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Genome-wide association study of resistance to anthracnose in pepper (Capsicum chinense) germplasm



Anthracnose is a fungal disease caused by Colletotrichum spp. that has a significant impact on worldwide pepper production. Colletotrichum scovillei is the most common pathogenic anthracnose-causing species in the Republic of Korea.


The resistances of 197 pepper (Capsicum chinense) accessions deposited in Korea’s National Agrobiodiversity Center were evaluated for their response against the virulent pathogens Colletotrichum acutatum isolate ‘KSCa-1’ and C. scovillei isolate ‘Hana’) in the field and in vitro methods for three consecutive years (2018 to 2020). The severity of the disease was recorded and compared between inoculation methods. Six phenotypically resistant pepper accessions were selected based on three years of disease data. All of the selected resistant pepper accessions outperformed the control resistant pepper in terms of resistance (PI 594,137). A genome-wide association study (GWAS) was carried out to identify single nucleotide polymorphisms (SNPs) associated with anthracnose resistance. An association analysis was performed using 53,518 SNPs and the disease score of the 2020 field and in vitro experiment results. Both field and in vitro experiments revealed 25 and 32 significantly associated SNPs, respectively. These SNPs were found on all chromosomes except Ch06 and Ch07 in the field experiment, whereas in the in vitro experiment they were found on all chromosomes except Ch04 and Ch11.


In this study, six resistant C. chinense accessions were selected. Additionally, in this study, significantly associated SNPs were found in a gene that codes for a protein kinase receptor, such as serine/threonine-protein kinase, and other genes that are known to be involved in disease resistance. This may strengthen the role of these genes in the development of anthracnose resistance in Capsicum spp. As a result, the SNPs discovered to be strongly linked in this study can be used to identify a potential marker for selecting pepper material resistant to anthracnose, which will assist in the development of resistant varieties.

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Pepper (Capsicum spp.) is one of the most popular and widely grown horticultural crops worldwide due to its variety of products, uses, and forms of consumption [1]. It is native to the tropical areas of South and Central America and has around 38 described species with various morphological differences, mainly noticed in the fruits and related to size, shape, color, and level of pungency [2]. However, of those, only five species are considered domesticated plants: Capsicum annuum L., Capsicum chinense Jacq., Capsicum baccatum L. (var. pendulum), Capsicum frutescens L., and Capsicum pubescens Ruiz et Pav [3]. C. chinense (Habanero pepper) is diploid (2n = 24) and is predominantly self-pollinated. However, cross-pollination plays an important role in increasing the number and quality of fruits as well as facilitating higher seed production [4].

Although Capsicum spp. have rich nutritional and economic value, their production has been seriously hampered by several pests and diseases [5]. Of these, Colletotrichum is a large genus that includes many important species and prevalent fungal pathogens that cause various tropical and subtropical fruit and vegetable diseases [5]. The genus Colletotrichum was ranked as the world’s eighth-most important phytopathogenic fungus [6]. There are several Colletotrichum species that cause pepper anthracnose, including Colletotrichum acutatum (teleomorph Glomerella acutata), Colletotrichum capsici (synonym Colletotrichum dematium), Colletotrichum coccodes, Colletotrichum gloeosporioides (teleomorph Glomerella cingulata), and [7]. C. acutatum and C. gloeosporioides are the most damaging and widely distributed anthracnose disease-causing species [8, 9].

Anthracnose of pepper (Capsicum spp.) has become a major limiting factor in pepper production, causing significant economic losses in different countries, including China, Korea, India, Indonesia, and Thailand [8, 10,11,12,13]. In Korea, the C. acutatum species complex stands out as the predominant pathogen responsible for the disease, infecting both immature and mature pepper fruits [14]. Specially, Colletotrichum scovillei is the major causal agent of anthracnose of chili in Korea [15]. Typical anthracnose symptoms on pepper fruit include sunken necrotic tissues and concentric rings of acervuli, which reduce fruit quality [11]. Anthracnose disease causes pre-and post-harvest pepper fruit rot and reduces their marketability. Various disease management strategies have been implemented to control anthracnose in pepper, including crop management systems (crop rotation, proper soil drainage, and field cleaning of infected plant parts), biological agents, and/or chemicals, and cultivation of resistant genotypes [5, 16]. However, the development of resistant cultivars is the best long-term strategy to control the disease. This is because it reduces the amount of money that is spent on chemical and mechanical methods of disease control [17, 18] and allows a more sustainable crop management.

Genetic analysis studies have provided insights into the inheritance patterns of resistance to Colletotrichum species in chili crops, highlighting the importance of species, isolates, and fruit maturation stages [19]. Marker-Assisted Selection (MAS) has emerged as a crucial tool in the chili crop improvement program, enabling the efficient selection of multiple resistance genes for pyramiding into a single genotype [20]. Research findings have revealed varying inheritance patterns for anthracnose resistance in different pepper varieties. For instance, an interspecific cross between “Yeoju” and ‘Daepoong-cho’ demonstrated that resistance against C. acutatum is controlled by a monogenic recessive gene [21]. In contrast, genetic analysis of C. chinense (PBC932) revealed mostly dominant resistance observed in chromosome P5 for both green and red fruit [22]. Similarly, investigations on the C. annuum breeding line ‘83–168’ identified a single dominant gene responsible for resistance against C. acutatum [23]. Dominant resistance genes have also been reported in C. baccatum PBC80 [24], while differential gene expression at different fruit maturity stages has been observed in C. chinense and C. baccatum [25]. Understanding the genetic basis of resistance inheritance is crucial for developing effective breeding strategies and selecting resistant cultivars against Colletotrichum pathogens in pepper crops.

Several methods have been developed and used to select a core collection with maximum genetic diversity based on morphological characteristics and/or passport information [26]. Approaches such as genotyping-by-sequencing (GBS) are currently being used to genetically characterize germplasm collections based on single nucleotide polymorphisms (SNPs) [27] to develop a unique genetic profile for each accession, allowing analysis of the genetic diversity in the accession collection and the genetic relationships between germplasms [28,29,30]. Genome-wide association studies (GWAS) provide high resolution through accumulated historical recombination in natural populations, varieties, breeding materials, and collections of landraces [31]. The purpose of a GWAS is to ascertain the associations between genotypes and phenotypes by comparing the allele frequencies of genetic variants between individuals who are ancestrally similar but have phenotypic differences and by analyzing genetic variants across multiple genomes to identify those that are statistically associated with a particular disease or trait [32].

