Abstract
Anthocyanins have significant functions in stress tolerance in pepper
(Capsicum annuum L.) and also benefit human health. Nevertheless, the
key structural genes and regulatory genes involved in anthocyanin
accumulation in pepper fruits are still not well understood and fine
mapped. For the present study, 383 F[2] plants from a cross between the
green-fruited C. annuum line Z5 and the purple-fruited line Z6 was
developed. Two separate bulked DNA pools were constructed with DNAs
extracted from either 37 plants with high anthocyanin content or from
18 plants with no anthocyanin. A combination of specific-locus
amplified fragment sequencing (SLAF-seq) and bulked segregant analysis
(BSA) was used to identify candidates for regions associated with
anthocyanin accumulation. We identified a total of 127,004 high-quality
single nucleotide polymorphism (SNP) markers, and detected 1674
high-quality SNP markers associated with anthocyanin accumulation.
Three candidate anthocyanin-associated regions including the intervals
from 12.48 to 20.00 Mb, from 54.67 to 56.59 Mb, and from 192.17 to
196.82 Mb were identified within a 14.10-Mb interval on chromosome 10
containing 126 candidate genes. Based on their annotations, we
identified 12 candidate genes that are predicted to be associated with
anthocyanin expression. The present results provide an efficient
strategy for genetic mapping of and valuable insights into the genetic
mechanisms of anthocyanin accumulation in pepper fruit, and allow us to
clone and functionally analyze the genes that influence anthocyanin
accumulation in this species.
Introduction
Anthocyanins are soluble flavonoid plant pigments that confer colors
ranging from bright red-orange to blue-violet or black [[40]1] to
fruits, flowers, seeds, leaves, and stems. Anthocyanins are involved in
pigmentation, attraction of seed distributors and pollinators [[41]2,
[42]3], and protection against photo-oxidative damage in plants
[[43]4], and might have health-promoting effects in humans [[44]5,
[45]6]. Anthocyanins are accumulated in the palisade and mesophyll
cells of purple or black leaves and in the outer mesocarp of black and
violet fruit [[46]1]. The violet or black pigmentation of pepper is
also due to high levels of anthocyanin [[47]1, [48]7]. Therefore, new
pepper cultivars with high anthocyanin content could both improve
stress tolerance in pepper plants and enhance health benefits for
humans.
Extensive studies of the biosynthetic pathway leading to anthocyanins
have found 12 structural genes and three transcription factors (TFs)
involved in the pathway [[49]8–[50]10]. Anthocyanin biosynthesis begins
in the phenylpropanoid pathway followed by the flavonoid pathway
([51]S1 Fig) [[52]8]. Phenylpropanoid pathway enzymes phenylalanine
ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate CoA
ligase (4CL) transform phenylpropanoid into 4-coumaroyl-CoA. In the
flavonoid pathway, the product of condensation of one 4-coumaroyl-CoA
with three malonyl-CoA is converted into dihydroflavonol by the enzymes
chalcone synthase (CHS), chalcone isomerase (CHI), and flavanone
3-hydroxylase (F3H). Dihydroflavonol is then transformed into
dihydromyricetin, a colorless molecule, by the enzyme flavonoid
3´,5´-hydroxylase (F3´5´H). This compound is subsequently reduced by
dihydroflavonol 4-reductase (DFR) and ultimately converted into blue
delphinidin by anthocyanidin synthase (ANS). All other anthocyanins are
then derived from delphinidin. The predominant anthocyanin in pepper
(C. annuum L.) is delphinidin-3-trans-coumaroylrutinoside-5-glucoside
[[53]10–[54]12]. The delphinidins undergo further modification via
glycosylation catalyzed by UDP glucose-flavonoid
3-O-glycosyl-transferase (UFGT/3GT), 5/7-O-glycosyl- transferases
(5GT/7GT) [[55]13, [56]14], rhamnosylation catalyzed by rhamnosyl
transferases (RTs) [[57]15], acylation catalyzed by acyl transferases
(AT) [[58]16], and methylation catalyzed by methyltransferases (MTs)
[[59]17]. The anthocyanins are subsequently transported into the
vacuole, which might be mediated by glutathione S-transferase (GST)
[[60]18, [61]19], anthocyanic vacuolar inclusions (AVIs) in the
cytoplasm [[62]20], and anthocyanin permease (ANP) [[63]8, [64]9]. A
series of structural genes that encode these anthocyanin biosynthetic
enzymes have already been cloned. Each of these genes is expressed in a
specific way depending on the tissue or developmental stage. The
sequences of these genes are also highly conserved across plant species
[[65]8, [66]10].
A regulatory complex comprised of the basic helix-loop-helix (bHLH)
protein R2R3-MYB plus WD40 repeats (MYB-bHLH-WD40) interacts with
structural gene promoters to modulate the expression of anthocyanin
structural genes [[67]7, [68]12, [69]21, [70]22]. Among these, MYB
appears to be an important determinant in anthocyanin accumulation
[[71]10]. MYB and WD40 TFs control the expression of some late (F3’5’H,
DFR, or 3GT) and early anthocyanin biosynthesis pathway structural
genes (CHS, CHI, or F3H) in pepper fruits [[72]9]. However, this result
differs from that of Borovsky et al. (2004), which found that only the
expression of the late structural genes (DFR, ANS) is dependent on the
incompletely dominant gene anthocyanin (A) [[73]15]. The A locus, which
encodes a MYB TF (CaMYB), controls the expression of anthocyanin in
various tissues of pepper [[74]15, [75]23–[76]25]. Li et al. (2013)
also found that CaMYB1 and CaMYB2 which were homologous to CaMYB might
regulate the expression of anthocyanin biosynthesis in green fruits of
hot pepper [[77]26]. Ben-Chaim et al. (2003) mapped the pepper A locus,
which is homologous to tomato anthocyanin 1 (ANT1) and petunia
anthocyanin 2 (AN2) [[78]15], onto pepper chromosome 10 [[79]24]. In
addition, the A locus is linked to a major-effect quantitative trait
locus (QTL) (fs10.1) controlling pepper fruit shape [[80]24, [81]27].
