*3.3. Construction of Linkage Mapping*

In order to construct a pepper genetic linkage map, 1639 SNPs were utilized for linkage grouping using the SNP matrix from GBS analysis. As such, the linkage map consisted of 1550 SNP markers on 16 linkage groups (LG) (Table 2 and Figure 2). The linkage map covered a total length of 828.449 cM with an average distance of 0.676 cM between adjacent markers (Table 2 and Figure 2). Next, LG\_04 showed the maximum lengths, which were 78.045 cM (chromosome 04) in the largest LG, whereas LG\_05-2 showed the minimum lengths, which were 15.952 cM (chromosome 05) in the smallest LG (Table 2). The number of mapped SNPs per chromosome ranged from the minimum 11 in chromosome 10 to the maximum 172 in chromosome 03, with an average number of 96.875 SNP markers per LG (Table 2 and Figure 2). Moreover, the correlation coefficient between genetic and physical maps was estimated among the 16 linkage groups. Chr.07 exhibited the highest correlation coefficient of 0.876, and the average correlation coefficient was 0.670.

**Table 2.** Summary of the pepper genetic linkage map constructed using SNP markers derived from genotyping-by-sequencing (GBS) analysis for bacterial wilt (BW) disease resistance. The higher Pearson's correlation coefficient indicates the closer correlation.


**Figure 2.** *Cont*.

**Figure 2.** Distribution of single-nucleotide polymorphism (SNP) markers on 16 linkage groups of F2 pepper population. The pepper genetic linkage map consisted of 1550 SNP markers derived from genotyping-by-sequencing (GBS) analysis. SNP names were shown on the right side of the linkage map and genetic distances (cM) between SNPs on the left.

#### *3.4. QTL Analysis for Bacterial Wilt (BW) Resistance*

In order to analyze significant quantitative trait loci (QTL) regions, the 1000 permutation was tested at *p* < 0.05, and the LOD threshold was calculated as 5.5 by QTL cartographer. Notably, the LOD values ranged from 5.69 to 7.06, which was detected in chromosome 01 via CIM (Figure 3A). On the basis of the threshold levels, we designed one QTL, *pBWR-1*, named the pepper Bacterial Wilt Resistance-1 on chromosomes 01 (Figure 3B). In addition, the significant LOD ≥ 5.5 regions identified at the *pBWR-1* QTL region were within LG\_01-1 on chromosome 01. The *pBWR-1* QTL was located between the ch01\_47793963 and ch01\_161127926 markers on chromosome 01 with twelve markers (Figure 3B and Table 3). The explained phenotypic variance of the QTL was ranged from 20.13 to 25.16% (Table 3).

**Figure 3.** (**A**) Quantitative trait loci (QTL) plot. QTL associated with bacterial wilt (BW) disease resistance in the F2 population derived from the parental lines of KC352 and 14F6002-14 in the linkage groups obtained via CIM. The LOD threshold is indicated by the red-colored horizontal line. (**B**) Physical map of chromosome 01 with SNP markers used for the mapping of BW resistance locus as shown in Figure 2. The QTL positions (LOD ≥ 5.5) and SNP markers of BW resistance are indicated with a black bar combined with red-colored vertical lines above the linkage map.


**Table 3.** Summary of significant QTLs (LOD ≥ 5.5) regions with composite interval mapping (CIM) analysis.

<sup>a</sup> R2, proportion of phenotypic variance explained by a major QTL; <sup>b</sup> |d/a|, estimation of gene action; A, (additive effect) 0–0.20; PD, (partial dominance) 0.21–0.80; <sup>c</sup> D, (dominance) 0.81–1.20; OD, (overdominance) >1.20. <sup>d</sup> Number of gene was selected with the 1 Mb to the left and right of the corresponding marker.

#### *3.5. Prediction and Annotation of Candidate Genes*

In order to identify candidate genes within the major QTL region, the number of genes was selected with the 1 Mb to the left and right of the corresponding markers, and a total of 31 candidate genes were annotated on the basis of CM334 reference genome, Swiss-Prot, and the NCBI non-redundant protein (NR) databases (Table 4). In addition, functional classification of 31 predicted genes was further analyzed along with the Kyoto Encyclopedia of Genes Genomes (KEGG) pathway and Gene Ontology (GO) term (Table 4). As such, the analysis of GO term enrichment identified one gene (CA01g20110) annotated with "defense response" (GO: 0006952), and the KEGG analysis identified one gene (CA01g20130) with "plant–pathogen interaction" (KO: 13457). Overall, four of the 31 candidate genes were assigned to defense-associated genes; CA01g18650 in ch01\_156505108 marker and CA01g20130 (putative disease resistance protein RPM1), CA01g20110 (Thaumatin-like protein), and CA01g20140 (NB-ARC domain-containing protein) in ch01\_161127926 marker were predicted as disease-resistance proteins. Taken together, the annotated four diseaseresistance/defense-associated genes would be crucial candidate genes for *pBWR-1* QTL in the present study.



