Abstract
Background Capsicum is one of the major vegetable crops grown world-wide. Current subdivision in clades and species is based on morphological traits and coarse sets of genetic markers. Fruits broad variability has been driven by breeding programs and has been mainly studied by linkage analysis.
Results We discovered 746k variable sites by sequencing 1.8% of the genome in a collection of 373 accessions belonging to 11 Capsicum species from 51 countries. We describe genomic variation at population-level, confirm major subdivision in clades and species, and show that the known subdivision of C. annuum in two groups separates large and bulky fruits form small ones. In C. annuum, we identify four novel loci associated with phenotypes determining the fruit shape, including a non-synonymous mutation in the gene Longifolia 1-like (CA03g16080).
Conclusions Our collection covers all the economically important species of Capsicum widely used in breeding programs, and represent the widest and largest study so far in terms of the number of species and genetic variants analyzed. We identified a large set of markers that can be used for population genetic studies and genetic association analyses. Our results foster fine genetic association studies and foresee genomic variability at population-level.
Background
Pepper (Capsicum spp.) is one of the major vegetable crops grown worldwide, largely appreciated for its economic importance and nutritional value. Pepper originated in North-West and South America and then expanded and diversified in the other Southern and Central American regions. The taxonomic arrangement of the genus have been recently revised establishing the existence of 35 species distributed in 11 clades,[1] the most important of which are Annuum (C. annuum, C. frutescens, C. chinense) Baccatum (C. baccatum, C. praetermissum, C. chacoense) and Pubescens (C. pubescens). Population structure of Capsicum spp. inferred from genetic markers mostly reflects the known taxonomic grouping, however it highlights sub-groups within C. annuum and admixture between C. chinense and C. frutescens.[2, 3] Even if these studies were carried out on a large number of accessions, both are based on small number of markers (28 simple sequence repeats, SSR, on 1,400 accessions and 48 simple nucleotide polymorphisms, SNP, on 3,800 accessions), therefore the population structure of the different Capsicum species has never been investigated deeply.
Pepper presents a wide diversity in fruit size and shape achieved after a successful selection process. The early steps of domestication involved key traits such as non-deciduous fruits and the orientation of fruit tip from erect to pendant. Further steps of selection resulted in wide variability of fruit shape and size comparable to other Solanaceae.[4] Nevertheless, unlike other cultivated Solanaceae, e.g. tomato,[5, 6, 7, 8] the genetics underlying fruit shape in pepper is limited to findings from quantitative trait loci (QTL) analysis in a few biparental mapping populations (Supplementary Table 1), mostly phenotyped with low-throughput techniques,[9, 10, 11, 12, 13, 14, 15, 16] and/or limited to the identification of QTLs.[17, 18, 19, 20] As a consequence, major genetic loci underlying Capsicum fruit variability have not yet been identified.
Technical advances in high-throughput phenotyping allow to precisely measure the determinants of fruit shape and size using standard ontologies,[21] while the availability of a reference sequence[22] and population-based sequence data enables the discovery and fine mapping of genomic variants. The combination of these two factors lays the foundation for a better investigation of the genetics of fruit size and shape through genome-wide association studies (GWAS), as carried out for capsacinoid content[23, 24] and peduncle length[25], but never performed for fruit morphology attributes in Capsicum.
In this study we discovered 746k high-quality polymorphic sites from analyzing sequence data of 373 pepper accessions from eleven species of Capsicum and measured thirty-eight fruit shape and size attributes in 220 C. annuum accessions. We used these data to (i) uncover genomic properties of the pepper genome, (ii) describe population structure within the Capsicum genus at a resolution never achieved before, (iii) study natural selection, and (iv) discover significant association between genetic markers and traits related to pepper fruit shape and size in C. annuum. Finally, we discovered a non-synonymous change in the sequence of Longifolia 1-like gene associated with variance in C. annuum fruit elongation.
