Skip to content

Advertisement

  • Research article
  • Open Access

An SNP-based saturated genetic map and QTL analysis of fruit-related traits in cucumber using specific-length amplified fragment (SLAF) sequencing

Contributed equally
BMC Genomics201415:1158

https://doi.org/10.1186/1471-2164-15-1158

  • Received: 27 May 2014
  • Accepted: 11 December 2014
  • Published:

Abstract

Background

Cucumber, Cucumis sativus L., is an economically important vegetable crop which is processed or consumed fresh worldwide. However, the narrow genetic base in cucumber makes it difficult for constructing high-density genetic maps. The development of massively parallel genotyping methods and next-generation sequencing (NGS) technologies provides an excellent opportunity for developing single nucleotide polymorphisms (SNPs) for linkage map construction and QTL analysis of horticultural traits. Specific-length amplified fragment sequencing (SLAF-seq) is a recent marker development technology that allows large-scale SNP discovery and genotyping at a reasonable cost. In this study, we constructed a high-density SNP map for cucumber using SLAF-seq and detected fruit-related QTLs.

Results

An F2 population of 148 individuals was developed from an intra-varietal cross between CC3 and NC76. Genomic DNAs extracted from two parents and 148 F2 individuals were subjected to high-throughput sequencing and SLAF library construction. A total of 10.76 Gb raw data and 75,024,043 pair-end reads were generated to develop 52,684 high-quality SLAFs, out of which 5,044 were polymorphic. 4,817 SLAFs were encoded and grouped into different segregation patterns. A high-resolution genetic map containing 1,800 SNPs was constructed for cucumber spanning 890.79 cM. The average distance between adjacent markers was 0.50 cM. 183 scaffolds were anchored to the SNP-based genetic map covering 46% (168.9 Mb) of the cucumber genome (367 Mb). Nine QTLs for fruit length and weight were detected, a QTL designated fl3.2 explained 44.60% of the phenotypic variance. Alignment of the SNP markers to draft genome scaffolds revealed two mis-assembled scaffolds that were validated by fluorescence in situ hybridization (FISH).

Conclusions

We report herein the development of evenly dispersed SNPs across cucumber genome, and for the first time an SNP-based saturated linkage map. This 1,800-locus map would likely facilitate genetic mapping of complex QTL loci controlling fruit yield, and the orientation of draft genome scaffolds.

Keywords

  • SLAF-seq
  • Genetic map
  • SNP
  • Cucumis sativus L
  • QTL analysis

Background

Cucumber (Cucumis sativus L., 2n = 2x = 14) is one of the most important vegetable crops cultivated worldwide, immature fruits of which are consumed cooked, processed, or fresh in a considerable amount. Agricultural production of cucumbers and gherkins accounted for more than 2 million hectares of land yielding 62 million tons of produce in 2010 (http://faostat3.fao.org). However, cucumber has a very narrow genetic base and lack of molecular polymorphism [14], which impedes the construction of saturated genetic maps and map-based cloning of horticultural important genes. In the past decades, cucumber linkage maps were mostly composed of dominant markers [i.e. random amplified polymorphic DNAs (RAPDs), and amplified fragment length polymorphisms (AFLPs)], and did not reach saturated (average marker distance less than 2 cM) due to insufficient marker number [59].

Draft genome assemblies of three cucumber lines (9930, Gy14, B10) [1012] successively published provide a good opportunity for developing simple sequence repeats (SSRs) as co-dominant markers in map construction [13]. Several SSR-based maps have been developed with 100 ~ 300 or even more markers [11, 1419], and genes controlling cucumber scab resistant (Ccu), compact growth (cp), Zucchini yellow mosaic virus resistance (zym) , uniform immature fruit color (u), tuberculate fruit (Tu), spine color and mature fruit color (B), and dull skin (D) were mapped or fine mapped [4, 17, 2024]. The inter-subspecific genetic map with 995 SSRs constructed by Ren et al.[14] is the most saturated, followed by an intra-varietal map containing 735 loci [11]. Unfortunately, over one quarter of the mapped SSRs in this inter-subspecific map were found clustering in chromosomes 3, 4, 5, 6, and 7 due to the small mapping population [77 recombinant inbred lines(RILs)] and possible chromosomal rearrangements between the two parents (cultivated cucumber Gy14 and the wild C. sativus var. hardwickii PI 183967). Two consensus maps were developed in cucumber to increase marker density, which were constructed by Zhang et al. (1369 marker loci) [25], and Yang et al. (1681 marker loci), respectively [26]. Both of them employed the Gy14 × PI 183967 map with 995 SSRs for map integration [16], whereas marker orders in recombination suppression regions in the 1369-point map were not well placed. The 1681-locus consensus map overcame this drawback by the integrating intra-varietal map by Yang et al.[11], and improved marker orders and density in three chromosomes particularly in chromosome 4. Despite the high marker density in consensus maps, it is still difficult to construct saturated maps for F2 or RIL populations derived from intra-varietal crosses to conduct QTL analysis and molecular mapping in certain populations.

Single-nucleotide polymorphisms (SNPs) are the most abundant and stabile form of genetic variation in most genomes, which have become the marker type of choice in many evolutionary and ecological studies [2729]. The advent of massive parallel next-generation sequencing (NGS) technologies has made it possible for high-throughput identification and genotyping of SNPs. However, whole-genome deep re-sequencing is still cost-prohibitive for sequencing and genotyping large populations and usually not necessary [30]. Reduced representation library (RRL) sequencing is one strategy to bring down the cost through genome reduction [25, 31, 32]. Restriction-site associated DNA sequencing (RAD-seq) reduces genome complexity by sequencing only the DNA fragments with restriction sites in spite of length, and has been proven to be a useful tool for SNP discovery and genetic mapping [3335]. 2b-RAD is a streamlined RAD approach that sequences uniform fragments generated by type IIB restriction endonuclease, which is suitable for species with large genomes including humans [17]. Recently, specific-length amplified fragments sequencing (SLAF-seq) was developed as a modified RRL sequencing strategy for de novo SNP discovery and genotyping of large populations [36], which has generated high-density genetic maps with abundant SNPs for common carp, sesame and soybean [3638].

Studies have shown that a number of fruit-related traits are controlled by quantitative trait loci (QTLs) in cucumber, such as fruit weight and fruit shape index (i.e., fruit length, diameter, length/diameter ratio and length/stalk ratio) [8, 3945]. Fruit length and weight are two traits that significantly correlate with yield and commercial quality of the cultivated cucumber. With a SRAP (sequence-related amplified polymorphism)-based genetic map (257 loci), Yuan et al.[40] identified five QTLs for fruit weight and seven QTLs for fruit length. More recently, Cheng et al.[42] identified five QTLs for immature fruit length using 234 SSRs on LGs 1, 4, and 6. Miao et al.[43] detected three QTLs for immature fruit length on LG5 and LG6 with a previously constructed map (245 SSRs). Five QTLs conditioning fruit weight were also identified on LG1 and LG5 [41]. However, the information is still insufficient for map-based gene cloning for cucumber yield and fruit quality improvement.