Therefore, this experiment was conducted using a total of 197 C. chinense accessions from different countries. These accessions were evaluated for their resistance to anthracnose both in the field and in vitro in different crop years to identify resistant germplasm. The experiment started in 2018 with the C. acutatum isolate ‘KSCa-1’. In 2019, a highly virulent and widely distributed C. scovillei isolate named ‘Hana’ was identified. Consequently, the studies conducted in 2019 and 2020 utilized the ‘Hana’ isolate. The study aims to identify SNPs associated with anthracnose resistance and the underlying genes through GWAS analysis. The findings from this study will provide valuable insights for future breeding programs, ultimately leading to the development of improved disease-resistant pepper varieties.


Evaluation of C. chinense germplasm resistance against anthracnose

The resistance of 197 pepper (Capsicum chinense) accessions deposited in the Republic of Korea’s National Agrobiodiversity Center were evaluated for their response against the virulent pathogen C. acutatum isolates ‘KSCa-1’ and C. scovillei isolate ‘Hana’ in field and in vitro methods for three consecutive years (2018 to 2020). Multiple genes of the isolate ‘Hana’ were sequenced and its identity as C. scovillei was confirmed as presented in the phylogenetic tree (Supplementary Figure S1). The in vitro experiment was carried out to assess C. chinense resistance to isolate ‘KSCa-1’ in 2018 and isolate ‘Hana’ in 2020. Disease resistance testing in the field was carried out in 2019 and 2020 against the C. acutatum isolate “Hana”. Table 1 summarizes the distribution of germplasm by disease severity score for each year. During the 2018 in vitro experiment with isolate ‘KSCa-1’, the number of germplasms according to the disease scores was as follows: 74 (0–1), 56 (1–2), 30 (2–3), and 37 (3–4), whereas in the 2020 in vitro experiment with isolate “Hana”, the number of germplasms according to the disease score was 48 (0–1), 39 (1–2), 50 (2–3), and 60 (3–4). Regarding the field experiment in 2019, 124 germplasms had a disease score that ranged from 0 to 1, 39 (1–2), 20, (2–3), and 14 (3–4). On the other hand, in the 2020 field experiment, 26 germplasms had a disease score of 0–1, 73 (1–2), 62 (2–3), and 36 (3–4).

Table 1 The frequency distribution of C. chinense germplasm based on disease score in both in vitro and field inoculation methods from 2018 to 2020

The correlation analysis was conducted based on the disease response of C. chinense germplasm between the testing years and inoculation methods (Fig. 1). Regardless of inoculation methods, a positive correlation was observed between testing years. A relatively strong positive correlation was observed between the field inoculation experiments of 2019 and 2020 (r = 0.39***). The correlation between 2018 and 2020 in the in vitro experiment was positive (r = 0.12). The correlation between field and in vitro experiments in 2020 showed a positive correlation (r = 0.25***).

Fig. 1
figure 1

Spearman correlation coefficient between the experimental method and years, based on the anthracnose disease severity scores of the C. chinense population. The significance level is represented by asterisks (***), indicating a high level of significance (p < 0.001)

A PCA plot was generated using disease data from 197 accessions collected between 2018 and 2020 (Fig. 2). PC1 and PC2 accounted for 66.6% of the total variance, with PC1 alone explaining 42.5% of the variance. The main contributors to PC1 were the field disease data from 2019 to 2020, along with the in vitro disease data from 2020. On the other hand, the primary contributor to PC2 was the in vitro disease data from 2018. The accessions were also grouped based on the average disease score values from all experiment, as shown in Fig. 2. Accessions with disease scores ranging from 0 to 1 (resistant) are represented by the color cyan, those with scores from 1 to 2 (moderately resistant) are represented by green, scores from 2 to 3 (susceptible) are represented by violet, and scores from 3 to 4 (highly susceptible) are represented by the color red.

Fig. 2
figure 2

PCA of pepper germplasms based on anthracnose disease severity scores. The color cyan represents accessions classified as resistant (R), green represents moderately resistant (MR), violet represents susceptible (S), and red represents highly susceptible (HS)

Selection of resistant C. chinense accessions

Six C. chinense accessions that showed resistance to anthracnose in all testing years and inoculation methods were selected based on their disease severity score by comparing them with susceptible and resistant controls (Table 2). These six resistant accessions are IT229207, IT236717, IT261436, IT261448, IT284059, and IT305437. Compared to the resistant control (PI 594,137), the selected germplasm showed better resistance to both experimental conditions across the testing years. Of the six resistant genetic resources, five were native to South America (one from Bolivia, one from Brazil, one from Colombia, and two from Peru), and one from Hungary. The resistant control (PI 594,137) had severity scores of 1.4, 1.2, and 2.7 in 2018, 2019, 2020 (field), and 2020 (in vitro), respectively, whereas the susceptible control (Manitta) had a disease score of 4.0 in all testing years regardless of experiment conditions.

Table 2 Six selected resistant accessions for anthracnose in both in vitro and field inoculation tests

Genome-wide association analysis

The GBS library was constructed from 197 pepper accession and sequenced using the Illumina Hiseq 2000 platform (Illumina, Madison, WI, USA) and generated approximately 1.2 billion sum of mapped reads with an average mapping depth of 16.96× for a single accession. The summary of GBS statistics of 197 pepper germplasm summarized in Table S1. The SNP matrix was generated and classified as homozygous (SNP read depth ≥ 90%), heterozygous (40% ≤ SNP read depth ≤ 60%) and etc. SNP (20% ≤ read rate < 40%, 60% < read rate < 90%) (Table S2). The SNPs filtering processes are described in Table 3. From the sequences of 197 pepper accession 1,034,421 SNPs were detected with minor alleles frequency (MAF > 5%) covering 12 chromosomes.

Finally, a total of 53,518 SNPs were obtained after filtering with the combination of MAF (> 5%) and missing data (< 30%). The number of SNPs retained on all 12 chromosomes within a 1 Mb window size is presented in Fig. 3. Genome-wide association analysis was conducted with 53,518 SNPs to identify SNP markers associated with anthracnose resistance. the 2020 field and in vitro experimental disease severity scores was used for GWAS analysis. The association analysis results for both the in vitro and field experiments of 2020 were visualized in Manhattan and QQ plots (Fig. 4A and B). A total of 57 significantly associated SNPs were found in both experimental conditions (Tables 3 and 4). The details of all significantly associated SNPs in both experimental conditions are summarized in Table S3. There were seven significant SNPs on Ch02, Ch06, and Ch02, and six SNPs on Ch01, Ch07, and Ch09. Relatively, the smallest number of significant SNP markers was found on Chr04 and Chr11, with 1 SNP on each chromosome. Based on the GWAS analysis of field and in vitro experiment data, the number of significantly associated SNPs was 25 and 32, respectively. In the GWAS results of the 2020 field experiment, significantly associated SNPS were detected across chromosomes except on Chr06 and Chr07. The number of significantly associated SNPs on Chr09, Chr08, Chr05, and Chr03 were 5, 4, 3 and 3, respectively (Table 3). According to the 2020 in vitro experiment, 14 of the 25 significantly associated SNPs were found in protein-coding genes, while the other 11 were found in the intergenic region. Regarding the 2020 in vitro experiment, the SNPs that were strongly linked were found on all chromosomes except Chr04 and Chr11. The number of SNPS on Chr06, Chr07, Chr01 and Chr02 was 7, 6, 5 and 5, respectively (Table 4). Among the 32 SNPS, 19 were found on protein coding genes and 13 were in the intergenic regions. All six SNPs discovered on Chr07 were in the intergenic region (Table 3). Multiple SNPs were found in the following genes; CA05g03560, CA08g18220, CA10g01780, CA06g12230, and CA12g05320 (Tables 3 and 4). Figure 5 displays box plots of selected SNPs significantly associated with anthracnose based on the C. chinense GWAS panels.