Results
Genomic diversity of the Capsicum genus at 746k variable sites
The germplasm collection presented here covers all the economically important species of Capsicum widely used in breeding programs. It includes 373 accessions belonging to eleven Capsicum species, of which five are wild (Table 1). Two hundred and twenty C. annuum accessions were already described.[26] With the exception of 48 accessions for which the geographical origin is unknown, the remaining accessions are from 51 countries (Supplementary Table 2, Figure 1).
Genomic DNA extracted from accessions was digested with the restriction enzyme ApeKI obtaining more than 7.5M master tags[26] that were assembled in 605,076 genomic regions or fragments. The cumulative sequence length of the fragments is 48,869,949 bp, corresponding to 1.8% of the genome, with average depth-of-coverage 5.8. Fragments are scattered across the genome. Average fragment size is 81 bp (standard deviation is 42.7 bp), and the range of distance between two consecutive fragments varies from 3 to 278,100 bp (average 4,551 bp). The majority of the fragments (85.9%) are intergenic.
By aligning sequence reads along the reference genome,[22] we identified 1,382,545 polymorphic sites, of which 746,450 have Phred-scaled quality scores (QUAL) >10 and were considered for all downstream analyses (Figure 2a). These figures compared to the cumulative sequence length of the fragments suggest that 1.5% of the genome is variable in Capsicum. Up to 95% of variants are single nucleotide polymorphisms (SNP, 82.4%) or multi-nuclotide polymorphisms (MNP, 12.5%). Insertions and deletions (InDels) represent 1.9% of all variants and their size ranges from deletions of 30 nucleotides to insertions of 20 nucleotides. InDels of three or multiples of three nucleotides are more frequent in genic region compared to inter-genic ones, suggesting a preference for InDels that add or remove triplets over those causing frame-shifts mutations (Supplementary Figure S1). Variants are not equally distributed in the genome, and in fact, only 37.1% of the fragments have at least one variant. Average number of variants per 100 bp is 9.4 and 12.3 in genic and intergenic regions respectively (Figure 2b) and this difference is significant (Mann-Whitney test p-value <2×10e-16). Finally, based on the annotations,[22] only 5.22% of variants (38,964) fall within exons.
For six of the eleven Capsicum species with >10 accessions, we further investigated genomic diversity. The average number of segregating sites per species is 440,600 and it is roughly proportional to sample size. C. annuum is an exception, as it seems less variable than expected given the number of haploid sequences, which likely reflects the recent loss of diversity due to intensive selective breeding (Figure 2c). In fact, C. annuum accessions are among the least heterozygous (Figure 2d) and also those with the lowest nucleotide diversity (Figure 2e). By contrast, the wild species C. chacoense has the highest values of heterozygosity per individual (Figure 2d) and nucleotide diversity per site (π, Figure 2e), even having the lowest number of segregating sites compared to domesticated species with a similar number of haploid sequences (Figure 2c).
To summarize, we discover 746k variable sites by analyzing 605k fragments of average size 81 bp that cover 1.8% of the genome of 373 Capsicum accessions. Variant density is not uniform, and in fact, only 37.1% of the fragments contain variants. Variant density is significantly lower in genic regions compared with intergenic ones. The majority of variants are single nucleotide changes. Due to our use of Genotype By Sequencing (GBS) and reference-guided analysis, we were only able to discover InDels up to a few tens of bp. Among species, C. annuum is the least diverse while C. chacoense is the most variable, despite possible variant detection bias due to the fact that the reference sequence belongs to C. annuum.
Population structure of Capsicum reveals strong subdivisions with little or no admixture among species
We investigated the population structure of the nine Capsicum species with at least two accessions (Table 1) using data from the 746k variable sites and three approaches. Phylogenetic reconstruction confirms clustering of accessions in species (Figure 3a), as observed in similar studies based on plant morphological characteristics[27, 28] or a smaller number of genetic markers.[3, 29, 2] Nevertheless, this is to date the largest phylogenetic study in terms of genomic markers analyzed in Capsicum species. We used principal components analysis (PCA) to summarize the observed genetic variation among accessions and species. The first two components explain the 24% of the observed variation separating three main domesticated species: C. annuum, C. baccatum, and C. chinense. While describing almost one-fourth of the variance, clustering within the first two components is not complete, and a number of accessions are positioned in between clusters. The third and fourth components (5% of explained variance) separate C. pubescens and C. chacoense between them and from the cluster of domesticated species (Figure 3b). While C. chacoense is a wild species, C. pubescens is domesticated, although one of the least easy to breed.[30] PCA clustering is not related to geography (Supplementary Figure S2).
