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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)

Journal of the Korean Society of International Agriculture Vol.36 No.4 pp.414-422
DOI : https://doi.org/10.12719/KSIA.2024.36.4.414

Development of an F₂ Population and GBS Analysis for Identifying QTL Associated with Resistance to Bacterial Wilt in Pepper

Sena Choi^*, Jundae Lee^**, Eun Young Yang^***, Yul-Kyun Ahn^***†

^*Urban Agriculture Reseach Division, National Institute of Horticultural and Herbal Science, Rural Development Administration, Jeonju-55365, Republic of Korea
^**Department of Horticulture, Chonbuk National University, Jeonju 54896, Korea
^***Department of Vegetable Crops, Korea National College of Agriculture and Fisheries, Jeonju-54874, Republic of Korea

^† Corresponding author (Phone) +82-63-238-9153 (E-mail) aykyun@korea.kr

Received November 12, 2024 Review November 29, 2024 Accepted November 29, 2024

Abstract

Pepper is one of the most important vegetables in South Korea. It is a key ingredient in kimchi, the nation’s staple dish, and serves as the primary raw material for producing gochujang, a commonly used condiment in Korean cooking. As a result, numerous pepper varieties have been developed, including those that yield more fruit or have milder pungency. However, farmers who grow peppers tend to prefer varieties that are resistant to pests and diseases. Bacterial wilt (BW) is one of the most devastating diseases affecting peppers and is transmitted through the soil. To breed pepper varieties resistant to bacterial wilt using molecular breeding techniques, it is essential to first identify the Quantitative Trait Locus (QTL) that confers resistance to this disease. This requires conducting locus analysis with resistant cultivars. In this study, an F₂ population was developed by selfing F₁ hybrids, which were obtained by crossing a resistant cultivar with a susceptible cultivar, to identify QTLs associated with bacterial wilt resistance. Genotyping-by-Sequencing (GBS) analysis will be performed using the F₂ population, and the results will be utilized for QTL mapping.

Key Words : pepper , bacterial wilt , F₂ population , genotyping-by-sequencing , single nucleotide polymorphism , quantitative trait loci , Ralstonia solanacearum

고추 풋마름병 저항성 관련 QTL 탐색을 위한 F₂ 집단 구성과 GBS 분석

최세나^*, 이준대^**, 양은영^***, 안율균^***†

^*국립원예특작과학원 도시농업과
^**전북대학교 농업생명과학대학 원예학과
^***국립한국농수산대학교 원예학부 채소학과

초록

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INTRODUCTION

Relative to its population size, South Korea is one of the largest consumers of pepper in the world (Yoon et al., 1996). Although the cultivation area and production value of peppers have been declining with each passing year (Statistics Korea, 2023), peppers remain one of the most important crops in South Korea. As the cultivation area gradually decreases, the preventing pest and disease damage has become increasingly important. While soil sterilization and timely pesticide application can serve as important preventive measures, the most fundamental solution may be the cultivation of pest- and diseaseresistant varieties.

Bacterial wilt is one of the diseases of pepper infected by the soil-borne, vascular pathogen Ralstonia solanacearum. Typical disease symptoms include browning of the xylem, foliar epinasty and lethal generalized wilting. Wilting symptoms probably result from the extensive bacterial colonization of the xylem and massive exopolysaccharide production, which rapidly induce vascular dysfunction (Genin, 2010). Bacterial wilt is a common disease in tropical and subtropical areas of the globe, and is an emerging disease in some temperate areas (Elphinstone, 2005). Another reason why R. solanacearum is a major constraint to the production of a number of economically important agricultural crops and ornamental plants is its very wide host range. As a whole, this bacterium is able to infect more than 53 botanical families (Hayward, 1991a) that represent more than 200 host plant species including pepper. In South Korea, bacterial wilt disease is found in 60 % of pepper fields exhibiting wilting symptoms, and 20.3 % of tomato greenhouses were reportedly contaminated with R. solanacearum Chungbuk province in South Korea (Lee and Kim, 2022;Yun et al., 2004). Control of bacterial wilt in infested soils is very difficult. It is generally considered that crop rotation with a nonhost crop is of minimal value because of the wide range of crop and weed hosts of the pathogen (Kelman, 1953;Hayward, 1991b). The most efficient and sustainable strategy to control bacterial wilt disease is the development of resistant varieties (Jiang, 2017).