Table 3 Summary of SNPs filtering process
Table 4 Significant SNPs associated with anthracnose resistance in GWAS analysis of 2020 in vitro experiment
Table 5 Significant SNPs associated with anthracnose resistance in GWAS analysis of 2020 field experiment
Fig. 3
figure 3

Distribution of 53,518 SNPs across all 12 chromosomes. The figure shows the number of SNPs within 1 Mb window size and reflects the SNP density on each chromosomes used during the present study for GWAS

Fig. 4
figure 4

Manhattan (A) and quantile–quantile (QQ) (B) plots resulting from the genome-wide association study (GWAS) for anthracnose resistance in pepper. Red dotted line indicates the GWAS threshold (p < 0.001)

Fig. 5
figure 5

Boxplots depicting the significantly associated SNPs with anthracnose in C. chinense based on the in vitro (a–e) and field (f–i) experiments. The x-axis represents the chromosome number and position (bp), while the y-axis represents the disease severity score. The median is indicated by the horizontal line within the box, and the mean is represented by the ‘x’ symbol


Developing anthracnose-resistant Capsicum cultivars relies on identifying resistance sources and understanding the genetic basis, crucial for combating this economically important disease [33]. In the current study, six resistant accessions to C. acutatum were found in the C. chinense population during field and in vitro disease evaluation methods, and their disease scores fall between 0 and 1 (Table 2). In vitro inoculation of isolate KSCa-1 in 2018 had weak correlations with field and in vitro experiments in 2019 and 2020. This is mainly because the degree of severity in 2018 using isolate KSCa-1 was comparatively low in most of the pepper accessions tested. The PCA analysis effectively demonstrates the distinction between resistant and susceptible accessions, irrespective of experimental type and isolates, based on the overall average disease score values (Fig. 2). Global efforts to enhance anthracnose resistance in Capsicum cultivars necessitate evaluating diverse pepper germplasm resources for selecting resistant materials, including previous studies on capsicum spp. from different countries [34, 35]. According to the previous studies, a major resistance locus controlled Capsicum anthracnose [36], and a single recessive gene was responsible for resistance to C. capsici in ‘PBC932’ [21]. Another study showed that the resistance of C. chinense “PBC932” against C. acutatum was controlled by two complementary dominant genes in green fruits and two recessive genes in red fruits [37].

In this study, a GWAS was conducted using 53,518 SNPs to identify significantly associated SNPs for anthracnose resistance, revealing their presence across all chromosomes, including Ch05, Ch07, Ch10, and Ch12. This widespread distribution highlights the extensive genetic basis of anthracnose resistance in the investigated Capsicum species. Moreover, the inheritance analysis of the PBC932 cultivar, known for its resistance to both green and red fruit stages, supported our findings by demonstrating the involvement of well-known genes within the genomic interval on chromosome P5 [19, 24, 25]. These findings provide valuable insights into the major genes or quantitative trait loci (QTL) contributing to anthracnose resistance, particularly in relation to specific pathogens and Capsicum species. Additionally, previous studies have reported similar results, identifying several QTLs associated with anthracnose resistance on chromosomes P5, P7, P10, and P12 [19].

An SNP (Ch11: 3,654,665 bp) showed a significant association with anthracnose resistance was detected within a gene responsible for encoding the receptor-like kinase protein. Receptor kinases play a vital role as receptors that bind to molecules involved in signaling pathways [38]. These kinases are members of several gene families that play important roles in extracellular signal recognition and transduction [38]. Plant receptor kinases, for example, are involved in a wide range of physiological and biochemical processes induced by plant hormones and environmental cues, such as self-incompatibility, endosperm and pollen development regulation, flower shedding, response to brassinosteroid and plant disease and environmental stress resistance [39, 40]. Two SNPs significantly associated with anthracnose resistance were identified in Ch01 (37,429,407 bp) based on the in vitro experiment, and in Ch02 (146,421,570 bp) based on field conditions. These SNPs are located within two genes, CA01g29280 and CA02g17350, which encode serine/threonine-protein kinases (Tables 3 and 4). Serine/threonine protein kinases (STKs) are receptor proteins that mediate signal transduction in plant defense responses [38]. STKs are primarily involved in the recognition and transduction of pathogen-derived signals during plant-microbe interactions [38]. A previous study focused on exploring the contribution of four candidate genes that encode STKs to anthracnose resistance in the common bean (Phaseolus vulgaris L.) [41]. The study revealed significantly higher expression levels of these genes in the resistant genotype compared to the susceptible genotype. Specifically, Phvul.001G243600 and Phvul.001G243700 genes exhibited approximately 15-fold and 90-fold higher expression levels in the resistant genotype, respectively, even before inoculation [41]. Several STK resistance genes have been cloned from various plant species, including Pto and Prf from tomato [42, 43], Xa21 from rice (Song et al., 1995), RPS5 and PBS1 from Arabidopsis [44, 45], and Rpg5 from barley [46]. Pto encodes a serine/threonine protein kinase that confers resistance to the bacterial pathogen Pseudomonas syringae pv. tomato, which expresses the avirulence gene avrPto [42]. The tobacco NrSTK gene encodes a serine/threonine protein kinase with conserved motifs typical of protein kinases, with high sequence similarity to the tomato serine/threonine kinase Pto, which was cloned and studied for resistance to black shank disease [38]. The study concludes that the NrSTK gene, which encodes a serine/threonine protein kinase, functions as a black shank resistance gene in tobacco [38]. The result of our study also strengthens that the serine/threonine-protein kinase gene has a role in anthracnose resistance, as mentioned in previous studies about its involvement in disease resistance. Protein phosphorylation/dephosphorylation are important signaling events in plants that are triggered by biotic and abiotic stress, as well as developmental pathways [47,48,49]. Serine/threonine protein kinases (EC are host immune receptors that have been shown to play a role in effector-triggered immunity (ETI) in plants by phosphorylating the OH group of serine or threonine residues, resulting in a functional change in the target protein [50, 51]. The critical elucidation of specific protein kinases’ physiological roles in plants, under abiotic and biotic stress, is essential for comprehending and identifying key players in defense pathways. Furthermore, protein kinases are regarded as suitable targets for gene modification, contributing to crop improvement [52, 53].

