Single Nucleotide Polymorphism Marker Discovery by Transcriptome Sequencing in Capsicum

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i

A THESIS FOR THE DEGREE OF MASTER OF SCIENCE

Single Nucleotide Polymorphism Marker Discovery

by Transcriptome Sequencing in Capsicum

고추 전사체를 통한

단일염기다형성 분자마커개발

August 2014

PHILLIP CHOE

MAJOR IN HORTICULTURAL SCIENCE

DEPARTMENT OF PLANT SCIENCE

i

Single Nucleotide Polymorphism Marker Discovery

by Transcriptome Sequencing in Capsicum

UNDER THE DIRECTION OF DR. BYOUNG-CHEORL KANG

SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL

OF SEOUL NATIONAL UNIVERSITY

BY

PHILLIP CHOE

MAJOR IN HORTICULTURAL SCIENCE

DEPARTMENT OF PLANT SCIENCE

THE GRADUATE SCHOOL OF SEOUL NATIONAL UNIVERSITY

August 2014

APPROVED AS A QUALIFIED DISSERTATION OF PHILLIP CHOE

FOR THE DEGREE OF MASTER OF SCIENCE

BY THE COMMITTEE MEMBERS

CHAIRMAN

_____________________________

Jin Hoe Huh, Ph.D

.

VICE-CHAIRMAN

_____________________________

Byoung-Cheorl Kang, Ph.D.

MEMBER

_____________________________

Eun Jin Lee, Ph.D.

i

Single Nucleotide Polymorphism Marker Discovery

by Transcriptome Sequencing in Capsicum

PHILLIP CHOE

DEPARTMENT OF PLANT SCIENCE

THE GRADUATE SCHOOL OF SEOUL NATIONAL UNIVERSITY

ABSTRACT

Backcross breeding is the method most commonly used to introgress

new traits into elite lines. Conventional backcross breeding requires at least

4-5 generations to recover the genomic background of the recurrent parent.

ii

Marker-assisted backcrossing (MABC) represents a new breeding approach

that can substantially reduce breeding time and cost. For successful MABC,

highly polymorphic markers with known positions in each chromosome are

essential. Single nucleotide polymorphism (SNP) markers have many

advantages over other marker systems for MABC due to their high abundance

and amenability to genotyping automation. To facilitate MABC in hot pepper

(Capsicum annuum), we utilized expressed sequence tags (ESTs) to develop

SNP markers in this study. For SNP identification, we used Bukang F1-hybrid

pepper ESTs to prepare a reference sequence through de novo assembly. We

performed large-scale transcriptome sequencing of eight accessions using the

Illumina Genome Analyzer (IGA) IIx platform by Solexa, which generated

small sequence fragments of about 90-100 bp. By aligning each contig to the

reference sequence, 58,151 SNPs were identified. After filtering for

polymorphism, segregation ratio, and lack of proximity to other SNPS or

exon/intron boundaries, a total of 1,910 putative SNPs were chosen and

positioned to a pepper linkage map. We further selected 412 SNPs evenly

distributed on each chromosome and primers were designed for high

throughput SNP assays and tested using a genetic diversity panel of 27

Capsicum accessions. The SNP markers clearly distinguished each accession.

These results suggest that the SNP marker set developed in this study will be

valuable for MABC, genetic mapping, and comparative genome analysis.

iii

Key words:

Capsicum annuum, expressed sequence tag (EST),

linkage map, marker-assisted breeding (MAB), single

nucleotide polymorphism (SNP), next generation

sequence (NGS)

iv

CONTENTS

ABSTRACT ... i

CONTENTS ... iv

LIST OF TABLES ... vii

LIST OF FIGURES ... viii

LIST OF ABBREVIATIONS ... ix

INTRODUCTION ...

01

LITERATURE REVIEWS ...

04

Molecular marker Developments in the past ... 04

Use of molecular marker in Capsicum ... 07

SNP marker discovery ... 08

EST-derived marker discovery ... 08

v

Sequencing technology development ... 10

LITERATURE CITED ...

14

MATERIALS AND METHODS ...

22

Plant material preparation for eight accessions ... 22

RNA extraction for each sample ... 22

Sequencing of transcriptomes for eight accessions ... 25

Quality trimming for sequences ... 25

Reference sequence preparation ... 27

Repeat Masking on the reference sequence ... 27

Alignment to the reference sequence... 27

SNP calling and filtering ... 28

Linkage mapping ... 28

Polymorphism survey and genetic diversity ... 32

vi

RESULTS ...

