High-density Genetic Map Construction and QTL Identification for Black Rot and Clubroot Resistance in Cabbage (Brassica oleracea L.)

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High-density Genetic Map Construction and QTL

Identification for Black Rot and Clubroot Resistance

in Cabbage (Brassica oleracea L.)

BY

JONGHOON LEE

FEBRUARY, 2015

MAJOR IN CROP SCIENCE AND BIOTECHNOLOGY DEPARTMENT OF PLANT SCIENCE

COLLEGE OF AGRICULTURAL AND LIFE SCIENCES THE GRADUATE SCHOOL OF SEOUL NATIONAL UNIVERSITY

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,

High-density Genetic Map Construction and QTL

Identification for Black Rot and Clubroot Resistance

in Cabbage (Brassica oleracea L.)

JONGHOON LEE

Department of Plant Science

The Graduate School of Seoul National University

GENERAL ABSTRACT

Black rot and clubroot are devastating diseases which cause significant yield and quality losses in Brassica oleracea. In order to detect quantitative trait loci (QTL) for resistance to these diseases, we constructed two high-density genetic

maps using two different F2 populations, molecular markers derived from whole

genome resequencing and genotyping-by-sequencing methods. Whole genome resequencing of two cabbage parental lines, C1184 and C1234, which were used for black rot study, has enabled rapid dCAPS marker discovery. Reference genome-guided mapping and SNP calling revealed 674,521 SNPs, and 117 markers showing polymorphism between both parental lines were developed and validated. A total 368 markers was used for linkage map construction, which covers 1467.3 cM with an average interval of 3.88 cM between adjacent markers. Black rot resistance in the mapping population was evaluated in three

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,,

and three minor QTLs, which include 21 disease resistance-related genes. Genotyping-by-sequencing (GBS) method was applied for construction of a high resolution genetic map and mapping of clubroot resistance genes. A total of 18,187 polymorphic GBS markers between C1220 and C1176 were detected and 4,103 SNPs, which were genotyped for 78 F2 plants, were used for linkage mapping and resulted in nine linkage groups, spans 879.9 cM with an average interval of 1.15 cM. This GBS map was a useful tool for refinement of mis-assembled regions of the reference genome of cabbage. Clubroot resistance in

the populations against race 2 (YC) and race 9 (GN) were evaluated using F2:3

progenies, and two major QTLs for YC and one major QTL for GN isolate were identified. These genetic maps and QTLs identified in this study will provide valuable information for cabbage breeding.

Key words: Brassica oleracea L., Whole-genome resequencing, Genetic linkage map, Genotyping-by-sequencing, Black rot, Clubroot, QTL

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,,,

LIST OF TABLES

Table 1-1. Summary of whole-genome resequencing data for B. oleracea lines

--- 27

Table 1-2. Summary of SNPs detected from B. oleracea whole-genome

resequencing data and development of dCAPS markers for validation --- 28

Table 1-3. Description of polymorphic markers between C1184 and C1234 used

in this study --- 33

Table 1-4. Results of chi-square goodness-of-fit tests of the observed

segregation ratios with the genotyped markers among F2 plants --- 40

Table 1-5. Distribution of molecular markers on the cabbage genetic map -- 44 Table 1-6. QTLs identified for resistance to Xcc KACC 10377 --- 48 Table 1-7. NBS-LRR-encoding genes in black rot resistance QTL regions

identified for B. oleracea in this study, and syntenic orthologs in closely related species --- 52

Table 2-1. Description of PCR-based polymorphic markers between C1220 and

C1176 used in this study --- 73

Table 2-2. Overview of GBS sequence data which was generated and aligned to

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9,,

Table 2-3. Summary of detected SNPs between C1220 and C1176 in each

chromosome --- 83

Table 2-4. Distribution of GBS loci and PCR-based markers on the cabbage

genetic map --- 86

Table 2-5. Composition of the reference pseudo-molecule of cabbage --- 90 Table 2-6. QTLs for resistance to Plasmodiophora brassicae, position of the

QTL on the map, LOD scores, additive and dominant effects, and percentage of variance explained --- 94

Table 2-7. Clubroot resistant genes and QTLs reported in B. rapa and B. oleracea, and positional comparison with the genetic map in this study --- 99

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9,,,

LIST OF FIGURES

Figure 1-1. Representative black rot disease symptoms on leaves of B. oleracea

after spraying with Xcc suspension --- 23

Figure 1-2. Distribution of SNPs in the pseudo-chromosomes of B. oleracea

--- 29

Figure 1-3. Genotype scoring for the F2 progenies by developed dCAPS

markers --- 32

Figure 1-4. Genetic linkage map of cabbage constructed using 368 markers

--- 43

Figure 1-5. Disease index distribution of F2 population, evaluated by average

scores resulted from inoculated F3 plants --- 46

Figure 1-6. Linkage map containing QTL regions and LOD profiles for black

rot resistance --- 47

Figure 1-7. Epistatic interaction between BRQTL-C1 and BRQTL-C3 --- 49 Figure 1-8. Syntenic relationships among crucifer species of QTL regions

containing genes encoding NBS-LRR proteins --- 53

Figure 2-1. Examples of grouping and naming GBS SNP markers --- 76 Figure 2-2. Distribution of sequencing data for each sample after filtering

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,;

process and mapping ratio of generated data --- 81

Figure 2-3. Distribution of GBS-SNPs and genetic bins in 3Mb intervals along

nine pseudo chromosomes --- 84

Figure 2-4. Genetic linkage map of cabbage constructed using 735 GBS loci,

representing 4,103 SNP markers, and 32 PCR-based markers --- 87

Figure 2-5. Comparison between physical position of SNPs in nine pseudo

chromosomes and the loci in genetic map --- 89

Figure 2-6. Disease index distribution of F2 population, evaluated by average

scores resulted from inoculated F3 plants --- 93

Figure 2-7. Epistatic interaction between CRQTL-GN_1 and CRQTL-GN_2

--- 95

Figure 2-8. Genotyping results of scaffold00122_p2 and scaffold00040 with

their potential adjacent blocks --- 98

Figure 2-9. Comparison of syntenic regions containing identified CRQTLs in

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;

LIST OF ABBREVIATIONS

BAC bacterial artificial chromosome

BLAST basic local alignment search tool

CIM composite interval mapping

CR clubroot resistance

CTAB cetyltrimethylammonium bromide

dCAPS derived cleaved amplified polymorphic sequences

DH doubled haploid

ECD European clubroot differential

EST expressed sequence tag

GATK Genome analysis toolkit

GBS genotyping by sequencing

IBP intron-based polymorphic

LOD logarithm of odds

LRR leucine-rich repeats

MAS marker-assisted selection

MIP MITE insertion polymorphism

MITE miniature inverted transposable

NBS nucleotide binding sites

NGS next-generation sequencing

PE paired-end

QTL quantitative trait loci

SNP single nucleotide polymorphism

SSR simple sequence repeat

Xcc Xanthomonas campestris pv. campestris

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GENERAL INTRODUCTION

Brassica oleracea L. is one of the most important vegetable crops, which

is mainly consumed as a vegetable in worldwide with morphologically various forms such as cabbages, broccoli, brussels sprouts, cauliflowers, kohlrabi, kalian, and kales. About 76 million tons of Brassica vegetables were produced in 2010, with a value of 14.85 billion dollars [1]. Besides the economic importance, cabbage is considered as a valuable plant for study genome evolution because it is a CC genome, which is one of three basic diploid

Brassica species, belonged in the U’s triangle [2]. B. oleracea offers unique

insights into polyploidy evolution, as it results from multiple ancestral polyploidy events and a final Brassicea-specific triplication event [3].