Borovsky and Paran (2011) mapped fs10.1 to 0.3 cM from the its nearest
molecular marker, CT11, and 6.3 Mb from ANT1, which is the ortholog of
the pepper A gene in tomato, according to the tomato genome assembly
release 2.30 ([82]http://solgenomics.net/) [[83]27]. The pepper genome
sequence was published in 2014 [[84]28], so the physical distance
between fs10.1 and CT11 in pepper was not known at that time.
Informative, saturated linkage maps are important for fine mapping
quantitative traits. Due to the lack of saturated linkage maps in
pepper, the anthocyanin accumulation trait had also not yet been finely
mapped in this species.
The genome sequence of pepper [[85]28] will enable fine mapping of
anthocyanin accumulation and other traits in pepper. Efficient
identification of genes or QTL linked to plant traits of interest will
become possible by combining specific-locus amplified fragment
sequencing (SLAF-seq) with bulked-segregant analysis (BSA)
[[86]29–[87]34]. SLAF-seq is a type of next-generation,
reduced-representation genome sequencing strategy for efficient
identification of single-nucleotide polymorphisms (SNPs), while BSA
allows subsequent rapid screening of bulked pools of DNA to identify
molecular markers linked closely to the target allele of a gene or QTL
controlling a trait of interest [[88]35, [89]36].
Here, BSA combined with SLAF-seq was first time to be used to map the
anthocyanin accumulation trait in pepper by pooling DNAs from F[2]
plants with distinct anthocyanin accumulation phenotypes from a cross
between the inbred C. annuum lines Z5 and Z6 as parents. The purple
pepper line Z6 has high anthocyanin content with purple stems and
fruit, while the green pepper line Z5 has no anthocyanin with green
stems and fruit. Although a lot of anthocyanin-related genes have been
isolated so far, but only A locus in pepper has been mapped.
Nevertheless, the anthocyanin accumulation-related genes including A
locus have not been fine mapped due to the lack of saturated linkage
maps in pepper and candidate genes for bHLH, WD40 and variation of
anthocyanin have not been identified. Thus, the main objective of this
study was to identify genome regions and candidate genes related to
anthocyanin accumulation-related genes in pepper, and to develop SNP
markers to provide diagnostic SNP markers for molecular marker-assisted
selection of anthocyanin accumulation genes and lay a foundation for
cloning.
Materials and methods
Plant materials
We used the green-fruited C. annuum line Z5 and purple-fruited C.
annuum line Z6 as parental lines in the present study. Z6 is an
anthocyanin-pigmented variety (0.96 mg anthocyanin/100 g fresh weight)
that has purple stems and fruit, while Z5 contains no anthocyanins and
has green stems and fruit ([90]S1 Table). We obtained an F[1] hybrid
from a Z5 × Z6 cross, and developed the F[2] mapping population of 383
plants by self-pollinating the F[1] hybrid. The parental plants, and
F[1] and F[2] populations were grown in a greenhouse at Beijing
Vegetable Research Center, Beijing Academy of Agriculture and Forestry
Sciences, Beijing, China.
Extraction and measurement of anthocyanin content
Pepper fruits were harvested at 20 d post-anthesis following the method
of Aza-González et al. (2013) [[91]8]. The pepper fruit pericarps were
dissected and immediately immersed into liquid nitrogen, and the frozen
pericarps were stored at -80 °C. Extraction and measurement techniques
described in Lee et al. (2005) [[92]37] were used to isolate and
quantify anthocyanins in pericarp tissue. In brief, three aliquots of
20.00 g of frozen pulverized pericarp tissues were extracted in 100 mL
of 0.1% (v/v) hydrochloric acid in methanol for 2 h at RT with lights
on, and then were filtered. The filtered extract was then transferred
to a 250-mL volumetric flask and the residue was re-extracted with
hydrochloric acid in methanol at least three times until a
spectrophotometrically colorless extract was obtained. Two aliquots of
50 mL of combined extracts were condensed to dryness in a rotary
evaporator (Buchi, Flawil, Switzerland), and then were diluted with
either 0.025 M potassium chloride (pH 1.0) or 0.4 M sodium acetate (pH
4.5) in separate 25-mL volumetric flasks. A test aliquot was then
diluted to determine the dilution factor with pH 1.0 buffer until
reaching an absorbance of 0.2 to 0.8 at 520 nm (A520). The absorbances
at both 520 and 700 nm of test aliquots diluted with pH 1.0 or pH 4.5
buffer were then measured. The anthocyanin concentration as
cyanidin-3-glucoside equivalents was calculated as: Anthocyanin
(cyanidin-3-glucoside equivalents, mg/L) =
[MATH: A×MW×DF×103ε×1 :MATH]
, where A = (A520nm–A700nm)[pH 1.0] –(A520nm–A700nm)[pH 4.5]; MW (the
molecular weight of cyanidin-3-glucoside) = 449.2 g/mol; DF = the
dilution factor determined above; l = cm path length; ε = molar
extinction coefficient of cyanidin-3-glucoside; and 10^3 convert from g
to mg.