Finally, admixture analysis supports subdivision into seven clusters (Figure 3c, Supplementary Figure S3). Clusters correspond mostly to species, with the exception of the further subdivision of C. annuum into two sub-clusters with some admixed individuals, as observed in other studies.[26, 3, 2] Most accessions belong to only one cluster, with the median coefficient of membership to the best-matching cluster being 0.99 (mean ±sd is 0.9471 ±0.13, Supplementary Figure S4). Nevertheless, some of the accessions seem misplaced probably because of mislabeling or misclassification[31] by germplasm providers. Like PCA, admixture clustering is not related to geography (Supplementary Figure S5).
In conclusion, the population structure analysis presented here shows that accessions cluster according to species without regards to their geographical origin and according to the known phylogenetic relationships. There is little or total lack of admixture among species and instead, deep clustering analysis reveals a major subdivision within C. annuum, as observed before.[26, 3, 2]
Insight into C. annuum fruit variability from high resolution phenotyping
Fruit shape and size varies widely between pepper accessions. We carried out phenol-typing of the 220 accessions of C. annuum at thirty-eight phenotypic traits (Figure 4), in order to describe and understand the range of variation of C. annum fruits. For each accession, each trait value is the average of forty-eight measures: accessions were grown in triplicate and for each triplicate eight fruit were considered, for a total of twenty-four fruits per accession. Fruits were longitudinally sectioned in half and each section measured. Trait values were scored from the images obtained with a CanoScan LiDE. Measured traits can be classified into two broad categories according to if they are related to fruit shape or size, and the shape category is further organized in eight classes (Supplementary Table S3).
Traits display a wide variability relative to each other and in fact the average coefficient of variation (CV) is 61% (median CV= 45%) and the largest CV is >300%, suggesting that in many cases the standard deviation exceeds the mean value of the trait (Figure 5a). The less variable trait is the Pericarp Area (CV=7%), i.e. the ratio of the area within the pericarp boundary to the area of the fruit, suggesting that fruits tend to have comparable skin thickness. Other traits with CV<10% are ProximalEccentricity, Eccentricity, and PericarpThickness, all in fact related to the thickness of the pericarp and mostly related to fruit latitudinal section features and eccentricity (Supplementary Table S3). By contrast, the most variable trait is the HAsimmetryOb (CV=346%), which represents the fruit length as the average distance between a horizontal line through the fruit at mid-height and the midpoint of the fruit’s height at each width. Other traits with CV >100 are VAsymmetry, Obovoid, and DistalIndentationArea, all defining the fruit shape (Supplementary Table S3). Pairwise Spearman’s rank correlations among all possible pairs of the 38 traits are statistically significant with p-value ≥10e-7 in 48.9% of the cases (Supplementary Table S4) and it is possible to observe clusters of correlation pairs (Figure 5b). While fruit thickness does not show significant variability, the major phenotypic variation involves traits determining the fruit asymmetry and the distal fruit-end shape.
Subdivision of C. annuum accessions into two clusters (Figure 3c) is associated with significant differences in traits related to fruit size (Figure 5c). To remove admixed accessions, samples from C. annuum were assigned to genetic clusters determined in the admixture analysis if membership to the cluster was >90%. Of the 38 traits, Area, MaximumWidth, WidthMidHeight, and Perimeter are significantly different between the two genetic clusters (Figure 5c). With the exception of the pair Perimeter-WidthMidHeight, all other pairs among the four traits are positively and significantly correlated, and their CV range from 50% (Perimeter) to 82% (Area, Figure 5a,b). Fruit size is significantly different between the two genetic clusters, with Cluster 1 having bulkier and larger fruits relative to Cluster 2. This suggests that differences in fruit size in C. annuum might be caused by genetic differences.