In this study, a high-throughput and cost-effective SLAF-seq approach was employed to generate an SNP-based genetic map for cucumber, which contained 1,800 high quality SNPs and spanned 890.79 cM with an average marker interval of 0.50 cM. Physically, we anchored 183 of the ‘9930’ draft genome scaffolds with 168.9 Mb sequences. Two mis-assembled scaffolds were verified. Nine QTLs controlling fruit length and weight were detected on four linkage groups.

Results

High-throughput SLAF sequencing and genotyping

A total of 10.76 Gb raw data was generated from Illumina sequencing and SLAF library construction, which contained 75,024,043 pair-end reads with a length of 100 bp. The GC (guanine-cytosine) content was 46.29%, and Q20 ratio (a quality score of 20) was 83.78%. In the paternal inbred line (NC76), 2,555,002 reads and 32,121 SLAFs were generated, with an average coverage of 79.54-fold for each SLAF. In the maternal line (CC3), the number of reads produced for 31,898 SLAFs was 3,050,383, and the average cover for each SLAF marker was 95.63-fold. For the analysis of the F2 mapping population, 84,900 to 367,251 reads were generated for the development of 16,497 to 28,155 SLAF markers for each plant; the marker coverage ranged from 4.34 to 13.81-fold, with an average of 7.74-fold (Figure 1a). The average count of SLAFs per individual was 22,866 (Figure 1b, distribution of DNA fragments digested by enzyme on the genome is presented in Additional file 1: Figure S5).
Figure 1
Figure 1

Coverage and number of markers for each of the F 2 individuals. The x-axes in both a and b indicate each of the F2 individuals; the y-axe in a indicates marker coverage, and the y-axe in b indicates the number of markers developed for each F2 plant.

After correcting or discarding low-depth SLAF tags, 52,684 high-quality SLAFs were identified, among which 5,044 were polymorphic with a polymorphism rate of 9.57% (Table 1, Additional file 2: Table S1). The parental lines were given with different alphabets as genotypes to determine segregation patterns, and 4,817 from the 5,044 polymorphic SLAFs were successfully encoded and grouped into eight segregation patterns (ab × cd, ef × eg, hk × hk, lm × ll, nn × np, aa × bb, ab × cc and cc × ab) following a genotype encoding rule (Figure 2; Additional file 2: Table S4). Since the two parents (NC76 and CC3) are homozygous cucumber inbred lines with genotypes of aa and bb, only the 4,227 markers that fell into the aa × bb segregation pattern were used in linkage analysis (Figure 2).
Table 1

Discovery of SLAF markers

Type

Number of SLAF markers

Number of reads

Ratio

Polymorphism SLAF

5,044

4,085,646

9.56%

Non-polymorphism SLAF

47,640

28,164,020

90.44%

Total

52,684

32,249,666

100.00%

Figure 2
Figure 2

Number of markers for each segregation pattern.

Genetic linkage map

After removing incomplete and significant segregation distortion markers, 1,800 SNPs were retained for genetic map construction. JoinMap 4.0 assigned all the 1,800 markers to seven LGs of cucumber (Table 2, Additional file 3). The average integrity of the mapped SNP markers reached 94.74%, representing a relatively high map quality. Details of this SNP-based cucumber map are presented in Additional file 4 and summarized in Table 2.
Table 2

Summary of the SNP-based cucumber genetic map

Linkage group ID

No. of mapped SNPs

Map length(cM)

Marker interval(cM)

Gaps < =5

scaffolds anchored

Physical length (Mb)

1

321

136.48

0.43

100.00%

36

25.93

2

252

131.20

0.52

100.00%

26

20.66

3

344

193.65

0.56

99.13%

32

34.25

4

199

104.89

0.53

100.00%

26

19.13

5

312

139.14

0.45

100.00%

24

24.97

6

206

119.31

0.58

100.00%

21

27.18

7

166

66.12

0.40

100.00%

18

16.76

Total

1,800

890.79

0.50

100.00%

183

168.88

The total genetic length of the SNP map was 890.79 cM in seven linkage groups with a mean marker distance of 0.5 cM between adjacent markers. The largest linkage group (LG3) contained 344 SNPs, while the smallest LG7 had 166 SNPs. On average, there were 257 SNP markers in each linkage group. The genetic distances of seven linkage groups spanned 66.12 cM (LG7) ~ 193.65 cM (LG3), with mean marker intervals ranged from 0.40 cM to 0.58 cM. ‘Gap < = 5′ value, which reflected linkage degree among markers, were 100.00% on all linkage groups except for LG3 (99.13%).

Validation of the SNP-based genetic map

The quality of this genetic map was evaluated by heat maps which directly reflected recombination relationships among markers in seven linkage groups (Additional file 5). Each cell represented a recombination rate between two adjacent markers, the level of which was visualized by different colors ranging from yellow to purple (yellow indicated a lower recombination rate; purple indicated a higher rate). Heat maps indicated SNP markers in most LGs were well ordered.

All the mapped SNPs were used to anchor and orient scaffolds of ‘9930’ draft genome assemblies. Physically, 183 scaffolds were anchored onto the SNP map covering 69.00% (168.9 Mb) of the ‘9930’ draft genome sequences (Table 2). Only 44 of the 1,800 SNP markers did not have BLAST hits. Locations of all mapped loci on the SNP linkage map in the‘9930’ draft genome assembly Version 2.0 are provided in Additional file 4. For most scaffolds, more than one SNP was assigned to the same scaffold (~10 SNPs per scaffold). In several cases, the order of scaffolds oriented by the genetic map disagreed with that in the draft genome assembly. For example, we anchored scaffold000063_5 and scaffold000063_1 to the distal end and one third region of chromosome 5 respectively, whereas their positions in the genome assemblies were ~ 3 Mb and ~ 0.3 Mb, respectively. We thereby designed two single-copy gene probes (Csa015370 from scaffold000063_5, and Csa019548 from scaffold000063_1) (see Methods) to verify their positions by fluorescence in situ hybridization (FISH). FISH images showed that scaffold000063_5 was at the distal end on chromosome 5 and scaffold000063_1 were in a one third region (~10 Mb) on the same chromosome. Thus these two scaffolds were mis-assembled (Figure 3).
Figure 3
Figure 3

Two scaffold mis-assemblies revealed by single-copy gene FISH. a Locations of two single-copy genes Csa015370 (scaffold000063_5, red) and Csa019548 (scaffold000063_1, green) on cucumber pachytene chromosome spreads were indicated with arrows.Scale bar = 5 μm. b Ideogram showing the physical positions of two scaffolds revealed by FISH and in the ‘9930’ draft genome assemblies.