Extraction and pooling of DNA
Young leaves were harvested from both parents and F[2] plants and
genomic DNA was isolated using the N-cetyl N,N,N-trimethylammonium
bromide (CTAB) method [[93]38]. The two DNA pools for bulked segregant
analysis (BSA) were constructed by combining equal amounts of DNA from
high- or low-anthocyanin F[2] plants chosen based on measured
anthocyanin concentrations ([94]S1 Table). The high-anthocyanin pool
(H-pool) was comprised of DNA from 37 plants with high anthocyanin
content that ranged from 0.90 to 7.31 mg per 100 g fresh weight, and
the low-anthocyanin pool (N-pool) was comprised of DNA from 18 plants
with no measurable anthocyanin content. DNA isolated from parental
lines Z5 and Z6 and the two pools of F[2] DNA were used for
construction and sequencing of SLAF libraries.
Construction and high-throughput sequencing of SLAF libraries
An initial restriction enzyme digestion experiment was performed in the
pepper line CM334 to select the appropriate restriction enzyme for SLAF
library construction as per Xu et al. (2015) [[95]39]. RsaI (New
England Biolabs, Ipswich, MA, USA) was chosen for digesting the
parental and bulked genomic DNAs because it resulted in even
distribution of SLAFs on each pepper CM334 chromosome ([96]S2 Fig).
Single-nucleotide A overhangs were added to the resulting DNA fragments
using Klenow fragment (USB, Cleveland, OH, USA), and then fragments
were ligated with dual-index sequencing adaptors [[97]40] and amplified
by PCR (polymerase chain reaction). Amplified fragments were then
purified, pooled, and screened for the optimal fragment size range for
construction of SLAF libraries, as described by Sun et al. (2013)
[[98]36] with minor modifications. DNA fragments of 364 to 414 bp were
identified and subjected to paired-end sequencing to identify SLAFs
using the Illumina HiSeq 2500 platform (Illumina, Inc., San Diego, CA,
USA) at Beijing Biomarker Technologies Corporation
([99]http://www.biomarker.com.cn).
Evaluation for the SLAF libraries
To validate sequencing procedures and accuracy, we used rice (Oryza
sativa L. japonica) as a control with the version 7.0 rice reference
genome ([100]http://rice.plantbiology.msu.edu/) with the same library
construction and sequencing methods as used for the Z5 × Z6 mapping
population to evaluate the SLAF libraries constructed for pepper.
First, BWA software was used to compare the rice control sequencing
reads with those of the pepper reference genome [[101]41]. As
[102]Table 1 shows, we achieved a typical 84.94% efficiency of mapping
paired-end reads in the rice control. Because complete digestion by a
restriction enzyme improves SLAF experiments, enzyme digestion
efficiency was evaluated as an index of optimal SLAF results, which can
be affected by genome complexity, DNA purity, and restriction enzyme
digestion completeness. Second, the efficiency of digestion of rice
genomic DNA in the present study was 93.81% ([103]Table 1), which was
adequate for construction of these SLAF libraries. Finally, the read
lengths of the paired-end reads of the rice control that mapped to the
rice reference genome ranged from 364 to 414 bp ([104]S3 Fig). These
results indicated the construction of SLAF libraries were accurate.
Table 1. Efficiency of mapping paired-end reads and enzyme digestion in the
rice control.
Mapped reads Efficiency (%) Enzyme digestion Efficiency (%)
Mapped paired-end reads 84.94 Complete digestion 93.81
Mapped single-end reads 4.58 Partial digestion 6.19
Unmapped reads 10.48 — —
[105]Open in a new tab
Analysis of SLAF-seq data
Raw sequence reads from each sample generated on the Illumina HiSeq
2500 platform were subjected to a series of quality control procedures
including assessment of sequence quality scores and guanine-cytosine
(GC) content to ensure reliable and unbiased reads [[106]36]. More than
80% of sequences in the four libraries had quality scores greater than
or equal to Q30 (where a quality score of Q30 indicates 0.1% error
probability, or a 99.9% probability of sequence accuracy). We used BWA
software to align clean sequence reads against the C. annuum cv.
Criollo de Morelos 334 reference genome CM334
([107]http://peppergenome.snu.ac.kr/download.php, version 1.55)
[[108]28, [109]41]. BLAT [[110]42] was then used to cluster all
paired-end reads that had distinct index data by sequence similarities
among the two parents and the pooled libraries. Sequences that were
over 90% identical were then considered as a single SLAF locus or tag.
Detecting high-quality SNPs
We used GATK [[111]43] and SAMtools [[112]44] software to detect
single-nucleotide polymorphisms (SNPs). We designated the intersection
of SNPs detected by both GATK and SAMtools as the ultimate set of SNPs
to subject to further analysis. SnpEff software [[113]45] was then used
to annotate whether SNPs were located upstream or downstream from a
nearby gene, or in an intergenic region, and whether they were
synonymous or non-synonymous mutations with reference to annotated gene
models from the pepper reference genome. SNPs were filtered prior to
association analysis according to the following criteria: SNPs with
multiple alleles were excluded; SNPs with sequencing depth of less than
4-fold in each F[2] pool or parental DNA were omitted; SNPs with
identical genotypes between pools were eliminated; and SNPs exhibiting
recessive alleles that were not inherited from the recessive parent
were excluded. Ultimately, the resulting high-quality SNPs were then
used in association analysis.
Association analysis
Both Euclidean distance (ED) [[114]43] and a SNP-index algorithm
[[115]46, [116]47] were used to conduct the association analysis. The
ED of the allele frequencies of each SNP was calculated between the
H-pool and N-pool, as described by Hill et al. (2013) [[117]43]:
[MATH: ED=(AN−p<
/mi>ool−AH−pool)2+(CN−p<
/mi>ool−CH−pool)2+(GN−p<
/mi>ool−GH−pool)2+(TN−p<
/mi>ool−TH−pool)2
:MATH]
, where A, C, G, or T refers to the frequency of each nucleotide.
