ± 3.05 26.30 ± 11.53 6.15 ± 1.45 4.59 ± 2.43
n/a n/a 3.00–42.65 2.38–8.70 0.87 ± 14.88 5.09–62.23 3.34–10.20 1.26–12.03
58.54 ± 27.68 6.79 ± 1.05 17.43 ± 6.56 4.50 ± 1.10 4.23 ± 2.09 24.79 ± 8.61 6.37 ± 1.41 4.18 ± 1.90
5.60–127.77 4.58–9.78 7.67–36.64 2.47–9.48 1.17 ± 11.81 10.85–53.29 2.40–9.63 1.15–9.44
OL: ovary length, OD: ovary diameter, IFL: immature fruit length, IFD: immature fruit diameter, IFS: immature fruit shape index, MFL: mature fruit length, MFD: mature fruit diameter, MFS: mature fruit shape index. n/a data not available. 216
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Fig. 2. Frequency distribution of ovary length (OL), ovary diameter (OD), immature fruit length (IFL), diameter (IFD) and shape index (IFS), mature fruit length (MFL), diameter (MFD) and mature fruit shape index (MFS) among RIL population in two environments.
and 3 QTLs were for MFS. 9 QTLs include ifl1.1, ifl2.1, ifd1.1, ifs1.1, ifs2.1, mfl1.1, mfl2.1, mfs1.1 and mfs2.1 could be detected both in the two experiments, and 5 QTLs ifd2.1, mfl3.1, mfd1.1, mfd2.1 and mfd6.1 were detected only in F5-2017S, 3 QTLsifl3.1, ifs3.1 and mfs3.1 were detected only in F5-2016S, and 4 QTLs ol1.1, ol2.1, ol3.1 and od2.1 for ovary fruit shape were only detected during the F5-2017S.
linkage map (Fig. 3), and detailed information of all mapped makers on the linkages map was presented in supplementary Table S1. A total of 90 markers were localized in a 7 linkage groups with a total length of 584.64 cM with averaged 83.52 cM for each group and 6.50 cM between adjacent markers. The lengths of individual chromosome in the 7 linkage groups varied from 42.41 cM (chromosome 5) to 128.83 cM (chromosome 1), and the map interval varied in 4.49 cM (chromosome 2) to 11.34 cM (chromosome 4). Chromosomes 1 and 3 showed the most markers which mapped on 18 markers, chromosome 4 and chromosome5 had the lowest marker number which only 6 markers. All the detailed information of each groups of linkage map had showed in supplementary Table S2.
3.3.1. QTL for ovary length and diameter LOD score of 3 QTLs ol1.1, ol2.1 and ol3.1 which were detected using phenotypic data of ovary length (OL) collected in F5-2017S were higher than 3. ol1.1 was a major-effect QTL which explained 25.68% of the phenotypic variation for ovary length, and it was located between SSR20354 and SSR17922 on Chr1. Another major-effect QTL ol2.1 for OL trait was detected which explained 24.44% of the phenotypic variation and located between UW080619 and SSR22558 on Chr2. ol3.1 explained 4.77% of the phenotypic variation and located between CS24 and SSR22514 on Chr3. One QTL od2.1 was detected for ovary diameter in F5-2017S, which explained 34.44% of phenotypic variation and located between SSR10518 and SSR22558 on Chr2. ol2.1 had a negative additive effect, and the other three QTLs showed a positive additive effect.
3.3. QTL analysis Phenotypic data of OL, OD, IFL, IFD, IFS, MFL, MFD and MFS from the two experiments were used in QTL analyses. We performed the whole genome scan for QTL number and location with CIM using a 10 cM window size. For the 8 fruit shape traits, a 1000 permutation tests (P = 0.05) was determined to obtain the LOD threshold to declare significance of QTL. Each QTL were estimated its support QTL score, R2 value, additive effects and 1.5-LOD support interval. The whole genome scan of all QTL detected across the seven chromosomes was provided in Supplemental Fig S2. The summary of all detected QTLs were presented in Table 3. A total of 21 QTLs were detected for 8 traits related to fruit shape were detected on 7 chromosomes. Among them, 3 QTLs were for OL traits, 1 QTL was for OD traits, 3 QTLs were for IFL, 2 QTLs were for IFS, 3 QTLs were for IFS, 3 QTLs were for MFL, 3 QTLs were for MFD,
3.3.2. QTL for the length, diameter and fruit shape index of immature fruit QTL analysis of the immature fruit length (IFL) trait identified 3 QTL with LOD scores greater than 3 from the two environments. In the experiment of F5-2016S, three QTLs ifl1.1, ifl2.1 and ifl3.1 about immature fruit length were detected. QTL ifl1.1 was a major-effect QTL which explained 20.71% of the phenotypic variation and located 217
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OL: ovary length, OD: ovary diameter, IFL: immature fruit length, IFD: immature fruit diameter, IFS: immature fruit shape index, MFL: mature fruit length, MFD: mature fruit shape index, MFS: mature fruit shape inde x . ns: meant there was not significant. * P < 0.05. ** P < 0.01.
1 1 0.533** 1 −0.285* −0.642** 1 0.137 −0.620** −0.2 1 −0.165 −0.326** 0.560** 0.910** 1 0.617** −0.183 −0.304* 0.833** 0.575** 1 0.640** 0.880** −0.216 −0.494** 0.599** 0.916** 1 0.680** 0.628** 0.592** −0.048 −0.349** 0.508** 0.574** IFL IFL IFD IFD IFS IFS MFL MFL MFD MFD MFS MFS
1 −0.499** 0.315* 0.722** −0.420** −0.211 0.336* 0.570** 0.347* 0.526** −0.063 −0.423** 0.324* 0.597** OL OD 2016 2017 2016 2017 2016 2017 2016 2017 2016 2017 2016 2017
1 −0.460** −0.653** 0.518** 0.458** −0.507** −0.683** −0.604** −0.684** 0.257 0.511** −0.527** −0.701**
1 0.507** −0.460** −0.512** 0.878** 0.633** 0.809** 0.572** −0.078 −0.295* 0.670** 0.540**
1 −0.554** −0.225 0.587** 0.877** 0.591** 0.790** −0.135 −0.484** 0.549** 0.845**
1 0.296* −0.693** −0.495** −0.454** −0.471** 0.078 0.460** −0.375** −0.502**
1 −0.453** −0.577** −0.416** −0.473** 0.329* 0.346** −0.426** −0.482**
2017 IFS 2016 IFS 2017 IFD 2016 IFD 2017 IFL 2016 IFL OD OL Trait
Table 2 Spearman’s rank correlation coefficients among OL, OD, IFL, IFD, MFL, MFD in RILs populations for two experiments.
2016 MFL
2017 MFL
2016 MFD
2017 MFD
2016 MFS
2017 MFS
T. Zhang, et al.
between UW083745 and SSR05723 on Chr1. ifl2.1 was also major-effect QTL for IFL trait could explain 24.44% of the phenotypic variation and located between UW080619 and SSR22558 on Chr2. ifl3.1 was a minor-effect QTL which R 2 was 7.27% and located on Chr3 with the flanking markers of SSR22158 and SSR03918. 2 QTLs ifl1.1 and ifl2.1 were detected in the experiment of F5-2017S, which explained 19.19% to 28.53% phenotypic variance (Table 3; Fig S2). ifl1.1 and ifl2.1 were identified as two major QTLs both under two different environments and which had a negative additive effect for immature fruit length and the minor QTL ifl3.1 was not identified in the experiment of F5-2017S. QTL analysis of the immature fruit diameter (IFD) trait was carried out two environments and 2 QTLs ifd1.1 and ifd2.1 were identified. One major-effect QTL ifd1.1 was detected in F5-2016S explained 29.22% of the phenotypic variation for IFD and located between SSR04805 and SSR23049 on Chr1. Two QTLs ifd1.1 and ifd2.1 detected in F5-2017S explained 7.83% and 21.87% of variation for the IFD. ifd2.1 was a major-effect QTL was located between SSR10518 and SSR22558 on Chr2 and ifd1.1 was a minor-effect QTL was located between UW083745 and SSR23049 on Chr1. ifd2.1 was only detected in F52017S environment and ifd1.1 could detected both under two environments, and it showed ifd1.1 was a stable QTL and had a positive additive effect for IFD. 3 QTLs ifs1.1, ifs2.1 and ifs3.1 were identified basing on the data of immature fruit shape index (IFS) with LOD scores greater than 3 under two different environments. ifs1.1, ifs2.1 and ifs3.1 detected in F52016S explained 7.85%, 6.78% and 76.42% of variation of the IFS trait. ifs1.1 was located between UW083745 and SSR23049 on Chr1, ifs2.1 was located between UW080619 and SSR22558 on Chr2, and ifs3.1 was located between CS24 and SSR20258 on Chr3, respectively. It indicated that ifs3.1 was a major-effect QTL under the environment of 2016 spring. Two major-effect QTLS mfs1.1 and mfs2.1 detected in F5-2017S explained 18.35% and 39.64% of variation of the IFS, ifs1.1 was located between UW083745 and SSR23049 on Chr1, and ifs2.1 was located between SSR10518 and SSR22558 on Chr2. ifs1.1 and ifs2.1 were identified as two stable QTLs both detected under two different environments and which had an positive additive effect for IFS and the major-effect QTL ifs3.1 was failure identified in the experiment of F52017S. 3.3.3. QTL for the length, diameter and fruit shape index of mature fruit 3 QTLs mfl1.1, mfl2.1 and mfl3.1 were identified basing on the data of mature fruit length (MFL) with LOD scores greater than 3 under two different environments. mfl1.1 and mfl2.1 detected in F5-2016S explained 28.34% and 47.46% of variation of the MFL trait. mfl1.1 was located between SSR04805 and SSR23049 on Chr1 and mfl2.1 was located between UW080619 and SSR22558 on Chr2. It indicated that these 2 QTLs were major ones under the environment of 2016 spring. Three QTLs mfl1.1, mfl2.1 and mfl3.1 detected in F5-2017S explained 17.57%, 29.27% and 3.82% of variation of the MFL (Table 3; Fig S2). mfl1.1, mfl2.1 were two major-effect QTLs and mfs1.1 was located between SSR03860 and SSR05723 on Chr1, and mfs2.1 was located between SSR10518 and SSR22558 on Chr2. mfl3.1 was a minor-effect QTL which located between CS24 and SSR03918 on Chr3. Comparing the 1.5-LOD-support intervals markers of mfl1.1 and mfl2.1 in the experiment of F5-2016S and F5-2017S, we identified mfl1.1 and mfl2.1 were stable and major-effect QTLs and had a negative additive effect for the trait of MFL. 3 major-effect QTLs mfd1.1, mfd2.1, mfd6.1 were identified basing on the data of mature fruit diameter (MFD) with LOD scores greater than 3 in the F5-2017S environments, and it was failure to detect any QTLs in the experiment of F5-2016S. mfd1.1, mfd2.1 and mfd6.1 detected in 2017 spring could explain 16.20%, 11.42% and 18.10% of variation of the MFD. mfd1.1 was located between SSR20354 and SSR17922 on Chr1, mfd2.1 was located between SSR10518 and SSR22558 on Chr2, and mfd3.1 was located between SSR11798 and UW039897 on Chr6, respectively. Both the mfd2.1 and mfd6.1 had a 218
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Fig. 3. The linkage group of cucumber constructed based on RIL population of a cross between Q16 and Q24. The left of the linkage group were indicated the cumulative distances among these markers indicated in cM and the name of markers were showed on the right.
