Keywords

6.1 Introduction

Pepper consists of a vast variation in morphological traits such as fruit color, size, and shape (Fig. 6.1), fruit quality traits such as metabolic contents of phytonutrients, yield-related traits such as response to biotic and abiotic stresses and shoot growth traits such as flowering time and plant architecture. Mapping the loci governing this variation and identification of the causative genes has been done in the last thirty years by exploiting natural variation that exists within several Capsicum species and in a more limited scale by induced variation mostly for flowering and shoot architecture traits. Early mapping studies in pepper have been summarized by Paran et al. (2006) and by Paran (2013). More recent review focused on mapping of economically important traits in the perspective of translational research (Ramchiary et al. 2014). The present chapter will focus on recent mapping studies that utilized newly developed genomic tools as well as on QTL studies for mapping major plant growth and fruit-related traits.

Fig. 6.1
figure 1

Natural variation of fruit morphology in pepper

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6.2 Ultra-High-Density Maps

With the advent of reference genome sequences of pepper (Kim et al. 2014; Qin et al. 2014) and affordable genomic tools such as single-nucleotide polymorphism (SNP) arrays and low-coverage next-generation sequencing (NGS)-based methods, several high-density maps have been constructed and used for mapping in pepper in recent years which are described below.

In the study of Han et al. (2016), an ultra-high-density bin map was constructed in a Capsicum annuum intraspecific recombinant inbred line (RIL) population from the cross of Perennial and Dempsey. SNP markers were detected by low-coverage (1X) resequencing of the RILs. Over 1 million SNPs were detected between the parents and used to construct a bin map in which all SNPs within a window of defined size were regarded as a bin using the sliding window approach. Using this approach, 2578 bins were used to construct the map that spans 1372 cM. Each of the 12 chromosomes consisted of 154–370 bins per chromosome in an average density of 1 bin/0.5 cM, thus providing a highly saturated map for efficient QTL mapping. A total of 18 plant architecture, leaf, flower, and fruit traits were measured, and a total of 86 QTLs were detected in multiple environments. This study confirmed the results from other QTL analyses for the occurrence of major fruit weight and fruit shape QTLs in chromosomes 2 and 3 (Ben Chaim et al. 2001; Barchi et al. 2009; Chunthawodtiporn et al. 2018).

Two RIL populations, an intraspecific cross of C. annuum Early Jalapeno × CM 334 (NM) and an interspecific Capsicum frutescens BG2814-6 × C. annuum NuMex RNaky (FA) cross, were used to construct ultra-high-density maps (Hill et al. 2015). Polymorphism detection was done using a pepper GeneChip containing 31,196 unigene expressed sequence tags (EST; Ashrafi et al. 2012). In total, 3878 and 16,167 EST markers were mapped in the NM and FA populations, respectively. The markers in the two maps were clustered into 783 and 2105 bins (markers with zero recombination were considered as a single bin) in the NM and FA populations, respectively. Because the maps were based on gene-based markers, they allowed syntenic comparison between pepper and other Solanaceae species and comparative mapping of common traits. Similar to other pepper interspecific crosses, translocation between chromosomes 1 and 8 was observed in the FA map and the high-density map allowed to precisely locate the translocation breakpoint to a specific bin in the chromosome. The use of a high number of common markers in both maps allowed to compare recombination rate and markers’ distortion in an intraspecific cross compared to an interspecific cross.

A more recent SNP Illumina Infinium array consisting of 16,000 SNPs was developed as a public tool to aid pepper breeding and mapping (Hulse-Kemp et al. 2016). The SNPs were selected based on resequencing of 22 pepper lines representing chili and bell-fruited types. The utility of the array was tested by constructing a high-density map from an interspecific cross of C. frutescens Tabasco × C. annuum blocky-type P4. A total of 5546 markers were mapped into 12 linkage groups and arranged in 1362 genetic bins. The present map and the above-mentioned FA map were compared using a common set of 822 markers and found to be highly similar. Important advantages of the Infinium SNP array are low rate of missing data, accurate calling of heterozygotes, and rapid downstream processing of the raw data.