33

Sequencing of hot pepper accessions and quality trimming ... 34

Alignment of each accession to the reference sequence... 35

SNP discovering and SNP filtering ... 37

Development and validation of SNP markers ... 39

DISCUSSION ...

47

REFERENCES ...

51

vii

LIST OF TABLES

Table 1. Comparisons of various markers

Table 2. Next generation DNA Sequencing technology

Table 3. Description of Capsicum used in this study

Table 4. Sequencing features of each accession

Table 5. Sequencing information from eight pepper accessions

Table 6. Alignment result of each accession to the reference

Table 7. SNP filtering process

Table 8. The number of SNPs and density in each pepper linkage group

Table 9. Origins of 27 accessions used for polymorphism survey of the SNP

markers

viii

LIST OF FIGURES

Figure 1. Alignment to the reference and finding SNPs

Figure 2. SNP Selection through filtering process

Figure 3. Overall Process for SNP discovery

Figure 4. Location of 412 SNP markers on pepper linkage map

ix

LIST OF ABBREVIATION

AFLP amplified fragment length polymorphisms

BGI

Beijing Genome Institute

BWA Burrows-Wheeler aligner

CCD

charge-coupled device

COSII conserved ortholog set II

DEPC diethyl pyrocarbonate

DNA deoxyribonucleic acid

EST

expressed sequence tag

GA

genome analyzer IIx

HRMA high-resolution melt analysis

KRIBB Korea Research Institute of Bioscience & Biotechnology

LINE long interspersed nuclear element

LTR

long terminal repeat

MAB marker assisted breeding

x

MAS marker assisted selection

NGS

next generation sequencing

NICEM National Instrumentation Center for Environmental Management

PTP

Pico Titer Plate

RAPD random amplified polymorphic DNA

RFLP restriction fragment length polymorphisms

RT

reverse transcriptase

SNP

single nucleotide polymorphism

SOLiD sequencing by oligo ligation and detection

SSR

simple sequence repeat

1

INTRODUCTION

Hot pepper (Capsicum annuum) is a crop of major economic

importance that is commercially cultivated in China, Korea, the East Indies,

and the United States of America, among many other countries (Shao et al.,

2008). Worldwide production of hot peppers has been estimated to be 14-15

million tons a year (Weiss, 2002). In fact, hot pepper is the vegetable

accounting for the largest planting area in Korea.

In commercial breeding, new traits are commonly introduced into elite

breeding lines using conventional backcross methods, which involve time

consuming efforts to transfer target genes into the genetic background of a

recipient parent. Recently marker-assisted backcrossing (MABC), which is

more efficient and faster than conventional backcrossing, has been generally

accepted as an advanced plant breeding technique (Blum et al., 2002). For the

successful application of MABC, factors to consider include the number of

target genes to be transferred, the genome size of the crop species, and the

availability of a highly saturated map. Among those factors, the availability of

highly polymorphic markers with known positions in each chromosome is

critical (Varshney et al., 2009). Although molecular markers based on

restriction fragment length polymorphism (RFLP), amplified fragment length

polymorphism (AFLP), and random amplified polymorphic DNA (RAPD)

have been developed and used in practical plant breeding (Imelfort et al., 2009;

2

Jung et al., 2010; Kang et al., 2001; Paran et al., 2004; Yoo et al., 2003), such

markers still have limitations for use in MABC due to the difficulty of finding

polymorphisms among breeding lines or of high throughput genotyping.

Recently, single nucleotide polymorphism (SNP) markers have captured

attention because of their potentiality for high-throughput detection and

computerization with automated platforms (Jung et al., 2010; Vignal et al.,

2002; Yi et al., 2006).

SNPs are single-base differences in DNA between accessions. In

plants, SNPs generally occur in populations once every few hundred base

pairs (Metzker, 2005). SNP markers can be developed through several

methods. For example, SNPs can be identified by simply comparing a

candidate sequence to a reference sequence (Nicolai et al., 2012), by whole

genome sequencing (WGS; Goff et al., 2002; The Arabidopsis Genome

Initiative, 2000), or by sequence alignment of expressed sequence tags (ESTs)

to a reference sequence (Kota et al., 2001; Labate and Baldo, 2005; Jones,

2009). For the crops like hot pepper, in which the genome is huge, ESTs have

been adopted as an alternative to WGS and as a substrate for cDNA

array-based expression analyses (Kim et al., 2008; Rudd, 2003). ESTs are a few

hundred base pairs of sequence derived from randomly selected cDNA clones

prepared from specific tissues, and EST sequencing is inexpensive compared

to WGS. These characteristics led us to test whether SNP markers could be

developed from several pepper accessions using ESTs generated with next

generation sequencing (NGS) technology.