Genetics of fruit shape in C. annuum
Having observed concordance between genetic and phenotypic clustering in C. annuum we next aimed at understanding the genetic basis underlying the natural variation in fruit size. We carried out genome-wide association analyses between the thirty-eight traits and the 746k genetic markers identified in this study using a univariate linear mixed model. To account for cryptic population structure and relatedness we integrated in the model a relatedness matrix estimated from the accession genotypes as random effects. In addition, we included the first two PCs (35.7% of explained variance) as fixed effects. SNPs were filtered for missingness (< 5%), minor allele frequency (>1%), and Hardy-Weinberg equilibrium (p-values >0.001), leaving 559,684 SNPs for the association analyses.
Association tests identified eight variants on three chromosomes, significantly associated (Bonferroni corrected p-value <1.78e-08) with seven traits (Table 2, Figure 6a, Supplementary Figure S6). All the seven traits are highly and significantly correlated (Figure 5b) and contribute to determine whether a fruit is circular or elongated (Supplementary Table S3).
On chromosome 10 we observe two clusters of variants separated by 4.7 Mbp associated with traits determining whether a fruit is squared or pointed through evaluation of asymmetry, blockiness and the shape of the distal end of the fruit (Figure 6b). The first cluster includes four intergenic variants in a 24 kb region (three of them are in complete linkage) associated with the traits Ovoid and ProximalFruitBlockiness. Both traits specify to what extent a fruit is squared. Ovoid is quantified as the ratio of the area of the fruit above and below middle height (aA/aB Figure 6b), while the ProximalFruitBlockiness is the ratio of the width at the upper blockiness position to width mid-height (wA/wB Figure 6b). In both cases, values close to 1 indicate a rather square fruit, while values >1 are typical of pointed fruits. For the strongest associated SNP 10:33557960 each minor allele confers a more pointed fruit by reducing the ratio for both Ovoid (β=-0.15 aA/aB, P=5.43E-09) and for ProximalFruitBlookiness (β=-0.24 wA/wB, P=4.04E-10).
The second cluster on chromosome 10 includes two intergenic variants 10 bp apart and in full linkage, associated with the trait DistalAngleMaoro that reflects how much a pepper is pointed and measures the angle between best-fit lines drawn through the fruit perimeter on either side of the distal end point: the smaller the angle the more pointed the pepper. Each minor allele for variants 10:28759675 and 10:28759675 increases is estimate to increase the angle by 18° (β=18.47°, SE = 2.96, P=4.97E-09).
On chromosome 11 we detect a significant association between one intergenic SNP and the Ellipsoid trait. The Ellipsoid index fits precision of the actual shape of the fruit to an ellipse: smaller values indicate ellipsoidal fruits and measures the fraction of area left out of a fitted ellipse (a.o.e.). The range of Ellipsoid in our data vary from 0.002 a.o.e. to 0.21 a.o.e., and the effect of each minor allele increases of the a.o.e.(β=0.04 a.o.e., SE = 0.006, P=4.39E-09), contributing to produce pointed fruit.
On chromosome 3, the SNP 3:183386147 is significantly associated with four traits also related to fruit shape: Circular, Ellipsoid, FruitShapeIndexExternalI, and LobednessDegree (Figure 6c, Table 2). Similar to Ellipsoid, Circular fits precision of the actual shape of the fruit to a circle: smaller values indicate circular fruits and measures the fraction of area left out of a fitted circle (a.o.c.). The range of values for Circular in our data set is 0.04-0.46 a.o.c. and each minor allele increase the a.o.c. (β=0.10 a.o.c., SE = 0.01, P=1.36E-08), as well as the a.o.e. (β = 0.03, SE = 0.005, P=1.58E-10) contributing therefore to make the fruit elongated. Consistently, the minor allele increases the LobednessDegree, i.e. the dispersion of several distances from the center of weight to the perimeter, up to 16.97mm (β = 16.97 mm, SE = 2.45,P=1.38E-10). Finally, the FruitShapeIndexExternalI, i.e. the ratio of the maximum height to the maximum width (mA/mB), is increased by the minor allele (β = 0.91mA/mB, SE = 0.145, P=4.93E-09). Overall, the alleles of the SNP 3:183386147 specify whether a fruit is circular or elongated as measured by effect size on four phenotypes. The signal at the SNP 3:18338614 is the only one significant in region (±2 Mb) containing 26 genes (Figure 7a) and there is not high linkage disequilibrium with other genetic variants.