Euclidean distances were squared (ED^2) to reduce noise and increase
the effect of large ED values. The ED values from the H-pool and N-pool
were fitted using a Loess regression analysis [[118]43], and the
significance threshold for marker-trait associations was set at three
standard deviations above the median of the Loess-fitted values.
Regions with Loess-fitted values exceeding this threshold were
considered as the candidate genomic regions associated with the
anthocyanin content of pepper in this cross.
The SNP-index algorithm is a useful tool for identifying significant
between-pool differences in genotype frequencies. We used SLAF depth to
represent genotype frequency in our SNP-index algorithm [[119]48]. We
calculated the ΔSNP-index as follows: ΔSNP-index = SNP-index
(H-pool)–SNP-index (N-pool), where SNP-index (N-pool) = N[Np]/(N[Np] +
H[Np]) and SNP-index (H-pool) = H[Hp]/(H[Hp] + N[Hp]). N[Np] and H[Np]
indicate the depth of the N-pool derived from Z5 and Z6, respectively;
and N[Hp] and H[Hp] represent the depth of the H-pool derived from Z5
and Z6, respectively. Thus, the ΔSNP-index equals zero if the
SNP-indices of the N-pool and the H-pool are equal. A ΔSNP-index closer
to 1 suggests that high anthocyanin content is almost completely
associated with one genotype, and that the associated SNPs are closely
linked to genomic regions conferring high anthocyanin content. In
contrast, a ΔSNP-index closer to –1 indicates that the associated SNPs
are linked to genomic regions conferring low anthocyanin content. We
calculated the confidence coefficient for each ΔSNP-index then fitted
ΔSNP-index values using the SNPNUM method [[120]44]. The genomic
regions associated with a trait are identified when the fitted marker
values are greater than the 99% confidence coefficient threshold.
Ultimately, the intersections of candidate regions identified by both
the ED and SNP-index approaches were designated as the final candidate
anthocyanin-related regions. We then plotted ED, SNP-index, and
ΔSNP-index data individually, and used CIRCOS 0.66
([121]http://circos.ca/) to plot a circular graph of the distributions
of chromosomes, genes, SNPs, ED values, and ΔSNP-indices.
Candidate gene annotation and screening
The functional annotations of candidate genes were explored using
blastx alignment with default parameters to sequences at the Cluster of
Orthologous Groups of proteins database (COG,
[122]http://www.ncbi.nlm.nih.gov/COG/), Gene Ontology (GO,
[123]http://www.geneontology.org/), the Kyoto Encyclopedia of Genes and
Genomes (KEGG, [124]http://www.genome.jp/kegg/), the NCBI non-redundant
protein database (NR, [125]ftp://ftp.ncbi.nih.gov/blast/db/), and
Swiss-Prot ([126]http://www.uniprot.org/). All genes that could be
related to the synthesis, storage, or regulation of anthocyanin
accumulation, as well as any genes containing SNPs resulting in
non-synonymous mutations were chosen for further analysis as candidate
genes.
Results
Analysis of sequence data and identification of SNPs
A total of 52.76 Mb and 27.65 Mb of clean sequence reads with an
average read length of 50 bp were obtained in the SLAF libraries for
the two pools and parents, respectively ([127]Table 2). The average
percentage of Q30 bases was 94.50% and the GC content was 37.03%,
indicating a majority of high-quality bases. Using BWA, we could map
over 90.81% of the clean sequence reads to the CM334 pepper reference
genome, indicating the level of sequencing accuracy. After clustering,
771,025 SLAFs were evenly distributed on the CM334 pepper reference
chromosomes ([128]Fig 1A; [129]S2 Table). The numbers of SLAFs ranged
from 41,197 on chromosome 08 to 73,838 on chromosome 01, 603,960 from
the N-pool, 556,567 from the H-pool, 491,731 from Z6, and 496,868 from
Z5. The average SLAF sequencing depths were 23.45-, 25.77-, 33.83-, and
45.74-fold in Z6, Z5, the H-pool, and the N-pool, respectively.
Table 2. Sequence data summary for parental lines Z5 and Z6 and bulked H-pool
and N-pool.
Sample Clean_Reads Q30 (%) GC (%) Mapped reads rate (%) Number of SLAFs
Total depth Average depth
N-pool 31,297,954 94.76 36.70 90.85 603,960 27,624,290 45.74
H-pool 21,460,775 94.35 37.11 90.50 556,567 18,829,026 33.83
Z6 13,090,095 94.37 37.20 91.11 491,731 11,529,003 23.45
Z5 14,556,569 94.50 37.12 90.77 496,868 12,806,096 25.77
[130]Open in a new tab
N-pool, the library created from pooled DNA from plants with no
anthocyanin content; H-pool, and the library created from pooled DNA
from plants with high anthocyanin content; Mapped reads rate (%), the
proportion of the clean sequence reads mapped to the CM334 reference
genome relative to the total clean reads; Q30, a quality score of 30
indicates 0.1% error probability or 99.9% sequence accuracy; GC,
guanine-cytosine content; SLAF, specific-locus amplified fragment.
Fig 1. Distribution of SLAFs and SNPs on each chromosome of pepper.
[131]Fig 1
[132]Open in a new tab
The x-axis and y-axis represent chromosome length and chromosome
number, respectively. The distance between two adjacent yellow bars
indicates 1 Mb on the chromosome, and black lines indicate SLAFs or
SNPs.