QTL mapping for fruit length and weight

Phenotypic data (including family means, standard errors and distribution of fruit length and weight) in both F2 and F2:3 families are presented in Additional file 6. Nine QTLs were detected for fruit length (fl for immature fruit length, and mfl for mature fruit length) and fruit weight (fw for immature fruit weight) (Table 3). Immature fruit length had four QTLs, and the most prominent QTL designated fl3.2, accounted for 44.60% of the observed phenotypic variance (Additional file 1: Figure S2). Twelve SNPs covered this interval (Table 3 and Additional file 5). A QTL designated fl3.1 explained 39.10% of phenotypic variance and was also located on LG3. The effects of other QTLs (fl1.1 and fl6.1) were comparatively smaller.
Table 3

Genetic mapping and QTL analysis of cucumber fruit traits in F 3 populations

Trait

QTL

LG

Pos. IM (cM)

Closest marker

Start marker

End marker

LOD

% Expl.

No.of SNPs in mapped region

       

Minimum

Maximum

Minimum

Maximum

 

Fruit length

fl1.1

1

45.3-51.6

Maker12120

Maker15971

Maker10660

2.25

3.56

7.60

11.60

15

fl3.1

3

136.4-137.1

Marker5482

Marker17152

Marker5894

14.03

14.32

38.50

39.10

4

fl3.2

3

150.5-155.9

Marker14961

Marker17179

Marker1137

14.42

16.97

39.30

44.60

17

fl6.1

6

57.0-66.7

Marker13991

Marker3014

Marker11267

4.34

5.13

14.00

16.30

25

Mature fruit length

mfl1.1

1

45.0-50.3

Marker13691

Marker15971

Marker10158

4.15

5.03

13.60

16.00

11

mfl1.2

1

100.1-103.0

Marker16756

Marker16756

Marker12772

4.55

5.24

14.60

16.60

5

mfl3.1

3

150.5-157.3

Marker7219

Marker17179

Marker1225

12.00

13.32

34.00

36.90

19

Fruit weight

fw3.1

3

115.2-117.1

Marker6431

Marker7424

Marker5909

8.31

9.43

25.20

28.30

11

 

fw3.2

3

146.8-153.5

Marker15161

Marker12129

Marker12274

8.16

9.59

24.60

28.30

12

Three QTLs were detected for mature fruit length, with the largest effect displayed by mfl3.1, explaining 36.39% of the observed phenotypic variance (Table 3). Sixty-six SNPs were discovered within the chromosomal region of mfl3.1. mfl1.1 (Additional file 1: Figure S4) and mfl1.2, two QTLs for mature fruit length, were detected on LG1 with maximum LOD scores of 5.03 and 5.46, respectively. Notably, mfl1.1 and fl1.1 shared the same start marker (Marker15971), as well as a physical length of 5 cM in the mapped region.

Two QTLs for immature fruit weight (fw) were detected, which both explained 28.30% of the observed fruit weight variance at maximum (Additional file 1: Figure S3). QTL fw3.1 spanned an interval between 115.2 ~ 117.1 cM, where eleven SNPs were identified; the closest marker linked with fw3.1 was Marker643. The other QTL, fw3.2, was flanked by Marker12129 and Marker12274, and twelve SNPs covered this chromosomal region.

Discussion

A cost-efficient method of rapid SNP discovery and genotyping using SLAF-seq

NGS-based marker discovery and genotyping technologies provide a good opportunity for developing SNP markers, which are being applied to many studies [4648]. Whole-genome deep re-sequencing and low coverage sequencing is very costly for large populations and usually unnecessary for linkage mapping and quantitative trait locus mapping. Reduced-representation sequencing offers an approach to sample and sequence a small set of genome regions instead of the whole genome [25, 30]. RAD-seq sequences short DNA fragments with restriction sites that are digested by restriction endonuclease despite the length of those fragments. It has been applied for SNP discovery and linkage map construction in organisms such as stickleback, rainbow trout, barley, and ryegrass [34, 4951]. 2b-RAD is a streamlined RAD approach that uses type IIB restriction endonuclease to produce uniform fragments sequenced through NGS platforms [17]. It can screen almost every restriction site in the genome and is simpler than existing RAD protocols. However, one drawback of 2b-RAD is the read length (33 ~ 36 bp) constrained by type IIB activity. SLAF-seq is a recently developed enhanced RRL sequencing strategy for de novo SNP discovery and genotyping of large populations [36]. Like 2b-RAD, SLAF-seq can also adjust marker identification and genotyping to meet personalized research purposes. The read length in SLAF-seq ranges between 30 ~ 50 bp, which may provide efficient locus discrimination as compared to that in 2b-RAD (33 ~ 36 bp). SLAF-seq was used to construct saturated SNP-based genetic maps in sesame (1,233 SNPs), soybean (5,308 SNPs), and develop 7E chromosome-specific molecular markers for Thinopyrume longatum [37, 38, 52]. In this study, we generated 10.76 Gb raw data, 75,024,043 pair-end reads, developed 32,121 SLAFs through high-throughput SLAF sequencing. 1,800 polymorphic SNPs were identified for linkage map construction.

The saturated SNP-based genetic linkage map in cucumber

In the present study, we report an SNP-based genetic linkage map in cucumber using SLAF sequencing technology. Due to the narrow genetic base in cucumber, it has been difficult to construct high-density genetic maps from intra-varietal crosses to facilitate genetic mapping and QTL analysis of important traits (disease resistance, fruit yield etc.). To date, the most saturated intra-varietal map is constructed by Yang et al. with 735 SSRs and a mean marker interval of 0.96 cM [11]. The SNP map developed herein contained 1,800 SNPs and a majority of them were anchored to ‘9930’ draft genome scaffolds (Table 2). As compared with the 735-SSR map [11], the number of mapped loci, marker density (from 0.96 cM to 0.50 cM), and total map length (706.7 cM vs. 890.79 cM) is significantly improved in the SNP genetic map. The marker number in this individual map is also an increase in comparison with the two consensus maps by Zhang et al.[53] and Yang et al.[26]. We anchored 183 scaffolds of ‘9930’ genome scaffolds covering 168.9 Mb of the cucumber genome, which is exceptionally not an increase contrasted to the 735-point (237 scaffolds, 193.3 Mb), and two integrated maps (1369-point map, 172.5 Mb; 1681-point map, 275 scaffolds and 202.3 Mb). This suggests more SNP markers are required to anchor scaffolds and cover the entire physical distance of cucumber genome. Marker locations on the genetic map and in the scaffolds could infer the quality of both the genetic map and the ‘9930’genome scaffolds. It was demonstrated in several studies that there are mis-assembled scaffolds in the published cucumber draft assemblies [11, 21, 54, 55]. In our study, two mis-assembled scaffolds in chromosome 5 were verified by single-copy gene FISH experiment. This indicated that the SNP-based genetic map constructed herein could be applied to detect mis-assembled scaffolds. However, further efforts are needed to address all the scaffold positions as well as improve quality of the SNP map.