interval for each QTL by the flanking markers, and the markers physical location was list in the Tabel S1 based on the Gy14 draft genome assemblies. A total of 5 consensus QTLs were detected in this study based on the consistent QTL judgment criteria as described in the previous method (Table 4). ifl1.1, ifd1.1, ifs1.1, mfl1.1 and mfs1.1 were share the same or overlapping 1.5-LOD-support intervals on Chr1 treat as a consensus QTL named FS1.1, ol1.1 and mfd1.1 were share another same or overlapping 1.5-LOD-support intervals on Chr1 regarded as a consensus QTL named FS1.2, ol2.1, od2.1, ifl2.1, ifd2.1, ifs2.1, mfl2.1, mfd2.1 and mfs2.1 were share the same or overlapping 1.5-LOD-support intervals on Chr2 deemed as a consensus QTL named FS2.1, ol3.1, ifl3.1, ifs3.1, mfl3.1 and mfs3.1 were share same or overlapping 1.5-LODsupport intervals on Chr3 regarded as a consensus QTL named FS3.1, and mfs6.1 as a major-effect QTL with R 2 > 10% was detected treated as a consensus QTL named FS6.1. It was clear that FS6.1 was worked on the mature fruit radial, FS3.1 played roles in fruit elongation, the others 3 QTLs FS1.1, FS1.2 and FS2.1 acted in both fruit elongation and diameter, especially the FS2.1 had an important role for fruit elongation and radial throughout the whole of fruit development.
positive additive effect for the MFD while mfd1.1 exhibited a negative additive effect. 3 QTLs mfs1.1, mfs2.1 and mfs3.1 were identified basing on the data of mature fruit shape index (MFS) with LOD scores greater than 3 under two different environments. mfs1.1, mfs2.1 and mfs3.1 detected in F52016S explained 23.32%, 24.71% and 12.56% of variation of the MFS trait. mfs1.1 was located between SSR04805 and SSR23049 on Chr1, mfs2.1 was located between SSR10518 and SSR22558 on Chr2, and mfs3.1 was located between SSR16133 and SSR20258 on Chr3, respectively. It indicated that these 3 QTLs were major ones under the environment of 2016 spring. mfs1.1 and mfs2.1 detected in F5-2017S explained 16.39% and 33.37% of variation of the MFS (Table 3; Fig S2), and mfs1.1 was located between SSR23049 and SSR20354on Chr1, and mfs2.1 was located between SSR10518 and SSR22558on Chr2. Comparing the flank markers of mfs1.1 and mfs2.1 in the experiment of F52016S and F5-2017S, we identified mfs1.1 and mfs2.1 were stable and major QTLs and have a negative additive effect for the trait of mature fruit shape index. With phenotypic data collected in two environments over two years, we identified 21 QTLs for 8 fruit shape traits. There was a 1.5-LOD 219
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Table 3 The QTL mapping results of cucumber fruit shape. Experiment
QTL
F5-2017S
Ovary shape
F5-2016S
immature fruit shape
F5-2017S
F5-2016S
mature fruit shape
F5-2017S
ol1.1 ol2.1 ol3.1 od2.1 ifl1.1 ifl2.1 ifl3.1 ifd1.1 ifs1.1 ifs2.1 ifs3.1 ifl1.1 ifl2.1 ifd1.1 ifd2.1 ifs1.1 ifs2.1 mfl1.1 mfl2.1 mfs1.1 mfs2.1 mfs3.1 mfl1.1 mfl2.1 mfl3.1 mfd1.1 mfd2.1 mfd6.1 mfs1.1 mfs2.1
Chr.
1 2 3 2 1 2 3 1 1 2 3 1 2 1 2 1 2 1 2 1 2 3 1 2 3 1 2 6 1 2
LOD value
5.33 6.43 4.85 8.82 6.26 6.81 3.21 7.67 9.74 9.52 17.85 4.86 6.93 3.61 4.43 5.18 12.34 7.45 11.61 4.39 7.07 4.7 6.26 4.81 3.2 4.12 4.07 5.11 4.37 10.03
R2(%)
Additive effect
25.68 29.41 4.77 34.44 20.71 24.44 7.27 29.22 7.85 6.78 76.42 19.19 28.53 7.83 21.87 18.35 39.64 28.34 47.46 23.32 24.71 12.56 17.57 29.27 3.82 16.20 11.42 18.10 16.39 33.37
6.217 −6.69 0.348 0.803 −4.228 −3.98 −1.286 0.738 0.105 0.094 0.018 −3.326 −3.801 0.315 0.57 0.081 0.112 −5.297 −6.672 −1.113 −1.022 −0.168 −3.829 −5.046 −1.105 −0.584 0.493 0.215 −0.757 −1.122
Peak
120 57.97 38 58 78 57.97 38 76 76 57.97 39.19 76 60 76 60 76 60 72 60 70.97 60 40 84 57.97 40 126 54 46 96 57.97
1.5-LOD Interval Left(cM)
Right(cM)
SSR20354(115.77) UW080619(57.29) CS24(20.35) SSR10518(46.20) UW083745(70.97) UW080619(42.06) SSR22158(0) SSR04805 (68.03) UW083745(70.97) UW080619(42.06) CS24(20.35) UW083745(70.97) SSR10518(46.20) UW083745(70.97) SSR10518(46.20) UW083745(70.97) SSR10518(46.20) SSR04805(68.03) UW080619(57.29) SSR04805(68.03) SSR10518(46.20) SSR16133(11.17) SSR03860(73.81) SSR10518(46.20) CS24(20.35) SSR20354(115.77) SSR10518(46.20) SSR11798(37.34) SSR23049(86.58) SSR10518(46.20)
SSR17922(132.24) SSR22558(68.62) SSR22514(39.19) SSR22558(68.62) SSR05723(98.64) SSR22558(68.62) SSR03918(130.87) SSR23049(86.58) SSR23049(86.58) SSR22558(68.62) SSR20258(49.17) SSR17922(132.24) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR20258(49.17) SSR05723(100.57) SSR22558(68.62) SSR03918(130.87) SSR17922(132.2) SSR22558(68.62) UW039897(54.92) SSR20354(115.77) SSR22558(68.62)
Chr: chromosome, R2: phenotypic variation explained.
4. Discussion
Most of the studies had revealed there were weak correlations between fruit diameter at different fruit stages (Yuan et al., 2008; Bo et al., 2015; Miao et al., 2011; Pan et al., 2017). In our study, there were very significantly positive correlations between ovary diameter (OD) and immature fruit diameter (IFD) and mature fruit diameter (MFD), and there was also significantly positive correlations between immature fruit shape index (IFS) and mature fruit shape index (MFS). It indicated that the fruit shape of cucumber might be decided at the very early fruit development stage. Reports showed that there was a positive correlation between the fruit length and diameter (Wei et al., 2016; Weng et al., 2015), however, we found that there was a negative correlation between the fruit length and diameter in the three different stages. It indicated that there might be different relationship between the fruit length and fruit diameter in different populations.