3

NGS is a fast and low cost method for the large-scale generation of

reliable and robust transcript sequences and identifying and characterizing

genetic polymorphisms in plants (Imelfort et al, 2009, Metzker, 2010). The

Illumina Genome Analyzer (IGA) used to be the most widely used platform

based on amplified sequencing features generated by bridge PCR (Shendure

and Ji, 2008). Since the IGA reads only short sequences (75-100 base pairs;

Flicek and Birney, 2009), SNPs can be identified by either de novo assembly

of short sequence reads or alignment to the reference. Several factors can

interfere with correct sequence alignment; these include missing calls of

overlapping genotypes (Anney et al., 2008), false discovery of polymorphic

SNPs (Pettersson et al., 2008), homozygote to heterozygote miscalls (Teo et

al., 2007), and allelic dropout (Pompanon et al., 2005).

Here, we describe a process for SNP development from transcriptome

sequencing of peppers. The resulting SNP primers clearly distinguished

between 27 tested Capsicum cultivars, demonstrating that the SNP markers

developed in this study will be useful resources to facilitate MABC in pepper.

4

LITERATURE REVIEW

Molecular marker Developments in the past

Molecular marker is a DNA sequence which can be detected and its

further inheritance can be monitored (Kumar, 2009). With development of

molecular markers, the applications in breeding also have been increased in

terms of selecting, backcrossing, and pyramiding (Collard, 2008). There are

two types of markers classified; One with hybridization-based, such as

restriction fragment length polymorphisms (RFLP) (Botstein, 1980), and the

other with PCR-based, such as random amplified polymorphic DNA (RAPD)

and amplified fragment length polymorphisms (AFLP) (Paran, 1998; VOS,

1995). RFLP is known as the first DNA profiling technic which breaks down

the DNA with restrict enzyme and put those cut-fragment under

electrophoresis (Beckmann, 1983). Although, its high reliability in

determining homozygosis/heterozygosis (Winter & Kahl, 1993), it is time and

labor consuming (Powell, 1996). RAPD method amplifies random segment of

DNA with arbitrary primers. Having advantage of small amount of template

DNA and no sequence data for primer construction, however its drawback is

low reproducibility and non-locus-specificity (Tingey, 1993). AFLP is

believed a transition between RFLP and PCR in terms of selectively

amplifying restriction fragments after breakdown of genomic DNA. The

strength is their abundance in genome, high replicability and not requiring for

5

primer construction sequence data (Mueller & Wolfenbarger, 1999). However,

its requirement for high molecular weight of DNA, difficulty of recognizing

allele, and less reproducibility are considered as main disadvantages (Majer,

1996). Recent year, simple sequence repeat (SSR), which is a simple

combination of 1-6 short motifs, became popular because of their high

genomic abundance and co-dominance of alleles (Morgante et al., 2002).

Despite of all achievement in developing various types of markers, today,

Single Nucleotide Polymorphism (SNP), which is a single base change in a

DNA sequence, becomes promising marker in their highly abundance and less

prone to mutations (Giordano et al. 1999; Vignal, 2002).

Moreover, SNP markers have captured the attentions in recent years

due to their availability for high-throughput detections and computerized with

automated platforms (Mammadov et al. 2012). It is powerfully effective for

genome mapping and identification of candidate genes for Quantitative Trait

Loci (QTL) (Liu and Cordes, 2004). SNPs are widely used in marker assisted

selection (MAS) or marker assisted backcrossing (MABC) in today’s

agriculture as mass finding technologies have been developed in the last few

decades. (Table 1)

6

Table 1. Comparisons of various markers

Features

RFLP

RAPD

AFLP

SSR

SNP

First introduce (Year)

1974

1990

1995

1992

1994

DNA requirement (µg)

10

0.02

0.5-1.0

0.05

0.05

PCR based

No

Yes

Yes

Yes

Yes

DNA quality

High

High

Moderate

Moderate

High

Development cost

Low

Low

Moderate

High

High

Cost per analysis

High

Low

Moderate

Low

Low

Reference

Grodzicker et al. Williams et al. Vos et al.

Akkaya et al. Jordan et al

(1974)

(1990)

(1995)

(1992)

(1994)

Reprinted and edited from “Potential of Molecular Markers in Plant Biotechnology” by Kumar et al. 2009. Plant Omics

Journal. 2:141-162.

Reprinted and edited from “Molecular Markers: History, Features and Applications” by Maheswaran. 2004. Advanced Biotech.

Aug:17-24.