QTL analysis of fruit-related traits

This study is the first attempt to conduct QTL analysis using a NGS-derived genetic map in cucumber. Fruit weight and length are two traits that have direct impacts on field yield of cucumber, thus are important for cultivar improvements. We identified 9 QTLs for fruit weight and length of mature and immature fruits on three linkage groups. Three QTLs were detected on LG1; one was on LG6, and five on LG3 for fruit yield traits. Two pairs of QTLs for mature fruit length and immature fruit length shared similar location intervals, fl1.1 and mfl1.1, fl3.1 and mfl3.1. QTLs located in two adjacent marker intervals might take function as one locus. In the previous studies, QTLs controlling fruit length were detected on all of the seven chromosomes/linkage groups using F2 or RIL populations based on different types of markers (i.e. SRAP, SSR, and morphological markers). For example, Cheng et al.[42] and Miao et al.[43] identified five QTLs (on LG1, LG4 and LG6) and three QTLs (on LG5 and LG6) that explained 7.1% ~ 14.1% of the phenotypic variation. Among 8 QTL s detected by Yuan et al.[40], the QTL on LG4 explained 23.32% of the fruit length variance, which was a dominant QTL. Both of the two predominant QTLs on LG3 in the present study explained ~ 40% of the observed variance. We identified two QTLs for immature fruit weight on LG3, which shared one linkage group with the report by Yuan et al.[40], whereas no common LGs with the results from Chen et al.[41]. The differences in QTL number, position and phenotypic variance explained might be attributed to different mapping populations (genotypes, population size etc.) and environment effects [6, 8, 39, 40, 56].

Generally, the detected QTL regions in most studies were covered by two markers. Nevertheless the chromosomal interval for QTLs detected in the present study could cover up to 20 SNPs. For example, the chromosomal region accounted for fl6.1 on LG6 (57.0 ~ 66.7 cM) covered 25 SNPs (Additional file 5 and Additional file 6, Additional file 1: Figure S2). This increase in mapped markers is likely to facilitate fine mapping of these QTLs in further studies.

Conclusion

We generated 10.76 Gb raw data, 75,024,043 pair-end reads and 52,684 high-quality SLAFs using SLAF sequencing. A high-resolution genetic map for cucumber was constructed containing 1,800 SNPs with a total genetic length of 890.79 cM. The mean distance between adjacent markers was 0.50 cM. This SNP-based genetic map could be applied to anchor and orient draft genome scaffolds. Physically, 183 scaffolds from the ‘9930’ draft genome assemblies were anchored. Although number of anchored scaffolds was not really an increase compared to existing cucumber consensus maps, the SNP map derived from de novo sequencing of a reduced representation genome could still be used to evaluate the quality of draft scaffold assemblies. Two mis-assembled scaffold were verified by FISH with newly developed single-copy genes. We further applied this linkage map to detect QTLs controlling fruit-related traits. Nine QTLs were identified for fruit length and weight. To date, this study is the first report of large-scale SNP identification and genotyping in cucumber. The 1,800-SNP map constructed herein would likely facilitate fine genetic mapping of fruit-related QTLs and orientation of draft genome scaffolds.

Methods

Plant materials and phenotypic evaluation

Cucumber inbred lines ‘CC3’ and ‘NC76’ were used as female parent and male parent, respectively. The fruits of CC3 female plant were characterized as non-netting, long fruits (20 ~ 30 cm) with smooth skin, white spines, and green/creamy mature fruit color. NC76 had short fruit (7 ~ 10 cm) with coarse skin, heavy netting, black spines and red mature fruit color. A population of F2 with 148 individuals was produced from a single F1 plant, and was then self-pollinated to generate the F3 families. Considering that plants might die of disease and other factors, 12 plants were randomly selected from the F3 families and planted. Phenotypic data from 10 healthy plants were used to represent each F2 individual. Fruit-related traits (immature and mature fruit length, and immature fruit weight) for each plant of the F2 population and F3 family were evaluated for genetic mapping and QTL analysis. The fruit-related traits were measured according to the standards published by Yuan et al.[8].

Two parental lines (CC3 and NC76), derived F1, F2, and F3families were grown in a greenhouse at Jiangpu Cucumber Research Station of Nanjing Agricultural University (JCRSNAU), Nanjing, China. The soil media was 25% peat + 25% cinder + 50% perlite. The F2 population was planted in March 2012, and evaluations were conducted on both immature (10 days after pollination) and mature (30 ~ 45 days after pollination) cucumber fruits. The evaluated traits were fruit length (fl for immature fruits and mfl for mature fruit, cm; from fruit apex to the pedicel attachment), fruit weight (fw for immature fruit weight, g). These traits were also measured in F3 family fruit in the spring of 2013 under the same growing conditions as F2 population. Individual plants were spaced 40 cm apart in rows placed 60 cm apart. All measurements were taken on individual plants and averaged within each F3 family. Data were analyzed with analysis of variance and partial correlations using Microsoft Excel 2000 [57].

DNA extraction

Young healthy leaves from two parents as well as 148 F2 individuals were collected and frozen in liquid nitrogen, then transferred to a -70°C freezer. Total genomic DNA was extracted from each leaf sample following the cetyltrimethyl ammonium bromide (CTAB) method described by Murray et al.[58]. The concentration and quality of extracted DNA were examined by electrophoresis in 1% agarosegels with a standard lambda DNA, and anND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA).

SLAF library preparation and sequencing

A pre-experiment was designed to evaluate the enzymes and sizes of restriction fragments to generate large number and high-quality SLAFs. The uniformity of sequencing depth of different fragments was controlled by selecting a tight length range (about 30 ~ 50 bp) and performing a pilot PCR amplification, only fragments with similar amplification features on the gel were maintained. SLAF library construction was performed following the procedures as described by Sun et al. [36] and Zhang et al. [37]. After the digestion (enzyme MseIwas used), polymerase chain reactions (PCR) and purification of genomic DNA, fragments(with indices and adaptors) of 330 ~ 380 bp were isolated using Gel Extraction Kit (Qiagen) and subjected to PCR amplification (barcode2 was added). The products were gel purified, and DNA fragments (SLAFs) of 330 ~ 380 bp were recovered and diluted for sequencing. Pair-end sequencing was performed on Illumina High-seq 2000 sequencing platform (Illumina, Inc; San Diego, CA, U.S.) at Biomarker Technologies Corporation in Beijing (http://Biomarker.com.cn/). Each cycle was real-time monitored during sequencing, we also calculated the ratio of high quality reads with quality scores higher than Q20 (a quality score of 20; indicating a 1% chance of an error, and thus 99% confidence) in the raw reads and guanine-citosine (GC) content as standards to control the quality.