A second Illumina Infinium SNP array was developed and utilized for mapping and diversity analysis in pepper (Cheng et al. 2016). A set of 15,000 SNPs was selected based on resequencing of the cultivars, BA3 and B702, of which approximately 8200 loci were anchored to the Zunla genome assembly (Qin et al. 2014) and scored in various populations. An interspecific cross of BA3 (C. annuum) × YNXML (C. frutescens) was used to construct an F2 mapping population. The population was genotyped with 5828 SNPs and phenotypically scored for erect/pendant fruit orientation controlled by the up locus which has been previously assigned to chromosome 12 (Lefebvre et al. 1995). A major locus, Up12.1, that controls the trait was mapped to a 4.5 Mb region containing 65 genes in the latter chromosome. Furthermore, the SNP array was used to evaluate the genetic diversity of a panel of 399 C. annuum elite and landrace pepper lines originated from China. The relative low genetic diversity level found within this panel indicates the need to broaden the genetic variation of the germplasm used for breeding.

Genomewide association study (GWAS) was conducted in a diverse collection of 94 C. annuum accessions to identify significant genomic regions affecting capsaicinoids content and fruit weight (Nimmakayala et al. 2016). SNPs’ discovery and genotyping were done by the genotyping-by-sequencing (GBS) method (Elshire et al. 2011). A total of 66,960 SNPs were identified among the accessions and mapped to the reference genome of CM334 (Kim et al. 2014), of which a set of 7331 SNPs was used for the QTL study. For both traits, multiple genomic regions with relatively small effects were found to contain significant SNPs. Several significant SNPs were found in candidate genes that have related biological function in other species. For capsaicinoid content, 30 and 56 SNPs were found to be associated with capsaicin and dihydrocapsaicin, respectively; 14 SNPs were common for both traits. Both capsaicinoid content and fruit weight are important traits for pepper domestication. In accordance, many significant SNPs for these traits were located within regions in the genome that exhibits selective sweep signatures.

6.3 Genetic Mapping in C. baccatum

Most mapping populations in pepper have been constructed in C. annuum intraspecific crosses or in interspecific crosses between C. annuum and Capsicum chinense or C. frutescens. Few genetic studies have been performed in the other cultivated species Capsicum baccatum and Capsicum pubescens. C. baccatum consists of both cultivated and wild subspecies and possesses high variability of fruit-related traits and sources for resistance to diseases such as anthracnose and powdery mildew. Therefore, mapping efforts have been performed in the latter species to aid in mapping and introgression of the resistance genes and other traits for use in breeding (Kim et al. 2010; Lee et al. 2010; Eggink et al. 2014; Mahasuk et al. 2016). To determine the genome structure of C. baccatum and its comparison to C. annuum, an intraspecific C. baccatum cross was used to map 395 SNPs identified by resequencing the two mapping parents (Lee et al. 2016). Comparison of the map to the C. annuum reference genome of CM334 revealed translocations between chromosomes 1 and 8 as previously shown in interspecific crosses in Capsicum. Furthermore, additional reciprocal translocations were detected between chromosomes 3 and 5 and between chromosomes 3 and 9. These translocations may act as genetic barriers between C. baccatum and C. annuum and explain the difficulties in crossing these species.

To study population diversity in C. baccatum, a panel of 283 and 94 accessions of C. baccatum and C. annuum, respectively, was genotyped by genotyping by sequencing (GBS; Elshire et al. 2011) and assessed for population structure, linkage disequilibrium (LD) and QTL mapping by GWAS analysis. Approximately 13,000 SNPs were detected in the C. baccatum panel (Nimmakayala et al. 2016). The population was phenotyped for peduncle length that differentiates cultivated and wild accessions. Significant associations were detected in 10 out of the 12 chromosomes, cumulatively explaining 21% of the variation for the trait.

The potential of using C. baccatum for the improvement of fruit quality was tested by introgressing multiple chromosome segments into C. annuum backgrounds. Multi-parent backcrossing coupled with embryo rescue allowed the construction of BC2S1 population from an interspecific cross of C. annuum and C. baccatum which was evaluated for attributes of fruit quality and subjected for QTL mapping (Eggink et al. 2014). Fruit phenotyping included volatile profiling, chemical composition, morphology, and sensory attributes. Subsequently, near-isogenic lines were developed to confirm QTLs detected in the BC2S1 population. The QTL with the strongest effect (LOD = 40.1) was detected for immature fruit color. This QTL likely corresponds to GOLDEN2-like (CaGLK2) that was identified as controlling chlorophyll content in the immature fruit (Brand et al. 2014). Sensory and metabolomic analyses allowed the identification of a QTL allele originated from C. baccatum that confers a strong effect on volatile content and flavor in chromosome 3. Since this QTL is mapped to a small introgression without apparent linkage drag, it is an important candidate for use in breeding for improved flavor. Additional potential sources for improved flavor are a QTL for increased content of sugars in chromosome 3 that does not coincide with reduced fruit size. Furthermore, QTLs for increased content of terpenoids were detected in chromosomes 1 and 10; their phenotypic effect on plant adaptation is yet to be determined.

