Genome-wide association study and candidate gene identification for agronomic traits in 182 upward-growing fruits of C. frutescens and C. annuum

Pepper agronomic traits serve as pivotal indicators for characterizing germplasm attributes and correlations. It is important to study differential genotypic variation through phenotypic differences of target traits. Whole genome resequencing was used to sequence the whole genome among different individuals of species with known reference genomes and annotations, and based on this, differential analyses of individuals or populations were carried out to identify SNPs for agronomic traits related to pepper. This study conducted a genome-wide association study encompassing 26 key agronomic traits in 182 upward-growing fruits of C. frutescens and C. annuum. The population structure (phylogenetics, population structure, population principal component analysis, genetic relationship) and linkage disequilibrium analysis were realized to ensure the accuracy and reliability of GWAS results, and the optimal statistical model was determined. A total of 929 SNPs significantly associated with 26 agronomic traits, were identified, alongside the detection of 519 candidate genes within 100 kb region adjacent to these SNPs. Additionally, through gene annotation and expression pattern scrutiny, genes such as GAUT1, COP10, and DDB1 correlated with fruit traits in Capsicum frutescens and Capsicum annuum were validated via qRT-PCR. In the CH20 (Capsicum annuum) and YB-4 (Capsicum frutescens) cultivars, GAUT1 and COP10 were cloned with cDNA lengths of 1065 bp and 561 bp, respectively, exhibiting only a small number of single nucleotide variations and nucleotide deletions. This validation provides a robust reference for molecular marker-assisted breeding of pepper agronomic traits, offering both genetic resources and theoretical foundations for future endeavors in molecular marker-assisted breeding for pepper.


Diversity assessment of major agronomic traits
The 26 agronomic traits of peppers investigated in this study were classified into 12 quality traits and 14 quantitative traits (Tables S1-S3).Plant height (unit: cm) has a maximum value of 122.20 and a minimum value of 40.50; weight per fruit (unit: g) maximum value is 5.46, the minimum value is 0.23; longitudinal diameter of fruit (unit: mm) maximum value is 83.87, the minimum value is 15.48.The results showed that the Shannon-Wiener index for qualitative and quanlitative traits ranged from 0.95 to 2.07 and 0.03 to 0.95, (average 0.49), with mean values of 1.82 and 0.49, respectively.Except for locule number, the Shannon-Wiener index for quantitative traits surpassed that of qualitative traits, signifying a more extensive diversity in quantitative traits.The Correlation analysis of the 14 quantitative traits data indicated that there are 57 pairs of traits with extremely significant www.nature.com/scientificreports/correlation between C.frutescens and C. annuum resources, of which 39 pairs are positively correlated and 18 pairs are negatively correlated.Notably, the maximum correlation coefficient between weight per fruit and longitudianl diameter of commercial fruits is 0.93 (Fig. 1).Also, the Lowest correlation coefficient of 0.001 was found between weight per fruit and plant height.(Table S4).The majority of the 26 agronomic traits demonstrated substantial phenotypic variations, affirming the suitability of the population for GWAS analysis.

Whole genome resequencing analysis
A total of 107 escaped peppers sample and 75 pod pepper samples underwent re-sequencing, revealing a GC content ranging from 34.01 to 35.36%.The Q20 proportion fell between 96.14 and 98.6%, while Q30 exceeded 90%.Mapping rates varied from 96.85 to 99.53%, with an average of 99.02%, and an average sequencing depth of 9.62X.The average 1X coverage (minimum 1 base coverage) was 90.47%, and the average 5X coverage (minimum 5 base coverage) was 76.79% (Table S5).These results attest to the high sequencing and gene mapping quality, establishing a robust foundation for subsequent analyses.
Employing the Genome Analysis Toolkit (GATK) for SNP detection yielded a total of 64,110,473 identified SNPs.The distribution of these variations was visually represented using a circos plot (Fig. 2).Uniform distribution of SNPs and Indels across chromosomes underscired indicating the reliability of the SNP and Indel data.