However, developing cultivars resistant to bacterial wilt is not a simple task. Because some researches have studied that bacterial wilt resistance is involved in a quantitative inheritance and is polygenically governed by multiple genes (≥2 genes) in the pepper cultivar Mie-Midori (Matsunaga, 1998). Identifying key loci associated with bacterial wilt resistance, or multiple loci, can be an efficient approach for developing resistant cultivars. To this end, it is necessary to analyze the sequences of both resistant and susceptible cultivars to identify single nucleotide polymorphisms (SNPs), and then utilize the more resistant lines from the F₂ population, obtained by selfing the F₁ hybrids of the two cultivars, to search for QTLs conferring resistance to bacterial wilt.

GBS technology is a simple and robust method that is practical as a high-throughput genotyping tool for a large number and huge amount of DNA as well as for complex genomes of crop species (Elshire, 2011;Baldwin, 2012). This study aims to establish a foundation for identifying genomic loci associated with resistance to bacterial wilt in peppers by utilizing GBS technology to explore SNPs and conducting phenotypic analysis of the F₂ population.

MATERIALS AND METHODS

Plant materials and population development

The resistant line to bacterial wilt, FRH1, and the susceptible line, Saengnyeok 211, were acquired from the National Institute of Horticultural & Herbal Science and used to generate the F₂ population. As control lines, Konesian hot (resistant) and Geonchowang (susceptible) were utilized, both of which are commercial varieties commonly used in Korea. A total of 180 F₂ lines were developed by crossing Saengnyeok 211 (susceptible to bacterial wilt) with FRH1 (resistant to bacterial wilt), followed by self-pollination of the resulting F1 lines (Fig. 2).

Preparation of bacterial strain

Ralstonia solanacearum ‘WR-1 isolate’ was obtained from the National Institute of Horticultural & Herbal Science. The bacterial culture was grown on Nutrient Agar (NA) medium. Typically, the bacterial wilt isolate is preserved at -70℃ in a suspended state. To ensure viability, the isolate was streaked on an NA medium plate and incubated for approximately 48 hours at 28℃. Isolated colonies were obtained by using an inoculating loop to streak the strain onto NA medium plates. The number of NA medium plates used was based on the required number of plants for inoculation. These plates were then incubated again at 28℃ for 48 hours. Once fully cultured, a small amount of clean water was added to the medium, and the strain was collected using a spreader. The adequate concentration diluted suspension of colony is 107∼108 colony- forming units(CFU)ㆍmL^-1.

Inoculation method

Pepper plants were initially cultivated in 72-cell plastic trays, and inoculation was carried out once the plants had developed 6-8 main leaves. To inoculate, the roots of the pepper plants were lightly wounded using a sharp knife, followed by adding 10 mL of bacterial suspension into each cell of the plastic trays. Immediately after inoculation, the trays were kept in a warm, humid environment (28-3 0℃, 85-90%). After 2-3 days, the trays were transferred to conditions with a temperature of 25-28℃ and approximately 60% humidity. Resistance evaluation was conducted 14 days post-inoculation. Disease severity was assessed using a disease index (DI) scale from 0 to 4 as follows: 0, healthy; 1,∼30% wilted; 2,∼50% wilted; 3, 80% wilted; 4, fully wilted (Fig. 3).

DNA extraction

DNA was isolated from young leaves of the F₂ population prior to inoculation with the bacterial wilt strain. Leaves were placed in 2.0 mL microcentrifuge tubes containing two 5 mm stainless steel beads to facilitate grinding with a Tissue Lyser (Retsch). Immediately after collecting leaves from pepper plants, the tubes were stored in liquid nitrogen. The freeze-dried leaf samples were subsequently ground using the Tissue Lyser, followed by the addition of 700 μL CTAB buffer. Samples were incubated in a 65℃ water bath for 15 minutes, then cooled to room temperature. Next, 700 μL of chloroform was added, and samples were centrifuged at 4℃ for 15 minutes at 13,000 rpm. Following centrifugation, 600 μL of supernatant was transferred to fresh 1.5 mL microcentrifuge tubes and combined with 700 μL of 70% ethanol. The tubes were inverted several times to mix, and mixtures were centrifuged again at 4℃ for 5 minutes at 13,000 rpm. The resulting supernatant was discarded, and the pellet was washed with 1,000 μL of absolute ethanol. After an additional centrifugation at 4℃ for 5 minutes at 13,000 rpm, the supernatant was removed, and the pellet was air-dried at room temperature. Finally, DNA was dissolved in 50 μL of distilled water and treated with 0.1 μL of 10 mgㆍmL⁻¹ RNase solution (Biosesang). DNA concentration was measured using NanoVue (GE Healthcare) and diluted to a final concentration of 100 ngㆍμL⁻¹.