Black rot and clubroot, caused by Xanthomonas campestris pv.

campestris and Plasmodiophora brassicae, respectively, are the most

devastating diseases causing severe losses of quality and yield in Brassica species including B. oleracea [4, 5]. In order to control both diseases during cabbage cultivation, breeding of resistant cultivars is considered as the most effective method. Many studies tried to identify resistance genes or QTLs to both diseases, but the resistant traits are known as complex and quantitatively controlled by multiple genes in B. oleracea. Genetic variations of the pathogens and different genes conferred race-specific resistance have made development of resistant cultivars to be difficult.

Recently, two draft genome sequences of B. oleracea were reported [1, 6]. Approximately 540 Mb of reference genome was assembled and 45,758 gene models were predicted by Liu et al [1], whereas 488 Mb of sequence was completed and 59,225 genes were annotated in Parkin et al [6]. Although there are differences between two reference genomes, they can provide insights into

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the dynamics of Brassica genome evolution and divergence, and serve as important resources for genomics-based breeding of Brassica crops beyond marker assisted breeding.

Here we constructed two high-resolution genetic maps for two different populations by two different methods using the reference genomes. For black rot disease study, a genome wide SNPs derived from whole-genome resequencing of two parental cabbage lines were used for development of

dCAPS markers. The developed markers applied to genotyping F2 populations.

On the other hand, GBS analysis with 80 plant materials, which consisted of

two parental lines and 78 F2 progenies, for clubroot disease study. Both

reference-based analyses of sequencing data provided useful information for QTL mapping of resistance against two diseases. Finally, the QTLs identified herein and the genetic map will be valuable for breeding applications in B.

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LITERATURE REVIEW

Evolution of Brassicaceae species

The angiosperm family Brassicaceae (Cruciferae) contains 338 genera

and approximately 3,700 species [7]. Among them, several plants have been regarded as scientifically and agriculturally important species, such as the first model plant species Arabidopsis thaliana, whose reference genome was reported in 2000 [8], and the Brassica crops, which are consumed in worldwide with various purposes such as edible and industrial oilseed, vegetable, condiment, and fodder crops [9]. This family shows a worldwide distribution, except Antarctica [7], of which six Brassica species have mainly been cultivated,

B. rapa, B. nigra, B. oleracea, B. juncea, B. napus, and B. carinata.

The genetic relationships of six Brassica species was well described by U’s triangle [2] (Figure 1). Three diploid species contain the basic chromosome sets; B. rapa (AA, 2n=20), B. nigra (BB, 2n=16), and B. oleracea (CC, 2n=18). The genomes of three allotetraploids, B. juncea (AABB, 2n = 36), B. napus (AACC, 2n = 38), and B. carinata (BBCC; 2n = 34), were derived by spontaneous hybridization among the three diploid species, followed by chromosome doubling [2, 10]. After the divergence between Arabidopsis and

Brassica lineage occurred in approximately 18 MYA [1, 10], Brassica species

have undergone a Brassica-lineage-specific whole-genome triplication [3], and then Brassica species underwent concerted gene loss and sequence before the divergence of three diploid genomes [10]. Their relationships have been confirmed by chromosome pairing and artificial synthesis of the amphidiploids, nuclear DNA content and sequence analysis, and the use of genome-specific markers [9]. Since the completion of the first plant genome sequence in 2000,

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genomics in the Brassicacea has largely focused on direct comparisons between

A. thaliana and the species of interest, and has suggested perspective of genome

changes associated with polyploidy [11, 12].

Figure 1. U’s triangle showing the genetic relationships among six cultivated Brassica species.

The comparative studies across the Brassicaceae a set of 24 genomic blocks (A-X) within the ancestral karyotype, which represent the conserved segments [12]. Recognition of the ancestral karyotype and these genomic building blocks facilitated comparisons between A. thaliana and Brassica and has provided a basis for family-wide comparative genomics in the Brassicaceae. In addition, the study of Brassicaceae genome evolution will be rapidly developed because reference genomes of Brassica species were recently reported.

Completed reference genome of B. rapa was published in 2011 [13], and two reference genomes of B. oleracea were reported by two different research

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groups using different materials [1, 6] (Table 1). These genomes will support studies of the large range of morphological variation found within B. oleracea, which includes sexually compatible crops such as cabbages, cauliflower and broccoli that are important for their economic, nutritional and potent anticancer value. In addition, a reference genome of B. napus was also reported in 2014 [14]. These data provide insights into the dynamics of Brassica genome evolution and divergence, and serve as important resources for Brassica vegetable and oilseed crop breeding.

Table 1. Summary of two reported reference genomes of B. oleracea

Pseudo-molecule (bp) Scaffolds (bp) Total (bp)

Estimated

genome size Coverage Liu et al. [1] 385,006,588 130,333,472 515,340,060 630 Mb 81.8 % Parkin et al. [6] 446,885,882 41,736,625 488,622,507 645 Mb 75.8 %

Complete reference genomes and whole genome resequencing

A high-quality, reference genome sequence provides access to the relatively complete gene catalog for a species, the regulatory elements that control their function and a framework for understanding genomic variation [15]. Publication of the first plant genome sequence, A. thaliana [8], gave rise to an expansion in genomics-based research and gene annotation to explore orthologous genes in other plants [15]. This complete reference genome triggered sequencing of other plant species along with advancement in sequencing technologies. This trend to produce draft genomes could affect the ability of researchers to address biological questions of speciation and recent evolution or to link sequence variation accurately to phenotypes [15, 16].

The growing availability of reference genome sequences led to high-throughput genotyping. This was initially accomplished by adopting microarray

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technology. However, some serious limitations remain for the array-based method. It is laborious, time-consuming, and expensive to design, produce, and process microarrays suited for specific mapping populations [17].

The development of the next-generation sequencing technology could suggest promising methods for genotyping and genetic mapping. Reference-based alignment derived by whole genome resequencing can easily identify abundant and high-confident SNPs in genome-wide. In addition, simultaneously sequencing of a large number of samples using a multiplexed sequencing strategy can provide effective genotyping and genetic mapping [18]. These technical advances have paved the way for the development of a sequencing-based high-throughput genotyping method that combines advantages of time and cost effectiveness, dense marker coverage, high mapping accuracy and resolution, and more comparable genome and genetic maps among mapping populations [17].