7

Use of molecular marker in Capsicum

Over the last decade, molecular markers have become a new wave of

breeding technologies because of their many advantages such as cost and time

savings. Capsicums is the most economical crops undergone marker discovery

and thousands of markers have been placed into a genetic map. First

inter-specific hybrid of Capsicum annuum x Capsicum chinense map was

constructed and later RFLP markers developed in 19 linkage groups (Tanksley,

1988; Prince et al, 1993; Kim et al. 1997). In addition, total 13 linkage groups

were created by inter-specific F2 population of Capsicum annuum x Capsicum

chinense using PCR based markers, mostly AFLP (Livingstone, 1999).

Another map was constructed with 150 RFLP and 430 AFLP markers in 16

linkage groups (Kang, 2001). With all these previously discovered markers,

integrated map was constructed consisting of AFLP, RFLP, and RAPD (Paran,

2004). Recently, SSR marker, which are easy applicable and sensitive in

between species, are widely used (Yi et al, 2006). There has been successful

positioning of orthologous markers between tomato and pepper genomes

using F2 of intra-specific varieties, Capsicum frutescens x Capsicum annuum,

additional conserved ortholog set II (COSII) markers into pepper map (Wu,

2009). Despite of all different types of valuable markers and their discoveries,

SNP marker in Capsicum have become most popular markers because of their

high abundance and easy detection of polymorphism (Jung, 2010).

8

SNP marker discovery

SNPs are single base differences in DNA between accessions (Trick,

2009). In general, the average occurrence in population is once every few

hundred base pairs (Metzker, 2005). Various methods were applied to

discover SNPs in many crops regardless their genomes are completely

sequenced or not. For rice, direct sequencing verified SNPs (Feltus et al.

2004), and sequence/resequence method was used in grapevine (Lijavetzky et

al., 2007), target induced local lesion in genomes (McCallum et al., 2000) and

TaqMan assay (Lee et al., 1999) were applied to detect SNPs. Other

techniques are high-resolution melt analysis (HRMA) (McGlauflin et al., 2010)

and the sequence alignment using expressed sequence tag (EST) database in

barley, tomato, and maize (Kota et al., 2001; Labate and Baldo, 2005; Jones,

2009). EST database mining is an efficient SNP discovery method and first

introduced in human SNP finding (Picoult-Newberg, 1999), but it is limited to

the sufficient ESTs with existing genotype in the database.

EST-derived marker discovery

Sequencing/resequencing methods are limited to when whole genome

sequence is known. Yet, these only have been successful in small genome

plants with whole genome reference, such as rice and Arabidopsis (Yamamoto

et al., 2010; Ossowski et al., 2008). Therefore, in the absence of reference

sequence, de novo assembly can be used as an alternative reference, which is a

9

computerized algorithm that put together small sequences to make a full

length sequence (Ashrafi, 2012). Upon assemble thousands of sequence

fragments to make a long reliable reference, the most widely used and publicly

available in terms of the number of sequences and the total nucleotide count is

EST (Rudd, 2003). ESTs are short single sequences fragments consisting of

few hundred base pair, which are derived by randomly selected cDNA clones

(Hong, 1998). Since, ESTs are abundant in database, and represent portion of

expressed gene, it has been widely used in marker development. For

Capsicum, there are many researches successfully finding markers using ESTs;

1,201 SSRs from 10,232 EST (Yi, 2006), SNPs and SSRs discovery using

Sanger-EST and Illumina sequence assembly (Ashrafi, 2012), and Roche 454

pyrosequencing and de novo assembly of Capsicum annuum ESTs (Nicolai,

2012), and transcriptome profiling with SSRs and SNPs discovery from EST

using 454 pyrosequencing (Lu, 2012).

SNP discovery in other crops by using ESTs

There have been many researches which develop markers from

mining ESTs, because, crop ESTs are public accessible and by assembling

these, we may get big portion of cDNAs. Group of researchers have found 62

SNPs and 12indels in 21 Unigenes of Tomato (Labate and Baldo, 2005). By

deep re-sequencing of a reduced representation library (RRL) and scaffolds in

Soybean, up to 25,047 SNPs have found and among them only 1790 SNPs

10

become as markers (Hyten, 2010). Recently, numbers of SNPs and SSR in the

pepper have been found by using Maor, Early Jalapeno and Criollo de

Morelos (Ashrafi et al. 2012). These great achievements are all for the

improvement and cost down of sequencing technology. In recent days, NGS

techniques are widely applied for SNP marker development because it

produces millions sequence reads in a single run that make sequencing process

relatively fast and cut down the cost (Hayes et al., 2007).