GBS analysis

A total of 182 lines, including 180 F₂ lines derived from a cross between Saengnyeok 211 and PRH1, along with the parental lines Saengnyeok 211 and PRH1, were analyzed using GBS by SEEDERS (Daejeon, Korea). Following DNA quality and quantity verification via agarose gel electrophoresis, the GBS library was constructed.

First, genome reduction was performed by digesting each sample with the restriction enzyme ApeK1, creating a reduced representation library (RRL). Only fragments within a specific size range (approximately 100–400 bp) were sequenced using the HiSeq 2000 system (Illumina, San Diego, CA, USA), resulting in DNA sequence reads from a small portion of the genome.

Next, a custom barcode adaptor was ligated to the sticky ends produced by the restriction enzyme to ensure unique identification of each sample, along with Illumina’s standard forward and reverse adaptors. Subsequently, all samples were pooled into a single tube, after which the remaining steps of Illumina’s standard library preparation procedure were carried out. DNA sequencing was then conducted on a single lane of an Illumina next-generation sequencing (NGS) machine, producing a large file containing sequences from all pooled samples.

To retrieve DNA sequences for each sample, all sequence reads containing the unique barcode associated with each sample were extracted from the pooled data. This produced individual DNA sequence files for each sample, which were then aligned to a reference genome to assess alignment confidence and quality for each read base. A multi-sample genotype caller was applied to generate genotype calls and associated quality metrics. The application of various quality thresholds and heuristics results in a final table of genome-wide genotype data from the sequenced sample (Myles, 2013).

RESULT

Evaluation of resistant degrees to bacterial wilt in pepper

The F₂ population was utilized to assess the resistance level against bacterial wilt. A total of 180 F₂ lines were inoculated with the bacterial strain, with genomic DNA extracted from each pepper plant prior to inoculation. Once the susceptible line, Geonchowang, began to wilt, resistance levels were evaluated. The resistance was classified into five disease index (DI) categories on a scale from 0 to 4, corresponding to disease severity. Within the 180 F₂ lines, 19 plants were rated at DI 0, 26 plants at DI 1, 57 plants at DI 2, 51 plants at DI 3, and 27 plants at DI 4 (Fig. 4; Table 1). This distribution displayed a left-skewed distribution (Fig. 5A). The observed 1:3 ratio, assuming that DI 0 and DI 1 plants are resistant (R) and DI 2 through DI 4 plants are susceptible (S), suggests that a major gene associated with bacterial wilt resistance behaves as a recessive gene (Fig. 5B; Table 2). However, further analysis is required to definitively conclude that the major gene is recessive.

In this study, the reference genome used was Capsicum annuum cv. CM334 ver. 1.55. The findings indicated a total of 30,242 genes with a cumulative gene length of 73.6 Mbp, distributed across chromosomes totaling 2,753.5 Mbp (Table 4).

Each sample’s clean reads, after demultiplexing and trimming, were aligned to the reference genome. A total of 389.7 million reads were mapped, averaging 1.8 million reads per sample, covering 72.41% of the total reads. The mapped regions collectively covered 1,587.4 Mbp, with an average mapped length of 7.5 Mbp per sample, accounting for 0.2768% of the reference genome coverage (Table 5).

Following the construction of the SNP matrix using raw SNPs from two parental lines and 180 F₂ lines, samples meeting quality control criteria were categorized into homozygous, heterozygous, and other classifications. On average, 13,253 SNPs were identified per sample, with 8,470 being homozygous, 2,305 heterozygous, and 2,477 falling into other categories (Table 6).