GATK [[133]43] and SAMtools [[134]44] software were used to identify a
total of 836,852 SNPs, whose properties are shown in [135]S3 Table. The
most SNPs, 127,688, were detected on chromosome 12, while the fewest,
17,073, were detected on chromosome 08 ([136]S2 Table). These SNPs were
mapped to each chromosome on the CM334 reference genome as shown in
[137]Fig 1B. A total of 127,004 high-quality SNPs remained after
filtering for use in the association analysis to find candidate
anthocyanin-associated regions in pepper.
Association analysis of anthocyanin content
The ED value at each SNP locus was calculated for the 127,004
high-quality SNP markers. We squared ED values to reduce noise, and
then fitted the association values by Loess regression analysis
[[138]43]. The threshold for association between markers and the
anthocyanin trait was set to 0.27, or three standard deviations above
the median of all the Loess-fitted values. When the Loess-fitted value
was greater than 0.27, we detected three candidate regions on
chromosome 05 for anthocyanin accumulation, and four candidate regions
on chromosome 10 for this trait ([139]Fig 2). As shown in [140]Table 3,
we identified 270 genes in a total interval of 80.12 Mb on chromosome
05. On chromosome 10, we identified 64 genes in the interval from
11,722,320 to 20,005,690 bp, 20 genes in the interval from 53,805,574
to 57,945,748 bp, 68 genes in the interval from 191,899,653 to
196,817,467 bp, and 15 genes in the interval from 210,028,131 to
211,321,646 bp. A total of 437 genes and 6268 high-quality SNP markers
were found in these candidate regions, among which we identified two
genes containing SNPs resulting in non-synonymous mutations. As shown
in [141]S4 Table, a total of 7537 and 2781 SNP markers were detected in
parents and bulked pools, respectively, and more than 97.00% of these
SNPs were annotated as located in the intergenic regions.
Fig 2. Association values for anthocyanin accumulation on each pepper
chromosome from Euclidean distance-based association analysis.
[142]Fig 2
[143]Open in a new tab
The 12 pepper chromosomes are represented along the x-axis, and the
association values based on Euclidean distance (ED) are indicated along
the y-axis. ED-based association values at each SNP location are
represented by colored dots. The red dashed line represents the
association threshold and black line indicates Loess-fitted values.
When ED-based association values are higher, stronger association of a
SNP with anthocyanin accumulation is indicated.
Table 3. Summary information for SNP-index and Euclidean distance-based
association analyses.
ChrID Start (bp) End (bp) Interval size (Mb) Number of genes Number of
SNPs
Euclidean distance
Chr05 53,667,767 93,840,115 40.17 124 3,238
Chr05 100,693,328 103,396,658 2.70 8 37
Chr05 132,573,706 169,821,807 37.25 138 891
Chr10 11,722,320 20,005,690 8.28 64 961
Chr10 53,805,574 57,945,748 4.14 20 341
Chr10 191,899,653 196,817,467 4.92 68 601
Chr10 210,028,131 211,321,646 1.29 15 199
SNP-index
Chr10 12,479,910 20,005,690 7.53 52 912
Chr10 54,672,471 56,593,567 1.92 6 226
Chr10 192,166,533 196,817,467 4.65 68 536
[144]Open in a new tab
Chr, chromosome; SNP, single-nucleotide polymorphism.
ΔSNP-index values were determined by subtracting the H-pool SNP-index
from the N-pool SNP-index. Average SNP-indices for the H-pool and
N-pool were plotted in 400-SNPsliding windows in 1-SNP steps in the
CM334 genome assembly. The SNP-indices of the H- and N-pools and the
ΔSNP-index are shown plotted in [145]Fig 3A, [146]Fig 3B, and [147]Fig
3C, respectively. When the fitted SNP marker values exceeded the
threshold at a 99% confidence coefficient, three candidate regions for
anthocyanin accumulation were detected on chromosome 10 by examining
ΔSNP-index values ([148]Fig 3C; [149]Table 3). There were 52 genes in
the intervals from 12,479,910 to 20,005,690 bp, six genes in the
interval from 54,672,471 to 56,593,567 bp, and 68 genes in interval
from 192,166,533 to 196,817,467 bp, or a total of 126 genes in a 14.10
Mb candidate region interval. One gene containing a SNP resulting in a
non-synonymous mutation was found in the interval from 194,974,062 to
194,974,736 bp. In addition, 1674 high-quality SNP markers were
identified and mapped in the candidate regions ([150]Table 3), and 2069
and 926 SNP markers we identified between the parents and bulked H-pool
and N-pool, respectively ([151]S4 Table). A total of 97.20% of the SNP
markers in parents and 96.54% of the SNP markers in bulked pools were
annotated as located in intergenic regions.
Fig 3.
[152]Fig 3
[153]Open in a new tab
Graphs of H-pool (a) and N-pool (b) indices and ΔSNP-index (c) for
SNP-index-based association analysis. The 12 chromosomes are
represented along the x-axis, and the SNP index or ΔSNP-index values
are shown along the y-axis. The black line indicates fitted SNP-index
or ΔSNP-index values. The red, blue, and green lines indicate the
thresholds at the 99%, 95%, and 90% confidence coefficients,
respectively.
Final candidate regions were determined by overlapping the ED and
SNP-index association analysis results to identify regions tightly
associated with anthocyanin content. The final candidate regions were
the same as those identified by SNP-index association analysisin the
intervals from 12.48 to 20.00 Mb, from 54.67 to 56.59 Mb, and from
192.17 to 196.82 Mb on chromosome 10.