4.1. Correlation between fruit length and correlation between fruit diameter of different stages in cucumber There were highly correlation between fruit length, and the same as the diameter of different stages. The length and diameter decided the cucumber fruit shape which was closely related with the high quality and yield. In our study, the correlation between fruit length in different stages was higher. The correlation ranged from 0.315 to 0.722 which was at a significant level or a very significant level among the length of different stages even in the different year. The correlation among the ovary diameter and immature fruit diameter, mature fruit diameter also reached to significant level. Reports showed that there were highly correlation between fruit length in different stages (Weng et al., 2015; Zhu et al., 2016), and it indicated that the fruit length might be decided in certain one or two main development stages. In our study, it showed that immature fruit length (IFL) and mature fruit length (MFL) had significantly and positively correlation with ovary length (OL), suggesting that the fruit length of later stage might be influenced by the ovary length at the very early stage.
4.2. Factors affecting QTL detect efficiency in this study QTL mapping was an efficient method to analyze genes which controlled quantitative traits in plants. However, the key factor for mapping QTL loci was the genetic map. (Zou et al., 2012; GutierrezGonzalez et al., 2011). In this study, the genetic map covered 584.64 cM
Table 4 Consensus fruit shape QTLs detected in the Q16 × Q24 RIL populations. Consensus QTL
Supporting QTL
Chr.
Peak location (cM) a
Experiments detecting the QTL
FS1.1 FS1.2 FS2.1 FS3.1 FS6.1
ifl1.1, ifd1.1, ifs1.1, mfl1.1, mfs1.1 ol1.1, mfd1.1 ol2.1, od2.1, ifl2.1, ifd2.1, ifs2.1, mfl2.1, mfd2.1, mfs2.1 ol3.1, ifl3.1, ifs3.1, mfl3.1, mfs3.1 mfs6.1
1 1 2 3 6
76.0 120.0 58.0 38.0 46.0
F5-2016S, F5-2016S, F5-2016S, F5-2016S, F5-2017S
Chr: chromosome. a:QTL peak location using F5-2017S data except for FS1.2 was from F5-2016S. 220
F5-2017S F5-2017S F5-2017S F5-2017S
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selection in cucumber, there will be more and more alleles or QTLs in cucumber in the cucumber breeding. Therefore, it is urgent to identify different genes or QTLs which are responsible for the different traits which are important to high quality and yield.
of whole genome of cucumber and the average interval between adjacent markers was 6.50 cM. Comparing with the genetic map constructed by others (Wang et al., 2016; Yuan et al., 2008; Zhang et al., 2012; Yang et al., 2013a,b; Ren et al.,2009), there were fewer markers in our study. The primer polymorphism rate in this study was only 10.96% which had a lower polymorphism rate between parent lines and there was uneven distribution of markers among different chromosomes. There were higher marker numbers on chromosomes 1, 2 and 3, and fewer marker numbers on chromosome 4 and chromosome 5. However, the 4 linkage group and 5 linkage group were covered with almost all genomes of chromosome 4 and chromosome 5, and the detected QTLs mainly located on chromosome1, chromosome2 and chromosome3, which implied the linkage group in this study could effective detected the QTLs controlled fruit shape and size. In addition, the environment was also an important factor for QTL mapping. There have many reports showed that QTL and environment have interactions in lots of traits (Prudent et al.,2009; Williams et al.,2008). In this study, cucumber fruit shape as a quantitative character had an influence by environment. There was no QTL locus detected for the MFD in F5-2016S but 3 QTLs in F5-2017S which indicate that in F5-2016S experiments have a huge environment influenced to MFD. However, the effect method for QTL detection influenced by environment is using multiple experiments. In this study, we had two environments through two years to map to QTLs for cucumber fruit shape which could effectively reduce the impact of environment.
5. Conclusion In the current study, we developed a SSR genetic map using a RIL population and surveyed the fruit shape traits under 2 environments over 2 years. Based on the genetic map and fruit shape traits, QTL analysis revealed five consensuses QTL, FS1.1, FS1.2, FS2.1, FS3.1 and FS6.1 which were related to the shape of cucumber fruit. Acknowledgment This study was supported by grants from the National Natural Science Foundation of China [No.31572143]. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.scienta.2019.01.062. References Amanullah, S., Liu, S., Gao, P., Zhu, Z.C., Zhu, Q.L., Fan, C.L., Luan, F.S., 2018. QTL mapping for melon (Cucumis melo L.) fruit traits by assembling and utilization of novel SNPs based CAPS markers. Sci. Hortic. 236, 18–29. https://doi.org/10.1016/j. scienta.2018.02.041. Ando, K., Carr, K.M., Grumet, R., 2012. Transcriptome analyses of early cucumber fruit growth identifies distinct gene modules associated with phases of development. BMC Genomics 13 (1). https://doi.org/10.1186/14712164-13-518. 518-518. Bo, K.L., Ma, Z., Chen, J.F., Weng, Y., 2015. Molecular mapping reveals structural rear rangements and quantitative trait loci underlying traits with local adaptation in semiwild Xishuangbanna cucumber (Cucumis sativus L. var. Xishuangbannanesis Qi et Yuan). Theor. Appl. Genet. 128, 25–39. https://doi.org/10.1007/s00122-0142410-z. Boonkorkaew, P., Hikosaka, S., Sugiyama, N., 2008. Effect of pollination on cell division, cellenlargement, and endogenous hormones in fruit development in a gynoecious cucumber. Sci. Horic. 116 (1), 1–7. https://doi.org/10.1016/j.scienta.2007.10.027. Broman, K.W., Wu, H., Sen, S., Churchill, G.A., 2003. R/qtl: QTL mapping experimental crosses. Bioinformatics 19 (7), 889–890. https://doi.org/10.1093/bioinformatics/ btg112. Cheng, Z.C., Gu, X.F., Zhang, S.P., Miao, H., Zhang, R.W., Liu, M.M., Yang, S.J., 2010. QTL analysis for fruit length of cucumber. China Vegetables 12 (1), 20–25 (in Chinese). Colle, M., 2015. Cucumber (Cucumis sativus L.) Fruit Development: Factors Influencing Fruit Size, Shape and Resistance to Phytophthora capsici. PhD dissertation. Michigan State University, East Lansing, MI, USA. Colle, M., Weng, Y., Kang, Y., Ophir, R., Sherman, A., Grumet, R., 2017. Variation in cucumber (cucumis sativus L.) fruit size and shape results from multiple components acting pre-anthesis and post-pollination. Planta 246 (4), 641–658. https://doi.org/ 10.1007/s00425-017-2721-9. Dijkhuizen, A., Staub, J.E., 2002. QTL conditioning yield and fruit quality traits in cucumber (Cucumis sativus L.). J. New Seeds 4 (4), 1–30. https://doi.org/10.1300/ J153v04n04_01. Fu, F.Q., Mao, W.H., Shi, K., Zhou, Y.H., Asami, T., Yu, J.Q., 2008. A role of brassinosteroids in earlyfruit development in cucumber. J. Exp. Bot. 59 (9), 2299–2308. https://doi.org/10.1093/jxb/ern093. Gillaspy, G., Ben-David, H., Gruissem, W., 1993. Fruits: a developmental perspective. Plant Cell 5 (10), 1439–1451. https://doi.org/10.2307/3869794. Gutierrez-Gonzalez, J.J., Vuong, T.D., Zhong, R., Yu, O., Lee, J.D., Shannon, G., Ellersieck, M., Nguyen, H.T., Sleper, D.A., 2011. Major locus and other novel additive and epistatic lociinvolved in modulation of isoflavone concentration insoybean seeds. Theor. Appl. Genet. 123 (8), 1375–1385. https://doi.org/10.1007/s00122-0111673-x. Jiang, L., Yan, S., Yang, W., Li, Y., Xia, M., Chen, Z., Wang, Q., Yan, L., Song, X., Liu, R., Zhang, X., 2015. Transcriptomic analysis reveals the roles of microtubule-related genes and transcription factors in fruit length regulation in cucumber (Cucumis sativus L.). Sci. Rep. 5, 8031. https://doi.org/10.1038/srep08031. Kennard, W.C., Havey, M.J., 1995. Quantitative trait analysis of fruit quality in cucumber: QTL detection, confirmation, and comparison with mating design variation. Theor. Appl. Genet. 91 (1), 53–61. https://doi.org/10.1007/BF00220858. Li, Y.H., Yang, L.M., Pathak, M., Li, D.W., He, X.M., Weng, Y., 2011. Fine genetic mapping of cp: a recessive gene for compact (dwarf) plant architecture in cucumber, Cucumis sativus L. Theor. Appl. Genet. 123 (6), 973–983. https://doi.org/10.1007/s00122011-1640-6. Li, X.X., Zhu, D.W., 2005. Descriptors and Data Standard for Cucumber. China Agriculture
4.3. QTLs about fruit shape and size of cucumber Pan et al. (2017) had summarize fruit size QTL analysis based on the F2 and F3 populations derived from WI7200 × WI7167 and proposed 8 consensus QTLs (FS1.1, FS1.2, FS2.1, FS3.1, FS4.1, FS5.2, FS5.3 and FS7.1) to explain the genetic mechanism of fruit size and shape in cucumber. We compared the 1.5-LOD interval of 8 QTLs detected by Pan et al. (2017) with 5 QTLs in this study, and it revealed that 4 QTLs FS1.1, FS1.2 and FS2.1 detected in this study were consistent with FS1.1, FS1.2 and FS2.1 QTLs by Pan et al. (2017). Weng et al. (2015) had detected the QTL for cucumber fruit size based on three populations (F2, F3 and RIL) derived from Gy14 × 9930 cross. The QTL analysis showed that there were 12 consensus QTLs (FS1.1, FS1.2, FS2.1, FS2.2, FS3.1,FS3.2, FS3.3, FS4.1, FS5.1, FS6.1, FS6.2, and FS7.1) for fruit size which explained fruit size in alone or in combination the others for fruit length and diameter at one stage or multiple stages of fruit developments. Basing on the 1.5-LOD interval of 12 QTLs detected by Weng et al. (2015), we found that the FS1.2, FS2.1, FS3.1 and FS6.1 in our study were consistent with the FS1.1, FS2.1, FS3.1 and FS6.2 in Weng et al. (2015) but failed to reveal the QTL FS1.2 at the chromosome 1 Weng et al. (2015) suggested that FS1.1 was involved the growth of ovary size, FS2.1 was only involved the radial growth of cucumber, FS3.1 controlled the fruit elongation just in immature stage and mature stage and FS6.1 could act in both fruit elongation and widening, which was different from the result of our study. There were also some other QTL loci screened about fruit shape and size in cucumber (e.g., Yuan et al., 2008; Kennard and Havey, 1995; Miao et al., 2011; Dijkhuizen and Staub, 2002; Wang et al., 2014; Wei et al., 2014; Bo et al., 2015; Zhu et al., 2016). A number of QTLs of fruit shape and size were identified, QTLs e FS1.1 locus which controlled immature fruit length and explain the variation of the trait 7.1%–14.1% were also reported by Cheng et al. (2010), and QTLs related with FS1.2 locus which controlled the mature fruit length and mature fruit diameter were identified and these loci explained phenotypic variation (R2) were 9.0% to 14.2% b (Miao et al., 2011). In addition, FS2.1 locus which controlled the immature fruit length and FS3.1 which controlled the immature and mature fruit length were also identified (Yuan et al., 2008; Wei et al., 2014). It was reasonable that the mapping studies in different genetic populations on fruit shape and size showed different differences and partial agreement. With the domestication and diversity 221