Genotyping-by-sequencing

Genotyping-by-sequencing (GBS) is one of sequence-based genotyping approaches using NGS technology, and has been demonstrated as a robust method for genome-wide profiling of complex populations. GBS uses restriction enzymes to reproducibly capture a targeted portion of the genome enabling high levels of multiplexing while obtaining sufficient sequencing coverage. GBS has been successfully applied for a range of studies including genetic mapping [19, 20], assaying genetic diversity and population structure [21], and genomic selection [22]. While a number of different restriction enzymes and adapter combinations have been used, the commonality of these approaches is the use of restriction enzymes to capture a reduced representation of the genome. This targeted portion of the genome flanking restriction sites is ligated to DNA-barcoded adapters that enable multiplexed sequencing of many

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individuals on a single sequencing run. To date, the use of GBS approaches has largely focused on sequencing with the Illumina GAII and HiSeq platforms [23]. Recently, the use of semiconductor devices for non-optical genome sequencing has been demonstrated [24] and the technology has currently become available for routine sequencing with the Ion Torrent PGM and Proton [25]

The flexibility of GBS in regards to species, populations, and research objectives makes this an ideal tool for plant genetics studies. As the phenomenal increase in NGS output continues, many research questions that were once out

of reach will be resolved through the application of these approaches[23].

Black rot disease in Brassica oleracea

Black rot, caused by bacterium Xanthomonas campestris pv. campestris (Xcc), is the most destructive disease in crucifer crops [5]. Xcc enters leaves not only through insects, or mechanically wounded tissue but also through hydathodes at leaf margins and spreads through vascular tissue, clogging vessels and producing V-shaped chlorotic lesions [26]. Such symptoms lead to a systemic infection in susceptible plants so that quality and yield of infected plants substantially decrease. Crop debris and cruciferous weed are potential inoculum sources in field [27]. The pathogen can be retained in seeds via vessels and causes severe incidence in descent seedlings; consequently, Xcc is difficult to prevent by agricultural practices such as seed treatment, crop rotation and use of agrochemicals. Thus, utilization of Xcc resistant cultivars is one of the most effective approaches to minimize crop loss from infection of the pathogen.

Nine races of Xcc have been identified to date from pathogenicity tests based on the interaction between differential cultivars and races [28]. According to several researches about screening for Xcc resistance [29, 30], races 1 and 4 are the most important races and their appearance was relatively more

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predominant worldwide than other races, 2, 3, 5, and 6 in B. oleracea [31]. Additionally, resistance to race 3 and race 5 is common, but resistance to race 1 is very rare [28]. There have been a variety of opinions concerning the Xcc resistance in cabbage whether resistant trait is qualitative or quantitative, or is controlled by dominant or recessive genes [28, 30, 32-34].

Specific resistance genes have not been identified so far, probably because the number of mapped DNA markers has not been sufficient. In addition, comparison of the QTLs identified by previous authors was quite difficult because no anchor markers can align the linkage maps contracted by different authors, and furthermore, some of the linkage maps did not follow the international nomenclature established for the C genome of B. oleracea [28, 35].

Clubroot disease in Brassica oleracea

Clubroot disease, caused by the soilborne, obligate plant pathogen

Plasmodiophora brassicae infects all cruciferous vegetable and oil crops,

including Brassica rapa, B. oleracea, B. napus, and other Brassica species [4]. This disease is one of the most economically important diseases of Brassica crops worldwide. The pathogen causes abnormal cell enlargement and uncontrolled cell division of infected roots, thus deforming them with characteristic clubs [36]. As a result, nutrient and water uptake by infected roots is inhibited; the growth of the aerial parts of host plants becomes stunted, the aerial parts become yellowish in color and wilt in direct sunlight; and crop yield and quality are reduced [5, 37, 38]. It is difficult to control by cultural practices or chemical treatments, because the pathogen has an ability to survive in soil as resting spores for long periods without host plants [39]. Therefore, breeding of resistant cultivars is regarded as the most effective method for controlling clubroot disease. Genetic variation of P. brassicae is the most troublesome problem in the breeding of clubroot resistance cultivars. In order to elucidate

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differentiation of the virulence, two kinds of differential host set were released and used to identify pathogen races, Williams classification [37] and European Clubroot Differential (ECD) set [40].

Although several research groups identified various sources of clubroot resistance through the screening of a large number of germplasm, completely resistant accessions have rarely been identified in various B. oleracea subspecies [41]. Some of the resistant sources have widely been used in breeding programs and genetic studies for morphologically diverse B. oleracea. Most of genetic studies for clubroot resistance concluded that inheritance of this trait in B. oleracea is controlled by many alleles [42-47], and at least 27 QTLs for resistance to various isolates have been found in B. oleracea so far [41, 47].

B. oleracea plant pyramiding of different CR genes resulted in high resistance

to P. brassicae pathotypes [46], thus accumulation of several CR genes by MAS is necessary.

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genes between B. rapa and B. oleracea. Theoretical and Applied Genetics 2010, 120(7):1335-1346.

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CHAPTER 1 Genome-wide SNP identification and QTL mapping for black rot

resistance in cabbage (Brassica oleracea L. var. capitata L.)

ABSTRACT

Black rot is a destructive bacterial disease responsible for large yield and quality losses in Brassica oleracea. To detect quantitative trait loci (QTL) for black rot resistance, we performed whole-genome resequencing of two cabbage parental lines, one resistant and one susceptible, using Illumina Hi-seq 2000. Approximately 11.5 Gb of sequencing data was produced from each parental line. Reference genome-guided mapping and SNP calling revealed 674,521 SNPs between the two cabbage lines, with an average of one SNP per 662.5 bp. Among 167 dCAPS markers derived from candidate SNPs, 117 (70.1%) were validated as bona fide SNPs showing polymorphism between parental lines. We then improved the resolution of a previous genetic map by adding 103 markers including 87 SNP-based dCAPS markers. The new map composed of 368 markers and covers 1467.3 cM with an average interval of 3.88 cM between adjacent markers. We evaluated black rot resistance in the mapping population

in three independent inoculation tests using F2:3 progenies and identified one

major QTL and three minor QTLs. We identified 21 disease resistance-related genes harboring TIR-NBS-LRR domains from the three QTL regions and conducted comparative analysis with related species.

Key words: Cabbage, Whole-genome resequencing, Genetic linkage map, Black rot, QTL

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INTRODUCTION

Cabbage (Brassica oleracea L.) is one of the most important vegetable crops, and is consumed as a food worldwide due to its healthy compounds for humans. Besides its economic importance, cabbage is considered a valuable plant for the study of genome evolution because it contains a CC genome, which represents one of three basic diploid Brassica species in the U’s triangle [1]. Recently, two draft genome sequences of B. oleracea were reported [2, 3], and the availability of this reference genome enhances our understanding of the genome architecture of B. oleracea and the evolution of Brassica species, as well as facilitates identification of genes associated with important traits for crop improvements.