6.4 QTLs for Economically Important Traits

High yield, early flowering, biotic and abiotic stress tolerance, enriched metabolite content, desired fruit size and shape and reduced postharvest water loss have been major targets for pepper improvement mostly by classical breeding efforts. More recently, molecular breeding techniques such as QTL mapping and introgression, identification of causative genes, and molecular marker development have been utilized for breeding enhancement. A compilation of QTL data for various economical traits associated with plant and fruit growth is presented in Table 6.1, and the major results are summarized in the following paragraphs.

Table 6.1 List of QTLs for plant growth and fruit traits in pepper
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6.4.1 Plant Growth

Shoot architectural components such as the length of the primary stem, internode length, leaf size, degree of lateral branching, and timing of flowering initiation determine the overall plant growth. QTLs for plant development in a cross of Yolo Wonder × Criollo de Morelos 334 RIL population were identified by Barchi et al. (2009). Colocalization of QTLs affecting flowering time, primary axis (stem) length, internode length, axis growth speed, and internode growth time was observed in chromosomes P2, P4, P9, and LG47 which may indicate pleiotropic effects of these QTLs. Twelve QTLs for several plant growth traits were detected in an intraspecific C. annuum doubled haploid (DH) population, explaining 14–34% of the phenotypic variability for various plant architectural traits (Mimura et al. 2010). The strongest QTL effect was detected for flowering time in LG8 that was not conclusively assigned to a specific chromosome. QTLs for pepper growth traits such as leaf size, plant height, flowering time, days to breaker fruit stage, and branching were also mapped in an RIL population from an interspecific cross of 2814-6 (C. frutescens) × NuMex RNaky (C. annuum). A total of 23, 15 and 17 QTLs mostly with minor effects were identified for leaf traits, floral traits, and whole plant morphology, respectively (Yarnes et al. 2012). Additionally, a major QTL for plant height was reported in LG5 (Dwivedi et al. 2015), and two major QTLs for lateral branch number were reported in chromosome 2 (Han et al. 2016).

Several studies have focused specifically on mapping flowering time QTLs. A major QTL for flowering time was identified in chromosome 2 in a cross of C. annuum blocky-fruited type accession (early flowering) and C. annuum var. glabriusculum wild accession (late flowering) (Fig. 6.2, Borovsky et al. 2015). This QTL was co-localized with the flowering suppressor gene CaAPETALA2 that is disrupted in the EMS (ethylmethane sulfonate)-induced early flowering mutant. The same genomic region has been detected as a major flowering time QTL in an independent study in a C. annuum × C. frutescens cross (Tan et al. 2015). Additional five minor QTLs were detected in the latter study. In addition to these QTLs, several flowering promoter and suppressor genes that also control shoot architecture were identified by using EMS-induced mutants including CaJOINTLESS (Cohen et al. 2012), CaBLIND (Jeifetz et al. 2011), CaS (Cohen et al. 2014) and CaFASCICULATE (Elitzur et al. 2009).

Fig. 6.2
figure 2

Reprinted from Borovsky et al. (2015) by permission

QTL mapping of flowering time in a cross of C. annuum accession PI 527325 (USDA early) and C. annuum var. glabriusculum wild accession PI 511887 (USDA late). a Pictures of the early- and late-flowering parents used for QTL mapping. b Distribution of flowering time in the F2 population. c Interval QTL mapping of flowering time in a region containing CaAP2 in chromosome 2.

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Trichome density on the plant stem and flower calyx varies among accessions and are often present in wild accessions and landraces. QTL mapping for the trait was performed in intraspecific C. annuum populations involving CM334 as a trichome-rich parent. A major QTL, Ptel1, controlling trichome density in the main stem and in the calyx was detected in LG24 corresponding to Chromosome 10 (Kim et al. 2011). Additional 10 minor QTLs were detected in other chromosomes. Recently, an RIL population from a cross of CM334 × Maor was analyzed for several fruit and growth traits including the degree of stem pubescence that was scored by a visual scan (Chunthawodtiporn et al. 2018). Three QTLs were detected in chromosomes 2, 10, and 11, the QTL in chromosome 10 having the largest effect on the trait which likely corresponds to Ptel1. Two candidate genes, TRICHOME BIREFRINGENCE-LIKE 5 and a C2H2 zinc-finger transcription factor which are putatively involved in the formation of trichomes based on Arabidopsis studies, were located in the vicinity of the QTL.