**Table 4.** *Cont.*


**Table 4.** *Cont.*

#### **4. Discussion**

BW is one of the most destructive pepper diseases worldwide, leading to the reduction of yield and production in pepper cultivation [16,63]. 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 [18–20]. Thus, it is indispensable to breed resistant pepper cultivars against BW. Although molecular marker-assisted selection (MAS) for BW resistance can contribute to a rapid selection of BW-resistant breeding in pepper crops, a few studies have determined QTL regions [17,36,62]. In this study, we performed QTL analysis to develop molecular markers that are associated with BW resistance in pepper (*Capsicum annuum*) by evaluating the 94 F2 recombinant lines obtained by a cross between a resistant and a susceptible parental line. We first constructed a genetic linkage map using GBS approach and identified significant QTL regions on chromosome 01 associated with BW resistance.

#### *4.1. Construction of Pepper Genetic Map*

GBS is a genome-wide genotyping, powerful, and straightforward approach that takes advantage of enzyme-based genome analysis, thereby conferring a rapid and cost-effective analysis of the huge and complex genome in organisms. The GBS tool has been widely utilized in genotyping segregated plants via the combination of a high-throughput nextgeneration sequencing (NGS) technology to generate multiplexed libraries using barcoded adapters [64,65]. With the application of GBS analyses, it has been reported that a large amount of barely SNPs are produced, and ≥34,000 SNPs are mapped onto its reference genome sequences, and ≥20,000 wheat SNPs are constructed onto its reference map [65]. Moreover, the method has identified 9998 SNPs and 64,754 SNPs located on the peach and pea genomes, respectively [66,67]. Notably, recent studies have explored and evaluated 1,399,567 SNP in onion using a GBS library [50], and a total of 91,132 raw SNPs were uncovered via a QTL study involved in flowering time in perilla [49]. In addition to this, current applications of GBS analysis have exhibited a total of 22,446 SNPs for QTL mapping of the resistance against the cucumber mosaic virus in a cucumber crop as well as a total of 66,405 SNPs for *Phytophthora capsici* resistance in a pepper crop [48,59]. In the present study, a total of 628,437 raw SNPs were identified and successfully genotyped with 94 F2 offspring using GBS. Around 108 Gbp of raw data (Tables S1 and S2) and a total 1639 SNPs were finally produced, and the SNP markers were shown with genome-wide distribution, covering the whole pepper genome (Figure 2). A total of 1550 SNP markers were ultimately constructed on a genetic linkage map, which comprised 16 LGs, including one linkage group on chromosome 03, 04, 06, 07, 08, 09, 11, and 12 as well as two linkage groups on chromosome 01, 02, 05, and 10, respectively (Figure 2 and Table 2). In general, SNP markers used for genetic mapping are based on the polymorphic markers of the parent. When the polymorphism of the parent is absent in a large region within the middle of the genome, separating two linkage groups on one chromosome can often be produced although the genetic map is physically one chromosome. In particular, the phenomena often occur when working with breeding lines. Indeed, previous reports have shown that Yellow lupin (*Lupinus luteus* L.) possesses 26 chromosomes, but 40 linkage groups were constructed for QTL mapping via NGS approaches [68], and a linkage group of LG01 was divided into two LGs on chromosome 01 in perilla via GBS [49]. Recently, it has been determined that linkage map is constructed with the resistance trait of powdery mildew from F5 pepper population via GBS. The LG07 is separated into two linkage groups on chromosome 07 [69].