Black rot is one of the most devastating diseases to crucifers including B.

oleracea and is caused by the vascular bacterium Xanthomonas campestris pv. campestris (Pammel) Dowson (Xcc). The disease infects the host plants through

hydathodes, wounded tissue, insects and stomata [4, 5]. The main disease symptoms are V-shaped chlorotic lesions at the margins of leaves, necrosis and darkening of leaf veins, which lead to serious production losses in vegetable crops [6]. Accordingly, development of cultivars resistant to black rot has been a priority for breeders.

Several methods have been attempted to control black rot disease, including crop diversification and rotation, production of disease-free seed, pre-treatment of seed with bactericide, elimination of potential pathogen sources such as infected crop debris and weeds, and planting of resistant cultivars [7]. Among these, utilization of resistant cultivars is one of the most effective and efficient ways to reduce disease incidence and crop loss. However, the development of commercially acceptable resistant varieties has proven to be

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extremely difficult due to the lack of studies on genetics and breeding for resistance in cabbage. Two major factors hinder black rot resistance breeding in

B. oleracea: multigenic control of resistance and emergence of new races of the

pathogen that overcome host resistance [8]. Nine races of Xcc have been identified [9], among which races 1 and 4 are the major pathogens causing black rot disease in B. oleracea crops [10]. Therefore, obtaining B. oleracea cultivars that have resistance to both races is considered a prerequisite to control black rot disease [11].

Molecular markers are highly useful for genomic analysis and allow to track exploration of heritable traits and the corresponding genomic variation. DNA markers are now key components of crop improvement programs, and are applied to identify cultivars, analyze genetic diversity, construct linkage maps and identify quantitative trait loci (QTL). Advances in molecular markers have facilitated the identification of interesting traits via marker-assisted selection (MAS) in plant improvement. Marker-based approaches represent an effective and rapid strategy for identifying and transferring useful genes in breeding programs [12]. Furthermore, the identification of markers linked to QTL can allow analysis of the consistency of QTL effects across different environments and genetic backgrounds, and increase the frequency of favorable alleles during selection [13]. Several QTLs for black rot resistance in B. oleracea have been reported, including two on linkage groups 1 and 9, and two additional QTLs on linkage group 2 [13], as well as two other major QTLs on linkage groups 2 and 9, and two minor QTLs on linkage groups 3 and 7 [14]. Moreover, three QTLs

analyzed using SNP markers in the F2 mapping population derived from a cross

between resistant cabbage and susceptible broccoli were found on linkage groups 2, 4 and 5, and exhibited significant effects in black rot resistance [4]. Recently, three further QTLs for black rot resistance were also detected in linkage groups 5, 8 and 9 [5]. In total, 14 QTLs with major and minor effects

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have been mapped on eight different B. oleracea chromosomes, suggesting that resistance to black rot disease is complex and quantitatively controlled by multiple genes in B. oleracea.

Successful QTL mapping requires a large number of genetic markers [15]. Markers based on simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) are commonly used due to their advantages over other types of genetic markers. SSR markers are highly reproducible, highly polymorphic, but not amenable to automation. However, next-generation sequencing (NGS) technology makes SNP markers preferable to SSR markers [16]. SNPs have proved to be universal as well as the most abundant forms of genetic variation even among individuals of the same species [17]. Therefore, SNP markers exhibit higher polymorphism than SSR markers [18, 19].

In this study, we have resequenced two parental cabbage lines up to 20x genome coverage and conducted a genome-wide survey for SNPs. We validated the SNPs and developed derived cleaved amplified polymorphic sequences (dCAPS) markers for resistance against black rot disease. The genome-wide catalog of SNPs, the high-density map derived from a mapping population generated from elite cabbage breeding lines with a narrow genetic background, and the QTLs reported herein will be valuable for both breeding and genetic researches in B. oleracea.

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MATERIALS AND METHODS

Plant materials and whole-genome resequencing

Two cabbage (Brassica oleracea L. var. capitata) inbred lines, C1184 and C1234, were selected as parents to develop a mapping population. The two lines show different responses to black rot disease; C1184 is susceptible to X.

campestris pv. campestris (Xcc), whereas C1234 is resistant. The mapping

population consisted of 97 F2 plants generated by crossing between C1184 and

C1234, as described previously [19]. The 97 F2 plants were vernalized and

self-pollinated to produce seeds of F3 progenies for inoculation tests. All plant

materials examined in this study were obtained from Joeun Seeds Co. (Chungcheongbuk-Do, Korea).

Genomic DNAs were extracted from approximately 5 g samples of young leaves from the cabbage parental lines, following the modified cetyltrimethylammonium bromide (CTAB) protocol [20]. The quality and quantity of the DNA were examined using a NanoDrop ND-1000 (NanoDrop Technologies, Inc., USA). More than 5 μg extracted DNA was randomly sheared and quantified using DNA 1000 kit (Agilent Technologies, Inc., USA) according to the manufacturer’s protocol. Sequencing with constructed shotgun libraries of C1184 and C1234 was performed by Illumina Hi-seq 2000. Fragmentation, library construction, and sequencing were carried out by the National Instrumentation Center for Environmental Management (NICEM; Seoul, Korea).

SNP discovery and dCAPS marker design

Overall process of SNP discovery was performed by following the framework described by DePristo et al [21]. Briefly, Illumina paired reads from

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the parental lines were aligned to the reference sequence of B. oleracea [3] using Bowtie2 program [22]. Then, read grouping and removal of PCR duplicates were carried out using Picard (http://picard.source forge.net). Misalignments caused by INDELs were corrected by local re-alignment using Genome Analysis Toolkit (GATK) and candidate SNPs were called using Variant Caller, a utility in GATK [23]. To filter variants and avoid false positives, candidate SNPs exhibiting any of the following characteristics were removed: (1) mapping quality score lower than 4; (2) quality less than 30; (3) less than 10x or more than 45x mapping depth.

Initially, SNPs of C1184 and C1234 relative to the reference genome were called separately. All of the identified SNP positions from both parental lines were then merged and compared to each other, and promising SNPs for this research between C1184 and C1234 were identified. The selected SNPs were used to develop dCAPS markers using the dCAPS Finder 2.0 program (http://helix .wustl.edu/dcaps) for design of nearly-matched primers including SNP positions. After designing such mismatched primers for each SNP, the

opposite primers were designed using the Primer3 program (http://primer3.wi.

mit.edu/). All primers were synthesized by Macrogen (Seoul, Korea).