Sequencing technology development

Since, DNA sequencing technology has been developed, tremendous

amount of molecular data have been produced including marker, ESTs and

even whole genome sequence. However, those DNA sequences to date have

mainly relied on various version of Sanger biochemistry (Sanger, 1977). In

Sanger method, DNA is fragmented, cloned and transformed into bacterial

colony. Cycle sequencing reaction follows, generating ddNTP-terminated,

dye-labeled product, which will undergo high resolution electrophoretic

separation in capillaries. As fluorescently labeled fragment move through a

detector, spectrum is used to trace the sequence. Sanger method sequencing is

highly accurate as 99.9% and today the read achieve is up to ~ 1,000bp, but

their high cost with time consuming and labor intensity are the fact which

makes it less favorable (Shendure and Ji, 2008). With technical advancement,

over the last ten years, new methods called NGS have utterly changed the

11

DNA sequencing strategy. NGS do not rely on traditional Sanger method,

which is labeled DNA fragments are physically resolved by electrophoresis

(Richardson, 2010). Among various methods, Roche/454 is known as first

NGS system. It produces relatively long sequences of 200 - 300bp per read

with more than 400,000 reads per run (Bubnoff, 2008). In brief, encapsulated

bead-DNA complex are PCR-amplified to produce beads, which contain

thousands copies of the same template sequence. Those beads are attached to a

pico titer plate (PTP) which eventually undergoes the pyrosequencing reaction

as detecting the generated light (Metzker, 2005). Roche 454 doesn’t directly

read individual bases, but it reads the number of As, Cs, Gs, or Ts at the

current position (Brockman, 2008). Another popular one with short sequence

read of 35-75bp is Solexa, Illumina Genome Analyser II (Imelfort, 2009),

which is based on the concept of sequencing by synthesis technology. Fixed

adapter containing library is denatured and amplified to make clusters

containing thousands of the same DNA fragments. The library splices into

single strands followed by complementation of fluorescent-dye containing

four different kinds of nucleotides (ddATP, ddGTP, ddCTP, ddTTP) and the

signal is detected by charge-coupled device (CCD) camera (Liu, 2012). Later

in 2007, sequencing by oligo ligation and detection (SOLiD) sequencer was

commercialized which sequence by DNA ligase, which takes 5 days to

produce 3 - 4Gb of sequence data with an average read length of 25 - 35bp

(Mardis, 2008). In brief in the system, the fluorescent signals are captured

while the probes are complemented to the template strand followed by

vanishing process by the cleavage (Liu, 2012). HeliScope is unique with its

12

not requiring clonal amplification which led to small amount of materials, and

rather fluorescence detection system is used to directly recognize single DNA

through sequencing by synthesis (Thompson, 2011). It makes average length

of 25bp with high accuracy. Today’s rapid development in bioinformatics and

technology have led tremendous improvement in sequencing DNA and

furthermore mapping, therefore rapid development in practical application

such as MAS and MABC. (Table 2)

13

Table 2. Next generation DNA Sequencing technology

Feature generation

By synthesis

Cost

Paird-end 1

error

Read length

/megabase /instrument

454

Emulsion PCR

Polymerase

~$60

$500,000

Yes Indel

250bp

Solexa

Bridge PCR

Polymerase

~$2

$430,000

Yes Subst.

36bp

SOLiD

Emulsion PCR

Ligase

~$2

$591,000

Yes Subst.

35bp

HeliScope

Single molecule

Polymerase

~$1 $1,350,000

Yes Del

30bp

Note. Cost values are in US dollars.

Reprinted from “Next-generation DNA sequencing” by Jay Shendure & Hanlee Ji. 2008. Nature Biotechnology. 26:1135-1145.

Copyright 2008 by Nature Publishing Group.

14

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MATERIALS AND METHODS

Plant material preparation for eight accessions

Jeju (Capsicum annuum), LAM32 (C. annuum), Tean (C. annuum),

CM334 (C. annuum), SNU-001 (C. chinense), Yuwolcho (C. annuum),

PI201234 (C. annuum) and YCM334 (C. annuum) were grown in a growth

chamber with 12 h light at 25°C and 12 h dark at 18°C. Leaf tissues at the

same stage from the eight accessions were collected. Total RNA was isolated

from leaves of each accession with Trizol extraction buffer (Ambion, Carlsbad,

CA, USA) as described in the manufacturer’s protocol and used for

sequencing of transcriptomes. (Table 3)

RNA extraction for each sample

TRIzol reagent from Invitrogen was used for extracting RNA process

as follows the protocol of manufacturer’s instructions. 1 mL of TRIzol reagent

was added on each sample. After 5 minutes of phase separation at the room

temperature, chloroform of 0.2mL were added on followed by centrifuge at

12,000 x g for 15 minutes at 5 degree Celsius. The RNA was precipitated from

the aqueous phase by mixing with 0.5 ml of isopropanol followed by the

sample incubating in the room temperature for 10 minutes and centrifuged at

12,000 x g for 10 minutes at 4 Celsius. By removing the supernatant, the RNA

23

was washed with 75% ethanol. Air dried pallet is dissolved in diethyl

pyrocarbonate (DEPC) treated water.