Disease-resistant markers are primarily associated with resistance (R) proteins, the majority of which feature a domain comprising nucleotide-binding sites and carboxy-terminal leucine-rich repeat regions (NBS-LRR) [54,55,56,57,58]. The presence of NBS-LRR genes has been identified in numerous plant species, leading to significant advancements in disease resistance research [58]. A SNP (Ch03, 1,918,096 bp) was found in a gene that encodes CC-NBS-LRR protein (Table 3). Based on deduced N-terminal structural features, NBS-LRR can be classified into two subfamily groups: toll/interleukin-1 receptor (TIR-NBS-LRR; TNL) and coiled-coil domain (CC-NBS-LRR; CNL) [59, 60]. The disease resistance protein, which is resistance to P. syringae 5 (RPS5) in Arabidopsis thaliana, is a well-characterized CNL protein that confers resistance to Pseudomonas pv. tomato (Pto) strain DC3000 carrying the heterologous avirulence gene avrPphB [44, 61, 62].

The results of the BLAST searches indicate that the SNP marker on chromosome 02 (specifically, in the region between positions 163.20 and 164.20) corresponds to a gene annotated as an E3 ubiquitin ligase protein. Additionally, a similar finding was reported for a significantly associated SNP marker for stripe rust in wheat on chromosome 6AL (Tdurum_contig29607_413), which also encodes an E3 ubiquitin ligase protein [63]. The study by You et al. [64] suggests that E3 ubiquitin ligase proteins are important modules in plants for regulating innate immunity and programmed cell death. These proteins significantly contribute to enhancing antimicrobial defense mechanisms while preventing the occurrence of autoimmunity. Furthermore, the study indicates that E3 ubiquitin ligases are involved in broad-spectrum disease resistance, implying that they play a role in protecting plants against a wide range of pathogens. However, it is important to note that the specific functions and mechanisms of E3 ubiquitin ligases can vary among different genes and species. Therefore, further research would be necessary to determine the exact role and significance of these E3 ubiquitin ligase proteins in the context of the SNP markers found on chromosome 02 in the current study with respect to their function in anthracnose resistance and on chromosome 6AL with respect to their function in stripe rust resistance in wheat [64]. A significant SNP was found on Ch05 in a gene that encodes a putative pentatricopeptide repeat-containing protein (PRP). A study demonstrated that a PRP protein was involved in disease resistance and salt tolerance in rice [65]. While PPR genes are primarily involved in post-transcriptional regulation of gene expression in mitochondria and chloroplasts, they have been found to share common features with disease resistance genes (R genes) which are known for their role in plant defense against pathogens [66]. Further studies are essential to understand the functions of these genes in which the selected SNPs are found and their association with disease resistance.


This study evaluated pepper accessions collected from different countries for resistance to C. acutatum isolate ‘KSCa-1’ and C. scovillei isolate ‘Hana’ that cause anthracnose on capsicum spp. A phenotypic evaluation of C. chinense against anthracnose was carried out, and six resistant accessions were identified. These accessions showed better resistance compared to the resistant control pepper (PI 594,137). Based on the results of this experiment, the selected resistant materials can be used as a potential source of anthracnose resistance for pepper breeding and genetic studies. Using GWAS, SNPs associated with anthracnose resistance on different chromosomes and their positions in candidate genes were identified. Furthermore, the identified SNPs can be further investigated as potential markers for resistance screening of C. chinense genetic resources. As mentioned in several studies, genes that encode protein kinase receptors have been known as a source of resistance for several diseases. The presence of significantly associated SNPs in this study in a gene that encodes a protein kinase receptor, including serine/threonine-protein kinase, can be an indication of its involvement in anthracnose resistance development in Capsicum spp.

Materials and methods

Plant materials and pathogen (Colletotrichum acutatum and Colletotrichum scovillei)

In this study, 197 pepper germplasms from Capsicum chinense collected from different countries were used. This experiment was conducted for three consecutive years from 2018 to 2020. In 2018, ten plants from each accession were grown in greenhouses at the National Agrobiodiversity Center in Jeonju, and for two years from 2019 to 2020 at Hana Seed Co.‘s experimental sites in Ansang, Korea. This experiment was triplicated. Resistant control (PI 594,137) and susceptible control (Manitta) pepper germplasms were used. The pepper plants were grown according to the standard pepper cultivation methods of the Rural Development Administration (RDA, Jeonju, Korea) [67]. The field performances of the pepper accessions against C. acutatum and C. scovillei isolates were evaluated. The introduction number (IT) and geographic origin of the 197 C. chinense accessions used in this study are presented in Supplementary Table S4.

The single spore isolation was performed following the protocol described by Oo et al. [15] with minor modifications. The diseased fruits displaying characteristic symptoms were cut into small 5 mm fragments and surface sterilized using a 1% sodium hypochlorite (NaOCl) solution for 3 min. After three rinses with sterilized distilled water and drying on sterilized tissue paper [68], the prepared fruit fragments were placed on Petri dishes and incubated in a dark-light chamber at 25 ± 2 °C with a 12-hour light-dark cycle. Following a 2-day incubation period, spore layers were isolated using autoclaved toothpicks or glass sticks and mixed with distilled water in a tube. The spore-water mixture was then streaked onto Water agar media and allowed to grow for 3 days at 25 ± 2 °C [69]. Finally, a single isolated spore from the emerging fungus was transferred onto a potato dextrose agar (PDA) plate to establish a pure culture.

The ‘KSCa-1’ isolate has been identified as C. acutatum, whereas the ‘Hana’ isolate is classified as C. scovillei (Supplementary Figure S1). C. scovillei is within the C. acutatum species complex, indicating a close relationship between the two species [70]. Two isolates from C. acutatum and C. scovillei were used to evaluate the resistance of pepper accessions in different years of cultivation. In 2018, the ‘KSCa-1’ isolate was used for in vitro evaluation, and the isolate ‘Hana’ in 2019 (field experiment) and 2020 (field and in vitro experiments). Dr. Lee [36] kindly offered us the pathogenic fungus C. accutatum (KSCa-1 isolate). Isolate ‘Hana’ was identified from the pepper cultivation fields of Hana Seed Co. (Anseong, Korea) (Supplementary Figure S1).

Inoculum preparation

The inoculum preparation was performed according to the procedure of Kim et al. [34]. Potato dextrose agar (PDA) plates (Sigma Chemical Co., St. Louis, MO, USA) were used to grow the isolates at 28 °C under 16 h fluorescent light/ 8 h dark in a temperature-controlled incubation chamber. After the isolates had grown for seven days, the PDA plates were flooded with distilled water, and the fungal culture was gently scraped off the plates. A hemocytometer was used to adjust the inoculum to a density of 1.0 × 105 conidia mL− 1.