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Genetic analysis and QTL mapping of fruit length and diameter in a cucumber (Cucumber sativus L.) recombinant inbred line (RIL) population
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Tingting Zhang, Xvzhen Li, Yuting Yang, Xiao Guo, Qin Feng, Xiangyu Dong, Shuxia Chen College of Horticulture, Northwest A&F University, Yangling, Shaanxi, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Cucumber Fruit length and diameter Fruit shape index QTL mapping SSR
Fruit length and diameter are very important appearance quality which affects preference of consumers and quality of fruit. To improve the genetic mechanisms of cucumber fruit shape and size, an F2:5 recombinant inbred line (RIL)-population was derived from a cross between Q16 inbred lines which had slender fruit and Q24 inbred lines which had round fruit to screen fruit shape QTLs locus. 85 RILs, 15 F1 and 15 of each parental line were grown in the spring of 2016 and 2017 respectively. The length and diameter of ovary, immature fruit and mature fruit were measured at blooming day, 12 days after pollination and 35 days after pollination respectively. The RIL population was screened basing on the data related fruit using 89 SSR markers and 1 InDel marker. Twentyone QTLs loci were totally identified for fruit shape traits from RIL population. Comparing the flanking markers of 21 QTL loci allowed to 5 consensus QTLs FS1.1, FS1.2, FS2.1, FS3.1 and FS6.1 loci, in which FS3.1 played a role in fruit elongation, FS6.1 was important for the increase of fruit diameter, and the else 3 QTLs acted in both fruit elongation and diameter. The results from this study may increase the understanding of the genetic basis control of fruit shape and size and provide support for marker-assisted selection in cucumber with ideal fruit shape.
1. Introduction Cucumber (Cucumis sativus L.) belonged to the cucurbitaceae family and originated from the southern Himalayas, which was considered originating on Asian (Wei et al., 2016). Cucumber was an important vegetable around the world and it was usually consumed in raw or in preserved pickles at the immature period. The appearance quality of fruit largely influenced the preference of consumers, especially the fruit length and fruit diameter of the cucumber fruits, and the fruit shape has great influence on the fruit weight even the yield (Ramamurthy and waters, 2015; Amanullah et al., 2018). Actually, as one of the most important criteria, the traits of fruit length and diameter were always very important reference for breeders. After a long-term natural domestication and artificial selection basing on the C. sativus var. hardwickii, there were diverse cultivars and showed an increase variation of fruit shape and size. Currently, the cucumber were classified into several market types such as the Northern China type, Southern China type, Japanese Long Green type, European greenhouse type, wide type and so on (Colle, 2015). Every commercial type had different fruit
shape and size, and consumers of different locations had special preference to different kind of fruit shape. For example, the fruit shape of Northern China ecotype which was about 30 cm long, 4–5 cm wide was popular to people in the north of China, and the fruit shape of Southern China ecotype which was about 40 cm long and 5–6 cm diameter was popular to people in the south of China, and the fruit shape of European greenhouse type which was about 10 cm long and 3 cm wide was popular worldwide (Jiang et al., 2015; Pan et al., 2016). In fact, the shape of fruit was decided in some extent by the shape of ovary. The ovary exhibited a distinctive shape before female flowers were blooming and cell division occurs (Sinnott, 1936), which influenced the development of fruit and the length and diameter of the fruit. Reports showed that there was a strong correlation between the shape of ovary and the shape of fruit later (Wei et al., 2016). The shape of fruit was highly heritable and was influenced largely by the environmental factors (Ramamurthy and Waters, 2015; Perin et al., 2002). Notably, the shape and size of pre-anthesis ovary were influenced by increasing of cell division. During the period of 7 days pre-antheses to bloom, cucumber ovary showed a significantly increasing on the cell number,
Abbreviations: RIL, recombinant inbred lines; SSR, simple sequence repeats; INDEL, insertion deletion; PCR, polymerase chain reaction; OL, ovary length; OD, ovary diameter; IFL, immature fruit length; IFD, immature fruit diameter; IFS, immature fruit shape index; MFL, mature fruit length; MFD, mature fruit diameter; MFD, mature fruit shape index; CIM, composite interval mapping; LOD, polymerase chain reaction ⁎ Corresponding author. E-mail address: [email protected] (S. Chen). https://doi.org/10.1016/j.scienta.2019.01.062 Received 9 October 2018; Received in revised form 10 January 2019; Accepted 30 January 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.
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2.2. Field trials and phenotype of F5 population
and the cell size had remained constant, and the different cultivars owned diverse ovary shapes had a difference multiple of cell number increasing (Colle, 2015). After the female flowers were pollinated, the shape and size of cucumber fruit was decided both by the cell division and expansion (Gillaspy et al., 1993; Zhang et al., 2006; Ando et al., 2012). Cucumber fruit with different shape or size showed differences in cell number and size in the longitudinal versus transverse diameter (Jiang et al., 2015; Yang et al., 2013a,b). While the orientation and amount of the cell division influenced the fruit shape and size, the cell expansion played an important role for the rapidly increasing in fruit size. Then cell division speeded up and then slowed down until 8 days post pollination. The fruit cell number increased significantly after this stage, and the cell expansion followed with the cell division, and fruit size enlarged quickly (Fu et al., 2008; Boonkorkaew et al., 2008). Both the fruit length and diameter growth followed by the increase of cell number and size, but the diameter was somewhat behind the length development at the time of 4˜6 day post pollination (Yang et al., 2013a,b). Recently, people were so interesting with QTL loci of fruit shape that researchers began to study the loci through the method of QTL mapping. It was difficult to measure the fruit shape accurately. Generally, the fruit length and diameter were referred to the cucumber fruit shape and size, also the fruit shape index showed the ration of length to diameter for the fruit so it reflected the fruit shape at some extent and considered a trait reflected the shape of fruit despite it’s a complex trait (Pan et al., 2016). Studies showed that shape index had a strong correlation with fruit length and diameter of ovaries, immature fruit and mature fruit for cucumber (Colle et al., 2017; Weng et al., 2015), so the shape index was also used to describe the cucumber fruit shape in this study. There were numerous of QTL mapping studies performed to understand the genetic basis of fruit shape and size in cucumber recently. Kennard and Havey (1995) reported the QTL mapping of cucumber fruit length and diameter for the first time. Bo et al. (2015) mapped 5 QTLs for mature fruit length and 3 QTLs for mature fruit diameter based on the 124 RILs population developed from XIS cucumber × cultivated cucumber inbred line. Miao et al. (2011) mapped 3 QTLs for fruit length and 1 QTL for fruit diameter using 148 RILs population developed from 9110 Gt and 9930. Wei et al. (2014) identified 8 QTLs for immature fruit and mature fruit length, in which a major QTL loci fl3.2 in chromosome 3 explained 38.87% of phenotypic variation. These studies provided a sight for loci of fruit shape and size. However, most of these QTLs were different because of the different population used. In this study, we developed a SSR (simple sequence repeats)-based map that contained 90 molecular markers loci for cucumber using a recombinant inbred line (RIL) population from Q16 and Q24. We surveyed the length and diameter of ovary, immature fruit and mature fruit under 2 environments over 2 years in RIL population. QTL analysis revealed five consensuses QTL, FS1.1, FS1.2, FS2.1, FS3.1 and FS6.1 which were related to the shape of cucumber fruit.