24

Table 3. Description of Capsicum used in this study

Name

Species

Origin

Genetic-type Sample

Jeju

annuum

Korea

Landrace

Leaf

LAM32

annuum

India

Landrace

Leaf

Tean

annuum

Korea

Landrace

Leaf

CM334

annuum

Mexico

Landrace

Leaf

SNU-001

chinense

Venesula

Landrace

Leaf

Yuwolcho

annuum

Korea

Landrace

Leaf

PI201234

annuum

USA

Landrace

Leaf

25

Sequencing of transcriptomes for eight accessions

Jeju, LAM32 and Tean were sequenced via GAIIx sequencing with

116-bp single-end reads at the National Instrumentation Center for

Environmental Management (NICEM). CM334 sequencing data of 101-bp

paired-end reads were provided by Dr. Choi from the Plant Genomics

Laboratory at Seoul National University. SNU-001, Yuwolcho, PI201234 and

YCM334 were sequenced with the GAIIx sequencing platform using the

90-bp paired-end read sequencing method at Beijing Genome Institute (BGI).

(Table 4)

Quality trimming for sequences

The sequence data from each accession were trimmed to reduce the

quality deterioration in the 3’-end and 5’-end regions, which negatively affects

mapping and assembly. Sickle version 1.0 (https://github.com/najoshi/sickle)

was used for quality trimming.

26

Table 4. Sequencing features of each accession

Accessions

Species

length Single /paired

Institute

Features

Jeju

C. annuum

101bp x1

Single

NICEM

susceptible

LAM32

C. annuum

101bp x1

Single

NICEM

cmr2

Tean

C. annuum

101bp x1

Single

NICEM

susceptible

CM334

C. annuum

101bp x2

Paired

NICEM Phytophthora

SNU-001

C. chinense

90bp x 2

Paired

BGI

Capsiate

Yuwolcho

C. annuum

90bp x2

Paired

BGI

M0/L

PI201234

C. annuum

90bp x2

Paired

BGI

Phytophthora

YCM334

C. annuum

90bp x2

Paired

BGI

Phytophthora

Each accession is sequenced by Illumina GA IIx from different institute. Features column represents the resistance line

NICEM: National Instrumentation Center for Environmental Management, BGI: Beijing Genomics Institute

27

Reference sequence preparation

The reference sequence, assembled by commercially available CLC

Genomics Workbench software, comprised 31,196 contigs from EST

sequences that were mainly derived from Korean F1 hybrid line Bukang, for

which data are available at the Korea Research Institute of Bioscience &

Biotechnology (KRIBB) (Ashrafi et al., 2012; Kim et al., 2008).

Repeat Masking on the reference sequence

Repeat masking was performed on the reference sequence using the

727 repeat sequence library set up by the Plant Genomics Laboratory at Seoul

National University because of the presence in Capsicum of areas highly

abundant in repeat sequence (Yi et al., 2006). The data were collected by the

RMBlast program, which is mainly used in NCBI for finding matches between

two sequences and attempting to start alignments from these matched places

(Johnson et al., 2008). RepeatMasker from the Institute for Systems Biology

was used to screen DNA sequences for repeats and areas of low complexity.

Alignment to the reference sequence

Sequence alignments to the reference sequence were performed to

find SNPs (McPherson, 2009) using the Burrows-Wheeler Transform

algorithm from Burrows-Wheeler Aligner version 0.5.9rc1, which requires

28

low memory and so is suitable for reducing the analysis time (Li and Durbin,

2009). (Figure 1)

SNP calling and filtering

SNP data were collected using SAMtools software and an in-house

python script with strict criteria (Li et al. 2009). SNPs from sequencing depth

of 25x were filtered in the following order: 1) diallelic SNPs, 2) SNPs found

in more than six accessions, 3) SNPs with high PIC value, 4) SNPs for

Fluidigm probe design (at least 60 bp from any intron-exon junction or

another SNP). (Figure 2)

Linkage mapping

A genetic map was drawn by connecting candidate SNPs after the

filtering process to a hybrid of Capsicum frutescens cv. BG2814-6 x Capsicum

annuum cv. NuMexRNaky RIL population (119 RILs) built at the University

29

Accession 1

Reference

Accession 2

SNP

Figure 1. Alignment to the reference and finding SNPs.