Molecular marker analysis

The newly developed dCAPS markers were validated by examining polymorphisms between the two parental lines C1184 and C1234. Additional expressed sequence tag (EST)-based dCAPS, intron-based polymorphic (IBP), genomic SSR, and INDEL markers that were not included in the previous genetic map [19] were also analyzed in this study. Furthermore, five polymorphic markers based on miniature inverted transposable element (MITE)

insertion polymorphism (MIP) [24, 25] were also used for genotyping the F2

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PCR amplifications were performed in a total volume of 25 μL containing 20 ng genomic DNA template, 1 x PCR buffer, 20 pM each primer set, 0.2 mM each dNTP, 1 U Taq DNA polymerase (VIVAGEN, Korea). The amplicons of dCAPS markers were mixed with 3 U appropriate restriction enzymes (New England Biolabs, USA), the corresponding 1 X buffer, and 1 X BSA when necessary, then incubated at 37ଇ for more than three hours. The digested fragments of dCAPS markers and amplicons of other markers stained by ethidium bromide were visualized on a UV trans-illuminator after electrophoresis using 9% non-denaturing polyacrylamide gels or 1 % agarose gels depending on fragment size.

Inoculation test

Xanthomonas campestris pv. campestris KACC 10366, obtained from

the Korean Agricultural Culture Collection (KACC; Suwon, Korea) were used for the inoculation tests. Inoculum of the bacterium was scraped and cultured on tryptic soy agar (TSA) plates at 30ଇG for 48 hU Cultured bacteria were harvested using a spreader and diluted with distilled water to 0.125 OD at 600 nm to prepare bacterial suspension for inoculation.

Inoculation tests, carried out in 2012, 2013, and 2014 under the same conditions at the Korea Research Institute of Chemical Technology (Dae-jeon,

Korea), were performed with 10~15 F3 plants of each individual F2 plants

selected for genotyping analysis. The F3 seeds were sown and grown on 5 x 8

plastic pots for 20 d in a greenhouse. Afterwards, 20-d-old plants, usually at a stage with two sufficiently developed true leaves, were inoculated by spraying bacterial suspension until adaxial and abaxial surfaces of leaves were sufficiently wet. Each plastic pot (40 plants) received 80 mL bacterial suspension, and the inoculated plants were moved into a dew chamber with the

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temperature set at 28ଇ. After 48 h incubation, all plants were transferred to a room maintained at 25ଇ and 80 % humidity for further 7 d incubation with 12 h light/day, and disease symptoms on two inoculated leaves per each plant were surveyed. The severity of the black rot symptoms were recorded based on infected leaf area, with the following disease indices: (0) less than 15% , (1) 15-30%, (2) 30-55%, (3) 55-75%, (4) more than 75% leaf area showing black rot symptoms (Figure 1-1).

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Figure 1-1. Representative black rot disease symptoms on leaves of B.

oleracea after spraying with Xcc suspension. Disease indices are: (0) less

than 15% , (1) 15-30%, (2) 30-55%, (3) 55-75%, (4) more than 75% leaf area showing black rot symptoms.

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Map construction and QTL analysis

A total of 103 polymorphic markers were genotyped in the F2 population,

and the resulting scores were integrated into genotyping data used for a previous genetic map [19]. Linkage analysis and map construction were performed using JoinMap version 4.1 with the same parameters as in the previous study[19]. The Kosambi mapping function was used to convert recombination frequencies into genetic distances.

A disease index for each F2 individual was calculated as the mean grade

of 10~15 F3 seedlings. QTLs for Xcc resistance were evaluated using composite

interval mapping (CIM) analysis with QGene program. CIM was performed with LOD (logarithm of odds) threshold values that were estimated using 1,000 permutation tests at 5% significance with 0.5-cM scan intervals. Afterwards, epistatic interactions between loci were evaluated by using the freely available software QTL Network 2.1.

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RESULTS

Whole-genome resequencing of two cabbage parental lines and SNP detection

Whole genome sequencing data included about 114 million raw reads for C1184 and 113 million for C1234 (Table 1-1). The recently assembled B.

oleracea genome sequence consists of 488.6 Mb, including 446.9 Mb in 9

pseudo-chromosomes and 41.2 Mb of unanchored scaffolds, and corresponding to almost 75% of the estimated genome size (648 Mb) [3]. Our new sequencing data represented approximately 18-fold genome coverage for both parental lines based on the estimated genome size. We mapped each set of paired reads onto the nine pseudo-chromosomes of reference genome sequence. In total, almost 94 million raw reads (82.1%) and 88 million (77.6%) from C1184 and C1234, respectively, were successfully aligned to the reference genome. The average mapping depth was 21.2- and 20-fold for C1184 and C1234, respectively.

The total number of SNPs relative to the reference sequence and average SNP densities were very similar in both parental lines. Approximately 1.20 and 1.24 million high-quality SNPs are identified in C1184 and C1234, respectively, by comparison to the reference genome. On average, one SNP was detected in every 372.8 bp in C1184, and every 360.0 bp in C1234. Chromosome C03 of both lines had the most SNPs, whereas the least SNPs were found on chromosome C06 of C1184 and chromosome C04 of C1234.

These SNPs were merged and used to detect SNPs between the two parental lines (Table 1-2). As a result, a total of 674,521 SNPs were found throughout nine chromosomes, with an average of one SNP per 662.5-bp interval. The highest density of SNPs was found on chromosome C03, with one SNP per 541 bp, while the lowest density was on chromosome C05, with one SNP per 818.9 bp. Analysis of the distribution of SNPs per 100 kb along the

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nine chromosomes revealed areas of high and low SNP density on each chromosome (Figure 1-2).

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Table 1-1. Summary of whole-genome resequencing data for B. oleracea lines

C1184 C1234

Raw reads 114,454,524 113,830,992

Raw bases 11,559,906,924 11,496,930,192

Coverage of B.oleracea genome 17.8 x 17.7 x

GC (%) 36.1 35.6

Mapped reads 93,956,750 88,382,752

Mapped percentage (%) 82.1 77.6

Mapped bases 9,489,631,750 8,926,657,952

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T

able 1-2. Summary of SNPs detected fr

om

B. oleracea

w

h

ole-genome r

esequencing data and development of

dCAPS markers for

validation a(h) is the n u mber of markers th at show ed heter oz ygous r esults bPer

centage of the total

amplified d

CAPS markers that w

er e polymorphic Ch. Number of SNPs (average bp per SNP) V alidation Ref vs. C1 184 Ref vs. C1234 C1 184 vs. C1234 Amplified / Designed Polymorphic (h) a C01 122,191 (358.2) 114,778 (381.3) 66,197 (661.1) 31 / 35 20 (4) 64.5 % C02 149,730 (353.2) 161,246 (328.0) 74,741 (707.6) 14 / 17 10 (1) 71.4 % C03 196,150 (331.3) 205,306 (316.5) 120,1 15 (541.0) 13 / 22 11 (1) 84.6 % C04 136,815 (392.6) 132,144 (406.5) 86,999 (617.5) 14 / 20 8 (4) 57.1 % C05 130,557 (359.2) 132,887 (353.0) 57,417 (818.9) 15 / 18 14 (2) 93.3 % C06 87,712 (454.1) 102,422 (388.8) 63,017 (631.9) 28 / 34 16 (4) 57.1 % C07 119,275 (405.5) 128,978 (375.0) 69,905 (691.9) 8 / 15 5 (1) 62.5 % C08 108,586 (384.6) 113,956 (366.4) 68,361 (610.9) 21 / 26 18 (3) 85.7 % C09 147,866 (369.8) 149,581 (365.6) 67,768 (806.9) 23 / 35 15 (6) 65.2 % T otal 1,198,882 (372.8) 1,241,298 (360.0) 674,521 (662.5) 167 / 222 117 (26) 70.1 %

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Figur e 1-2.