Population structure and linkage disequilibrium analysis
The population structure analysis yields insights into the subdivision of the population into distinct subgroups.Integrating population structure analysis as a covariate proves effective in mitigating false positives resulting from population structure in GWAS analysis.Assuming the population can be divided into K = 2 ~ 10 subgroups, when K = 10 (Figure S2, Table S6), the lowest cv value is observed, signifying the optimal subdivision of all pepper germplasm resources into 10 groups (Fig. 3d).Conversely, at K = 1, the population remains undivided; at K = 2, clear stratification emerges, indicating the existence of two subgroups within the 182 pepper resources under study.Subgroup 1, denoted in red, comprises 107 materials, all of C.frutescens,while Subgroup, represented in blue, consists of 75 materials, exclusively of C.annuum.This aligns with the results of the phylogenetic tree and principal component analysis.
By using the NJ method to construct a phylogenetic tree, it is possible to more intuitively present the differences in varieties and the distance of genetic relationships within a population.The phylogenetic tree shows that the 182 individuals in the pepper population in this study can be roughly divided into two branches, namely group 1 and group 2, which are consistent with the results of population structure analysis (Fig. 3b).Further systematic evolutionary analysis of 107 samples of Capsicum frutescens from Hainan (Fig. 3c) reveals pronounced differentiation among different cities and counties.For instance, peppers from Baisha constitute the third branch, while those from Haikou and Wenchang belong to the sixth and seventh branches, respectively.Notably, within the same city or county, peppers sourced from different towns also exhibit significant divergence, such as the second branch.Despite Dongying Town, Luni Village, and Duowen Town, Duolang Village, both being located in Lingao County, they belong to distinct branches.Principal component analysis (PCA) classifies the 182 pepper germplasms into two major groups, C. frutescens and C. annuum (Fig. 3f).PC1, PC2, and PC3 elucidate 77.39%, 3.02%, and 2.09% of the genetic variation, respectively.C. annuum from outside the province clusters densely, indicating a close genetic relationship among the pepper germplasms.Conversely, C. frutescens from diverse location within Hainan province displays greater dispersion, reflecting a rich genetic background.Evaluation of genetic relationships based on selected SNP markers (Fig. 3e) reveals genetic relationship values, with materials exhibiting of -0.2 accounting for 48.45%, those between 0 and 1.2 constituting 29.86%, and those exceeding 1.2 making up 21.68%.These findings affirm the high genetic diversity of the germplasm resources used, conducive to a robust whole-genome association study with minimal interference.
Calculation of the linkage disequilibrium (LD) decay of the 182 pepper materials (Fig. 3a) demonstrates a decrease in R 2 with increasing physical distance.At an R 2 of 0.1, the LD decay distance for group 1 exceeds 1000 kb, while for group 2, it surpasses 500 kb.Notably, group 1 aligns with C. frutescens, and group 2 with C. annuum, indicating a faster LD decay rate for C. annuum, with the smallest LD decay distance observed in the C.frutescens population.This further substantiates the high genetic diversity within the C. frutescens population.
Genome-wide association study of 26 agronomic traits.Utilizing a subset of highly consistent SNPs derived from 182 pepper, we conducted a GWAS for 26 agronomic traits (Table 1).To enhance the reliability of GWAS results and mitigate the influence of population structure, we employed two statistical models: the Linear Mixed Model (LMM) and the Efficient Mixed Model Association www.nature.com/scientificreports/eXpedited (EMMAX).After comparing their performance through Q-Q plots, we identified the most suitable statistical model for subsequent GWAS analysis of each trait.The LMM proved optimal for 9 traits, while EMMAX was optimal for 17 traits, .For instance, stem length proved suitable for GWAS analysis using the LMM model   www.nature.com/scientificreports/(Fig. 4, Fig. S3).The optimal model may vary for different traits, underscoring the necessity of selecting the most appropriate model for GWAS analysis for each trait.GWAS analysis was conducted on 26 agronomic traits, with results detailed in Table S8.Fifteen traits, such as Plant height, Weight per fruit, and Longitudinal diameter of fruit, exhibited 929 significantly associated loci.Conversely, Branching type, Leaf surface, Leaf shape, and eleven other traits showed no significant associations (Table S7).
For instance, plant height was associated with a total of 49 SNPs ( 26 Table 1.Screening loci and gene counts for pepper traits identified through GWAS.
Genes associated with stem-related traits were primarily implicated in functions such as signal transduction mechanisms, replication, recombination and repair, transcription, posttranslational modification, protein turnover, chaperones, intracellular trafficking, secretion, and vesicular transport.Among these, 26 genes had an unknown function, and 10 genes lacked functional annotations (Fig. 5a).Regarding leaf-related traits, the annotated genes were predominantly involved in functions such as translation, ribosomal structure and biogenesis, signal transduction mechanisms, and posttranslational modification, protein turnover, chaperones, with 2 genes of unknown function and 3 genes not functionally annotated (Fig. 5b).Genes associated with flowerrelated traits were predominantly involved in functions such as posttranslational modification, protein turnover, chaperones, replication, recombination and repair, transcription, and signal transduction mechanisms, with 38 genes of unknown function and 12 genes lacking functional annotations (Fig. 5c).Finally, genes associated with fruit-related traits were mainly implicated in functions such as replication, recombination and repair, signal transduction mechanisms, posttranslational modification, protein turnover, chaperones, transcription, and carbohydrate transport and metabolism, with 92 genes of unknown function and 48 genes lacking functional annotations (Fig. 5d).www.nature.com/scientificreports/