Notes: Each accession is aligned to the reference sequence, and for those which have different single nucleotide

is the candidate for the SNPs.

30

Figure 2. SNP Selection through filtering process.

Note: Filter option 4; adjacent SNPs located within 60bp were discarded

70bp

80bp

30bp

10bp

20bp

Selected

Selected

Discarded

Cu

rren

t

p

ip

elin

e

New

p

ip

eli

n

e

Selected

Discarded

31

Figure 3. Overall Process for SNP discovery.

Reference Sequence

NGS on each accession

(EST, de novo assembly)

(Illumina GA IIx)

Repeat Masking

Quality Trimming

(RMBlast, RepeatMasker)

(Sickle version 1.0)

Alignments (BWA)

SNP calling (SAM tools, In-house Script)

Filtering (Customizing Option)

32

Polymorphism survey and genetic diversity

SNPs positioned in the linkage map were used to design locus-specific

markers for the Fluidigm® EP1TM genotyping system. To validate their

polymorphism and study the application of the SNP markers, 24 different C.

annuum accessionss (CM334, YCM334, Tean, Yuwolcho, ECW, Bukang A

line, Bukang C line, Lam32, Jeju, Dempsey, DRB, Perennial, 9093, ECW30R,

Takanotsume, 35001, 35009, RS202, RS203, RS205, VK-515R, VK515S,

LongSweet, AC2212) and three different C. chinense accessions (PI159236,

Habanero, SNU11-001) were employed. Genomic DNAs of each accessions

were extracted by the hexadecyl trimethyl ammonium bromide (CTAB)

method from young leaf tissues (Yang et al., 2012). Phylogenetic analysis was

carried out using the neighbor-joining method in Darwin5 software

(http://darwin.cirad.fr/darwin, Perrier et al., 2003). Tree construction was

based on the unweighted neighbor-joining method, and bootstraps were

determined from 1,000 replicates.

33

RESULTS

Sequencing of hot pepper accessions and quality trimming

We produced a total of 4.1-4.4 Gb with 36-38 million reads from Jeju,

LAM32 and Tean; a total of 7.1 Gb with 70 million reads from CM334; and a

total of 3.5-3.6 Gb with 39-40 million reads from SNU-001, Yuwolcho,

PI201234, and YCM334 (Table 1). After quality trimming with Sickle version

1.0, the collected data were reduced to 3.6-3.7 Gb with 35-37 million reads of

100-102 bp read length for Jeju, Tean, and LAM32; 5.3 Gb with 67 million

reads averaging 79.5 bp for CM334; and 3.4-3.5 Gb with 39-40 million reads

averaging 87-88 bp for SNU-001, Yuwolcho, PI201234, and YCM334. (Table

5)

34

Table 5. Sequencing information from eight pepper accessions

Cultivar

Origin

Raw reads

Trimmed reads

Bases

Reads

Read length (bp)

Bases

Reads

Read length (bp)

Jeju

Korea

4,341,320,416

37,425,176

116

3,690,172,704

36,990,461

99.8

LAM32

India

4,120,993,728

35,525,808

116

3,596,017,756

35,239,208

102

Tean

Korea

4,356,564,208

37,556,588

116

3,708,990,466

37,165,176

99.8

CM334

Mexico

7,111,852,582

70,414,382

101

5,297,351,327

66,591,676

79.5

SNU-001

Venezuela

3,575,010,600

39,722,340

90

3,440,874,878

39,338,990

87.5

Yuwolcho

Korea

3,579,091,560

39,767,684

90

3,438,315,513

39,381,505

87.3

PI201234

Germplasm in

USA

3,575,048,220

39,722,758

90

3,493,467,360

39,637,637

88.1

YCM334

Taiwan

3,525,119,100

39,167,990

90

3,444,541,024

39,081,800

88.1

35

Alignment of each accession to the reference sequence

The total acquired reference sequence of 21,665 kb was de novo

assembled from 31,196 contigs derived from Bukang ESTs, with an average

contig length of 696 bp. There was a total of 1,071 kb of interspersed repeat

elements (5,216 elements). Unclassified repeats accounted for 626 kb of this.

Of the remaining repeat sequence, the largest portion (278 kb) was annotated

as long terminal repeats (LTR), which are composed of several hundred base

pairs. Long interspersed nuclear elements (LINEs), which encode a reverse

transcriptase (RT) and other proteins, accounted for 118 kb. In addition, there

was 49 kb of DNA transposable elements, which tend to have short inverted

repeats at each end. In addition to these interspersed repeat elements, 102 kb

and 103 kb were marked as simple repeat and low complexity regions,

respectively. Therefore, a total of 1,276 kb (5.9%) was masked as repeat

sequences.