Distribution of SNPs in the pseudo-chr

omosomes of B. oleracea. SNPs w ithin 100-kb intervals ar e show n for (a) Refer ence vs. C1 184; (b) Refer ence vs. C1234; (c) C1 184 vs. C1234.

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Development of dCAPS markers and construction of genetic map

We used the SNPs between C1184 and C1234 for development of dCAPS markers. Based on the physical positions of all markers used in a previous genetic map for B. oleracea [19], new dCAPS markers were designed for the regions of low marker density. Among 167 markers amplified, 117 (70.1%) were polymorphic between the two parental lines (Table 1-2 and Table 1-3). Among the 117 polymorphic markers, 26 showed heterozygosity in one of parental lines (Table 1-2). We used 87 of these polymorphic dCAPS markers for

genotyping of each individual in the F2 population (Table 1-3 and Figure 1-3).

Additionally, 16 other types of polymorphic markers including five EST-based dCAPS markers, five MIP markers, three IBP markers, two genomic SSR markers, and one INDEL marker were also genotyped with the same population. Among 103 newly analyzed markers, 25 markers showed a segregation pattern

distorted from the 1:2:1 Mendelian ratio in the F2 population, based on

chi-square goodness of fit at the 0.05 probability level (Table 1-4). There were six segregation distortion regions (SDRs) in the previous map [19], and all dCAPS markers designed from the SDRs of C01 and C05 showed the same distortion ratio.

The 103 novel polymorphic marker loci (Table 1-3) were added to the previous 265 markers [19] to develop a higher density genetic map. All 368 markers were placed on the map, and a linkage map was generated with nine linkage groups (LGs) in which each LG had more than 32 markers (Table 1-5 and Figure 1-4). The improved B. oleracea genetic map spanned 1,467.3 cM, which is 135.4 cM more than the previous map, and the average distance between neighboring loci was reduced to 3.88 from 5.02 cM. Most of the new dCAPS markers were mapped to the originally estimated position of each chromosome sequence. The exceptions included BoRSdcaps1-35, which was designed on chromosome C01 but mapped to chromosome C02, and

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BoRSdcaps5-18, designed on chromosome C05 but mapped to chromosome C09.

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Figur

e 1-3. Genotype scoring for

the F

2

pr

ogenies by developed dCAPS markers (a) BoRSdcaps1-27 (b)

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T

able 1-3. Description of polymorphic markers betw

een C1

184 and C1234 used in this study

No. Marker name Marker typ e Forw ard Reverse Restriction enz yme 1 BoRSdcaps1-4 a Resequencing-d CAPS ACAGT TCAT GT AATG GGAAC A G TTAC A A GC AACAT TGTC AT CATCTTC T T T CT CC Alu ช 2 BoRSdcaps1-5 a Resequencing-d CAPS TTGA A T T AT TA CTGGT GTG T GC ATT AAAAG TA TTT T C AGAAG ATCTC AGAAA G C Alu ช 3 BoRSdcaps1-6 a Resequencing-d CAPS ATGA GTCCT TC TGGTC T GAC AG TAT TTAAC AATC T A T GAG TTG G CGA GACA T CG Cla ช 4 BoRSdcaps1-9 b Resequencing-d CAPS TAAT AAA AAT A AATA T AG GAT A A TT G G A TC TAAT TAA A T T C T GACTC GGT TC TGC BamH 5 BoRSdcaps1-10 a Resequencing-d CAPS ATGT GT TCAA T A CACCACACAA AAC TAAA GATCCA A T AAT ATA T T G G C TAGT T A Mse ช 6 BoRSdcaps1-11 a Resequencing-d CAPS GAGAG AAG AT ACCGGGA AAC ACT ATACG T AG TTA TTGA T T T G T AG AA A T CG A Cla ช 7 BoRSdcaps1-13 a Resequencing-d CAPS GGAA TAAC TGA C ATCGT GGAG A A TTT GAACCT TG AATG G CGA TTC TAAA A T C Cla ช 8 BoRSdcaps1-14 a Resequencing-d CAPS AAAAG AAT TG AGAT TAG T GG T GGAAA T CG A CATAG T TG TGC AAGT TTG T CT G C Cla ช 9 BoRSdcaps1-19 a Resequencing-d CAPS GCAAGA TACT A T TG TAGA TA TT AAC G A AT T AATCT GAA ACCTGAAC ATG AGG A EcoR 10 BoRSdcaps1-20 b Resequencing-d CAPS TCTA TAGG GAA TGCAA T T T ATC TTCAA G T A TTGA T G TTG GC CAGAAG AATC Sc aช 11 BoRSdcaps1-22 a Resequencing-d CAPS GGTT G T T CTG A C GGCAAG AT TTAG AGA AAT T AGTTC TCT T AC CAAAG T A Sc aช 12 BoRSdcaps1-24 b Resequencing-d CAPS TTCTC TTCTC A GACATCG ATGC TCCTCT T A TTAC T CGTAC T ATCCAAGCC A A C TC Mse ช 13 BoRSdcaps1-25 a Resequencing-d CAPS TAAG AGT TAA A T ACAA AGAA G C AAAT AT C G TTCG TTTCC A CCTCTAT TGA A A C T Cla ช 14 BoRSdcaps1-26 b Resequencing-d CAPS AAGAA ACTG A AGAAA TGG A A G CAGT ATTA TG TCAAG GGAACA AAGA TACCT AG C Alu ช 15 BoRSdcaps1-27 a Resequencing-d CAPS GCAAT TCCACA AATT AACT T T T T CT ATAT TCACA T T C AAAGA TCAT A A G A T C GA Cla ช 16 BoRSdcaps1-28 a Resequencing-d CAPS CGAGTCA TGG T CTGGA AACA CAGTA A CTGA A AGGCT T TAC A A GATG G AT Mbo ช 17 BoRSdcaps1-29 a Resequencing-d CAPS ATAGCCA AAA T AGTA G TT GTA A AAGAG G TA GCCAGTC T CCTCTCCTTCCT Rsa ช 18 BoRSdcaps1-32 a Resequencing-d CAPS ACCGGGTTA TTTTAA ATG GACA G TTAA A CCCTTA CTTGG GAA GATTGTTT C C Hpa ซ 19 BoRSdcaps1-33 a Resequencing-d CAPS ATAT TT GGA TG GCAAACCG TAA G ATAT TT AAT AT TCAAA AAT ATC T AAA G TA Rsa ช 20 BoRSdcaps1-35 a Resequencing-d CAPS CGTT TGT ATT T ATTCA T T G CCT TG TTAA AT TTAC A ATTG TT TT ATA C AAAA G C Alu ช

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T

able 1-3. Continued _No.