6.4.2 Fruit Traits

6.4.2.1 Fruit Size and Yield

Fruit size/weight QTLs have been identified in multiple studies (Table 6.1 and summarized by Paran and van der Knaap 2007; Hill et al. 2017). Several QTL studies in mapping populations consisting of crosses of a common blocky-fruited cv. Maor (C. annuum) with small-fruited C. annuum, C. frutescens, and C. chinense accessions have been carried out. Two major QTLs for fruit weight, fw2.1 and fw4.2, are conserved in the three Capsicum species. fw2.1 had the most significant effect in multiple populations (Ben Chaim et al. 2001; Rao et al. 2003; Zygier et al. 2005). A putative tomato orthologous fruit weight QTL, fw2.2, is located in a syntenic region in chromosome 2 (Frary et al. 2000). The gene that underlies fw2.2 in tomato is CELL NUMBER REGULATOR (CNR). However, the syntenic region in pepper consists of multiple genes associated with organ size regulation including the ortholog of OVATE, a fruit shape gene in tomato (Hill et al. 2017). Therefore, high-resolution mapping will be required to precisely map this QTL and identify the underlying gene.

Another possible orthologous fruit weight QTL in pepper and tomato is fw3.2 that is associated with the gene KLUH, a P450 coding enzyme in both species (Chakrabarti et al. 2013). A cluster of minor QTLs for fruit weight, fruit shape, fruit diameter, and pericarp thickness is located in P11 and P12 (Barchi et al. 2009). 16 significant SNPs associated with fruit weight were identified in a GWAS study of 94 accessions (Nimmakayala et al. 2016). Except for chromosome 7, all other chromosomes had at least one significant SNP. Out of the 16 SNPs, seven were located in known genes that control organ size such as STYLOSA, FASCIATED, WUSCHEL, and CLAVATA1.

The yield of pepper is affected by parameters such as number of fruits per plant, fruits weight, and total fruit yield. QTL mapping for these traits was performed in a C. annuum intraspecific RIL population (Dwivedi et al. 2015). A total of 10 QTLs were detected for yield-related traits. Colocalization of five QTLs in chromosome 2 (Qtofw.iivr-2.1, Qtfw.iivr-2.1, Qfw.iivr-2.1, Qnfp.iivr-2.1, and Qpt.iivr-2.1) with significant additive effects was identified which might be due to the linkage of different QTLs or pleiotropic effects of the same genes. Other QTL studies for fruit-related traits in pepper reported clustering of QTLs for fruit traits in the same region of chromosome 2 (Ben Chaim et al. 2001; Rao et al. 2003; Zygier et al. 2005; Barchi et al. 2009; Chunthawodtiporn et al. 2018).

Pericarp thickness is positively correlated with fruit weight (Ben Chaim et al. 2001), and therefore, QTLs for both traits are often located in common genomic positions. Two major QTLs for pericarp thickness were identified in different intraspecific populations of C. annuum in chromosomes 2 and 3 (Qpt.iivr-2.1, 3.1). Qpt.iivr-2.1 is located in the same genomic region in chromosome 2 that contains a QTL for fruit weight (Dwivedi et al. 2015; Naegele et al. 2014). Several linked QTLs for pericarp thickness were identified in chromosome 4 in a cross of C. annuum and C. frutescens (Yarnes et al. 2012). Several minor QTLs for pericarp thickness were also identified by Barchi et al. (2009).

An additional factor that may be associated with fruit size is locule number. The locule number locus, lcn2.1, in tomato affects fruit size via changing carpal numbers (Lippman and Tanksley 2001). Only limited data is available for mapping this trait in pepper. Low positive correlation between fruit weight and locule number was reported by Barchi et al. (2009). Few minor QTLs for locule number were identified in this cross; the strongest QTL being NloLG25.1.