For genetic linkage map construction, SNP genotyping and filtering utilized a unified SNP matrix from the two parental lines and 180 F₂ lines. In constructing the genetic linkage map, 1,636 SNP markers were selected to assess population similarity, factoring in marker quantity, genotype frequency, and time needed for grouping.

The SNP genotyping revealed 212,839 SNP loci, of which 63,461 met a minimum depth threshold of 3. Further filtering reduced these to 1,636 markers, which were employed in constructing the genetic linkage map after imputation and filtering steps. These steps removed markers with excessive missing data (<30%), low minor allele frequency (>20%), and significant segregation distortion (χ² test p-value <0.001) (Table 7).

DISCUSSION

Pepper is an important horticultural crop cultivated globally, valued for its diverse applications and benefits. Historically, pepper has been used both as a food ingredient and in traditional medicine, leading to its widespread and continuous cultivation by many farmers. Various factors influence pepper production, with disease being a major one that can significantly reduce yields. Of the diseases affecting peppers, bacterial wilt poses a particularly severe threat. It is difficult to manage BW disease owing to a wide array of plant host range, a huge number of diverse BW isolates, and its long survivability in pepper plants (Hayward,1991c;Denny, 2006; Hayward, 19). Thus, developing bacterial wilt-resistant pepper varieties is crucial. SNP markers have become extremely popular in plant molecular genetics due to their genome-wide abundance and amenability for high- to ultra-high-throughput detection platforms (Nadeem, 2018). The GBS method has been extensively employed for genotyping segregated plant populations by integrating high-throughput NGS technology, which enables the creation of multiplexed libraries through the use of barcoded adapters (Deschamps, 2012;Poland, 2012). Since the GBS tool possesses a wide applicability of QTL identification in pepper (C. annuum), genetic analysis of quantitative traits and high-resolutionlinked markers for BW would contribute to more accurate marker-assisted selection (MAS) in plant genetics and breeding via GBS (Lee, 2022). Advances in NGS technology have enabled the discovery of vast numbers of SNPs. In this study, genetic linkage mapping was conducted after identifying 1,600 SNPs spanning most pepper chromosomes.

With the large-scale availability of the sequence information and development of high-throughput technologies for SNP genotyping, SNP markers have been increasingly used for QTL mapping studies (Mammadov, 2012). By leveraging SNP markers, researchers can achieve high-resolution mapping of resistance loci, which enhances the precision of QTL mapping efforts. This approach is particularly useful for complex traits like bacterial wilt resistance, allowing breeders to better understand the inheritance of resistance and improve selection accuracy.

The SNP-based QTL mapping conducted here not only highlights specific genomic regions associated with resistance but also provides essential data for future marker-assisted selection (MAS) in breeding programs. With the advent of NGS-based methods, it was possible to sequence the whole genome of plant and trees in a short time (Younessi-Hamzekhanlu, 2022). Identifying SNP markers closely linked to bacterial wilt resistance enables early- generation selection, which is both time-efficient and resource- saving, thus accelerating the development of robust, disease-resistant pepper varieties.

Additionally, GBS is rapidly becoming popular for low-cost high-density genome-wide scans through multiplexed sequencing (Thomson, 2014). This genome-wide SNP data facilitates a comprehensive approach to detecting resistance alleles, making it possible to breed pepper varieties with enhanced resistance to bacterial wilt under various environmental conditions. In future studies, the validation of these QTLs in different genetic backgrounds would further solidify their applicability in pepper breeding programs globally.

적 요

고추 풋마름병은 역병, 흰가루병 등의 각종 병과 더불어 고 추에 많은 위해를 가하고 있는 병 중 하나로, 이에 연관된 유 전자는 양적 형질로 알려져 있으며, 이 유전자를 탐색하기 위 해 필요한 QTL을 수행하기 위해 다음과 같이 GBS 분석을 시 행함