Inoculation methods

In both in vitro and field experiments, inoculation was performed. In vitro experiments were carried out with the C. acutatum isolate ‘KSCa-1’ in 2018 and the C. scovillei isolate ‘Hana’ in 2020. Well-developed pepper fruits were harvested from pepper plants. The in vitro inoculation was performed according to the protocol described by Kim et al. [71] with minor modifications. The fruits were treated with 10% Clorox for 3 min, washed in sterile distilled water several times, and dried with sterile paper towels. We placed 10 fruits per accession in resealable plastic bags (25 × 30 cm) containing wet paper towels. The fruit surface was sprayed with inoculum adjusted to 1.0 × 105 conidia mL− 1 concentration. To induce disease, the inoculated peppers were immediately sealed to maintain moisture and placed in a growth chamber at 28 °C. After two days of incubation, the resealable plastic bags were opened for two hours at room temperature to prevent excessive moisture from forming on the fruits. Sterile distilled water instead of the conidial suspension was used as control. The incubation period lasted 14 days under the same conditions. This experiment was conducted in triplicate.

Field inoculation with the isolate ‘Hana’ was conducted in the experimental field of Hana Seed Co. located in Anseong, Korea. The isolate ‘Hana’ inoculum concentration used for the field inoculation was 1.0 × 105 conidia mL− 1. To introduce the pathogen into the field, the inoculum was diluted, and a spray inoculation method, similar to pesticide spray application, was employed to ensure widespread distribution and effective dissemination of the pathogen. The initial inoculation carried out around 110 days after planting and was conducted three times prior to the start of the monsoon season. In 2019, the spray inoculations took place on June 27, July 11, and July 24. Similarly, in 2020, the inoculations were carried out on June 27, July 3, and July 9, ensuring a sufficient exposure of the plants to the pathogen.

Disease severity assessment

In an in vitro experiment, the percentage of infected sites was calculated to evaluate the disease severity with an average of 14 days after inoculation. The disease severity of field inoculation was scored at 4 weeks post-inoculation. The disease severity in both experimental conditions was scored according to the method described by Ro et al. [35]. The scoring system used a range of 0 to 4 scales: 0 indicated no symptoms, 1 indicated symptoms with less than 10% disease incidence, 2 indicated symptoms with 11–20% incidence, 3 indicated symptoms with 21–40% incidence, and 4 indicated symptoms with 41–100% incidence. Supplementary Figure S2 illustrates the disease severity score in both experimental conditions. This scoring was applied to both non-wound inoculated and field-inoculated pepper accessions. The phenotypes were categorized based on their mean disease severity scores: those with a score of 0–1 were considered resistant (R), scores of 1–2 were classified as moderately resistant (MR), scores of 2–3 were categorized as susceptible (S), and scores of 3–4 were classified as highly susceptible (HS).

Genotyping and genome-wide association analysis

Genomic DNA was extracted using the CTAB method [72] from young leaves of each accession. The amount of DNA is quantified using the standard procedure of the Quant-iT PicoGreen dsDNA Assay Kit (Molecular Probes, Eugene, OR, USA) with the Synergy HTX Multi-Mode Reader (Biotek, Winooski, TV, USA) and normalized to 12.5 ng µL− 1. DNA was digested with ApeKI restriction enzyme (New England Biolab) at 75 °C for 3 h. Libraries for GBS were constructed according to previously described protocols [27, 73] with minor modifications. The GBS libraries were sequenced using Illumina Hiseq 2000 platform (Illumina, Madison, WI, USA) with 151 bp of paired-end reads. The cleaned reads generated after passing the pre-processing process were mapped to C. chinense reference genome v1.2 (

Barcode sequence was used for demultiplexing, followed by adapter sequence removal and sequence quality trimming. Adapter and barcode sequences were eliminated using the software Cutadapt (version 1.8.3) [74]. DynamicTrim and LengthSort programs of the SolexaQA (v.1.13) package [75] were used to remove low-quality sequences. For DynamicTrim, a phred score ≥ 20 was applied. For LengthSort, a short read length of ≥ 25 pb was applied. BWA (BurrowsWheeler Aligner, ver.0.6.1-r104) [76] generated cleaned reads, passed the preprocessing process, and performed mapping to the C. chinense reference genome v1.2 ( A SAM file was created, and default values were used, except for the following options: a seed length (− l) of 30, maximum differences in the seed (− k) of 1, number of threads (− t) of 16, mismatch penalty (− M) of 6, gap opening penalty (− O) of 15, and gap extension penalty (-E) of 8.

The clean reads were mapped to the reference genome sequence. The resulting SAM files were utilized for raw SNP discovery using SAMtools (0.1.16) [77], and consensus sequences were extracted from the obtained data. SNP validation was conducted using SEEDERS in-house script [78] before SNP detection; raw SNP detection was performed, and default values were used except for the following options: a minimum mapping quality for SNPs (− Q) of 30, minimum mapping quality for gaps (− q) of 15, minimum read depth (− d) of 3, minimum InDel score for nearby SNP filtering (− G) of 30, SNPs within INT bp around a gap to be filtered (− w) of 15, window size for filtering dense SNPs (− W) of 30, and maximum read depth (− D) of 165.

The SNP matrix was generated by removing the incorrectly identified SNP sites using SNP comparison between samples. SNPs were classified as homozygous (SNP read depth ≥ 90%) or heterozygous (40% ≤ SNP read depth ≤ 60%) and other SNPs, which did not meet the criteria for Homozygous or Heterozygous (Table S2). Finally, a total of 53,518 high-quality SNPs were generated for association analysis. Based on the location information of the reference genome sequence (C. chinense. v1.2,

Based on the response of pepper accessions to anthracnose disease across testing years, the 2020 field and in vitro experiments were deemed relatively representative for GWAS analysis, as disease severity was greater in 2020 than in other years. The association analysis was conducted using a dataset consisting of 53,518 SNPs. This analysis utilized the genomic association and prediction integrated tool (GAPIT3) package [79], which is integrated within the R statistical software program (version 4.0.2). The analysis employed a mixed linear model (MLM) [80]. The threshold value was set at p < 0.001 (-log (p) > 3.0) for declaring significant marker-trait association [81].

For the identification of candidate genes associated with the SNP of interest, we conducted a BLAST search using the Capsicum genome database (, C. annum.v.1.55) and the NCBI (National Center for Biotechnology Information) database. Our search focused on a 200 kb region surrounding the SNP, including 100 kb on each side. The flanking sequences of the SNP were obtained from the Capsicum chinense genome database. Subsequently, we compared these sequences against the Capsicum genome database and the NCBI database to identify genes or gene regions that exhibited similarity or alignment. This comprehensive approach allowed for the efficient identification of potential candidate genes associated with the SNP of interest. We reported the physical location of the SNPs within the reference genome of C. chinense using a 1 Mb window size. Additionally, we predicted the gene positions based on the C. annum reference and the NCBI reference genome regions.