85 F5 populations, 15 parental lines and 30 F1 individuals were grown in a plastic tunnel at the Horticulture Experimental Station (34° 16′ N, 108° 4′ E) of the College of Horticulture, Northwest A&F University, Yangling, Shaanxi Province, China at spring of 2016 and 2017 year respectively, which were named F5-2016S and F5-2017S. 5 plants were planted for every F5 RIL family. The 2–3 female flowers of each individual at about 14–17th node which were blooming were marked as 0 day and recorded as ovary, The fruit which were 12 days old was named immature fruits and the fruits which were 35 days old were named mature fruits according to Li and Zhu (2005). The length and diameter of immature fruits and mature fruits were recorded at 12 day and 35 day after blooming respectively. The ovary length (OL), ovary diameter (OD) and immature fruit diameter (IFD) were measured using digital caliper very gently. The immature fruit length (IFL), mature fruit length (MFL) and mature fruit diameter (MFD) were measured using a tap. The immature fruit shape index (IFS) and mature fruit shape index (MFS) were calculated followed the method by Li and Zhu (2005). 2–3 fruits from each F5 RIL individual were measured. Subsequent data (e.g., mean, coefficient of variation, range) were conducted in Microsoft Excel 2013. The correlation analysis was conducted among different traits, basing on the mean of individual plants measurement for RILs.
2.3. SSR markers analysis and linkage map development To screen the polymorphism markers between the parental lines, a total of 1505 cucumber or melon SSR primer pairs (Ren et al., 2009; Yang et al., 2012) distributed throughout the 7 chromosomes and 1 INDEL primer pair from the our lab were used. All the polymorphic markers between Q16 and Q24 were used to genotype the RIL population. The genomic DNA of the individuals was extracted using the CTAB method (Murray and Thompson, 1980). PCR amplification and gel electrophoreses were followed Li et al. (2011). The genetic linkage map was constructed based on the genotyping date from the RIL population and using the QTL IciMapping 4.0 software (http://www.isbreeding.net/software/?type=detail&id=18). Segregation distortion of each marker was tested using chi-squared (χ2) at an expected 1:1 segregation ratio.
2.4. QTLs mapping QTL analysis was performed by composite interval mapping (CIM) using R/qtl software package (http://www.rqtl.org/) (Broman et al., 2003; Weng et al., 2015). Using the data collected from individuals of RILs in two seasons, a whole genome scan was performed to detect the QTL of cucumber fruit shape traits procedures following Pan et al. (2017). To declare the presence of QTL, genome-wide LOD threshold was determined using 1000 permutations. In order to detect QTLs, a 1.5-LOD-support interval was calculated and defined by the left and right markers, and the interaction between QTLs in the same trait was examined by using the ‘effect plot’ function in R/qtl to conduct the interaction plots of QTL pairs. The name of the QTL was based on its chromosome location and traits names. For example, ol1.1 and ifl2.1 refer to the first QTL for the length of ovary and immature fruits on cucumber chromosome 1 and 2, respectively. A consensus QTL was judged by the following criteria: (1) if the QTL locus was detected both in the two environments; (2) if the QTL locus shared the same or overlapping 1.5-LOD-support intervals; (3) as a major-effect QTL with R 2 > 10% was detected at least one environment.
2. Materials and methods 2.1. Plant materials Two parental lines Q16 and Q24 were used to develop segregating populations in this study.Q16 was an inbred lines which was a typical Northern China ecotype with slim fruit shape and a length of above 35 cm, a diameter of about 3–4 cm at immature fruit stage, and a length of 40 cm at mature fruit stage. Q24 was a landrace which had round shape with 10 cm diameter both in immature and mature fruit period (Fig. 1). In this study, a single F1 plant from the cross between Q16 and Q24 was self-pollinated to produce an F2 population, and an 85 F5 recombinant inbred lines (RILs) were developed through single seed descent which were used during the 2016 and 2017 field trials. 215
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Fig. 1. Fruit morphology of Q16 and Q24. B are ovaries of Q16 (left) and Q24 (right). A and E are immature fruit shape of Q16 and Q24, respectively. C is an mature fruit of Q16 and D is an mature fruit of Q24.
3. Result
mature fruit shape in RILs shown in Fig.S1. The box-plots showed expression of these traits were stable in the two experiments. Therefore, these eight traits in both two experiments were used to the subsequent analysis. The Spearman’s rank correlation coefficients were calculated of all traits in RILs from two experiments. The correlation coefficients between the traits of fruit shape in 2016 spring and 2017 spring were showed in Table 2. The correlation was significantly or very significantly positive among the same trait in different stages even in the different year, such as the correlation among the fruit length of ovary and that of immature fruit, mature fruit was 0.315 and 0.347 respectively in the year of 2016, and the correlation among the fruit length ovary and immature fruit, mature fruit was 0.722 and 0.526 respectively in the year of 2017. All of the correlation of them reached to significant level (P < 0.05) or very significant level (P < 0.01). The correlation among the ovary diameter and immature fruit diameter, mature fruit diameter also reached to significant level. The fruit length and fruit diameter had a negative correlation in different fruit development stages, indicating that the elongating of the fruit would influence the thickening growth.
3.1. Phenotypic evaluations of fruit shape in RILs Two parental lines Q16 and Q24 had considerable differences in fruit shape and size including fruit longitudinal diameter, transverse diameter and fruit shape index both in the ovary fruit stage, immature fruit stage and mature fruit stage. Images of ovary, immature and mature fruits of parents were shown in Fig. 1. Q16 was above 35 cm longitudinal and the diameter was 3–4 cm in the immature fruit phase, the fruit of Q24 showed round shape which longitudinal diameter and the transverse diameter were about 10 cm (Fig. 1A and E). Similarly, both in the ovary phase and mature fruit phase showed Q16 as a slender fruit shape, while Q24 was spherical (Fig. 1B–D). We collected phenotypic data of 8 traits (OL, OD, IFL, IFD, IFS, MFL, MFS, and MFS) from RIL populations in two experiments (F5-2016S, F5-2017S) except for OL and OD in F5-2016S. The phenotypic performance and variation of the 8 fruit shape traits in parental lines, F1 and RILs population were presented in Table 1. The fruit length of F1 from Q16 × Q24 was about 34 cm long, and the diameter was 6 cm in the middle fruit size of parental lines. As with the F1 fruit shape, RILs had an average fruit long and diameter between the parental lines (Table 1). A frequency distribution of OL, OD, IFL, IFD, IFS, MFL, MFD and MFS among the RILs from two experiments were illustrated in Fig. 2. It showed that the fruit length, diameter and shape index in RILs were a normal or near-normal distribution. It suggested that these traits were quantitative one and suitable for QTL analysis. Three box-plots depicted the frequency distribution of the ovary shape, immature fruit shape and
3.2. Linkage map A total of 1505 SSR markers were screened between Q16 and Q24 and 165 primers (10.96%) showed polymorphic and clear bands. All 165 pairs primers were used for screening of polymorphic primers in the RILs, and finally 119 pairs of available polymorphic SSR primers were obtained after removing the primers with indistinguishable or unclear bands. Eventually there were 90 markers being mapped on the
Table 1 Phenotypic means and range of fruit shape traits of Q16, Q24 and their derived populations. Trais
OL OD IFL IFD IFSI MFL MFD MFS
Q16
Q24
F1
F5-2016S
F5-2017S
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Range
Mean ± SD
Range
45.52 ± 6.78 5.36 ± 0.38 32.75 ± 2.74 3.11 ± 0.60 10.53 ± 1.77 38.63 ± 10.40 5.66 ± 1.41 7.35 ± 2.98
12.37 ± 1.12 7.96 ± 0.62 9.57 ± 1.50 8.19 ± 1.30 1.14 ± 0.09 10.27 ± 0.60 9.03 ± 1.67 1.17 ± 0.04
34.30 ± 5.34 6.38 ± 0.69 24.29 ± 3.12 4.65 ± 0.72 5.23 ± 1.97 27.01 ± 8.98 6.23 ± 1.27 4.48 ± 1.68
n/a n/a 21.72 ± 9.65 5.42 ± 1.32 4.63 ± 3.05 26.30 ± 11.53 6.15 ± 1.45 4.59 ± 2.43
n/a n/a 3.00–42.65 2.38–8.70 0.87 ± 14.88 5.09–62.23 3.34–10.20 1.26–12.03
58.54 ± 27.68 6.79 ± 1.05 17.43 ± 6.56 4.50 ± 1.10 4.23 ± 2.09 24.79 ± 8.61 6.37 ± 1.41 4.18 ± 1.90
5.60–127.77 4.58–9.78 7.67–36.64 2.47–9.48 1.17 ± 11.81 10.85–53.29 2.40–9.63 1.15–9.44
OL: ovary length, OD: ovary diameter, IFL: immature fruit length, IFD: immature fruit diameter, IFS: immature fruit shape index, MFL: mature fruit length, MFD: mature fruit diameter, MFS: mature fruit shape index. n/a data not available. 216
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Fig. 2. Frequency distribution of ovary length (OL), ovary diameter (OD), immature fruit length (IFL), diameter (IFD) and shape index (IFS), mature fruit length (MFL), diameter (MFD) and mature fruit shape index (MFS) among RIL population in two environments.
and 3 QTLs were for MFS. 9 QTLs include ifl1.1, ifl2.1, ifd1.1, ifs1.1, ifs2.1, mfl1.1, mfl2.1, mfs1.1 and mfs2.1 could be detected both in the two experiments, and 5 QTLs ifd2.1, mfl3.1, mfd1.1, mfd2.1 and mfd6.1 were detected only in F5-2017S, 3 QTLsifl3.1, ifs3.1 and mfs3.1 were detected only in F5-2016S, and 4 QTLs ol1.1, ol2.1, ol3.1 and od2.1 for ovary fruit shape were only detected during the F5-2017S.
linkage map (Fig. 3), and detailed information of all mapped makers on the linkages map was presented in supplementary Table S1. A total of 90 markers were localized in a 7 linkage groups with a total length of 584.64 cM with averaged 83.52 cM for each group and 6.50 cM between adjacent markers. The lengths of individual chromosome in the 7 linkage groups varied from 42.41 cM (chromosome 5) to 128.83 cM (chromosome 1), and the map interval varied in 4.49 cM (chromosome 2) to 11.34 cM (chromosome 4). Chromosomes 1 and 3 showed the most markers which mapped on 18 markers, chromosome 4 and chromosome5 had the lowest marker number which only 6 markers. All the detailed information of each groups of linkage map had showed in supplementary Table S2.