SLAF-seq data analysis and genotyping

SLAF-seq data was operated using the software developed by Sun et al. [36]. All pair-end reads generated from SLAF-seq (with clear index information) were clustered according to sequence similarity, which could be inferred from one-to-one alignment by BLAT (-tileSize = 10 -stepSize = 5). Identical reads were merged to avoid repeat computing requirements, and sequences with over 90% similarity were grouped into one SLAF locus as described [36]. Minor allele frequency (MAF) evaluation was performed to define alleles in each SLAF. In the mapping populations of diploid cucumber, one locus can contain no more than four SLAF tags, thus the groups with over four tags were considered as repetitive SLAFs and excluded. In this study, SLAFs with a sequence depth of less than 148 were defined as low-depth SLAFs. Polymorphic SLAFs, which referred to SLAFs that contained 2 ~ 4 tags, were considered as potential markers. Those polymorphic SLAF markers were then assorted into eight segregation patterns as following: ab × cd, ef × eg, hk × hk, lm × ll, nn × np, aa × bb, ab × cc and cc × ab. Since the F2 mapping population was derived from two homozygous cucumber inbred lines with a genotype of aa or bb, therefore only the SLAF markers which had segregation patterns of aa × bb were used in map construction.

Genetic map construction and QTL analysis

Genotype data from the F2 mapping population was used to perform linkage analysis using JoinMap 4.0 software [59]. A logarithm of odds (LOD) threshold between 5 and 10 was adopted as indicator for clustering analysis with single linkage method. We employed the Kosambi mapping function to convert recombination percentages to genetic distance. Newly developed SNPs were named by Marker_. The SNP markers were ordered and grouped into seven linkage groups according to genomic information of cucumber. For all SNP markers, corresponding sequence for each SNP was BLAST against the ‘9930’ genome scaffold assemblies version 2.0 [22] (http://cucumber.genomics.org.cn) to anchor and orient scaffolds.

QTL analysis was conducted with MapQTL 4.0 software [60] using both interval mapping method and multiple-QTL model mapping (MQM) methods as described [61, 62]. Composite interval mapping (CIM) was adopted with a walking speed of 1 cM [63]. Two-LOD support intervals were constructed as 95% confidence intervals [64]. The significance of each QTL interval was tested by a likelihood-ratio statistic (LOD). The threshold of the LOD score for significance (P = 0.05) was determined using 1,000 permutations. Calculation of the percentage of phenotypic variance explained by each QTL (Expl.%) was done in MapQTL4.0 based on the population variance found within the segregation population.

Cytological validation with fluorescence in situhybridization

Pachytene chromosome preparations and slide treatment were performed as described previously [24, 54]. Design and isolation of single-copy gene probes were conducted followed the description by Lou et al.[54]. Two single-copy genes, Csa015370 and Csa019548 were selected randomly from scaffold000063_5 and scaffold000063_1, respectively. Flanking sequences of the genes were adopted for primer design to obtain amplification products > 2000 bp. The primers were designed using PRIMER PREMIER 5.0 software. The primer pairs 5′-TAACGCACTCAGTCACTCATTG-3′, 5′-CTTCTGCTTCGCTTCATCC-3′ and 5′-TCCCAACTCCCCACCACTT-3′, 5′-CGTAATCCTCCATCTTTTATCCTCT-3′ were used for amplifying two genes above, respectively. Amplified PCR products were resolved on 1% agarose gels (BIO-WEST) for 30 min at 120 V, and stained with ethidium bromide. Products of the expectedsize were cut from the gel and purified using a gel recovery kit (Promega). The single-copy genes from the purified PCR products were used for FISH. Csa015370 was labeled with digoxigenin-11-dUTP, and Csa019548 was labeled withbiotin-16-UTP. They were detected using a fluorescein isothiocyanate-conjugated antibiotin antibody and arhodamine-conjugated anti-digoxigenin antibody (Roche Diagnos-tics), respectively. The FISH experiment was performed as previously described [55]. Images were captured using a SENSYS CCD camera attached to an Olympus BX51 microscope and processed using ADOBE PHOTOSHOP 5.0 (Adobe Systems). The CCD camera was controlled using FISH view 5.5 software (Applied SpectralImagingInc).

Availability of supporting data

Raw sequence reads have been deposited in NCBI’s Sequence Read Archive (Study accession: SRP050237). Besides, sequence information of all SNPs is included as additional files.

Notes

Abbreviations

SLAF: 

Specific-length amplified fragments

SNP: 

Single-nucleotide polymorphism

SLAF-seq: 

Specific-length amplified fragments sequencing

MAS: 

Marker-assistant selection

LG: 

Linkage group

QTLs: 

Quantitative trait loci

RAPDs: 

Random amplified polymorphic DNA

AFLPs: 

Amplified fragment length polymorphisms

SSRs: 

Simple sequence repeats

RILs: 

Recombination inbred lines

RRL: 

Reduced-representation library

RAD: 

Restriction-site associated DNA

NGS: 

Next-generation sequencing

SRAP: 

Sequence-related amplified polymorphism

FISH: 

Fluorescence in situ hybridization.

Declarations

Acknowledgements

We thank Joyce Wanjiru Ngure (Department of Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing, China) for critical reading of the manuscript, and Huaigen Xin and Long Huang (Biomarker Technologies Corporation, 101300 Beijing, China) for their efforts in bioinformatics analysis. This research was partially supported by Key Program 31430075, General Programs 31272174, 31471872 and U1178307 from the National Natural Science Foundation of China, the National Basic Research Program of China (973 Program:2012CB113900), the ‘863’ Project (2012AA100102), the ‘111’ Project (B08025), and the National Key Technology R&D Program of the Ministry of Science and Technology of China (2013BAD01B04-10).

Authors’ Affiliations

(1)
State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University, Weigang Street No.1, Nanjing, 210095, China