Marker name Marker typ e Forw ard Reverse Restriction enz yme 21 BoRSdcaps2-1 a Resequencing-d CAPS TGT T GCT T TA G TTT ATA A TCT T TTT AAA G C TGTAC A TCT A T CCATTCA AGTC GAG Alu ช 22 BoRSdcaps2-2 Resequencing-d CAPS ATGA TAT GAA A T GACT T GA GT T T TA TA TT A TTT ATA T TCCA CGCCATAG G T T TTA Mse ช 23 BoRSdcaps2-6 a Resequencing-d CAPS TAT TTT TG TAA GATCCAGC ATC A CA TGGTC T CT TCT T AT TTG AAAAC AAACC T T Mse ช 24 BoRSdcaps2-9 b Resequencing-d CAPS CCATACT TGAC CCAGATG AAG AGAAG G A T C GGAT TTA T CT G T GTG GAA AAAC AAG BamH 25 BoRSdcaps2-10 a Resequencing-d CAPS GGTCCCT TTGC T AAA AGA TGC AATT T GA GAGC CATTA A ACCT T T TTC A G C Alu ช 26 BoRSdcaps2-12 a Resequencing-d CAPS TTAG AAA TAA T CATAT GGCCT A A TAA A G CT TGTGC A T T AAA TTT ACAA TTCT TTG TT Hind ฌ 27 BoRSdcaps2-13 a Resequencing-d CAPS AGGT GAA TAG T CGTT TTA T CCA ACC TTCACA GGT AA TGTC TTT GAG T T CCTC G G Hae ฌ 28 BoRSdcaps2-14 Resequencing-d CAPS CTTT TACT TCTC TAAACCC TAAA AGT CT AG ATT T AGA GGT A G TAG G TTC GAT TCCA Xba ช 29 BoRSdcaps2-16 a Resequencing-d CAPS CATT TATC TGG A TT TTA G T TTT AGAT TA A G ATTA TT ACCGA C ACAACAAA A G CAT Alu ช 30 BoRSdcaps2-17 a Resequencing-d CAPS ACACCACTA A C GACCTA T ACT G CTC GGTT AGAC AAA TAT T TAC GACC AAAT C G A Cla ช 31 BoRSdcaps3-1 a Resequencing-d CAPS CGCTGCT G CTT CCTTAT T T T GTCTA AAT GAT ATGT TT TCCT TT TCT C G A Xho ช 32 BoRSdcaps3-4 a Resequencing-d CAPS TTT TT TT TTTCC AAACCAG AGT AAAA TC G A TGCAG T GTC A A TTT AAGA ACAA GAAT Cla ช 33 BoRSdcaps3-8 a Resequencing-d CAPS CACCGGATAGA GGAA TCCCGCA ATT G G ATC CACTTCA G CAG A TGT AGA TAT G GAA BamH 34 BoRSdcaps3-9 b Resequencing-d CAPS GATG ATT TGAC TGAG GAA TTT G TTC TAGA ATCA TT T TTT GTA A CTGC AGT T C TA Xba ช 35 BoRSdcaps3-12 a Resequencing-d CAPS ATGT TT GAAA T GAGTC AAA TGA C TAGA A GC AACCATCG G TA CGTAT T A T TTC T CA Alu ช 36 BoRSdcaps3-14 a Resequencing-d CAPS AAAT TTG T CT A T ATGC AAT ATA A TTA G G T A ATCATCC AATC G TTA A TCA AAA TGT Rsa ช 37 BoRSdcaps3-16 a Resequencing-d CAPS ATTG GT TGT A C AGATC GAGG AT AAA GAAT GAAG AA CATAA A CTT T C AAAGA A AG Hind ฌ 38 BoRSdcaps3-18 a Resequencing-d CAPS AAAAG GCCTA AACTTTCTTAG A TGC TTCCATC ATG T AGATTCCCTTA CATT G AT EcoR 39 BoRSdcaps3-19 a Resequencing-d CAPS ATCCAA T CTCA GAAA TGAA TT T GGAAG G T A GAGAA GGA GCCATATC ATCT A C AAA Rsa ช 40 BoRSdcaps3-20 a Resequencing-d CAPS AGACT T CCCAA AAAG TTTC AAG AGT GGAAA AGT TA AAGT GTT A TA A T TA TGG T A Rsa ช

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T

Marker name Marker typ e Forw ard Reverse Restriction enz yme 41 BoRSdcaps3-22 a Resequencing-d CAPS ATCT TTA AACG A TT TGAA ACAT TGGT T G TA AAAACCA ACCT GAAT TTCCT AA AAC Rsa ช 42 BoRSdcaps4-1 b Resequencing-d CAPS TCACGG TGAA A GGTT A TA TAG T AGTC TTCT TTG TG TTT GTACT T T G TA T T AT TT A Mse ช 43 BoRSdcaps4-2 a Resequencing-d CAPS TCGAA TCAAA T CCCTTGT AATC AACTT A G C AACTT ACCGAA TGT T TA AAT GT GGA Alu ช 44 BoRSdcaps4-6 b Resequencing-d CAPS CACGTAA A TAC G CTT T CAGT TT TCT TAAG TGGC AAA TTCT AATCC TA ATAT A G C Alu ช 45 BoRSdcaps4-8 a Resequencing-d CAPS TTCAA TGGC AT GTTG TT TT TT TT AAC T C G A TTT G CTG T CTC C TTCT TCAAA A T AG Xho ช 46 BoRSdcaps4-14 Resequencing-d CAPS CTGT TTT AAAC CACAAAA ACTA TCC TC T AG CTAAA GACTA T GAGCT G CCGT T T AG Xba ช 47 BoRSdcaps4-15 b Resequencing-d CAPS ACAAA TAT TTG AGTT T T GAT TG GTTAC A A G CGAAAA TG TTA CCAGTCAG T T A TTT T Alu ช 48 BoRSdcaps4-17 b Resequencing-d CAPS CTTCCCATGCATCCATGAATGC CTGAA TT A ATAG GAAA TG GATA T GCA GAC GATA Mse ช 49 BoRSdcaps4-18 a Resequencing-d CAPS CATAT G TGC A G AAACA TTG GTT GGTA A G GC CTGCTG AAG TG ACTTG ACTA TC TGA Hae ฌ 50 BoRSdcaps5-2 a Resequencing-d CAPS ACAACACT AAA GATCA TAA G CT TTAG T C G A CGCTAT TCTCT AGTG AAACG A AGAC Sa lช 51 BoRSdcaps5-3 a Resequencing-d CAPS AAGAA TAC T CT CATACG TTA A G AGCT A G TA TTT G TTCCC TG GGAT ATG AGT A A T Sc aช 52 BoRSdcaps5-4 a Resequencing-d CAPS TCAAA AACACA T CTCGA AACA T AGA ATCAA A CAACA A TAT T AA AGA A T CTC C C G Hpa ซ 53 BoRSdcaps5-6 b Resequencing-d CAPS AGGAC TAGCT T GTCT TTT T GA G C AGCAA G C TTCCAG A CCTT G TTA AGT GT TT TGT Hind ฌ 54 BoRSdcaps5-7 a Resequencing-d CAPS AATA T CT TAT T TTCTCA TA TAC T TGA T C T A ATGT ATCA AT T T AGG TGG T TC T T GG Xba ช 55 BoRSdcaps5-8 a Resequencing-d CAPS TGTG GCAA TGT AAAT ATCCT TG TAA TTCCTG A TG AC TATCA AGA AAG TAGT T TA Mse ช 56 BoRSdcaps5-9 a Resequencing-d CAPS TGGA ATCCAA A T ACGA GTA AAG AGTT A C C G CTCTGG GTCA A GAAA TAAG TTC C TA Hpa ซ 57 BoRSdcaps5-12 a Resequencing-d CAPS GAAA TTA TAT G AGAT TTGC GAA AAGA TTAA GCAT GAA ACGCATA A TG A GAGC TT A Mse ช 58 BoRSdcaps5-13 b Resequencing-d CAPS AACTTC AAAAC CAAAAACCAC T ATG AACTT GCAAG A C ATAACA AAG T GGCAA G T Sc aช 59 BoRSdcaps5-14 a Resequencing-d CAPS GGTT A CT TAA T CGTAA GAT TTA TAGA T C TC CTTCAGAAGAATCCCGTTTGA XhoI 60 BoRSdcaps5-15 a Resequencing-d CAPS TTGG GGA TCGA CGGTGC TTG TA GGAA TT G A GACCCCTTTCATGCATTGTT MboI