Statistical analysis

The disease assessment was summarized using Microsoft Excel. Pearson’s correlation between experiment methods and experiment years, as well as principal component analysis (PCA), were carried out using the R statistical software program (4.0.2 version).

Data Availability

The datasets used and/or analyzed during the current study are available in the supplementary file, and additional datasets can be provided by the corresponding author on request. The SNPs data used for GWAS analysis submitted to National Agricultural Biotechnology Information Center ( with the following accession number NV-0771.


  1. Sudr CP, Gonalves LSA, Rodrigues R, do Amaral Jnior AT, Riva-Souza EM, dos Bento S. Genetic variability in domesticated Capsicum spp as assessed by morphological and agronomic data in mixed statistical analysis. Genet Mol Res. 2010;9:283–94.

    Article  Google Scholar 

  2. Kalwij JM. Review of ‘The Plant List, a working list of all plant species’. J Veg Sci. 2012;23:998–1002.

    Article  Google Scholar 

  3. DeWitt D, Bosland PW. The complete chile pepper book: a gardener’s guide to choosing, growing, preserving, and cooking. 1st ed. London: Timber Press; 2009.

    Google Scholar 

  4. Cauich O, Quezada Euán JJG, Ramírez VM, Valdovinos-Nuñez GR, Moo-Valle H. Pollination of habanero pepper (Capsicum chinense) and production in enclosures using the stingless bee Nannotrigona perilampoides. J Apic Res. 2006;45:125–30.

    Article  Google Scholar 

  5. Ali A, Bordoh PK, Singh A, Siddiqui Y, Droby S. Post-harvest development of anthracnose in pepper (Capsicum spp): etiology and management strategies. Crop Prot. 2016;90:132–41.

    Article  CAS  Google Scholar 

  6. Dean R, Van Kan JAL, Pretorius ZA, Hammond-Kosack KE, Di Pietro A, Spanu PD, et al. The top 10 fungal pathogens in molecular plant pathology: top 10 fungal pathogens. Mol Plant Pathol. 2012;13:414–30.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Park K, Kim C. Identification, distribution and etiological characteristics of anthracnose fungi of red pepper in Korea. Plant Pathol J. 1992;8:61–9.

    Google Scholar 

  8. Voorrips RE, Finkers R, Sanjaya L, Groenwold R. QTL mapping of anthracnose (Colletotrichum spp.) resistance in a cross between Capsicum annuum and C. chinense. Theor Appl Genet. 2004;109:1275–82.

    Article  PubMed  Google Scholar 

  9. Sarath Babu B, Pandravada SR, Prasada Rao RDVJ, Anitha K, Chakrabarty SK, Varaprasad KS. Global sources of pepper genetic resources against arthropods, nematodes and pathogens. Crop Prot. 2011;30:389–400.

    Article  Google Scholar 

  10. Kim J-T, Park S-Y, Choi W-B, Lee Y-H, Kim H-T. Characterization of Colletotrichum isolates causing anthracnose of Pepper in Korea. Plant Pathol J. 2008;24:17–23.

    Article  CAS  Google Scholar 

  11. Than PP, Jeewon R, Hyde KD, Pongsupasamit S, Mongkolporn O, Taylor PWJ. Characterization and pathogenicity of Colletotrichum species associated with anthracnose on chilli (Capsicum spp.) in Thailand. Plant Pathol. 2008;57:562–72.

    Article  Google Scholar 

  12. Than PP, Prihastuti H, Phoulivong S, Taylor PWJ, Hyde KD. Chilli anthracnose disease caused by Colletotrichum species. J Zhejiang Univ Sci B. 2008;9:764–78.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Xia H, Wang X-L, Zhu H-J, Gao B-D. First Report of Anthracnose caused by Glomerella acutata on Chili Pepper in China. Plant Dis. 2011;95:219–9.

    Article  PubMed  CAS  Google Scholar 

  14. Kang B-K, Kim J-H, Lee K-H, Lim S-C, Ji J-J, Lee J-W, et al. Effects of temperature and moisture on the survival of Colletotrichum acutatum, the Causal Agent of Pepper Anthracnose in Soil and Pepper Fruit debris. Plant Pathol J. 2009;25:128–35.

    Article  CAS  Google Scholar 

  15. Oo MM, Lim G, Jang HA, Oh S-K. Characterization and pathogenicity of New Record of Anthracnose on various chili varieties caused by Colletotrichum scovillei in Korea. Mycobiology. 2017;45:184–91.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Saxena A, Raghuwanshi R, Gupta VK, Singh HB. Chilli Anthracnose: the epidemiology and management. Front Microbiol 2016;7.

  17. Agrios G. Plant pathology. 5th ed. Burlington, Ma. USA: Elsevier Academic Press; 2005.

    Google Scholar 

  18. Oo MM, Oh S-K. Chilli anthracnose (Colletotrichum spp.) disease and its management approach. Korean J Agricultural Sci. 2016;43:153–62.

    Article  Google Scholar 

  19. Sun C, Mao SL, Zhang ZH, Palloix A, Wang LH, Zhang BX. Resistances to anthracnose (Colletotrichum acutatum) of Capsicum mature green and ripe fruit are controlled by a major dominant cluster of QTLs on chromosome P5. Sci Hort. 2015;181:81–8.

    Article  CAS  Google Scholar 

  20. Ridzuan R, Rafii M, Ismail S, Mohammad Yusoff M, Miah G, Usman M. Breeding for Anthracnose Disease Resistance in Chili: progress and prospects. IJMS. 2018;19:3122.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kim SH, Yoon JB, Do JW, Park HG. A major recessive gene associated with anthracnose resistance to Colletotrichum capsici in chili pepper (Capsicum annuum L). Breed Sci. 2008;58:137–41.

    Article  Google Scholar 

  22. Pickersgill B. The genus Capsicum: a multidisciplinay approach to the taxonomy of cultivated and wild plants. Biol Zentralbl. 1988;107:381–9.

    Google Scholar 

  23. Lin Q, Kanchana-Udomkan C, Jaunet T, Mongkolporn O. Genetic analysis of resistance to pepper anthracnose caused by Colletotrichum capsici. Thail J Agric Sci. 2002:259–64.

  24. Mahasuk P, Taylor PWJ, Mongkolporn O. Identification of two new genes conferring resistance to Colletotrichum acutatum in Capsicum baccatum. Phytopathology®. 2009;99:1100–4.