We further investigated the population structure of C. annuum using PCA in order to perform PC-based selection scans[32] to find support for selection in the variants found in the GWAS. We used strict filtering on variants and samples to remove inter-species mixture and obtain a clean dataset of unadmixed C. annuum accessions to perform selections scans. The cleaned dataset consists of 212 C. annuum accessions and 100,773 variants. The principal components analysis is visualized in supplementary Supplementary Figure S7, where the first component mostly describes the genetic variance between European and Non-European samples and the second component captures the variance within the European samples. The first component is very likely to be associated with the ancestry component splitting the C. annuum accessions in the initial admixture plot from all species. The PC-based selection statistics of the top variants of the GWAS are reported in Supplementary Table S5 and Supplementary Figures S8 and S9 for most significant of the two first PCs. Only the 3:183386147 variant reaches nominal significance with a p-values of 0.015 and is thus not significant when taking multiple testing into account.
A non-synonymous change in the Longifolia 1-like gene is associated with variance in C. annuum fruit elongation
The SNP 3:183386147 has two alleles, T and C. In our collection, the T allele is fixed in C. pubescens, C. baccatum, and C. chacoense, and nearly fixed (0.98) in C. chinense and C. frutescens, while its frequency in C annuum is 0.76, suggesting that T could be the ancestral state (Table 3). The SNP 3:183386147 is located in the third exon of the gene CA03g16080, which has no homologs in the pepper genome (Figure 7b). CA03g16080 is predicted to code for the protein Longifolia 1-like, a protein homologous to the Arabidopsis LONGIFOLIA1 and LONGIFOLIA2 and to Oryza sativa (rice) SLG7. Both LONGIFOLIA and SLG7 activate longitudinal organ expansion. [33, 34, 35] The mutation T→C causes a change in the aminoacidic composition from Phenylalanine to Leucine, with the removal of an aromatic group.
We translated the DNA sequence of CA10g04730 into the corresponding protein sequence and predicted its secondary and tertiary structures to understand their structural properties and functional aspects. Functional terms associated to the protein indicate that it is a nuclear protein, as shown in Arabidopsis[33], with a possible involvement in protein binding and export from the nucleus, and RNA transport. The LONGIFOLIA 1-like protein contains 749 amino acids and is organized in a partial globular architecture, with three well-organized domains linked by segments without defined secondary structure (Figure 7c). The amino acid coded by the codon including the 3:183386147 T→C variant is the residue number 367 and is located at the edge of an α-helix of the central domain in a buried portion of the structure. The molecular modelling predictions indicate that the transition from phenylalanine to leucine in position 367 may cause a change in protein stability. The protein conformation with leucine seems more stable, probably due to the flexibility of its side chain, while the voluminous and rigid side chain of phenylalanine might induce a clash of side chains that might trigger a fold change, at least locally (Figure 7c).
Discussion
Capsicum is one of the most extensively domesticated plants and its fruits are among the most-widely consumed. Although fruit morphology is a main target in breeding programs, the genetic basis of fruit shape has been studied so far by linkage analysis, using coarse sets of markers and low resolution phenotypic data. Similarly, the population structure of the Capsicum genus has never been investigated with a fine set of genetic markers. The recent availability of the reference sequence of a few species of Capsicum allowed the exploration of the genome properties, including the identification of genomic rearrangements among species. Nevertheless, little is known about genetic diversity within species due to the lack of studies with samples of adequate size. Our study is the first population study that combine sample size and depth of markers to highlight genomic features at species level and provide first insight into the genetics underlying morphological traits of pepper fruits.