Visualization of results of genomic analysis of anthocyanin accumulation with
the combined SLAF-seq/BSA strategy
To more clearly visualize all of the results from the present study, we
plotted a circular graph ([154]S4 Fig) representing, in order from the
first circle to the fifth circle, the 12 chromosomes of pepper, the
distributions of genes, SNP densities, ED values, and ΔSNP-index
values.
Annotation of SNPs and genes in the candidate anthocyanin- associated region
We detected a total of 2069 and 926 SNP markers in the candidate
anthocyanin-associated genomic regions between the parents and between
the bulked pools, respectively ([155]S4 Table). Analysis using SnpEff
software [[156]45] indicated the 20 SNPs in the upstream regions of
genes, 2011 SNPs in intergenic regions, and 26 SNPs in the downstream
regions of genes between the parental lines. This analysis also
revealed 10 SNPs in the upstream regions of genes, 894 SNPs in
intergenic regions, and 13 SNPs in the downstream regions of genes
between the bulked pools. Additionally, there were only two SNPs
resulting in synonymous mutations and one SNP resulting in a
non-synonymous in coding regions, indicating that most SNP markers were
located in the intergenic regions ([157]S4 Table).
A total of 126 candidate genes related to anthocyanin content ([158]S5
Table) were identified within the three candidate regions on chromosome
10 according to the current CM334 pepper reference genome annotation.
Excluding 11 genes in the candidate regions that have not yet been
annotated in public databases, we found annotations for 38, 101, 18,
100, and 126 genes within the candidate anthocyanin-associated regions
in the COG, GO, KEGG, NCBI NR, and Swiss-Protdatabases, respectively
([159]Table 4). The 38 genes predicted by COG analysis were classified
by their putative functions ([160]Fig 4); 11 genes were classified as
‘general function prediction only’ and seven genes were associated with
‘transcription’. The putative functions of seven other genes were
classified as ‘replication, recombination and repair’. In addition,
four genes were associated with ‘carbohydrate transport and
metabolism’, ‘cell wall/membrane/envelope biogenesis’,
‘posttranslational modification, protein turnover, chaperones’, or
‘signal transduction mechanisms’, which accounted for 10.53% of all
genes annotated in the COG analysis ([161]S5 Table; [162]Fig 4).
Table 4. Annotation statistics for candidate anthocyanin-associated pepper
genes for anthocyanin accumulation.
Database NR GO SwissProt COG KEGG Total
Number of annotated 126 101 100 38 18 126
Number of CDS containing non-synonymous subsitutions 1 1 1 1 0 1
[163]Open in a new tab
Fig 4. Functional annotations of candidate anthocyanin-associated genes
according to the Cluster of Orthologous Groups of proteins database (COG).
[164]Fig 4
[165]Open in a new tab
The x-axis indicates the classification annotation in COG and the
y-axis represents the number of annotated candidate genes.
GO term enrichment analysis identified genes associated with enriched
GO terms from the molecular function, biological process, and cellular
component domains ([166]S5 Fig). Because some genes had more than one
annotation from different GO domains, genes could sometimes fall into
more than one functional category ([167]S5 Fig). The major enriched
functional categories in our data included those associated with
cellular components such as cell (GO:0005623), cell part (GO:0044464),
or organelle (GO:0043226); molecular function such as catalytic
activity (GO:0003824) or binding (GO:0005488); and biological process,
specifically biological regulation (GO:0065007), cellular process
(GO:0009987), metabolic process (GO:0008152), response to stimulus
(GO:0050896), or single-organism process (GO:0044699). These results
suggested that major metabolic changes in the biological process domain
and posttranslational modifications in the molecular function domain
might be involved in the regulation of anthocyanin content of pepper.
At the same time, the regulation of anthocyanin content could also be
associated with cell, organelle and cell part functions in the cellular
component domain. Additionally, directed acyclic graphs (DAG) for GO
terms were plotted using the Bioconductor package TopGO [[168]49] to
show the hierarchical parent-child relationships among GO terms
([169]S6–[170]S8 Figs). This analysis showed that the most-enriched
term was chloroplast thylakoid membrane (GO:0009535) in the cellular
component domain, DNA-directed RNA polymerase activity (GO:0003899) in
the molecular function domain, and negative regulation of abscisic
acid-activated signaling pathway (GO:0009788) in the biological process
domain, which might be involved in the regulation of pepper anthocyanin
content.
To better understanding of the functions of genes potentially involved
in anthocyanin accumulation, we performed KEGG analysis and identified
18 genes in 22 pathways using KEGG analysis ([171]Fig 5). One gene,
CA10g12720, was involved in the ‘endocytosis’ pathway in cellular
processes, and CA10g12690 was involved in the ‘plant hormone signal
transduction’ pathway that was associated with environmental
information processing. In addition, 10 genes were identified in seven
pathways in genetic information processing and 16 genes were identified
in 12 pathways related to metabolism. Four pathways were identified as
enriched (P <0.05) by KEGG enrichment analysis including more than two
genes with predicted functions in the ‘RNA polymerase’ (ko03020),
‘homologous recombination’ (ko03440), ‘pyrimidine metabolism’
(ko00240), or ‘purine metabolism’ (ko00230) pathways ([172]S6 Table)
that are associated with the regulation of gene expression.
Fig 5. Pathways identified as enriched in the candidate regions via KEGG
analysis.
[173]Fig 5
[174]Open in a new tab
The x-axis represents the number and percentage of annotated candidate
genes and the y-axis represents name of pathway in KEGG.
Candidate genes correlated with anthocyanin accumulation
Annotations suggested that 12 candidate genes could have functions
associated with anthocyanin accumulation in pepper ([175]Table 5).