본 연구는 고추 풋마름병의 연관 유전자좌 탐색을 목표 로 풋마름병 저항성과 관련 있는 SNP 탐색을 수행함
이를 위해 풋마름병에 이병성인 ‘생력211’ 품종과 저항 성 유전자를 가지는 ‘FRH1’이라는 품종을 실험 재료로 사용 하고, 유전자형 분석을 위해 ‘생력 211’과 ‘FRH11’ 품종을 교 배하여 F₂ 세대를 전개함
QTL 탐색을 위해 F₂ 집단에 풋마름병을 접종하여 저항 성이 큰 순서에 따라 0부터 4까지 다섯 단계로 나누어 표현형 데이터를 수집함
F₂ 집단의 180개체를 활용하여 염기 서열 분석 기술 중 하나인 GBS 방식으로 고추 유전자 전반을 아우를 수 있는 수 준의 SNP 정보를 수집하고, 부족한 부분은 모 부본을 재해독 (resequencing) 한 데이터를 활용하여 보충함
Barcode sequence를 활용하여 얻은 각 샘플의 raw reads 의 평균은 3.2 Mbp이고 분석된 총 raw reads 길이는 326.4 Mbp로 확인함
Trimming 단계를 거쳐 결과적으로 raw reads의 92.23% 에 해당하는 247.8 Mbp의 염기서열 데이터를 획득함
이 데이터와 표현형 데이터를 기반으로 향후 고추 풋마 름병 저항성과 관련된 유전자좌를 찾는 QTL mapping을 진행 할 예정임

ACKNOWLEDGMENTS

This research work was supported by the Cooperative Research Program for Agriculture Science and Technology Development [Project No: PJ0126712019], Rural Development Administration, Republic of Korea.

Figure

Fig. 1.

The morphological appearance of the two lines used in this study (A) Saengryeok 211 and (B) FRH1.

Fig. 2.

F₁ population generated from the parental lines.

Fig. 3.

Disease index (DI) of pepper after the inoculation of Ralstonia solanacearum.

Fig. 4.

F₂ population inoculated with Ralstonia solanacearum for phenotipic data. (A, B) Pre-inoculation and (C, D) post-inoculation.

Fig. 5.

Distributions of disease resistant degrees for the 180 F₂ population. (A) Disease index of 0 to 4; (B) the number of resistant and susceptible plants (DI0, 1=R; DI2-4=S).

Table

Table 1.

Disease index of resistance to bacterial wilt in pepper.

Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay
1	4	S	35	3	S	69	2	S	99	2	S	135	1	R	173	3	S
2	1	R	36	3	S	70	0	R	100	2	S	136	3	S	174	3	S
3	1	R	37	3	S	71	4	S	101	0	R	137	2	S	175	3	S
4	2	S	38	4	S	72	3	S	102	0	R	138	2	S	176	4	S
5	2	S	39	1	R	73	2	S	103	0	R	139	2	S	177	2	S
6	4	S	40	0	R	74	3	S	104	2	S	144	4	S	178	4	S
7	0	R	41	4	S	75	3	S	105	2	S	145	1	R	179	4	S
8	0	R	45	1	R	76	2	S	106	1	R	146	0	R	180	4	S
9	3	S	46	3	S	77	3	S	107	0	R	150	3	S	181	3	S
11	2	S	47	2	S	78	3	S	108	2	S	151	2	S	182	0	R
12	1	R	48	3	S	79	1	R	109	1	R	152	0	R	184	4	S
14	4	S	49	2	S	80	4	S	111	1	R	153	2	S	185	2	S
16	3	S	50	4	S	81	2	S	112	2	S	154	2	S	186	4	S
17	0	R	51	1	R	82	3	S	113	2	S	155	3	S	187	1	R
18	4	S	52	1	R	83	1	R	114	0	R	156	3	S	188	2	S
19	1	R	53	3	S	84	3	S	115	1	R	157	3	S	189	2	S
20	2	S	54	3	S	85	2	S	116	2	S	158	4	S	190	2	S
22	1	R	55	3	S	86	3	S	119	1	R	159	4	S	191	2	S
23	1	R	56	2	S	87	0	R	120	2	S	160	2	S	192	3	S
24	4	S	57	4	S	88	1	R	121	1	R	161	3	S	193	4	S
25	3	S	58	2	S	89	2	S	123	1	R	162	2	S	194	2	S
26	3	S	59	0	R	90	3	S	124	2	S	163	3	S	195	0	R
27	4	S	60	2	S	91	2	S	125	2	S	164	3	S	196	2	S
28	3	S	61	4	S	92	2	S	126	2	S	166	3	S	197	2	S
29	3	S	62	2	S	93	2	S	127	3	S	167	1	R	198	3	S
30	1	R	63	3	S	94	2	S	128	2	S	168	2	S	199	3	S
31	4	S	64	2	S	95	3	S	130	3	S	169	4	S	200	3	S
32	4	S	65	0	R	96	2	S	132	4	S	170	3	S	201	3	S
33	3	S	67	3	S	97	0	R	133	2	S	171	2	S	202	3	S
34	4	S	68	3	S	98	3	S	134	0	R	172	2	S	203	1	R

Table 2.