3.3.1. QTL for ovary length and diameter LOD score of 3 QTLs ol1.1, ol2.1 and ol3.1 which were detected using phenotypic data of ovary length (OL) collected in F5-2017S were higher than 3. ol1.1 was a major-effect QTL which explained 25.68% of the phenotypic variation for ovary length, and it was located between SSR20354 and SSR17922 on Chr1. Another major-effect QTL ol2.1 for OL trait was detected which explained 24.44% of the phenotypic variation and located between UW080619 and SSR22558 on Chr2. ol3.1 explained 4.77% of the phenotypic variation and located between CS24 and SSR22514 on Chr3. One QTL od2.1 was detected for ovary diameter in F5-2017S, which explained 34.44% of phenotypic variation and located between SSR10518 and SSR22558 on Chr2. ol2.1 had a negative additive effect, and the other three QTLs showed a positive additive effect.
3.3. QTL analysis Phenotypic data of OL, OD, IFL, IFD, IFS, MFL, MFD and MFS from the two experiments were used in QTL analyses. We performed the whole genome scan for QTL number and location with CIM using a 10 cM window size. For the 8 fruit shape traits, a 1000 permutation tests (P = 0.05) was determined to obtain the LOD threshold to declare significance of QTL. Each QTL were estimated its support QTL score, R2 value, additive effects and 1.5-LOD support interval. The whole genome scan of all QTL detected across the seven chromosomes was provided in Supplemental Fig S2. The summary of all detected QTLs were presented in Table 3. A total of 21 QTLs were detected for 8 traits related to fruit shape were detected on 7 chromosomes. Among them, 3 QTLs were for OL traits, 1 QTL was for OD traits, 3 QTLs were for IFL, 2 QTLs were for IFS, 3 QTLs were for IFS, 3 QTLs were for MFL, 3 QTLs were for MFD,
3.3.2. QTL for the length, diameter and fruit shape index of immature fruit QTL analysis of the immature fruit length (IFL) trait identified 3 QTL with LOD scores greater than 3 from the two environments. In the experiment of F5-2016S, three QTLs ifl1.1, ifl2.1 and ifl3.1 about immature fruit length were detected. QTL ifl1.1 was a major-effect QTL which explained 20.71% of the phenotypic variation and located 217
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OL: ovary length, OD: ovary diameter, IFL: immature fruit length, IFD: immature fruit diameter, IFS: immature fruit shape index, MFL: mature fruit length, MFD: mature fruit shape index, MFS: mature fruit shape inde x . ns: meant there was not significant. * P < 0.05. ** P < 0.01.
1 1 0.533** 1 −0.285* −0.642** 1 0.137 −0.620** −0.2 1 −0.165 −0.326** 0.560** 0.910** 1 0.617** −0.183 −0.304* 0.833** 0.575** 1 0.640** 0.880** −0.216 −0.494** 0.599** 0.916** 1 0.680** 0.628** 0.592** −0.048 −0.349** 0.508** 0.574** IFL IFL IFD IFD IFS IFS MFL MFL MFD MFD MFS MFS
1 −0.499** 0.315* 0.722** −0.420** −0.211 0.336* 0.570** 0.347* 0.526** −0.063 −0.423** 0.324* 0.597** OL OD 2016 2017 2016 2017 2016 2017 2016 2017 2016 2017 2016 2017
1 −0.460** −0.653** 0.518** 0.458** −0.507** −0.683** −0.604** −0.684** 0.257 0.511** −0.527** −0.701**
1 0.507** −0.460** −0.512** 0.878** 0.633** 0.809** 0.572** −0.078 −0.295* 0.670** 0.540**
1 −0.554** −0.225 0.587** 0.877** 0.591** 0.790** −0.135 −0.484** 0.549** 0.845**
1 0.296* −0.693** −0.495** −0.454** −0.471** 0.078 0.460** −0.375** −0.502**
1 −0.453** −0.577** −0.416** −0.473** 0.329* 0.346** −0.426** −0.482**
2017 IFS 2016 IFS 2017 IFD 2016 IFD 2017 IFL 2016 IFL OD OL Trait
Table 2 Spearman’s rank correlation coefficients among OL, OD, IFL, IFD, MFL, MFD in RILs populations for two experiments.
2016 MFL
2017 MFL
2016 MFD
2017 MFD
2016 MFS
2017 MFS
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between UW083745 and SSR05723 on Chr1. ifl2.1 was also major-effect QTL for IFL trait could explain 24.44% of the phenotypic variation and located between UW080619 and SSR22558 on Chr2. ifl3.1 was a minor-effect QTL which R 2 was 7.27% and located on Chr3 with the flanking markers of SSR22158 and SSR03918. 2 QTLs ifl1.1 and ifl2.1 were detected in the experiment of F5-2017S, which explained 19.19% to 28.53% phenotypic variance (Table 3; Fig S2). ifl1.1 and ifl2.1 were identified as two major QTLs both under two different environments and which had a negative additive effect for immature fruit length and the minor QTL ifl3.1 was not identified in the experiment of F5-2017S. QTL analysis of the immature fruit diameter (IFD) trait was carried out two environments and 2 QTLs ifd1.1 and ifd2.1 were identified. One major-effect QTL ifd1.1 was detected in F5-2016S explained 29.22% of the phenotypic variation for IFD and located between SSR04805 and SSR23049 on Chr1. Two QTLs ifd1.1 and ifd2.1 detected in F5-2017S explained 7.83% and 21.87% of variation for the IFD. ifd2.1 was a major-effect QTL was located between SSR10518 and SSR22558 on Chr2 and ifd1.1 was a minor-effect QTL was located between UW083745 and SSR23049 on Chr1. ifd2.1 was only detected in F52017S environment and ifd1.1 could detected both under two environments, and it showed ifd1.1 was a stable QTL and had a positive additive effect for IFD. 3 QTLs ifs1.1, ifs2.1 and ifs3.1 were identified basing on the data of immature fruit shape index (IFS) with LOD scores greater than 3 under two different environments. ifs1.1, ifs2.1 and ifs3.1 detected in F52016S explained 7.85%, 6.78% and 76.42% of variation of the IFS trait. ifs1.1 was located between UW083745 and SSR23049 on Chr1, ifs2.1 was located between UW080619 and SSR22558 on Chr2, and ifs3.1 was located between CS24 and SSR20258 on Chr3, respectively. It indicated that ifs3.1 was a major-effect QTL under the environment of 2016 spring. Two major-effect QTLS mfs1.1 and mfs2.1 detected in F5-2017S explained 18.35% and 39.64% of variation of the IFS, ifs1.1 was located between UW083745 and SSR23049 on Chr1, and ifs2.1 was located between SSR10518 and SSR22558 on Chr2. ifs1.1 and ifs2.1 were identified as two stable QTLs both detected under two different environments and which had an positive additive effect for IFS and the major-effect QTL ifs3.1 was failure identified in the experiment of F52017S. 3.3.3. QTL for the length, diameter and fruit shape index of mature fruit 3 QTLs mfl1.1, mfl2.1 and mfl3.1 were identified basing on the data of mature fruit length (MFL) with LOD scores greater than 3 under two different environments. mfl1.1 and mfl2.1 detected in F5-2016S explained 28.34% and 47.46% of variation of the MFL trait. mfl1.1 was located between SSR04805 and SSR23049 on Chr1 and mfl2.1 was located between UW080619 and SSR22558 on Chr2. It indicated that these 2 QTLs were major ones under the environment of 2016 spring. Three QTLs mfl1.1, mfl2.1 and mfl3.1 detected in F5-2017S explained 17.57%, 29.27% and 3.82% of variation of the MFL (Table 3; Fig S2). mfl1.1, mfl2.1 were two major-effect QTLs and mfs1.1 was located between SSR03860 and SSR05723 on Chr1, and mfs2.1 was located between SSR10518 and SSR22558 on Chr2. mfl3.1 was a minor-effect QTL which located between CS24 and SSR03918 on Chr3. Comparing the 1.5-LOD-support intervals markers of mfl1.1 and mfl2.1 in the experiment of F5-2016S and F5-2017S, we identified mfl1.1 and mfl2.1 were stable and major-effect QTLs and had a negative additive effect for the trait of MFL. 3 major-effect QTLs mfd1.1, mfd2.1, mfd6.1 were identified basing on the data of mature fruit diameter (MFD) with LOD scores greater than 3 in the F5-2017S environments, and it was failure to detect any QTLs in the experiment of F5-2016S. mfd1.1, mfd2.1 and mfd6.1 detected in 2017 spring could explain 16.20%, 11.42% and 18.10% of variation of the MFD. mfd1.1 was located between SSR20354 and SSR17922 on Chr1, mfd2.1 was located between SSR10518 and SSR22558 on Chr2, and mfd3.1 was located between SSR11798 and UW039897 on Chr6, respectively. Both the mfd2.1 and mfd6.1 had a 218
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Fig. 3. The linkage group of cucumber constructed based on RIL population of a cross between Q16 and Q24. The left of the linkage group were indicated the cumulative distances among these markers indicated in cM and the name of markers were showed on the right.