6.4.2.2 Fruit Shape

Despite the large variation in fruit shape that exists in pepper, only few shape attributes were studied that include fruit width, fruit length, and fruit shape index (length/width). The two most significant QTLs for fruit shape index were fs3.1 and fs10.1 that have been identified as controlling fruit elongation (Ben Chaim et al. 2001, 2003a, b; Borovsky and Paran 2011). Both QTLs control most of the trait variation in Capsicum, explaining 67 and 44% of the phenotypic variation for fs3.1 and fs10.1, respectively (Ben Chaim et al. 2003a, b). Major QTLs regulating fruit shape variation within C. annuum have been identified in multiple populations in chromosomes 1, 3, and 4 (Ben Chaim et al. 2001; Barchi et al. 2009; Naegele et al. 2014; Dwivedi et al. 2015; Han et al. 2016). Fruit shape QTLs were also identified in interspecific crosses between C. annuum and C. chinense in chromosomes 1, 3, 4, and 10 (Ben Chaim et al. 2003a; Zygier et al. 2005; Borovsky and Paran 2011). Four major QTLs in chromosome 4 and one QTL in each of the chromosomes 3, 4, and 11 were identified in a cross of C. annuum and C. frutescens (Yarnes et al. 2012). Fruit shape QTLs in chromosomes 10 and LG1_8 have been identified in a cross of C. annuum and C. baccatum (Eggink et al. 2014). To-date, none of the genes underlying natural fruit shape variation in pepper has been identified. A pepper homolog of OVATE, a tomato fruit shape QTL (Liu et al. 2002), was shown to be associated with fruit shape variation by down regulation using virus-induced gene silencing (VIGS; Tsaballa et al. 2011), indicating the possibility that the pepper gene may regulate fruit shape variation in natural populations.

6.4.2.3 Fruit Color and Chlorophyll Content

Since the fruit pigments are associated with fruit color, nutrition, and flavor, pepper fruit color is important to the breeder and to the consumer. The color of the ripe fruits is determined primarily by carotenoids and that of the immature fruits by anthocyanins and chlorophyll. Mutations in genes in the carotenoid biosynthesis pathway result in change of color from red to yellow or to orange (Popovsky and Paran 2000; Thorup et al. 2000; Huh et al. 2001; Borovsky et al. 2013). Additional pepper fruit color variation is associated with chlorophyll catabolism, anthocyanin accumulation and chloroplast compartment size (Borovsky et al. 2004; Borovsky and Paran 2008; Pan et al. 2013).

Both qualitative and quantitative variation in fruit pigment content exists, however, its genetic control in largely unknown. QTL analysis for chlorophyll content in the immature fruit in a cross between a dark green-fruited C. annuum inbred 1154 and a light green-fruited C. chinense accession PI 152225 revealed the presence of two major QTLs, pc8.1 and pc10.1, that control the trait (Fig. 6.3, Brand et al. 2012). One of the QTLs, pc10, was found to correspond to the pepper homolog of GOLDEN2-like transcription factor (GLK2) that controls chloroplast compartment size in the immature fruit (Brand et al. 2014). Major QTLs that regulates unripe fruit color were identified in chromosome 10 in an interspecific cross of C. annuum and C. baccatum and in an intraspecific C. annuum cross which likely correspond to pc10.1 (Eggink et al. 2014; Han et al. 2016).

Fig. 6.3
figure 3

Reprinted from Brand et al. (2012) by permission

Position of QTLs for pigment content in the F2 of 1154 × PI 152225. The most significant markers at the two QTLs are boxed. Numbers on the horizontal axis represent LOD values.

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6.4.2.4 Metabolites Content

A QTL study for metabolites content associated with flavor was performed in an interspecific cross between C. annuum and C. baccatum (Eggink et al. 2014). A strong effect on flavor was found in a small introgression of chromosome 3. This QTL explained 38.7% of the variation for odor and was associated with an intense odor of C. baccatum. NILs for this QTL showed an increase in intensity of the compound 6-methyl-4-oxo-5-heptenal and decrease of the compound (Z)-butanoic acid 3-hexenyl ester and 2-isobutyl-3-methoxypyrazine. Additional minor QTLs associated with sensory attributes were detected in different chromosomes. Two major QTLs that control variation in biochemical composition were identified in LG1_8 and LG10.1. A significant QTL was found for brix, glucose, fructose, malate, and citrate in LG1_8 that coincided with a major fruit size QTL. Because the C. baccatum allele was associated with an increased metabolite content and decreased fruit size, it was concluded that the increase in the metabolites concentration was a result of smaller fruits and not an effect of increased metabolism. In contrast, a QTL for increased BRIX value was detected in LG3 which was unaffected by fruit size. 46% of the variation in the metabolic content between the two parents was due to a group of 15 terpenoids controlled by QTLs in LG10.1 and LG1.