The number of resistant and susceptible plants to bacterial wilt.

Population	Number of plants	Expected ratio	χ2	probability
F2	45	135	180	1:3	0.000	1.00

Table 3.

Summary of sequencing with average and sum of raw reads and trimmed reads.

Statistics	Raw reads	Trimmed reads	Trimmed/Raw (%)
AVG	3,231,130	326,344,153	2,977,125	247,807,599	92.23
SUM	588,065,702	59,394,635,902	541,836,816	45,099,883,769	-

Table 4.

Pepper reference genome (C.annuum cv. CM334 ver. 1.55).

Pepper reference genome	Chromosome length (bp)	Number of gene	Gene length (bp)
total	2,753,501,687	30,242	73,613,116

Table 5.

Statistics of mapped data of 180 sequenced samples.

Statistics	Number of Total SNP	Number of Homozygous SNP (read depth ≥ 90%)	Number of Heterozygous SNP (40%≤read depth≤60%)	Others z
SUM	2,385,466	1,524,576	414,960	445,930
AVG	13,253	8,470	2,305	2,477

Table 6.

Statistics of SNPs detected in 180 sequenced samples.

^Z Others is the difficult case of distinguishing between homo/heterozygous.

Statistics	Number of Mapped reads	Percent of mapped reads (%)	Total length of mapped region (bp)	Reference genome coverage (%)
AVG	1,864,743	72.41	7,595,435	0.2768
SUM	389,731,286	-	1,587,445,970	-

Table 7.

The number of SNP markers used for the construction of the genetic linkage map.

Filtering step	Filtering item	SNP matrix loci of 180 samples
1	Total SNP loci	212,839
2	SNP genotyping (min. depth = 3)	63,461
3	SNP marker for constructing linkage map	1,636

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Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay

1	4	S	35	3	S	69	2	S	99	2	S	135	1	R	173	3	S
2	1	R	36	3	S	70	0	R	100	2	S	136	3	S	174	3	S
3	1	R	37	3	S	71	4	S	101	0	R	137	2	S	175	3	S
4	2	S	38	4	S	72	3	S	102	0	R	138	2	S	176	4	S
5	2	S	39	1	R	73	2	S	103	0	R	139	2	S	177	2	S
6	4	S	40	0	R	74	3	S	104	2	S	144	4	S	178	4	S
7	0	R	41	4	S	75	3	S	105	2	S	145	1	R	179	4	S
8	0	R	45	1	R	76	2	S	106	1	R	146	0	R	180	4	S
9	3	S	46	3	S	77	3	S	107	0	R	150	3	S	181	3	S
11	2	S	47	2	S	78	3	S	108	2	S	151	2	S	182	0	R
12	1	R	48	3	S	79	1	R	109	1	R	152	0	R	184	4	S
14	4	S	49	2	S	80	4	S	111	1	R	153	2	S	185	2	S
16	3	S	50	4	S	81	2	S	112	2	S	154	2	S	186	4	S
17	0	R	51	1	R	82	3	S	113	2	S	155	3	S	187	1	R
18	4	S	52	1	R	83	1	R	114	0	R	156	3	S	188	2	S
19	1	R	53	3	S	84	3	S	115	1	R	157	3	S	189	2	S
20	2	S	54	3	S	85	2	S	116	2	S	158	4	S	190	2	S
22	1	R	55	3	S	86	3	S	119	1	R	159	4	S	191	2	S
23	1	R	56	2	S	87	0	R	120	2	S	160	2	S	192	3	S
24	4	S	57	4	S	88	1	R	121	1	R	161	3	S	193	4	S
25	3	S	58	2	S	89	2	S	123	1	R	162	2	S	194	2	S
26	3	S	59	0	R	90	3	S	124	2	S	163	3	S	195	0	R
27	4	S	60	2	S	91	2	S	125	2	S	164	3	S	196	2	S
28	3	S	61	4	S	92	2	S	126	2	S	166	3	S	197	2	S
29	3	S	62	2	S	93	2	S	127	3	S	167	1	R	198	3	S
30	1	R	63	3	S	94	2	S	128	2	S	168	2	S	199	3	S
31	4	S	64	2	S	95	3	S	130	3	S	169	4	S	200	3	S
32	4	S	65	0	R	96	2	S	132	4	S	170	3	S	201	3	S
33	3	S	67	3	S	97	0	R	133	2	S	171	2	S	202	3	S
34	4	S	68	3	S	98	3	S	134	0	R	172	2	S	203	1	R