(49)

T

Marker name Marker typ e Forw ard Reverse Restriction enz yme 61 BoRSdcaps5-16 a Resequencing-d CAPS TTT TGCA ACCA CCAATT G TA TTCACT TAAC T AACCACCTA G A C ATA T TA Mse ช 62 BoRSdcaps5-17 a Resequencing-d CAPS ATTG GAGCCCC GAAGA TTAT TACATAAA GGA GCTGTTTTTA A T AGA TAT EcoR 63 BoRSdcaps5-18 a Resequencing-d CAPS ATT T GT AAT TA TTA TCCGAA AA TCTA T CTA ACCATT AGACC A CGACAACG XbaI 64 BoRSdcaps6-2 a Resequencing-d CAPS GTTTTATGGATACACGCCTCTACAC CTCACACATC A C ACATACA T AG CATGT T A Mse ช 65 BoRSdcaps6-4 b Resequencing-d CAPS GCACCAAT TT G A TAT T T T T T T T G GAAG T A C AAAAG AGA GG AGAGG AGA AG AAAT G Sc aช 66 BoRSdcaps6-5 a Resequencing-d CAPS GATCT ACCAAA TGGA TTG GAAC TC AAAT AAA TAG AAATC T TG ATA TTG TG G TA Rsa ช 67 BoRSdcaps6-9 a Resequencing-d CAPS CCGGTA T CTCT CCTAAG TAA T G A CA AATA T ACA AAC TTT AAAAC AAT TTGC A TC Cla ช 68 BoRSdcaps6-16 a Resequencing-d CAPS ATGT TAG AAT T TTGG AACA GAT TTCA TC GA TTTC ATT A TA T T CCCTCGG T AT GTC Cla ช 69 BoRSdcaps6-20 a Resequencing-d CAPS GTAA TAG T AA A T GGA ATT AACA AACTCC A A AGTTC AGA TAA AGGCACT T CAC G Hind ฌ 70 BoRSdcaps6-21 b Resequencing-d CAPS TAAT TGG T AA A T ACAA TAACAC AAGACA T C TAGTC GATC T T C ACCGTT TTG T T Cla ช 71 BoRSdcaps6-22 a Resequencing-d CAPS ATTG TGAC GAT CACCTTCCA TA C ATT T GA TCCAG A CCAGAA TTA A G CAC TT A Mse ช 72 BoRSdcaps6-25 a Resequencing-d CAPS AGAGC TTA AGG TTT TGA GAGA T T TCT T C T A AGCTG TGA GAA GTTG TGA A CCT T Xba ช 73 BoRSdcaps6-26 a Resequencing-d CAPS CTCGGGGC AT T C TTT TGT T A T A C ATGAC ATT T TC TAACT A TA TAG T AGA G T A Rsa ช 74 BoRSdcaps6-29 a Resequencing-d CAPS CCTTGG T CGA A T CTTCC TTG ATTA TGA AGA T T AACT T AA AT T TTT T G AT Mbo ช 75 BoRSdcaps6-30 a Resequencing-d CAPS TTGGCCC AAG T CCATCTA AT TCTAA T T GGT G T ACGTC T GG AT ATCAG A T Mbo ช 76 BoRSdcaps6-31 b Resequencing-d CAPS AAGCACG AGA TTA TAT T CT TC AAACA T G A T C C G T G TA C G TTT T GT G C TT TT Mbo ช 77 BoRSdcaps6-32 a Resequencing-d CAPS TGCAT A T T CCA AAGG TTA GAT G C GTCAT G TA ACT T GCAT TCAA GT AAAGG G T Rsa ช 78 BoRSdcaps6-33 b Resequencing-d CAPS ACATA T TA TGA T ACACG T AG TA ATGG A TT A TTGAC TAA T T T T GAA TTG TGGC TAA Mse ช 79 BoRSdcaps6-34 a Resequencing-d CAPS TGGCA T GT GTA A TTAC TAA GAT GAAG T CC G ATTCGC TAACC AACGAA ACG Hpa ซ 80 BoRSdcaps7-5 a Resequencing-d CAPS TTCACA AGACG A TCAA AAACA G A TAGA TCCAA T T T TCAA GAGC TT ATCA A AG Hind ฌ

High-density Genetic Map Construction and QTL Identification for Black Rot and Clubroot Resistance in Cabbage (Brassica oleracea L.)

High-density Genetic Map Construction and QTL

Identification for Black Rot and Clubroot Resistance

in Cabbage (Brassica oleracea L.)

High-density Genetic Map Construction and QTL

Identification for Black Rot and Clubroot Resistance

in Cabbage (Brassica oleracea L.)

JONGHOON LEE

Department of Plant Science

The Graduate School of Seoul National University

GENERAL ABSTRACT

CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATIONS

GENERAL INTRODUCTION

LITERATURE REVIEW

REFERENCE

CHAPTER 1

Genome-wide SNP identification and QTL mapping for black rot

resistance in cabbage (Brassica oleracea L. var. capitata L.)

ABSTRACT

INTRODUCTION

MATERIALS AND METHODS

RESULTS