Candidate gene screening and validation
The boxplots depicting four traits-weight per fruit, longitudinal diameter of fruits, transverse diameter of fruits, and placenta size-are illustrated in Fig. 6a-d.The numerical values of these four traits in C. annuum surpass those in C. frutescens.This, in conjunction with the qRT-PCR results from both C. annuum and C. frutescens, facilitates the analysis functional trends in genes.
Leveraging the outcome of gene functional annotation, we randomly selected 13 candidate genes associated with weight per fruit, longitudinal diameter of fruits, transverse diameter of fruits, and placenta size for qRT-PCR validation experiments shown, as depicted in Fig. 6e-h.These aimed to substantiate the influence of these genes on the growth of various traits.Notably, the expression level of the TPX2 gene in C. frutescens, characterized by smaller weight per fruit, significantly exceeded that in C. annuum, indicating its potential role in inhibiting weight per fruit in both species.Similarly, nsLTPs13 and NUTC RAC KER displayed significantly higher expression levels in the variety with smaller weight per fruit, suggesting their potential as genes inhibiting weight per fruit in both C. frutescens and C. annuum.In the context of the longitudinal diameter of commercial fruits, the expression levels of extensin-2-like and COP10 genes were significantly higher in C. annuum compared to C. frutescens, whereas SCPL13 exhibited significantly higher expression in C. frutescens.This implies that these extensin-2-like and COP10 may promote longitudinal diameter of fruits in both species, while SCPL13 may have an inhibitory role.Notably, no significant difference in the expression level of the extensin-1-like gene was observed between the two varieties.Examining the transverse diameter of fruits, the expression of the DDB1 gene in C. annuum with a larger diameter was significantly higher than that in C. frutescens with a smaller diameter, suggesting its potential role in promoting the transverse diameter of fruits in both species Conversely, the gene GAUT1 exhibited opposite expression levels in the two varieties, indicating its potential as a gene inhibiting this trait.No significant differences in the expression levels of the RKD4 gene were observed between the two varieties.For placenta size, SCPL51 displayed significantly higher expression in the smaller seed cavity of C. frutescens compared to the larger seed cavity of C. annuum.In contrast, HDT2 and HDAC9 showed significantly higher expression in C. frutescens, indicating their potential roles as genes inhibiting placenta size in both varieties.www.nature.com/scientificreports/

Cloning of GAUT1 and COP10 and sequence analysis
In the YB-4(C.frutescens) variety, cloned genes COP10 and GAUT1 exhibit individual nucleotide mutations and deletions compared to the gene sequences cloned in CH20(C.annuum) (Fig. 7), which may be the reason for controlling the longitudinal and transverse diameter of chili fruits.When comparing the amino acid sequences of GAUT1 and COP10 between the two varieties, YB-4 has 5 single nucleotide mutation points relative to the CH20 variety.These mutations include C → T, T → C, A → C, A → T, and G → A, among which only the A → C and A → T mutations result in changes in amino acids.The A → C base mutation causes the conversion of E (glutamate) to D (aspartate), while the A → T base mutation leads to the transformation of T (threonine) into S (serine) in the GAUT1 gene of YB-4 compared to CH20.In addition, in the COP10 gene, YB-4 relative to CH20 exhibits 3 base deletions and 1 base mutation of C to A. The deleted bases result in the absence of a glycine in the YB-4 variety, and furthermore, the single base mutation changes S (serine) to F (phenylalanine).