The germplasm collection presented here covers all the economically important species of Capsicum widely used in breeding programs, representing the largest study so far in terms of number of species and number of genetic variants analyzed. Although limited to the 746k high quality variable sites accessible in the 1.8% of the genome, this study is an order of magnitude larger than previous studies in terms of the number of analyzed variants [23, 25], and also considers many more samples. The list of segregating sites positions and allele frequencies that we identified is publicly available and constitute a valuable resource for future studies.
We estimate that approximately 1.5% of Capsicum genome might be variable, therefore we expect that a number of variable sites in the order of 107 might be discovered from population sequencing of the whole 3.6 Gbp of the genome. We discovered that genetic variants tend to form clusters since their average consecutive distance is lower than their average density. As expected genic regions are less variable than intergenic regions, suggesting greater selective constrains in genic regions.
Our work deciphers genetic variability at intra- and inter-species levels and associate it to morphological traits. We confirm and validate previous results showing that domesticated species tend to be less variable compared to wild ones[36] and that there is little admixture among species. We correlate to fruit size the major subdivision within C. annuum observed also in previous studies and never fully explained.[26, 2, 3] By determining that small and big fruits have different genetic backgrounds, we provide a rationale for genetic association studies for traits related to fruit morphology. In fact, we discover two significant associations. One links a clusters of genetic variants on chromosome 10 to traits determining whether a fruit is shaped or pointed. The other, a non-synonymous change on chromosome 3 in the gene Longifolia 1-like, is related fruit elongation and roundness. Both genomic regions overlap with QTLs linked to fruit shape and elongation previously identified in pepper[13, 12, 9, 18]. In particular major QTL detected on chromosome 3 (fs3.1) and confirmed in several studies (Supplentary Table 1) is located 10 Mbp upstream from the non-synonymous change in Longifolia 1-like, while a QTL on chromosome 10 (fs10.1) is located 100 Mb downstream the cluster of variants on chromosome 10. Those are certainly wide distances, probably related to the properties and limitations of QTL mapping, but it is encouraging to find overlap.
Among Solanaceae, the genetic basis of fruit shape have been extensively studied in tomato, leading to the identification of major QTLs and genes[7]. One of these, the Ovate-like gene CaOvate, has been reported in pepper to be involved in fruit elongation [37]. Beyond this, no other single genes information is reported, therefore, the genetic loci underlying fruit shape are not yet comprehensively explored. Despite the appropriateness of the promising association found in Longifolia 1-Like, further work will be necessary to fine map the signals that we found and fully understand the genetic asset behind pepper fruit shape and size.
Conclusions
We present results form analyzing a small fraction of the Capsicum genome that are informative on overall genomic variability, population structure and genetic association that demonstrate the potential of making discoveries from population studies in this species. We can therefore predict that future studies based on more extensive sequencing and that can exploit haplotype-based information will allow both a better understanding of the evolutionary history of pepper (including reconstructing the domestication process and identifying footprints of natural selection), and discoveries that can guide the breeding process and inform genomic selection. As an example, a more in depth study of the properties of the non-synonymous change in Longifolia 1-like can guide applications in breeding that aim to modify pepper fruit appearance.
Methods
Genotype by sequencing, variant calling, and phasing
Genotype by sequencing was carried out as described in Taranto et al.[26]. Reads were mapped to the reference genome[22] using BWA [38]. Variant calling was done using Freebayes v1.2.0 [39] with standard parameters. Missing genotypes were imputed and the imputed genotypes were phased using Beagle v4.1 [40, 41].
Population structure, phylogenetics, PCA
Model-based ancestry estimation was obtained using the ADMIXTURE software[42] with K ranging from 1 to 10. One thousand bootstrap replicates were run to estimate parameter standard errors. Ten-fold cross-validation (CV) procedure was performed and CV scores were used to determine the best K value.