interval for each QTL by the flanking markers, and the markers physical location was list in the Tabel S1 based on the Gy14 draft genome assemblies. A total of 5 consensus QTLs were detected in this study based on the consistent QTL judgment criteria as described in the previous method (Table 4). ifl1.1, ifd1.1, ifs1.1, mfl1.1 and mfs1.1 were share the same or overlapping 1.5-LOD-support intervals on Chr1 treat as a consensus QTL named FS1.1, ol1.1 and mfd1.1 were share another same or overlapping 1.5-LOD-support intervals on Chr1 regarded as a consensus QTL named FS1.2, ol2.1, od2.1, ifl2.1, ifd2.1, ifs2.1, mfl2.1, mfd2.1 and mfs2.1 were share the same or overlapping 1.5-LOD-support intervals on Chr2 deemed as a consensus QTL named FS2.1, ol3.1, ifl3.1, ifs3.1, mfl3.1 and mfs3.1 were share same or overlapping 1.5-LODsupport intervals on Chr3 regarded as a consensus QTL named FS3.1, and mfs6.1 as a major-effect QTL with R 2 > 10% was detected treated as a consensus QTL named FS6.1. It was clear that FS6.1 was worked on the mature fruit radial, FS3.1 played roles in fruit elongation, the others 3 QTLs FS1.1, FS1.2 and FS2.1 acted in both fruit elongation and diameter, especially the FS2.1 had an important role for fruit elongation and radial throughout the whole of fruit development.
positive additive effect for the MFD while mfd1.1 exhibited a negative additive effect. 3 QTLs mfs1.1, mfs2.1 and mfs3.1 were identified basing on the data of mature fruit shape index (MFS) with LOD scores greater than 3 under two different environments. mfs1.1, mfs2.1 and mfs3.1 detected in F52016S explained 23.32%, 24.71% and 12.56% of variation of the MFS trait. mfs1.1 was located between SSR04805 and SSR23049 on Chr1, mfs2.1 was located between SSR10518 and SSR22558 on Chr2, and mfs3.1 was located between SSR16133 and SSR20258 on Chr3, respectively. It indicated that these 3 QTLs were major ones under the environment of 2016 spring. mfs1.1 and mfs2.1 detected in F5-2017S explained 16.39% and 33.37% of variation of the MFS (Table 3; Fig S2), and mfs1.1 was located between SSR23049 and SSR20354on Chr1, and mfs2.1 was located between SSR10518 and SSR22558on Chr2. Comparing the flank markers of mfs1.1 and mfs2.1 in the experiment of F52016S and F5-2017S, we identified mfs1.1 and mfs2.1 were stable and major QTLs and have a negative additive effect for the trait of mature fruit shape index. With phenotypic data collected in two environments over two years, we identified 21 QTLs for 8 fruit shape traits. There was a 1.5-LOD 219
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Table 3 The QTL mapping results of cucumber fruit shape. Experiment
QTL
F5-2017S
Ovary shape
F5-2016S
immature fruit shape
F5-2017S
F5-2016S
mature fruit shape
F5-2017S
ol1.1 ol2.1 ol3.1 od2.1 ifl1.1 ifl2.1 ifl3.1 ifd1.1 ifs1.1 ifs2.1 ifs3.1 ifl1.1 ifl2.1 ifd1.1 ifd2.1 ifs1.1 ifs2.1 mfl1.1 mfl2.1 mfs1.1 mfs2.1 mfs3.1 mfl1.1 mfl2.1 mfl3.1 mfd1.1 mfd2.1 mfd6.1 mfs1.1 mfs2.1
Chr.
1 2 3 2 1 2 3 1 1 2 3 1 2 1 2 1 2 1 2 1 2 3 1 2 3 1 2 6 1 2
LOD value
5.33 6.43 4.85 8.82 6.26 6.81 3.21 7.67 9.74 9.52 17.85 4.86 6.93 3.61 4.43 5.18 12.34 7.45 11.61 4.39 7.07 4.7 6.26 4.81 3.2 4.12 4.07 5.11 4.37 10.03
R2(%)
Additive effect
25.68 29.41 4.77 34.44 20.71 24.44 7.27 29.22 7.85 6.78 76.42 19.19 28.53 7.83 21.87 18.35 39.64 28.34 47.46 23.32 24.71 12.56 17.57 29.27 3.82 16.20 11.42 18.10 16.39 33.37
6.217 −6.69 0.348 0.803 −4.228 −3.98 −1.286 0.738 0.105 0.094 0.018 −3.326 −3.801 0.315 0.57 0.081 0.112 −5.297 −6.672 −1.113 −1.022 −0.168 −3.829 −5.046 −1.105 −0.584 0.493 0.215 −0.757 −1.122
Peak
120 57.97 38 58 78 57.97 38 76 76 57.97 39.19 76 60 76 60 76 60 72 60 70.97 60 40 84 57.97 40 126 54 46 96 57.97
1.5-LOD Interval Left(cM)
Right(cM)
SSR20354(115.77) UW080619(57.29) CS24(20.35) SSR10518(46.20) UW083745(70.97) UW080619(42.06) SSR22158(0) SSR04805 (68.03) UW083745(70.97) UW080619(42.06) CS24(20.35) UW083745(70.97) SSR10518(46.20) UW083745(70.97) SSR10518(46.20) UW083745(70.97) SSR10518(46.20) SSR04805(68.03) UW080619(57.29) SSR04805(68.03) SSR10518(46.20) SSR16133(11.17) SSR03860(73.81) SSR10518(46.20) CS24(20.35) SSR20354(115.77) SSR10518(46.20) SSR11798(37.34) SSR23049(86.58) SSR10518(46.20)
SSR17922(132.24) SSR22558(68.62) SSR22514(39.19) SSR22558(68.62) SSR05723(98.64) SSR22558(68.62) SSR03918(130.87) SSR23049(86.58) SSR23049(86.58) SSR22558(68.62) SSR20258(49.17) SSR17922(132.24) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR23049(86.58) SSR22558(68.62) SSR20258(49.17) SSR05723(100.57) SSR22558(68.62) SSR03918(130.87) SSR17922(132.2) SSR22558(68.62) UW039897(54.92) SSR20354(115.77) SSR22558(68.62)
Chr: chromosome, R2: phenotypic variation explained.
4. Discussion
Most of the studies had revealed there were weak correlations between fruit diameter at different fruit stages (Yuan et al., 2008; Bo et al., 2015; Miao et al., 2011; Pan et al., 2017). In our study, there were very significantly positive correlations between ovary diameter (OD) and immature fruit diameter (IFD) and mature fruit diameter (MFD), and there was also significantly positive correlations between immature fruit shape index (IFS) and mature fruit shape index (MFS). It indicated that the fruit shape of cucumber might be decided at the very early fruit development stage. Reports showed that there was a positive correlation between the fruit length and diameter (Wei et al., 2016; Weng et al., 2015), however, we found that there was a negative correlation between the fruit length and diameter in the three different stages. It indicated that there might be different relationship between the fruit length and fruit diameter in different populations.
4.1. Correlation between fruit length and correlation between fruit diameter of different stages in cucumber There were highly correlation between fruit length, and the same as the diameter of different stages. The length and diameter decided the cucumber fruit shape which was closely related with the high quality and yield. In our study, the correlation between fruit length in different stages was higher. The correlation ranged from 0.315 to 0.722 which was at a significant level or a very significant level among the length of different stages even in the different year. The correlation among the ovary diameter and immature fruit diameter, mature fruit diameter also reached to significant level. Reports showed that there were highly correlation between fruit length in different stages (Weng et al., 2015; Zhu et al., 2016), and it indicated that the fruit length might be decided in certain one or two main development stages. In our study, it showed that immature fruit length (IFL) and mature fruit length (MFL) had significantly and positively correlation with ovary length (OL), suggesting that the fruit length of later stage might be influenced by the ovary length at the very early stage.
4.2. Factors affecting QTL detect efficiency in this study QTL mapping was an efficient method to analyze genes which controlled quantitative traits in plants. However, the key factor for mapping QTL loci was the genetic map. (Zou et al., 2012; GutierrezGonzalez et al., 2011). In this study, the genetic map covered 584.64 cM
Table 4 Consensus fruit shape QTLs detected in the Q16 × Q24 RIL populations. Consensus QTL
Supporting QTL
Chr.