References

  1. Dijkhuizen A, Kennard WC, Havey MJ, Staub JE: RFLP variation and genetic relationships in cultivated cucumber. Euphytica. 1996, 90 (1): 79-87.Google Scholar
  2. Horejsi T, Staub JE: Genetic variation in cucumber (Cucumis sativus L.) as assessed by random amplified polymorphic DNA1. Genet Resour Crop Evol. 1999, 46 (4): 337-350. 10.1023/A:1008650509966.View ArticleGoogle Scholar
  3. Knerr LD, Staub JE, Holder DJ, May BP: Genetic diversity in Cucumis sativus L. assessed by variation at 18 allozyme coding loci. Theor Appl Genet. 1989, 78 (1): 119-128. 10.1007/BF00299764.PubMedView ArticleGoogle Scholar
  4. Li YH, Wen CL, Weng YQ: Fine mapping of the pleiotropic locus B for black spine and orange mature fruit color in cucumber identifies a 50 kb region containing a R2R3-MYB transcription factor. Theor Appl Genet. 2013, 126 (8): 2187-2196. 10.1007/s00122-013-2128-3.PubMedView ArticleGoogle Scholar
  5. Serquen FC, Bacher J, Staub JE: Mapping and QTL analysis of horticultural traits in a narrow cross in cucumber (Cucumis sativus L.) using random-amplified polymorphic DNA markers. Mol Breeding. 1997, 3 (4): 257-268. 10.1023/A:1009689002015.View ArticleGoogle Scholar
  6. Fazio G, Staub JE, Stevens MR: Genetic mapping and QTL analysis of horticultural traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Theor Appl Genet. 2003, 107 (5): 864-874. 10.1007/s00122-003-1277-1.PubMedView ArticleGoogle Scholar
  7. Li XJ, Pan JS, Wang G, Tian LB, Si LT, Wu AZ, Cai R: Localization of genes for lateral branch and female sex expression and construction of a molecular linkage map in cucumber (Cucumis sativus L.) with RAPD markers. Prog Nat Sci. 2005, 15 (2): 143-148. 10.1080/10020070512331341900.View ArticleGoogle Scholar
  8. Yuan XJ, Li XZ, Pan JS, Wang G, Jiang S, Li XH, Deng SL, He HL, Si MX, Lai L, Wu AZ, Zhu LH, Cai R: Genetic linkage map construction and location of QTLs for fruit-related traits in cucumber. Plant Breeding. 2008, 127 (2): 180-188. 10.1111/j.1439-0523.2007.01426.x.View ArticleGoogle Scholar
  9. Fukino N, Yoshioka Y, Kubo N, Hirai M, Sugiyama M, Sakata Y, Matsumoto S: Development of 101 novel SSR markers and construction of an SSR-based genetic linkage map in cucumber (Cucumis sativus L.). Breeding sci. 2008, 58 (4): 475-483. 10.1270/jsbbs.58.475.View ArticleGoogle Scholar
  10. Huang SW, Li RQ, Zhang ZH, Li L, Gu XF, Fan W, Lucas WJ, Wang XW, Xie BY, Ni PX: The genome of the cucumber, Cucumis sativus L. Nat Genet. 2009, 41 (12): 1275-1281. 10.1038/ng.475.PubMedView ArticleGoogle Scholar
  11. Yang L, Koo DH, Li Y, Zhang X, Luan F, Havey MJ, Jiang J, Weng Y: Chromosome rearrangements during domestication of cucumber as revealed by high-density genetic mapping and draft genome assembly. Plant J. 2012, 71 (6): 895-906. 10.1111/j.1365-313X.2012.05017.x.PubMedView ArticleGoogle Scholar
  12. Wóycicki R, Witkowicz J, Gawroński P, Dąbrowska J, Lomsadze A, Pawełkowicz M, Siedlecka E, Yagi K, Pląder W, Seroczyńska A, Śmiech M, Gutman W, Niemirowicz-Szczytt K, Bartoszewski G, Tagashira N, Hoshi Y, Borodovsky M, Karpiński S, Malepszy S, Przybecki Z: The Genome Sequence of the North-European Cucumber (Cucumis sativus L.) Unravels Evolutionary Adaptation Mechanisms in Plants. PLoS One. 2011, 6 (7): e22728-10.1371/journal.pone.0022728.PubMed CentralPubMedView ArticleGoogle Scholar
  13. Cavagnaro PF, Senalik DA, Yang LM, Simon PW, Harkins TT, Kodira CD, Huang SW, Weng YQ: Genome-wide characterization of simple sequence repeats in cucumber (Cucumis sativus L.). BMC Genomics. 2010, 11 (1): 569-10.1186/1471-2164-11-569.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Ren Y, Zhang ZH, Liu JH, Staub JE, Han YH, Cheng ZC, Li XF, Lu JY, Miao H, Kang HX, Xie BY, Gu XF, Wang XW, Du YC, Jin WW, Huang SW: An Integrated Genetic and Cytogenetic Map of the Cucumber Genome. PLoS One. 2009, 4 (6): e5795-10.1371/journal.pone.0005795.PubMed CentralPubMedView ArticleGoogle Scholar
  15. Weng YQ, Johnson S, Staub JE, Huang SW: An Extended Intervarietal Microsatellite Linkage Map of Cucumber, Cucumis sativus L. HortSci. 2010, 45 (6): 882-886.Google Scholar
  16. Miao H, Zhang SP, Wang XW, Zhang ZH, Li M, Mu SQ, Cheng ZC, Zhang RW, Huang SW, Xie BY, Fang ZY, Zhang ZX, Weng YQ, Gu XF: A linkage map of cultivated cucumber (Cucumis sativus L.) with 248 microsatellite marker loci and seven genes for horticulturally important traits. Euphytica. 2011, 182 (2): 167-176. 10.1007/s10681-011-0410-5.View ArticleGoogle Scholar
  17. Wang S, Meyer E, McKay JK, Matz MV: 2b-RAD: a simple and flexible method for genome-wide genotyping. Nat Meth. 2012, 9 (8): 808-810. 10.1038/nmeth.2023.View ArticleGoogle Scholar
  18. Fukino N, Yoshioka Y, Sugiyama M, Sakata Y, Matsumoto S: Identification and validation of powdery mildew (Podosphaera xanthii)-resistant loci in recombinant inbred lines of cucumber (Cucumis sativus L.). Mol Breeding. 2013, 32 (2): 267-277. 10.1007/s11032-013-9867-3.View ArticleGoogle Scholar
  19. He XM, Li YH, Pandey S, Yandell B, Pathak M, Weng Q: QTL mapping of powdery mildew resistance in WI 2757 cucumber (Cucumis sativus L.). Theor Appl Genet. 2013, 126 (8): 2149-2161. 10.1007/s00122-013-2125-6.PubMedView ArticleGoogle Scholar
  20. Kang HC, Weng YQ, Yang YH, Zhang ZH, Zhang SP, Mao ZC, Cheng GH, Gu XF, Huang SW, Xie BY: Fine genetic mapping localizes cucumber scab resistance gene Ccu into an R gene cluster. Theor Appl Genet. 2011, 122 (4): 795-803. 10.1007/s00122-010-1487-2.PubMedView ArticleGoogle Scholar