Plant material, growth conditions, phenotyping
Our world-wide collection of seeds from 373 accession belonging to the genus Capsicum includes both domesticated (landraces) and wild varieties (Table 1). Information about the accessions, including variety name, country of origin is in Supplementary Table S2. Three replicates for each accession were grown in a randomized complete-block design in greenhouse under controlled conditions at day-night temperature set points of 25/18 C. At maturity, eight fruits from each replicate were harvested, cleaned and cut longitudinally in two sections. Each section was scanned with a CanoScan LiDE 210 photo scanner (Canon, Tokyo, Japan) at a resolution of 300 dpi. Thirty-eight morphometric quantitative traits were recorded and analyzed using the Tomato Analyzer v 3.0 software[43]. A brief description of each trait, its acronym, and evaluation methodology are summarized in Supplementary Table S3 and visualized in Figure 5. Correlation between traits was calculated using the Spearman’s rank correlation test.
Genome-wide association and PC-based selection scan
Genome-wide association analysis was performed using a linear mixed model implemented in GEMMA[44], controlling for admixture and relatedness between individuals as random effects using a genetic similarity matrix estimated from the data with the same software. Bonferroni-corrected genome-wide threshold for p-value is 1.786723e-08 and was calculated taking as reference a p-value of 0.01 and considering 559,684 markers. default filters in GEMMA.
We used PhonoGram[45] to display the chromosomal location along the pepper genome of the genome-wide signicantly associated SNPs.
PC-based selection scan was based on PC estimated using PCAngsd [46] as it is able to model the statistical uncertainty of genotypes and missing data and it has an implementation of the PC-based selection statistic of Galinsky et al. (2016)[32] working on genotype likelihoods. The filtering of variants was performed in ANGSD based on genotype likelihoods, and admixture analyses were performed in PCAngsd to filter out C. annuum samples which had an admixture proportion < 90% for the C. annuum cluster.
Protein modelling and analysis
The exon-intron structure of the CA03g16080 gene was drawn using the Gene Structure Display Server 2.0. [47]
The amino acidic sequence of the gene CA03g16080 was used to generate a 3D model of the protein and to evaluate structural properties. In the absence of templates suitable for applying the homology modelling approach, we used a strategy based on the integration of predictions by means of different tools. We used PredictProtein[48] I-Tasser (Iterative Threading ASSEmbly Refinement) [49] for structure predictions and protein modelling. I-Tasser returned five best models and we selected three out of them, having the higher C-scores (from −1.55 to −1.85) and excluded the remaining two models, due to their lower C-scores (i.e., −4.03 and −4.62, respectively). The best models obtained have been used for evaluating structural properties by visual inspection with molecular viewers, and we integrated a complete evaluation with the results of other prediction tools, as and MAESTRO (Multi AgEnt STability pRedictiOn tool)[50], a tool for evaluating the possible effect of amino acid substitution on protein stability.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Availability of data and materials
Raw genetic data is available for collaboration upon request. Aggregate data (e.g. list of variable sites, allele frequencies, average phenotypic measures) are available on the GitHub repository https://github.com/ezcn/Capsicum-genomics
Competing interests
The authors declare that they have no competing interests.
Funding
P.T., N.D.A, and T.C. acknowledge the GenHort project (PON02_00395_3215002) supported by the PON R&C 2007-2013 grant funded by the Italian Ministry of Education, University and Research in cooperation with the European Regional Development Fund (ERDF). P.T. acknowledge the G2PSOL project funded by the EU Horizon 2020 research and innovation program under grant agreement No. 677379.
Author's contributions
P.T. conceived and conducted the experiments. V.C., N.D.A., E.G., A.A. J.M., A.F., P.T. performed the analyses and worked at the interpretation of the results. V.C., N.D.A., E.G., P.T. wrote the manuscript. T.C. contributed to critically discuss the results. All authors reviewed and approved the manuscript.
Acknowledgements
We are thankful to Roberto Sirica, Barbara Greco, and Francesca Taranto for valuable help in the first stage of the project. We also thanks Carmine Cirillo for contributing the git repository.
Footnotes
Updated author info