    Article  PubMed  CAS  Google Scholar 

  25. Mahasuk P, Khumpeng N, Wasee S, Taylor PWJ, Mongkolporn O. Inheritance of resistance to anthracnose (Colletotrichum capsici) at seedling and fruiting stages in chili pepper (Capsicum spp). Plant Breeding. 2009;128:701–6.

    Article  Google Scholar 

  26. Corak KE, Ellison SL, Simon PW, Spooner DM, Dawson JC. Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection. Crop Sci. 2019;59:1107–21.

    Article  Google Scholar 

  27. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) Approach for High Diversity Species. PLoS ONE. 2011;6:e19379.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD, et al. Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci USA. 2013;110:453–8.

    Article  PubMed  CAS  Google Scholar 

  29. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 2013;14:R55.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Muñoz-Amatriaín M, Cuesta-Marcos A, Endelman JB, Comadran J, Bonman JM, Bockelman HE et al. The USDA Barley Core Collection: genetic diversity, Population structure, and potential for genome-wide Association Studies. PLoS ONE. 2014;9: e94688.

  31. Nordborg M, Tavaré S. Linkage disequilibrium: what history has to tell us. Trends Genet. 2002;18:83–90.

    Article  PubMed  CAS  Google Scholar 

  32. Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y, Martin AR, et al. Genome-wide association studies. Nat Rev Methods Primers. 2021;1:59.

    Article  CAS  Google Scholar 

  33. Cui L, Van Den Munckhof MC, Bai Y, Voorrips RE. Resistance to Anthracnose Rot Disease in Capsicum. Agronomy. 2023;13:1434.

    Article  CAS  Google Scholar 

  34. Kim J-S, Jee H-J, Gwag J-G, Kim C-K, Shim C-K. Evaluation on Red Pepper germplasm lines (Capsicum spp.) for resistance to Anthracnose caused by Colletotrichum acutaum. Plant Pathol J. 2010;26:273–9.

    Article  Google Scholar 

  35. Ro N-Y, Sebastin R, Hur O-S, Cho G-T, Geum B, Lee Y-J, et al. Evaluation of Anthracnose Resistance in Pepper (Capsicum spp.) Genetic Resources. Horticulturae. 2021;7:460.

    Article  Google Scholar 

  36. Lee J, Do JW, Yoon JB. Development of STS markers linked to the major QTLs for resistance to the pepper anthracnose caused by Colletotrichum acutatum and C. capsici. Hortic Environ Biotechnol. 2011;52:596–601.

    Article  CAS  Google Scholar 

  37. Lin SW, Gniffke PA, Wang TC. Inheritance of resistance to pepper anthracnose caused by Colletotrichum acutatum. Acta Hortic. 2007:329–34.

  38. Gao Y-L, Wang B-W, Xu Z-L, Li M-Y, Song Z-B, Li W-Z, et al. Tobacco serine/threonine protein kinase gene NrSTK enhances black shank resistance. Genet Mol Res. 2015;14:16415–24.

    Article  PubMed  CAS  Google Scholar 

  39. Morris ER, Walker JC. Receptor-like protein kinases: the keys to response. Curr Opin Plant Biol. 2003;6:339–42.

    Article  PubMed  CAS  Google Scholar 

  40. Morillo SA, Tax FE. Functional analysis of receptor-like kinases in monocots and dicots. Curr Opin Plant Biol. 2006;9:460–9.

    Article  PubMed  CAS  Google Scholar 

  41. Chen M, Wu J, Wang L, Mantri N, Zhang X, Zhu Z, et al. Mapping and genetic structure analysis of the Anthracnose Resistance Locus Co-1HY in the Common Bean (Phaseolus vulgaris L). PLoS ONE. 2017;12:e0169954.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Martin GB, Brommonschenkel SH, Chunwongse J, Frary A, Ganal MW, Spivey R, et al. Map-Based cloning of a protein kinase gene conferring Disease Resistance in Tomato. Science. 1993;262:1432–6.

    Article  PubMed  CAS  Google Scholar 

  43. Salmeron JM, Oldroyd GED, Rommens CMT, Scofield SR, Kim H-S, Lavelle DT, et al. Tomato prf is a Member of the leucine-rich repeat class of Plant Disease Resistance genes and lies embedded within the pto kinase gene cluster. Cell. 1996;86:123–33.

    Article  PubMed  CAS  Google Scholar 

  44. Warren RF, Henk A, Mowery P, Holub E, Innes RW. A mutation within the leucine-rich repeat domain of the Arabidopsis Disease Resistance Gene RPS5 partially suppresses multiple bacterial and Downy Mildew Resistance genes. Plant Cell. 1998;10:1439–52.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Swiderski MR, Innes RW. The Arabidopsis PBS1 resistance gene encodes a member of a novel protein kinase subfamily: a protein kinase required for disease resistance. Plant J. 2001;26:101–12.

    Article  PubMed  CAS  Google Scholar 

  46. Brueggeman R, Druka A, Nirmala J, Cavileer T, Drader T, Rostoks N, et al. The stem rust resistance gene Rpg5 encodes a protein with nucleotide-binding-site, leucine-rich, and protein kinase domains. Proc Natl Acad Sci USA. 2008;105:14970–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Afzal AJ, Wood AJ, Lightfoot DA. Plant receptor-like Serine Threonine Kinases: roles in signaling and Plant Defense. MPMI. 2008;21:507–17.

    Article  PubMed  CAS  Google Scholar 

  48. Cao A, Xing L, Wang X, Yang X, Wang W, Sun Y, et al. Serine/threonine kinase gene Stpk-V, a key member of powdery mildew resistance gene Pm21, confers powdery mildew resistance in wheat. Proc Natl Acad Sci USA. 2011;108:7727–32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Cao FY, Yoshioka K, Desveaux D. The roles of ABA in plant–pathogen interactions. J Plant Res. 2011;124:489–99.

    Article  PubMed  CAS  Google Scholar 

  50. Hardie DG. Plant protein serine/threonine kinases: classification and functions. Annu Rev Plant Physiol Plant Mol Biol. 1999;50:97–131.

    Article  PubMed  CAS  Google Scholar 

  51. Romeis T. Protein kinases in the plant defence response. Curr Opin Plant Biol. 2001;4:407–14.

    Article  PubMed  CAS  Google Scholar 

  52. Ramonell KM, Somerville S. The genomics parade of defense responses: to infinity and beyond. Curr Opin Plant Biol. 2002;5:291–4.

    Article  PubMed  CAS  Google Scholar 

  53. Sheen J. Discover and connect Cellular Signaling. Plant Physiol. 2010;154:562–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Meyers BC, Shen KA, Rohani P, Gaut BS, Michelmore RW. Receptor-like genes in the Major Resistance Locus of Lettuce are subject to Divergent Selection. Plant Cell. 1998;10:1833–46.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Ellis J, Dodds P, Pryor T. Structure, function and evolution of plant disease resistance genes. Curr Opin Plant Biol. 2000;3:278–84.