Discussion
Chili peppers exhibit notable variation in flower, leaf, and fruit-related traits, as well as diverse agronomic characteristics 24,25 .Key yield and quality attributes in chili breeding are typically influenced by multiple genes or QTL 26 , primarily used genetic mapping of agronomic traits in chili peppers has mostly focused on QTL methods 27 , the inherent low sequencing depth of this approach may lead to overlooking significant genotypes 28 .
With advancements in high-throughput sequencing technology, GWAS has emerged as a potent tool for localizing candidate genes 29 .Despite the application of GWAS in chili breeding, most studies have focused on a limited number of traits or disease resistance [30][31][32] .In our study, GWAS was conducted on 26 agronomic traits of chili peppers, revealing significant SNPs associated with these traits.The results highlight the substantial impact of GWAS on efficiently localizing genes linked to multiple agronomic traits in chili peppers.Agronomic traits in chili peppers encompass various aspects, including yield, quality, and resistance.Determinants of chili pepper yield include fruit longitudinal and transverse diameters, weight per fruit, and the fruit shape index.GWAS analysis is a valuable tool for localizing QTL and predicting candidate genes associated with fruit yield-related agronomic traits.This approach provides a theoretical basis for molecular marker-assisted breeding of chili peppers.In tomato research, next-generation sequencing sequencing technology has facilitated the identification of representative gene families controlling fruit size.Notably, genes such as CNR, SUN, and OVATE, belonging to these families, have been successfully cloned.Their roles in regulating fruit length and shape have been validated [33][34][35][36] .The insights gained from tomato studies have been applied to QTL localization in chili peppers, revealing the complex genetic structure controlling these quantitative genetic traits 37,38 .Conducting a multi-trait QTL analysis in chili pepper has identified 40 candidate genes associated with C. annuum traits.These genes are implicated in diverse functions, including defense response, metabolic processes oxidation-reduction, and phosphorylation 39 .Understanding the genetic basis of these traits is critical, as cell number and size are pivotal determinants of plant organ size, and any variation in these parameters can impact organ size significantly 40 .Fruit peduncle length, a crucial trait, is intricately regulated by cell number or size, influenced indirectly by hormones and multiple pathways.Kinases play essential roles in this regulation, affecting plant growth and development.In our study, SNPs related to fruit stalks were identified in key genes, including leucine-rich repeat receptor-like kinases (LRR-RLKs), serine/threonine protein kinase, ABC transporter gene, and RING finger protein.These genes are likely to play pivotal roles in growth, development, cell wall integrity, and elongation rate [41][42][43][44] .Our gene functional annotation identified genes related to plant height, first internode length, fruit length, and fruit stalk length.For instance, nsLTPs13 plays a crucial role in cell development and organogenesis 45,46 , while TPX2 family members are involved in various plant developmental processes 47 .NUTC RAC KER is implicated in defining asymmetric cell division and stabilizing tissue boundaries 48 , and genes like SCPL13, regulate cell elongation and locule numbers in rice 49 , Arabidopsis 50 and tobacco 51 .
The extensin-2-like protein actively participates in plant cell wall formation and assembly, influencing plant cell elongation functions 52 .COP10 primarily regulates plant growth and development,with a role in light signal transduction 53,54 .The gene DDB1, associated wit-h commercial fruit transverse diameter, acts as a damage sensor maintaining the balance between genome integrity and cell cycle progression 8 .Galacturonosyltransferase (GAUT)1 and GAUT7 are core components of a plant cell wall pectin biosynthetic homogalacturonan:galacturon osyltransferase complex 55 .SCPL51, associated with seeds per fruit, regulates cell elongation and carpel numbers 50 .Members of the SCP and SCPL families, expressed in m-ajor tissue types, play roles in various biochemical and cellular processes in plants [56][57][58] .St-udies have revealed that overexpression of SCPL41 reduces membrane lipid content, while is absence of SCPL41 increases membrane lipid content 59 .HDT1/2 influences the transitio-n from cell division to expansion in root tips by inhibiting the expression of GA2ox2 60 .The regulation of histone acetylation involves the antagonism between HDAC and histone acetyltransferases (HAT) , modulating enzyme activity 61 .
Our study involved the investigation of 929 significantly associated SNPs in 182 C. frutescens and C. annuum.chili pepper resources, resulting in the identification of 519 candidate genes after meticulous screening.Through qRT-PCR validation, genes such as GAUT1, COP10, and DDB1, associated with fruit-related traits in C. frutescens and C. annuum, were confirmed.Future endeavors will focus on identifying candidate genes and the subsequent cloning and functional analysis of genes regulating fruit length.These efforts aim to provide a solid foundation for the development of high-quality chili pepper varieties.