Seven genes of these genes have annotations clearly associated with
anthocyanin biosynthesis or metabolism. For example, CA10g04060 is
annotated as 4CL2 in the 4CL gene family [[176]50] and encodes a
predicted 4-coumarate-CoA ligase 2 in the ‘ubiquinone and other
terpenoid-quinone biosynthesis’ pathway (ko00130), or 4-coumarate-CoA
ligase (GO:0016207) in the phenylpropanoid pathway. CA10g03640 and
CA10g03760, which encode a predicted anthocyanin 5-aromatic
acyltransferase (5AT) and a predicted anthocyanidin 3-O-glucoside
6'-O-acyltransferase (3AT), respectively, were predicted to have
anthocyanin 5-O-glucoside 6'-O-malonyltransferase activity
(GO:0033810). CA10g03880, CA10g12640, and CA10g12650 were all annotated
as glucosyltransferases. CA10g03880 was homologous to 3GGT from Ipomoea
nil in anthocyanin biosynthesis pathway [[177]51]. CA10g12640 and
CA10g12650 were predicted to encode proteins with flavonol
3-O-glucosyltransferase activity (GO:0047893). Additionally, CA10g12890
(homologous to Solanum melongena cytochrome P450 76A2, CYP76A2) might
encode a protein with F3´5´H activity (GO:0033772). Three out of 12
candidate genes were homologs of regulatory genes that regulate
structural anthocyanin biosynthesis genes. CA10g03650 was homologous to
MYB39 from Arabidopsis thaliana, which encodes a MYB39 TF involved in
regulation of phenylpropanoid metabolism (GO:2000762). Meanwhile,
CA10g12810, which is homologous to Arabidopsis thaliana APL, also
encodes a MYB-family TF APL predicted to participate in the regulation
of anthocyanin metabolism (GO:0031537). Further, Zhou et al. (2016)
reported that APL was also involved in phenylpropanoid and flavonoid
biosynthesis pathways [[178]52]. CA10g12710, a homolog of nol10 in
Danio rerio was predicted to be a WD40 repeat protein based on its COG
annotation. In addition, CA10g03810 was homologous to Arabidopsis
thaliana GDSL esterase/lipase At5g45960, which was associated with
accumulation of anthocyanin in ultraviolet light-exposed plants
(GO:0043481). Finally, CA10g12840 was the only candidate gene
containing a SNP resulting in a non-synonymous mutation. CA10g12840
encodes a predicted subtilisin-like protease, which can bind to maltose
binding protein [[179]53] and, based on its COG annotation, is
associated with ‘Posttranslational modification, protein turnover,
chaperones’. The function of CA10g12840 in anthocyanin accumulation
needs to be further studied.
Table 5. Candidate genes related to anthocyanin accumulation in pepper.
Classification Gene name Gene_ID Start (bp) End (bp) Description
Structural genes
4CL2 CA10g04060 18,570,738 18,572,196 4-coumarate—CoA ligase 2
Nicotiana tabacum [[180]50]
5AT CA10g03640 12,745,342 12,746,112 Anthocyanin 5-aromatic
acyltransferase Gentiana triflora
3AT CA10g03760 14,981,576 14,981,851 Anthocyanidin 3-O-glucoside
6”-O-acyltransferase Perilla frutescens
3GGT CA10g03880 16,367,650 16,369,032 Anthocyanidin 3-O-glucoside
2”-O-glucosyltransferase Ipomoea nil [[181]51]
UGT73C3 CA10g12640 194,040,338 194,041,813 UDP-glycosyltransferase 73C3
Arabidopsis thaliana
UGT73C5 CA10g12650 194,053,311 194,053,472 UDP-glycosyltransferase 73C5
Arabidopsis thaliana
CYP76A2 CA10g12890 195,250,338 195,253,243 Cytochrome P450 76A2 Solanum
melongena
Transcription factors MYB39 CA10g03650 13,533,765 13,535,787
Transcription factor MYB39 Arabidopsis thaliana
nol10 CA10g12710 194,493,711 194,494,820 Nucleolar protein 10 Danio
rerio
APL CA10g12810 194,789,125 194,791,433 MYB family transcription factor
APL Arabidopsis thaliana [[182]52]
Anthocyanin accumulation in response to stress At5g45960 CA10g03810
15,633,033 15,636,114 GDSL esterase/lipase At5g45960 Arabidopsis
thaliana
Genes with non-synonymous SNPs ARA12 CA10g12840 194974062 194974736
Subtilisin-like protease Arabidopsis thaliana [[183]53]
[184]Open in a new tab
Discussion
The pepper genome is large, with an estimated size of 3.48 Gb
[[185]28], and such large genomes can be relatively costly to analyze
by low-coverage sequencing or whole genome deep resequencing. SLAF-seq
is a relatively new strategy for genome-wide discovery of SNPs and
genotyping on a large scale that can improve the throughput and
accuracy of high-coverage sequencing, and also make it more efficient
and cost effective [[186]36]. A new strategy that combines SLAF-seq
with BSA and takes advantage of both methods was used to analyze the
genetic control of anthocyanin accumulation in the present study. This
strategy also has been used for QTL analysis and linkage mapping in
various species such as rice [[187]29], cotton [[188]30], and melon
[[189]32], as well as pepper [[190]33, [191]34, [192]54]. For example,
we previous had successfully mapped the pepper first flower node trait
using the strategy, and the strategy had proven to be an effective
method to identify candidate region and genes linked to a specific
trait [[193]54]. Anthocyanins are one of the determinants of pepper
color, and also happen to increase abiotic stress tolerance in plants
and have benefits for human health [[194]4–[195]6]. In pepper,
virus-induced gene silencing (VIGS) has been used to analyze the
functions of structural genes and TFs that are part of the anthocyanin
biosynthetic pathway [[196]9, [197]10, [198]55, [199]56]. For example,
Zhang et al. (2015) revealed that silencing the R2R3-MYB TF CaMYB using
VIGS led to the repression of the majority of anthocyanin pathway
genes, except for PAL, C4H, and 4CL [[200]10]. However, to date, there
are no reports yet of the use of combined SLAF-seq and BSA for the
study of anthocyanin accumulation in pepper. The present study
represents the first application of a combined SLAF-seq/BSA strategy
for identification of genomic regions and genes linked to anthocyanin
accumulation in pepper.