The major QTL, pc8.1, affecting chlorophyll content in the immature fruit was also associated with an increase of other metabolites accumulated in the chloroplast such as tocopherols and carotenoids (Brand et al. 2012). This association is likely due to the effect of the QTL in modulating chloroplast compartment size. A QTL study was performed to dissect the molecular basis for variation in flavonoid content in a cross between C. annuum and C. chinense (Wahyuni et al. 2014). LCMS metabolic profiling of semi-polar metabolites allowed the identification of 52 annotated metabolites. A total of 279 mQTL were detected; however, most QTLs were clustered in few chromosomal regions creating QTL hotspots in chromosome 9. Furthermore, genes controlling flavonoids biosynthesis were mapped and some exhibited colocalization with mQTLs in chromosomes 1, 6, and 9.

6.4.2.5 Pungency

Pungency in pepper fruit is due to the unique accumulation of alkaloid compounds termed capsaicinoids. A single dominant gene at the Pun1 locus in chromosome 2 is required for the production of capsaicinoids (Stewart et al. 2005). In addition for the qualitative difference in the presence or absence of pungency, large variation in the capsaicinoid content exists which result in cultivars with varying degree of pungency. Several studies on QTL mapping for capsaicinoid content have been performed in diverse genetic backgrounds. Twelve QTLs were identified in six chromosomes by Yarnes et al (2012). Several QTLs were detected in chromosome 4, similar to Ben Chaim et al. (2006). A large effect QTL detected by bulked segregant analysis (BSA) was mapped in chromosome 7 (Blum et al. 2003). 14 significant SNPs scattered throughout the genome were associated with capsaicin and dihydrocapsaicin content in a GWAS study of 94 accessions (Nimmakayala et al. 2016). A QTL mapping study was also conducted in a cross of one of the hottest chili peppers ‘Bhut Jolokia’ (Lee et al. 2016). Two QTLs for capsaicin content were detected in chromosomes 3 and 6, while two different QTLs for dihydrocapsaicin content were detected in chromosome 2. A study conducted using a diversity panel of 40 lines consisting of 21 pungent and 19 non-pungent lines revealed several fixed regions for non-pungency (NP). Out of the 17 fixed regions for NP, 14 are overlapped with QTLs for fruit size or shape. The most significant fixed regions were located in chromosome 2, spanning PUN1 and CaOVATE. In addition to PUN1, six genes regulating capsaicin biosynthesis were located in NP regions in chromosomes 1, 3, and 6, implicating their importance in breeding of non-pungent cultivars (Hill et al. 2017). The large variation in QTLs positions in different genetic backgrounds implicates the complexity of this trait and that markers used for selection in breeding programs will have to be developed in a genotype-specific manner.

6.4.2.6 Fruit Postharvest Water Loss

Fruit postharvest water loss (PWL) results in reduction in the overall fruit quality and thus affects the marketing of peppers. Based on screening of a wide germplasm for variation in the trait, two parents that exhibited large difference in PWL were selected for QTL mapping in an interspecific cross between C. annuum and C. chinense. Two linked QTLs, PWL10.1 and PWL10.2, were identified for fruit PWL in chromosome 10 in multiple generations (Popovsky-Sarid et al. 2017). Several genes associated with cuticle biosynthesis, cell wall metabolism, and fruit ripening were identified as QTL candidates using transcriptome analysis of near-isogenic line (NILs) that differ for the QTL.

6.5 Concluding Remarks and Future Prospects

In recent years, numerous QTL studied have been conducted for economically important traits in pepper which can be exploited for introgression of beneficial QTL alleles into elite lines. The deciphering of the pepper genome sequence allowed searching for candidate genes that co-localize with the QTLs; however, only in few cases the causative genes underlying the QTLs were unequivocally identified. High-resolution mapping, expression studies, and functional assays for candidate genes will be required to expedite the discovery of such genes. One obstacle that hinders functional genomic studies in pepper is the lack of an efficient transformation system. The recent report on successful application of pollen-mediated transformation in pepper (Zhao et al. 2017) may open the way for a large-scale use of genome editing techniques in this species.