Conclusions
This study conducted a comprehensive genome-wide analysis of a population comprising 182 chili pepper resources, encompassing both C. frutescens and C. annuum, through whole-genome resequencing.The analysis yieded a set of 64,110,473 high-quality SNP markers, addressing 26 key agronomic traits, including plant height, Vol:.( 1234567890 www.nature.com/scientificreports/weight per fruit, fruit length, and seed cavity size.Utilizing GWAS, we identified 929 significant SNP-associated loci, unveiling 519 candidate genes.These genes play pivotal roles in plant growth and development, regulation of light signal transduction, and responses to salt stress.Furthermore, through meticulous gene annotation and assessment of expression patterns, certain candidate genes, namely GAUT1, COP10, and DDB1, were delineated.While these findings contribute significantly to understanding of genes related to chili pepper fruit traits, elucidating the precise mechanisms governing these traits requires further investigation.This study provides valuable genetic resources and theoretical foundation for future endeavors in molecular marker-assisted breeding of chili peppers.

Plant materials and treatment
In this study, the pepper research group at the School of Tropical Agriculture and Forestry, Hainan University.Experimental samples of hot peppers, including the collection of plant materials, were collected according to relevant institutions, national and international guidelines and legislation, with the appropriate permission of Hainan Provincial Department of Science and Technology.Systematically acquired a total of 182 samples comprising both Capsicum annuum and Capsicum frutescens (Table S9).Specifically, 107 instances of Capsicum frutescens were procured from diverse cities and counties in Hainan Province, It is a pre-collection of chili peppers distributed in villages, forests and ravines all over Hainan, while the remaining 75 specimens of pod pepper originated from cultivated resources in other provinces.Notably, all specimens exhibited distinctive features of tall plants and upward growth of fruits.To ensure methodological consistency,all pepper plants were cultivated in a randomized design with three replicates at Sanjia Town, Dongfang City, Hainan Province, during September 2022.Individual plant samples were tagged and tender leaves were wrapped in aluminum foil, frozen in liquid nitrogen, and stored for further use.

DNA extraction and sequencing
The genomic DNA from tender leaves was extracted using the cetyltrimethylam-monium bromide (CTAB) method.Subsequent to extraction, the concentration and quality of the genomic DNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific).Following the DNA quality assessments, libraries were constructed.These qualified libraries then underwent sequencing on the NovaSeq 6000 platform.The raw reads obtained from the sequencing process underwent a series of essential data filtering steps: (1) the removal of reads containing adapters; (2) filtering out reads with more than 10% N content; (3) elimination of reads with low-quality bases exceeding 50% at a quality value below 20.This filtering process yielded clean reads, which were utilized for subsequent data analysis.

SNP and indel calls
The Zunla-1 genome (https:// www.ncbi.nlm.nih.gov/ assem bly/ GCF_ 00071 0875.1),characterized by a size of approximately 2.9 Gb and a GC content of 34.5%, was used as the reference genome.Alignment of Clean Reads to the reference genome employed the Burrows-Wheeler Alignment (bwa-mem2 v2.2) 62 with the MEM algorithm.

Population evolutionary analysis
The MEGA X 64 software was utilized to construct phylogenetic trees for 182 species using the neighbor-joining method, and applying the Kimura 2-parameter model with 1000 bootstrap repetitions.The analysis of population structure for the investigated materials utilized the admixture 65 software based on SNP data.Clustering, spanning a predefined range of subgroups (K values: 1-10), determined the optimal number of subgroups based on the valley of the cross-validation error (CV error).To facilitate evolutionary analysis by revealing clustering patterns, principal component analysis was executed using the EIGENSOFT software, leveraging SNP data.Estimation of relateness between individuals in natural population was carried out using the GCTA 66 software.Additionally, the calculation of linkage disequilibrium between pairwise SNPs within a specified distance range (1,000 kb) on the same chromosome was accomplished with the PopLDdecay(v3.41) 67software.

EMMAX model formula
In these formulas, the calculation of sample-relatedness μ was performed as a random effect using GEMMA software.In cases where covariates the covariates, are present, denoted as W, they are treated as fixed effects.Here, X represents genotype, and Y represents phenotype.The parameters include β 0 as the fixed effect, β k as the marker effect, and η signifying the error term.Ultimately, each variant locus yielded an association result.The P-values derived from the whole-genome association analysis were subsequently employed to generate Manhattan and QQ plots.The selection selection of candidate thresholds was predicated on values of 0.1 and 0.01 divided by the number of valid marker loci post-quality control measures.Exploring additional candidate regions involved considering values below − log10 (p) = 5 as potential regions of interest.
Validation of candidate genes using real-time quantitative PCR (qRT-PCR) Statistical analysis of data,encompassing box plots and qRT-PCR, was conducted aceoss a spectrum of 20 varieties.This selection comprised both the top and bottom 10 with respect to the numerical values associated with each trait.Total RNA underwent reverse transcription to cDNA following the protocols outlined in the Roche reverse transcription reagent kit.Primer sequences corresponding to genes such as SCPL13, COP10, and DDB1 (Table 2) were designed using Premier 5.0 software, with Actin (Accession number: AY486137.1)serving as the reference gene.Subsequent qRT-PCR reactions utilized SYBR® Premix Ex Taq II, and the ensuring data analysis adhered to the 2 -ΔΔCT method.