    Article  PubMed  CAS  Google Scholar 

  56. Donald TM, Pellerone F, Adam-Blondon A-F, Bouquet A, Thomas MR, Dry IB. Identification of resistance gene analogs linked to a powdery mildew resistance locus in grapevine. Theor Appl Genet. 2002;104:610–8.

    Article  PubMed  CAS  Google Scholar 

  57. Kim J, Lim CJ, Lee B-W, Choi J-P, Oh S-K, Ahmad R, et al. A genome-wide comparison of NB-LRR type of resistance gene analogs (RGA) in the plant kingdom. Mol Cells. 2012;33:385–92.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Jang HA, Oo MM, Kim D-G, Yoon H-Y, Kim M-R, Lee K-S, et al. CC-NBS-LRR, a set of VvCRP markers, can distinguish cultivars with ripe rot resistance to Colletotrichum pathogens in grapevine. Hortic Environ Biotechnol. 2020;61:915–27.

    Article  CAS  Google Scholar 

  59. Li X, Kapos P, Zhang Y. NLRs in plants. Curr Opin Immunol. 2015;32:114–21.

    Article  PubMed  CAS  Google Scholar 

  60. Duxbury Z, Ma Y, Furzer OJ, Huh SU, Cevik V, Jones JDG, et al. Pathogen perception by NLRs in plants and animals: parallel worlds. BioEssays. 2016;38:769–81.

    Article  PubMed  Google Scholar 

  61. Simonich MT. A Disease Resistance Gene in Arabidopsis with specificity for the avrPph3 gene of Pseudomonas syringae pv. Phaseolicola. MPMI. 1995;8:637.

    Article  PubMed  CAS  Google Scholar 

  62. Cai H, Wang W, Rui L, Han L, Luo M, Liu N, et al. The TIR-NBS protein TN13 associates with the CC‐NBS‐LRR resistance protein RPS5 and contributes to RPS5‐triggered immunity in Arabidopsis. Plant J. 2021;107:775–86.

    Article  PubMed  CAS  Google Scholar 

  63. Shahinnia F, Geyer M, Schürmann F, Rudolphi S, Holzapfel J, Kempf H, et al. Genome-wide association study and genomic prediction of resistance to stripe rust in current central and northern european winter wheat germplasm. Theor Appl Genet. 2022;135:3583–95.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. You Q, Zhai K, Yang D, Yang W, Wu J, Liu J, et al. An E3 ubiquitin Ligase-BAG protein Module Controls Plant Innate Immunity and Broad-Spectrum Disease Resistance. Cell Host Microbe. 2016;20:758–69.

    Article  PubMed  CAS  Google Scholar 

  65. Qiu T, Zhao X, Feng H, Yang J, Peng Y-L, Zhao W. OsNBL3, a mitochondria-localized pentatricopeptide repeat protein, is involved in splicing nad5 intron 4 and responding to biotic and abiotic stresses. preprint. Preprints; 2020.

  66. Geddy R, Brown GG. Genes encoding pentatricopeptide repeat (PPR) proteins are not conserved in location in plant genomes and may be subject to diversifying selection. BMC Genomics. 2007;8:130.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Rural Development Administration (RDA). The technique of pepper cultivation. 2016.

  68. Nam MH, Park MS, Lee HD, Yu SH. Taxonomic re-evaluation of Colletotrichum gloeosporioides isolated from Strawberry in Korea. Plant Pathol J. 2013;29:317–22.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Prihastuti H, Cai L, Chen H, McKenzie E, Hyde K. Characterization of Colletotrichum species associated with coffee berries in northern Thailand. Fungal Divers. 2009;39:89–109.

    Google Scholar 

  70. Damm U, Cannon PF, Woudenberg JHC, Crous PW. The Colletotrichum acutatum species complex. Stud Mycol. 2012;73:37–113.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Kim KD, Oh BJ, Yang J. Differential interactions of a Colletotrichum gloeosporioides isolate with green and red pepper fruits. Phytoparasitica. 1999;27:97–106.

    Article  Google Scholar 

  72. Lee H-Y, Ro N-Y, Jeong H-J, Kwon J-K, Jo J, Ha Y, et al. Genetic diversity and population structure analysis to construct a core collection from a large Capsicum germplasm. BMC Genet. 2016;17:142.

    Article  PubMed  PubMed Central  Google Scholar 

  73. De Donato M, Peters SO, Mitchell SE, Hussain T, Imumorin IG. Genotyping-by-sequencing (GBS): a novel, efficient and cost-effective genotyping method for cattle using next-generation sequencing. PLoS ONE. 2013;8:e62137.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j. 2011;17:10.

    Article  Google Scholar 

  75. Cox MP, Peterson DA, Biggs PJ, SolexaQA. At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics. 2010;11:485.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Kim J-E, Oh S-K, Lee J-H, Lee B-M, Jo S-H. Genome-wide SNP calling using next generation sequencing data in Tomato. Mol Cells. 2014;37:36–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012;28:2397–9.

    Article  PubMed  CAS  Google Scholar 

  80. Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42:355–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Yuan Y, Xing H, Zeng W, Xu J, Mao L, Wang L, et al. Genome-wide association and differential expression analysis of salt tolerance in Gossypium hirsutum L at the germination stage. BMC Plant Biol. 2019;19:394.

    Article  PubMed  PubMed Central  Google Scholar 

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The authors would like to acknowledge the National Institute of Agricultural Sciences, RDA, Republic of Korea, for the financial support.


This work was financially supported by Establishment of Utilization System and Advancement in Quality of resources in Seed-Cluster Central Genebank (PJ01604012023) and Screening pepper germplasm for the resistance to phytophthora blight and anthracnose (PJ013251022020), National Institute of Agricultural Sciences, RDA, Republic of Korea.

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Conceptualization: N.R, B-C. K, H-C.K, J.R; data curation: O.H, J-Y.Y, Y-J.L, D-A.K; data analysis: M.H, H-J.W, Y-M.C, J-W.D, J-K.K; writing original manuscript: M.H, N.R, G.W.K; revise the manuscript: M.H, N.R, B-C. K. All authors read and approved the final manuscript.

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Correspondence to Nayoung Ro or Byoung-Cheorl Kang.

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Experimental research and field studies on plants, including the collection of plant material, in this study comply with relevant guidelines and regulations. The formal identification of the pepper species was conducted by Dr. Nayoung Ro. All plant materials have been deposited at the RDA Genebank (Republic of Korea). Each accession is accompanied by a unique introduction number (IT), which is stated in Supplementary Table S4.

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Ro, N., Haile, M., Hur, O. et al. Genome-wide association study of resistance to anthracnose in pepper (Capsicum chinense) germplasm. BMC Plant Biol 23, 389 (2023).

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