Population	Number of plants			Expected ratio	χ2	probability
Population	Resistant	Susceptible	Total	Expected ratio	χ2	probability
F2	45	135	180	1:3	0.000	1.00

Statistics	Raw reads		Trimmed reads		Trimmed/Raw (%)
Statistics	Sum of Raw reads	Total length (bp)	Sum of trimmed reads	Total length (bp)	Trimmed/Raw (%)
AVG	3,231,130	326,344,153	2,977,125	247,807,599	92.23
SUM	588,065,702	59,394,635,902	541,836,816	45,099,883,769	-

Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay	Sample No.	Disease Index	Bioassay

1	4	S	35	3	S	69	2	S	99	2	S	135	1	R	173	3	S
2	1	R	36	3	S	70	0	R	100	2	S	136	3	S	174	3	S
3	1	R	37	3	S	71	4	S	101	0	R	137	2	S	175	3	S
4	2	S	38	4	S	72	3	S	102	0	R	138	2	S	176	4	S
5	2	S	39	1	R	73	2	S	103	0	R	139	2	S	177	2	S
6	4	S	40	0	R	74	3	S	104	2	S	144	4	S	178	4	S
7	0	R	41	4	S	75	3	S	105	2	S	145	1	R	179	4	S
8	0	R	45	1	R	76	2	S	106	1	R	146	0	R	180	4	S
9	3	S	46	3	S	77	3	S	107	0	R	150	3	S	181	3	S
11	2	S	47	2	S	78	3	S	108	2	S	151	2	S	182	0	R
12	1	R	48	3	S	79	1	R	109	1	R	152	0	R	184	4	S
14	4	S	49	2	S	80	4	S	111	1	R	153	2	S	185	2	S
16	3	S	50	4	S	81	2	S	112	2	S	154	2	S	186	4	S
17	0	R	51	1	R	82	3	S	113	2	S	155	3	S	187	1	R
18	4	S	52	1	R	83	1	R	114	0	R	156	3	S	188	2	S
19	1	R	53	3	S	84	3	S	115	1	R	157	3	S	189	2	S
20	2	S	54	3	S	85	2	S	116	2	S	158	4	S	190	2	S
22	1	R	55	3	S	86	3	S	119	1	R	159	4	S	191	2	S
23	1	R	56	2	S	87	0	R	120	2	S	160	2	S	192	3	S
24	4	S	57	4	S	88	1	R	121	1	R	161	3	S	193	4	S
25	3	S	58	2	S	89	2	S	123	1	R	162	2	S	194	2	S
26	3	S	59	0	R	90	3	S	124	2	S	163	3	S	195	0	R
27	4	S	60	2	S	91	2	S	125	2	S	164	3	S	196	2	S
28	3	S	61	4	S	92	2	S	126	2	S	166	3	S	197	2	S
29	3	S	62	2	S	93	2	S	127	3	S	167	1	R	198	3	S
30	1	R	63	3	S	94	2	S	128	2	S	168	2	S	199	3	S
31	4	S	64	2	S	95	3	S	130	3	S	169	4	S	200	3	S
32	4	S	65	0	R	96	2	S	132	4	S	170	3	S	201	3	S
33	3	S	67	3	S	97	0	R	133	2	S	171	2	S	202	3	S
34	4	S	68	3	S	98	3	S	134	0	R	172	2	S	203	1	R

Development of an F2 Population and GBS Analysis for Identifying QTL Associated with Resistance to Bacterial Wilt in Pepper