#### *4.2. Genetic Inheritance of pBWR-1 QTL on Chromosome 01*

As mentioned above, it has been reported that the genetic analysis and identification of resistance genes play a crucial role in the field of crop breeding against BW [24,30,31,34–36,38,63]. Nonetheless, the genetic inheritance is poorly understood in the involvement of BW resistance in pepper crops, and the mechanism of genetic inheritance is still unclear since different pepper sources result in different values of BW resistance. In our results, we

evaluated 49 F2 offspring as the DI value 4 (the most severe symptoms, 76 to 100% wilted leaves), whereas 14 plants were evaluated as DI value 1 (no visible symptoms) with the comparison of the parental lines after BW inoculation (Figure 1). We observed susceptible plants 3.5 times more than resistant plants in the F2 population, implying that BW resistance might be a partially recessive trait in the pepper lines used for our experiment. In similar line with our result, the genetic inheritance of BW resistance has been unraveled, and the resistance homozygous recessive (rr) allele was identified using F2 populations derived from a cross between resistant Anugraha and susceptible Pusa Jwala of near-isogenic lines (NILs) in pepper crops [70]. On the contrary, it has been determined that the inheritance action of BW resistance is involved in an incomplete dominance with more than two BWresistance genes using the progeny derived from a cross between the *capsicum* Mie-Midori (resistant line) and the *capsicum* AC2258 (susceptible line) [29]. It has been also studied that the BW resistance is governed by two to five genes using the progeny derived from a cross between the PM687 (resistant line) and the Yolo (susceptible line) [34]. In addition to this, researches have demonstrated that the disease severity in F1 hybrids is close to or lower than the generation-means of mid-parent values [29,49], and the progeny derived from BVRC 1 (resistant line) and BVRC25 (susceptible line) [36] as well as from MC4 (resistant line) and Subicho (susceptible line) exhibited the association of more than two resistance genes against *R. solanacearum*, indicating that the BW resistance would be involved in a partial dominance effect [71]. Although the complex mechanism still needs to be elucidated, the contradictory findings might result from diverse factors, including the different pepper sources of breeding lines, the different inoculation methods, the bacterial isolates, the different criteria using DI calculation, and the different environmental growth factors. It is, therefore, of our interest to further study the complex action of genetic inheritance in the pepper source used for the experiment with the comparison of other BW-resistant lines.

#### *4.3. Detection of Major QTL Controlling Resistance to R. solanacearum*

Multiple management strategies have been actively developed and applied to control the BW disease. However, the effects have been limited and insufficient for the control of destructive BW disease owing to the different pepper sources, the bacterial isolates, and different inoculation methods as mentioned above [26–28,63]. Besides, high diseaseresistance phenotype is not always associated with a good performance of horticultural traits, such as good fruit shape, fruit size, fruit yield, and fruit quality [71]. Thus, it is crucial to understand the genetic basis for resistance to BW disease to utilize pepper breeding programs using MAS, thereby ultimately integrating BW resistance with desirable traits during a breeding process [47,48,71]. In previous studies, the pepper accessions of LS2341 and BVRC1 have been determined with a *Bw1* QTL and a major *qRRs-10.1* QTL underlying on chromosome 08 and 10, respectively [35,36]. Initially, Mimura et al. (2009) reported that the CAMS451 marker of *Bw1* QTL in linkage group 11 (LG11) was located on chromosome 01 with the comparison of a LG01 on chromosome 01 of SNU3 map, which was integrated by a genetic linkage map of an interspecific cross between *C. annuum* and *C. chinense* [35]. However, the group reported the linkage group was shifted to chromosome 08 from 01 again [72]. The current studies suggest that the LG 11 is possessed by chromosome 08 rather than 01 in *C. annuum*. Mathew (2020) recently reported that the pepper CAMS451 marker of *Bw1 QTL* lies on chromosome 08 (position: 122704651-124710667) and has annotated 44 defense-associated genes from 1 Mb upstream and downstream of the marker [73]. In addition to this, another research on the BW resistance against *R. solanacearum* demonstrated that a major *qRRs-10.1* QTL region is located on chromosome 10 (position: 56910000- 69110000, 111090000-183670000) in *C. annuum*. Interestingly, 54 genes were annotated, and five putative R genes lie on the regions of 193.4–196.3 Mb, which are nearly closed to the markers of ID10-194305124 and ID10-196208712 [36]. In contrast, in the present study, we identified the major *pBWR-1* QTL region on chromosome 01 via a CIM method using the GBS analysis on the basis of the threshold levels (Figure 3), which exhibited the remarkable LODs from 5.69 to 7.06 in LG\_01-1 (Table 3). Importantly, our finding shows that the

major *pBWR-1* QTL region underlies on chromosome 01 (position: 47793907–61496208, 130789593–161127926) with 12 markers, which encode 31 candidate genes in the region (Tables 3 and 4). These discrepancies of previous and our current result on the genome loci would result from a variety of materials and methods as aforementioned reasons.