  21. Li DW, Cuevas H, Yang LM, Li YH, Garcia-Mas J, Zalapa J, Staub JE, Luan FS, Reddy U, He XM, Gong ZH, Weng YQ: Syntenic relationships between cucumber (Cucumis sativus L.) and melon (C. melo L.) chromosomes as revealed by comparative genetic mapping. BMC Genomics. 2011, 12 (1): 1-14. 10.1186/1471-2164-12-1.PubMed CentralPubMedView ArticleGoogle Scholar
  22. Li Z, Zhang ZH, Yan PC, Huang SW, Fei ZJ, Lin K: RNA-Seq improves annotation of protein-coding genes in the cucumber genome. BMC Genomics. 2011, 12 (1): 540-10.1186/1471-2164-12-540.PubMed CentralPubMedView ArticleGoogle Scholar
  23. Zhang WW, He HL, Guan Y, Du H, Yuan LH, Li Z, Yao DQ, Pan JS, Cai R: Identification and mapping of molecular markers linked to the tuberculate fruit gene in the cucumber (Cucumis sativus L.). Theor Appl Genet. 2010, 120 (3): 645-654. 10.1007/s00122-009-1182-3.PubMedView ArticleGoogle Scholar
  24. Lysak M, Mandáková T: Analysis of Plant Meiotic Chromosomes by Chromosome Painting. Plant Meiosis. 2013, 990 (2): 13-24.View ArticleGoogle Scholar
  25. Van Tassell CP, Smith TP, Matukumalli LK, Taylor JF, Schnabel RD, Lawley CT, Haudenschild CD, Moore SS, Warren WC, Sonstegard TS: SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Meth. 2008, 5 (3): 247-252. 10.1038/nmeth.1185.View ArticleGoogle Scholar
  26. Yang LM, Li DW, Li YH, Gu XF, Huang SW, Garcia-Mas J, Weng YQ: A 1,681-locus consensus genetic map of cultivated cucumber including 67 NB-LRR resistance gene homolog and ten gene loci. BMC Plant Biol. 2013, 13 (1): 53-10.1186/1471-2229-13-53.PubMed CentralPubMedView ArticleGoogle Scholar
  27. Bourgeois YX, Lhuillier E, Cézard T, Bertrand JA, Delahaie B, Cornuault J, Duval T, Bouchez O, Milá B, Thébaud C: Mass production of SNP markers in a nonmodel passerine bird through RAD sequencing and contig mapping to the zebra finch genome. Mol Ecol Resour. 2013, 13 (5): 899-907. 10.1111/1755-0998.12137.PubMedView ArticleGoogle Scholar
  28. Stölting KN, Nipper R, Lindtke D, Caseys C, Waeber S, Castiglione S, Lexer C: Genomic scan for single nucleotide polymorphisms reveals patterns of divergence and gene flow between ecologically divergent species. Mol Ecol. 2013, 22 (3): 842-855. 10.1111/mec.12011.PubMedView ArticleGoogle Scholar
  29. Wang J, Luo MC, Chen Z, You FM, Wei Y, Zheng Y, Dvorak J: Aegilops tauschii single nucleotide polymorphisms shed light on the origins of wheat D-genome genetic diversity and pinpoint the geographic origin of hexaploid wheat. New Phytol. 2013, 198 (3): 925-937. 10.1111/nph.12164.PubMedView ArticleGoogle Scholar
  30. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML: Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011, 12 (7): 499-510. 10.1038/nrg3012.PubMedView ArticleGoogle Scholar
  31. Lucito R, Nakimura M, West JA, Han Y, Chin K, Jensen K, McCombie R, Gray JW, Wigler M: Genetic analysis using genomic representations. P Natl Acad Sci. 1998, 95 (8): 4487-4492. 10.1073/pnas.95.8.4487.View ArticleGoogle Scholar
  32. Altshuler D, Pollara VJ, Cowles CR, Van Etten WJ, Baldwin J, Linton L, Lander ES: An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature. 2000, 407 (6803): 513-516. 10.1038/35035083.PubMedView ArticleGoogle Scholar
  33. Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA: Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 2007, 17 (2): 240-248. 10.1101/gr.5681207.PubMed CentralPubMedView ArticleGoogle Scholar
  34. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA: Rapid SNP discovery and genetic mapping using sequenced RAD markers. PloS One. 2008, 3 (10): e3376-10.1371/journal.pone.0003376.PubMed CentralPubMedView ArticleGoogle Scholar
  35. Ogden R: Unlocking the potential of genomic technologies for wildlife forensics. Mol Ecol Resour. 2011, 11 (s1): 109-116.PubMedView ArticleGoogle Scholar
  36. Sun XW, Liu DY, Zhang XF, Li WB, Liu H, Hong WG, Jiang CB, Guan N, Ma CX, Zeng HP: SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PloS One. 2013, 8 (3): e58700-10.1371/journal.pone.0058700.PubMed CentralPubMedView ArticleGoogle Scholar
  37. Zhang YX, Wang LH, Xin HG, Li DH, Ma CX, Ding X, Hong WG, Zhang XR: Construction of a high-density genetic map for sesame based on large scale marker development by specific length amplified fragment (SLAF) sequencing. BMC Plant Biol. 2013, 13: 141-10.1186/1471-2229-13-141.PubMed CentralPubMedView ArticleGoogle Scholar
  38. Qi ZM, Huang L, Zhu RS, Xin D, Liu CY, Han X, Jiang HW, Hong WG, Hu GH, Zheng HK, Chen QH: A high-density genetic map for soybean based on specific length amplified fragment sequencing. PLoS One. 2014, 9 (8): e104871-10.1371/journal.pone.0104871.PubMed CentralPubMedView ArticleGoogle Scholar
  39. Dijkhuizen A, Staub JE: QTL Conditioning Yield and Fruit Quality Traits in Cucumber (Cucumis sativus L.) Effects of Environment and Genetic Background. Journal of New Seeds. 2002, 4 (4): 1-30. 10.1300/J153v04n04_01.View ArticleGoogle Scholar
  40. Yuan XJ, Pan JS, Cai R, Guan Y, Liu LZ, Zhang WW, Li Z, He HL, Zhang C, Si LT, Zhu LH: Genetic mapping and QTL analysis of fruit and flower related traits in cucumber (Cucumis sativus L.) using recombinant inbred lines. Euphytica. 2008, 164 (2): 473-491. 10.1007/s10681-008-9722-5.View ArticleGoogle Scholar
  41. Chen QJ, Zhang HY, Wang YJ, Li WY, Zhang F, Mao AJ, Cheng JH, Chen MY: Mapping and analyzing QTLs of yield-associated agronomic traits of greenhouse cucumbers. Scientia Agricultura Sinica. 2010, 43 (1): 112-122.Google Scholar
  42. Cheng ZC, Gu XF, Zhang SP, Miao H, Zhang RW, Liu MM, Yang SJ: QTL analysis for fruit length of cucumber. China Vegetables. 2010, 12: 20-25.Google Scholar