The trimmed reads of eight accessions (Table 5) were aligned to the

reference sequence and the range of alignment ratio between each accession

and the reference was 52.6~70.5%. (Table 6)

36

Table 6. Alignment result of each accession to the reference

Trimmed reads

Aligned reads

Alignment ratio (%)

Jeju

36,990,461

19,465,349

52.62

LAM32

35,239,208

20,122,901

57.10

Tean

37,165,176

20,983,023

56.46

CM334

66,591,676

36,054,433

54.14

SNU-001

39,338,990

24,801,440

63.05

Yuwolcho

39,381,505

26,073,255

66.21

PI201234

39,637,637

27,929,380

70.46

YCM334

39,081,800

25,936,924

66.37

37

SNP discovering and SNP filtering

Sequences with read depth over 25x were used for SNP discovery,

and those that were obviously distinguishable and different among accessions

were defined as SNPs. Based on these criteria, a total of 58,151 SNPs were

identified. From these, SNPs with only two types of genotypes and two alleles

were filtered out. Among the remaining 57,502 SNPs, 33,315 were recognized

as distinguishable in at least six different accessions. In the next step, SNPs

that showed a uniform segregation ratio were chosen. Among the 33,315

SNPs tested, 4,508 segregated 4:4 or 3:5 in the eight accessions. Finally, from

those 4,508 SNPs, 1,910 were selected based on not having any other SNPs

within 60 bp in either direction (Table 7)

38

Table 7. SNP filtering process

Filtering Criteria

No. of SNPs remaining

No. of filtered SNPs

Filtering percentage

(%)

Depth filtering (>25 X)

58,151

Genotype/allele

57,502

649

1.11

No. of distinguishable

accessions

33,315

24,187

42.06

Segregation ratio

4,508

28,807

86.46

Adjacent SNP

1,910

2,598

57.63

39

Development and validation of SNP markers

Among 1,910 SNPs, a total of 1,282 were positioned on the hot pepper map

produced by UC Davis. From these, we further selected 412 SNPs that showed

clear and repeatable polymorphism, and were evenly distributed over a ~3 cM

interval in each chromosome. (Supplementary Table 1) The linkage map

showing the location of the 412 SNPs is presented in Figure 4 and the SNP

number per chromosome is given in Table 8. The entire length of the genetic

map was 1,460.6 cM. There was an average of 34 SNPs in each linkage group,

with the P1_Wild (177.1cM) linkage group including the most SNPs. The P3

linkage group had the density (0.40 SNP/cM). The fewest SNPs mapped to

linkage group P8_Wild. The number of SNPs per cM ranged from 0.16 to

0.40, with an average of 0.28 SNPs per cM. Accordingly, there are averages of

1 SNPs per 3.55-cM interval in the genetic map (Figure 4, Table 8).

To validate the 412 SNP markers, 24 C. annuum and 3 C. chinense

accessions (Table 9) were tested for diversity of pepper genotypes (Figure 5).

Between C. annuum accessions, the average number of SNPs was 143,

ranging from 23 (ECW vs. ECW30R) to 205 (Takanotsume vs. YCM334). By

contrast, between bell-type accessions of C. annuum (9003, Dempsey,

ECW30R, ECW), the average number of SNPs was 51, ranging from 23

(ECW vs. ECW30R) to 69 (DRB vs. ECW). C. chinense accessions showed

an average of 23 SNPs, ranging from 17 (Habanero vs. SNU11-001) to 27

(Habanero vs. PI159236). The SNP number between C. annuum and C.

40

to 209 (Dempsey vs PI159236). Overall, the average SNP number between all

Capsicum accessions was 150, ranging from 17 (Habanero vs SNU11-001) to

209 (Dempsey vs PI159236). Based on the genetic similarity results, a cluster

dendrogram placed the 27 pepper accessions into two main clusters and

clearly differentiated each accession (Figure 5). The first main cluster (I)

comprised 15 different C. annuum accessions (9093, Dempsey, ECW30R,

ECW, DRB, YCM334, RS205, RS203, RS202, Jeju, LongSweet, 35001,

AC2212, CM334, Bukang A). The second cluster (II) consisted of 12 different

accessions including seven C. annuum accessions (35009, Yuwolcho, Tean,

Takanotsume, Bukang C, VK-515R, and VK515S), two wild species of C.

annuum accessions (Perennial and Lam 32) and three C. chinenese accessions