Cloning of GAUT1 and COP10
Gene full-length cloning primers were designed using Primer5.0 software based on the gene ID obtained from genomic data.The primers used were as follows: GAUT1-F, 5′-ATG AAG CCA GTA AGA CAA CTC CCC -3′ and GAUT1-R, 5′-TTA AGA ATC GAA CGA AAA TGG AGG -3′ for GAUT1 gene; COP10-F, 5′-ATG TAC GGC GGA GCA CCG -3′ and COP10-R, 5'-TCA CCT TGC AAA TCT CAG TGTCC-3′ for COP10 gene.The full-length sequences were amplified by PCR using 2 × Phanta @ Max Master Mix enzyme, and the correct bands were excised and purified using the Norvazo purification kit (DC301).The purified PCR products were then ligated with a TA cloning vector and transformed into competent Escherichia coli DH5α cells.Single colonies were picked for liquid PCR screening, and strains with correct and bright bands were selected for sequencing analysis by Nanshan Biotechnology Co., Ltd.

Statistical analysis
Data analysis involved the utilization of Microsoft Excel 2007 for data organization and the calculation of the frequency distribution for qualitative traits.Additionally, the maximum, minimum, and coefficient for variation of quantitative traits were calculated.Quality traits include branching type, leaf surface, leaf shape, style length, flower colour, anther colour, appendage at blossom end, colour of immature fruit, colour of mature fruit, persistent calyx at base of fruit, fruit shoulder shape and fruit shape.Quantitative traits included plant height, plant breadth, leaf length, leaf width, petiole length, first stem node length, stem thickness, weight per fruit, longitudinal diameter of fruit, transverse diameter of fruit, fruit peduncle length, thickness of flesh, plancenta size and number of locules.Standard deviation (SD) and mean (Mean) served as classification criteria, with each 0.5 SD interval designated as a level "i." Levels were systematically categorized from i = 1 to 10, and the frequency distribution P i for each level was determined.The computation of the Shannon-Weaver diversity index (H') was undertaken using the following formula: Table 2. qRT-PCR primer information.

Figure 1 .
Figure 1.Correlation analysis of quantitative traits.Red indicates positive correlation, while blue indicates negative correlation.The intensity of color reflects strength of the correlation.

Figure 2 .
Figure 2. Distribution of variant types on chromosomes for each sample.Outer to inner: chromosome coordinates (colored squares), SNP density distribution (orange), and Indel density distribution (blue).

Figure 3 .
Figure 3. Population structure and chain imbalance analysis of C. annuum and C. frutescens.(a) Linkage disequilibrium analysis.(b) Phylogenetic analysis of 182 C. annuum and C. frutescens.(c) Phylogenetic analysis of 107 Hainan native escaped pepper populations (C.frutescens).(d) Population structure analysis.(e) Kinship analysis (f) Principal component analysis.Blue in A, B and F represents group 1; red represents group 2.

Figure 4 .
Figure 4. Manhattan and Q-Q plots of the LMM association model for fruit peduncle length.Horizontal coordinates represent chromosome positions, vertical coordinates represent − log10(p) values, with green line representing a value of 5.Blue or red denotes 0.1/labeled amount and 0.01/labeled amount.Loci above the threshold line are candidate loci.

Figure 7 .
Figure 7. cDNA sequences and amino acid sequences of GAUT1 and COP10 gene clones.(a) Gene sequence of GAUT1 cloned in YB-4 variety.(b) Gene sequence of COP10 cloned in YB-4 variety.(c) Comparison of amino acid sequences of GAUT1 gene in two clones of CH20 and two clones of YB-4.(d) Comparison of amino acid sequences of GAUT1 gene in two clones of CH20 and YB-4 varieties.

Trait Model Chromosome Number of SNPs gene − log10(p)
Blue in A, B and F represents group 1; red represents group 2.Vol:.(1234567890)