Peak location (cM) a
Experiments detecting the QTL
FS1.1 FS1.2 FS2.1 FS3.1 FS6.1
ifl1.1, ifd1.1, ifs1.1, mfl1.1, mfs1.1 ol1.1, mfd1.1 ol2.1, od2.1, ifl2.1, ifd2.1, ifs2.1, mfl2.1, mfd2.1, mfs2.1 ol3.1, ifl3.1, ifs3.1, mfl3.1, mfs3.1 mfs6.1
1 1 2 3 6
76.0 120.0 58.0 38.0 46.0
F5-2016S, F5-2016S, F5-2016S, F5-2016S, F5-2017S
Chr: chromosome. a:QTL peak location using F5-2017S data except for FS1.2 was from F5-2016S. 220
F5-2017S F5-2017S F5-2017S F5-2017S
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selection in cucumber, there will be more and more alleles or QTLs in cucumber in the cucumber breeding. Therefore, it is urgent to identify different genes or QTLs which are responsible for the different traits which are important to high quality and yield.
of whole genome of cucumber and the average interval between adjacent markers was 6.50 cM. Comparing with the genetic map constructed by others (Wang et al., 2016; Yuan et al., 2008; Zhang et al., 2012; Yang et al., 2013a,b; Ren et al.,2009), there were fewer markers in our study. The primer polymorphism rate in this study was only 10.96% which had a lower polymorphism rate between parent lines and there was uneven distribution of markers among different chromosomes. There were higher marker numbers on chromosomes 1, 2 and 3, and fewer marker numbers on chromosome 4 and chromosome 5. However, the 4 linkage group and 5 linkage group were covered with almost all genomes of chromosome 4 and chromosome 5, and the detected QTLs mainly located on chromosome1, chromosome2 and chromosome3, which implied the linkage group in this study could effective detected the QTLs controlled fruit shape and size. In addition, the environment was also an important factor for QTL mapping. There have many reports showed that QTL and environment have interactions in lots of traits (Prudent et al.,2009; Williams et al.,2008). In this study, cucumber fruit shape as a quantitative character had an influence by environment. There was no QTL locus detected for the MFD in F5-2016S but 3 QTLs in F5-2017S which indicate that in F5-2016S experiments have a huge environment influenced to MFD. However, the effect method for QTL detection influenced by environment is using multiple experiments. In this study, we had two environments through two years to map to QTLs for cucumber fruit shape which could effectively reduce the impact of environment.
5. Conclusion In the current study, we developed a SSR genetic map using a RIL population and surveyed the fruit shape traits under 2 environments over 2 years. Based on the genetic map and fruit shape traits, QTL analysis revealed five consensuses QTL, FS1.1, FS1.2, FS2.1, FS3.1 and FS6.1 which were related to the shape of cucumber fruit. Acknowledgment This study was supported by grants from the National Natural Science Foundation of China [No.31572143]. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.scienta.2019.01.062. References Amanullah, S., Liu, S., Gao, P., Zhu, Z.C., Zhu, Q.L., Fan, C.L., Luan, F.S., 2018. QTL mapping for melon (Cucumis melo L.) fruit traits by assembling and utilization of novel SNPs based CAPS markers. Sci. Hortic. 236, 18–29. https://doi.org/10.1016/j. scienta.2018.02.041. Ando, K., Carr, K.M., Grumet, R., 2012. Transcriptome analyses of early cucumber fruit growth identifies distinct gene modules associated with phases of development. BMC Genomics 13 (1). https://doi.org/10.1186/14712164-13-518. 518-518. Bo, K.L., Ma, Z., Chen, J.F., Weng, Y., 2015. Molecular mapping reveals structural rear rangements and quantitative trait loci underlying traits with local adaptation in semiwild Xishuangbanna cucumber (Cucumis sativus L. var. Xishuangbannanesis Qi et Yuan). Theor. Appl. Genet. 128, 25–39. https://doi.org/10.1007/s00122-0142410-z. Boonkorkaew, P., Hikosaka, S., Sugiyama, N., 2008. Effect of pollination on cell division, cellenlargement, and endogenous hormones in fruit development in a gynoecious cucumber. Sci. Horic. 116 (1), 1–7. https://doi.org/10.1016/j.scienta.2007.10.027. Broman, K.W., Wu, H., Sen, S., Churchill, G.A., 2003. R/qtl: QTL mapping experimental crosses. Bioinformatics 19 (7), 889–890. https://doi.org/10.1093/bioinformatics/ btg112. Cheng, Z.C., Gu, X.F., Zhang, S.P., Miao, H., Zhang, R.W., Liu, M.M., Yang, S.J., 2010. QTL analysis for fruit length of cucumber. China Vegetables 12 (1), 20–25 (in Chinese). Colle, M., 2015. Cucumber (Cucumis sativus L.) Fruit Development: Factors Influencing Fruit Size, Shape and Resistance to Phytophthora capsici. PhD dissertation. Michigan State University, East Lansing, MI, USA. Colle, M., Weng, Y., Kang, Y., Ophir, R., Sherman, A., Grumet, R., 2017. Variation in cucumber (cucumis sativus L.) fruit size and shape results from multiple components acting pre-anthesis and post-pollination. Planta 246 (4), 641–658. https://doi.org/ 10.1007/s00425-017-2721-9. Dijkhuizen, A., Staub, J.E., 2002. QTL conditioning yield and fruit quality traits in cucumber (Cucumis sativus L.). J. New Seeds 4 (4), 1–30. https://doi.org/10.1300/ J153v04n04_01. Fu, F.Q., Mao, W.H., Shi, K., Zhou, Y.H., Asami, T., Yu, J.Q., 2008. A role of brassinosteroids in earlyfruit development in cucumber. J. Exp. Bot. 59 (9), 2299–2308. https://doi.org/10.1093/jxb/ern093. Gillaspy, G., Ben-David, H., Gruissem, W., 1993. Fruits: a developmental perspective. Plant Cell 5 (10), 1439–1451. https://doi.org/10.2307/3869794. Gutierrez-Gonzalez, J.J., Vuong, T.D., Zhong, R., Yu, O., Lee, J.D., Shannon, G., Ellersieck, M., Nguyen, H.T., Sleper, D.A., 2011. Major locus and other novel additive and epistatic lociinvolved in modulation of isoflavone concentration insoybean seeds. Theor. Appl. Genet. 123 (8), 1375–1385. https://doi.org/10.1007/s00122-0111673-x. Jiang, L., Yan, S., Yang, W., Li, Y., Xia, M., Chen, Z., Wang, Q., Yan, L., Song, X., Liu, R., Zhang, X., 2015. Transcriptomic analysis reveals the roles of microtubule-related genes and transcription factors in fruit length regulation in cucumber (Cucumis sativus L.). Sci. Rep. 5, 8031. https://doi.org/10.1038/srep08031. Kennard, W.C., Havey, M.J., 1995. Quantitative trait analysis of fruit quality in cucumber: QTL detection, confirmation, and comparison with mating design variation. Theor. Appl. Genet. 91 (1), 53–61. https://doi.org/10.1007/BF00220858. Li, Y.H., Yang, L.M., Pathak, M., Li, D.W., He, X.M., Weng, Y., 2011. Fine genetic mapping of cp: a recessive gene for compact (dwarf) plant architecture in cucumber, Cucumis sativus L. Theor. Appl. Genet. 123 (6), 973–983. https://doi.org/10.1007/s00122011-1640-6. Li, X.X., Zhu, D.W., 2005. Descriptors and Data Standard for Cucumber. China Agriculture
4.3. QTLs about fruit shape and size of cucumber Pan et al. (2017) had summarize fruit size QTL analysis based on the F2 and F3 populations derived from WI7200 × WI7167 and proposed 8 consensus QTLs (FS1.1, FS1.2, FS2.1, FS3.1, FS4.1, FS5.2, FS5.3 and FS7.1) to explain the genetic mechanism of fruit size and shape in cucumber. We compared the 1.5-LOD interval of 8 QTLs detected by Pan et al. (2017) with 5 QTLs in this study, and it revealed that 4 QTLs FS1.1, FS1.2 and FS2.1 detected in this study were consistent with FS1.1, FS1.2 and FS2.1 QTLs by Pan et al. (2017). Weng et al. (2015) had detected the QTL for cucumber fruit size based on three populations (F2, F3 and RIL) derived from Gy14 × 9930 cross. The QTL analysis showed that there were 12 consensus QTLs (FS1.1, FS1.2, FS2.1, FS2.2, FS3.1,FS3.2, FS3.3, FS4.1, FS5.1, FS6.1, FS6.2, and FS7.1) for fruit size which explained fruit size in alone or in combination the others for fruit length and diameter at one stage or multiple stages of fruit developments. Basing on the 1.5-LOD interval of 12 QTLs detected by Weng et al. (2015), we found that the FS1.2, FS2.1, FS3.1 and FS6.1 in our study were consistent with the FS1.1, FS2.1, FS3.1 and FS6.2 in Weng et al. (2015) but failed to reveal the QTL FS1.2 at the chromosome 1 Weng et al. (2015) suggested that FS1.1 was involved the growth of ovary size, FS2.1 was only involved the radial growth of cucumber, FS3.1 controlled the fruit elongation just in immature stage and mature stage and FS6.1 could act in both fruit elongation and widening, which was different from the result of our study. There were also some other QTL loci screened about fruit shape and size in cucumber (e.g., Yuan et al., 2008; Kennard and Havey, 1995; Miao et al., 2011; Dijkhuizen and Staub, 2002; Wang et al., 2014; Wei et al., 2014; Bo et al., 2015; Zhu et al., 2016). A number of QTLs of fruit shape and size were identified, QTLs e FS1.1 locus which controlled immature fruit length and explain the variation of the trait 7.1%–14.1% were also reported by Cheng et al. (2010), and QTLs related with FS1.2 locus which controlled the mature fruit length and mature fruit diameter were identified and these loci explained phenotypic variation (R2) were 9.0% to 14.2% b (Miao et al., 2011). In addition, FS2.1 locus which controlled the immature fruit length and FS3.1 which controlled the immature and mature fruit length were also identified (Yuan et al., 2008; Wei et al., 2014). It was reasonable that the mapping studies in different genetic populations on fruit shape and size showed different differences and partial agreement. With the domestication and diversity 221
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