  43. Miao H, Gu XF, Zhang SP, Zhang ZH, Huang SW, Wang Y, Cheng ZC, Zhang RW, Mu SQ, Li M: Mapping QTLs for fruit-associated traits in Cucumis sativus L. Scientia Agricultura Sinica. 2011, 44 (24): 5031-5040.Google Scholar
  44. Qi JJ, Liu X, Shen D, Miao H, Xie BY, Li XX, Zeng P, Wang SH, Shang Y, Gu XF, et al: A genomic variation map provides insights into the genetic basis of cucumber domestication and diversity. Nature Genet. 2013, 45 (12): 1510-1515. 10.1038/ng.2801.PubMedView ArticleGoogle Scholar
  45. Wenzel G, Kennard WC, Havey MJ: Quantitative trait analysis of fruit quality in cucumber: QTL detection, confirmation, and comparison with mating-design variation. Theor Appl Genet. 1995, 91 (1): 53-61.PubMedView ArticleGoogle Scholar
  46. Trick M, Long Y, Meng JL, Bancroft I: Single nucleotide polymorphism (SNP) discovery in the polyploid Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J. 2009, 7 (4): 334-346. 10.1111/j.1467-7652.2008.00396.x.PubMedView ArticleGoogle Scholar
  47. Ariyadasa R, Mascher M, Nussbaumer T, Schulte D, Frenkel Z, Poursarebani N, Zhou R, Steuernagel B, Gundlach H, Taudien S, Felder M, Platzer M, Himmelbach A, Schmutzer T, Hedley PE, Muehlbauer GJ, Scholz U, Korol A, Mayer KFX, Waugh R, Langridge P, Graner A, Stein N: A Sequence-Ready Physical Map of Barley Anchored Genetically by Two Million Single-Nucleotide Polymorphisms. Plant Physiol. 2014, 164 (1): 412-423. 10.1104/pp.113.228213.PubMed CentralPubMedView ArticleGoogle Scholar
  48. Ganal MW, Altmann T, Röder MS: SNP identification in crop plants. Curr Opin Plant Biol. 2009, 12 (2): 211-217. 10.1016/j.pbi.2008.12.009.PubMedView ArticleGoogle Scholar
  49. Hohenlohe PA, Amish SJ, Catchen JM, Allendorf FW, Luikart G: Next‒generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol Ecol Resour. 2011, 11 (s1): 117-122.PubMedView ArticleGoogle Scholar
  50. Chutimanitsakun Y, Nipper RW, Cuesta-Marcos A, Cistué L, Corey A, Filichkina T, Johnson EA, Hayes PM: Construction and application for QTL analysis of a Restriction Site Associated DNA (RAD) linkage map in barley. BMC Genomics. 2011, 12 (1): 4-10.1186/1471-2164-12-4.PubMed CentralPubMedView ArticleGoogle Scholar
  51. Cogan NI, Ponting RC, Vecchies AC, Drayton MC, George J, Dracatos PM, Dobrowolski MP, Sawbridge TI, Smith KF, Spangenberg GC: Gene-associated single nucleotide polymorphism discovery in perennial ryegrass (Lolium perenne L.). Mol Genet Genomics. 2006, 276 (2): 101-112. 10.1007/s00438-006-0126-8.PubMedView ArticleGoogle Scholar
  52. Chen SQ, Huang ZF, Dai Y, Qin SW, Gao YY, Zhang LL, Gao Y, Chen JM: The development of 7E chromosome-specific molecular markers for thinopyrum elongatum based on SLAF-seq technology. PLoS One. 2013, 8 (6): e65122-10.1371/journal.pone.0065122.PubMed CentralPubMedView ArticleGoogle Scholar
  53. Zhang WW, Pan JS, He HL, Zhang C, Li Z, Zhao JL, Yuan XJ, Zhu LH, Huang SW, Cai R: Construction of a high density integrated genetic map for cucumber (Cucumis sativus L.). Theor Appl Genet. 2012, 124 (2): 249-259. 10.1007/s00122-011-1701-x.PubMedView ArticleGoogle Scholar
  54. Lou QF, Zhang YX, He YH, Li J, Jia L, Cheng CY, Guan W, Yang SQ, Chen JF: Single-copy gene-based chromosome painting in cucumber and its application for chromosome rearrangement analysis in Cucumis. Plant J. 2014, 78 (1): 169-179. 10.1111/tpj.12453.PubMedView ArticleGoogle Scholar
  55. Lou QF, He YH, Cheng CY, Zhang ZH, Li J, Huang SW, Chen JF: Integration of high-resolution physical and genetic map reveals differential recombination frequency between chromosomes and the genome assembling quality in cucumber. PLoS One. 2013, 8 (5): e62676-10.1371/journal.pone.0062676.PubMed CentralPubMedView ArticleGoogle Scholar
  56. Zhuang JY, Lin HX, Lu J, Qian HR, Hittalmani S, Huang N, Zheng KL: Analysis of QTL × environment interaction for yield components and plant height in rice. Theor Appl Genet. 1997, 95 (5–6): 799-808.View ArticleGoogle Scholar
  57. Levine DM, Ramsey PP, Smidt RK: Applied Statistics for Engineers and Scientists: using Microsoft Excel and Minitable. 2001, Upper Saddle River, NJ: Prentice-Hall, Inc.Google Scholar
  58. Murray M, Thompson WF: Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980, 8 (19): 4321-4326. 10.1093/nar/8.19.4321.PubMed CentralPubMedView ArticleGoogle Scholar
  59. Van Ooijen J, Voorrips R: Joinmap 4.0. Software for the Calculation of Genetic Linkage Maps in Experimental Populations. 2006Google Scholar
  60. Van Ooijen J, Boer M, Jansen R, Maliepaard C: MapQTL 4.0. Software for the Calculation of Qtl Positions on Genetic Maps (User Manual). 2000Google Scholar
  61. Peichel CL, Nereng KS, Ohgi KA, Cole BLE, Colosimo PF, Buerkle CA, Schluter D, Kingsley DM: The genetic architecture of divergence between threespine stickleback species. Nat MapQTL. 2001, 414 (6866): 901-905. 10.1038/414901a.View ArticleGoogle Scholar
  62. Courbot M, Willems G, Motte P, Arvidsson S, Roosens N, Saumitou-Laprade P, Verbruggen N: A major quantitative trait locus for cadmium tolerance in Arabidopsis halleri colocalizes with HMA4, a gene encoding a heavy metal ATPase. Plant Physiol. 2007, 144 (2): 1052-1065. 10.1104/pp.106.095133.PubMed CentralPubMedView ArticleGoogle Scholar
  63. Wang WX, Huang SM, Liu YM, Fang ZY, Yang LM, Hua W, Yuan SX, Liu SY, Sun JF, Zhuang M: Construction and analysis of a high-density genetic linkage map in cabbage (Brassica oleracea L. var. capitata). BMC Genomics. 2012, 13 (1): 523-10.1186/1471-2164-13-523.PubMed CentralPubMedView ArticleGoogle Scholar
  64. Van Ooijen JW: Accuracy of mapping quantitative trait loci in autogamous species. Theor Appl Genet. 1992, 84 (7–8): 803-811.PubMedView ArticleGoogle Scholar

Copyright

Advertisement