Sequence data analysis indicated that accurate SLAF libraries were
constructed after choosing the appropriate restriction enzyme, sizes of
restriction fragments, and evaluating the SLAF libraries against a
control library prepared from rice corresponding to our previous study
[[201]54]. A total of 771,025 SLAFs evenly distributed along the CM334
reference pepper chromosomes were obtained from the SLAF libraries, and
the sequencing depths of SLAFs from both pools and parents were all
greater than 20×. For successful SLAF-seq, sequencing depth should
exceed 6× and quality scores should be greater than Q30 for [[202]36].
Therefore, our data indicate that we successfully constructed an
accurate and high-quality SLAF library for identification of the
candidate regions and genes associated with anthocyanin accumulation in
pepper.
Molecular markers accurate, high-resolution genetic maps are essential
for mapping QTL and for improving the efficiency of marker-assisted
selection [[203]57–[204]60]. However, some PCR-based molecular markers
such as amplified fragment-length polymorphism (AFLP) [[205]15,
[206]61], random amplified polymorphic DNA (RAPD) [[207]62, [208]63],
and simple sequence repeat (SSR) [[209]32, [210]64] markers are
relatively low-density, non-specific, and provide incomplete coverage.
In contrast, SNPs are high frequency markers with denser, genome-wide
[[211]65, [212]66] distributions than SSR or other markers [[213]66].
Their ease of automatic genotyping [[214]67] and high polymorphism make
SNPS valuable for molecular genetic analyses [[215]68]. Of the 836,852
SNPs we identified, over 17,073 SNPs were mapped onto the pepper
chromosomes, which resulted in denser coverage of the whole pepper
genome than in the Cheng et al. (2016) map [[216]69]. Our map will
provide higher marker density and increase accuracy for identifying
candidate genes. We used a set of 127,004 of high-quality SNPs to
perform association analysis and identify candidate
anthocyanin-associated regions in pepper.
Overlapping the results of ED-based and SNP-index-based association
analyses should improve prediction of candidate regions associated with
anthocyanin accumulation, as was done by Geng et al. (2016) for seed
weight in Brassica napus [[217]31]. SNP-index analysis is more accurate
and quantitative for evaluation of frequencies and inheritance of
parental alleles in the F[2] [[218]70]. We narrowed candidate
anthocyanin-associated regions down to three within an interval of
14.10 Mb on chromosome 10 that contained 1674 high-quality SNPs. The
annotations for these SNPs showed the locations of more than 96% of
them in intergenic regions, and would thus be useful for fine-mapping
of anthocyanin-related genes.
The pepper CM334 reference genome has been available since 2014
[[219]28], and has allowed comparisons of high-throughput sequencing
results from other pepper crosses, which helps to identify
polymorphisms throughout the pepper genome. Anthocyanin biosynthetic
pathway genes are incompletely dominant and quantitatively inherited in
the Solanaceae [[220]15, [221]23, [222]71, [223]72]. Although the
enzyme- and TF-encoding genes of the anthocyanin biosynthetic pathway
have been extensively studied, most of these genes had not yet been
fine mapped in pepper due to the lack of saturated linkage maps. With
an accurate and high-quality SLAF library, we identified three
candidate regions associated with anthocyanin accumulation on pepper
chromosome 10, as did previous reports in pepper [[224]15, [225]24,
[226]25, [227]27]. The A locus that controls anthocyanin accumulation
had previously been mapped to pepper chromosome 10 [[228]24, [229]25]
and found to be allelic to the fs10.1 locus 2.1 cM from the fs10.1
locus [[230]27]. Three candidate regions on chromosome 10 were
identified in our study, intervals that ranged from 1.92 to 7.53 Mb in
length. Additionally, two adjacent major QTLs (fap10.1, 106.4 cM and
fap10.2, 109.8 cM) [[231]72] associated with control of anthocyanin in
eggplant fruit were also mapped to chromosome 10. The ANT1 and AN2 loci
from tomato have also been mapped to chromosome 10 [[232]73]. A homolog
of AN2 from petunia has also been identified as a candidate gene for
the pepper A locus [[233]15], and its location corresponds to those of
tomato anthocyanin gainer and eggplant fap10.1 [[234]71], highlighting
the genetic similarities between the solanaceous species pepper,
tomato, and eggplant. Therefore, the 14.10-Mb interval on pepper
chromosome 10 associated with anthocyanin accumulation is a strong
candidate region that could harbor a gene(s) controlling anthocyanin
accumulation in pepper.
A series of structural genes that includes PAL, C4H, 4CL, CHS, CHI,
F3H, F3’5’H, DFR, ANS, GTs, ATs, and MTs [[235]8–[236]10,
[237]15–[238]17], and three regulatory genes including MYB, bHLH and
WD40 affect anthocyanin biosynthesis [[239]7, [240]12, [241]21,
[242]22]. However, there have been limited studies of the genetic
regulatory mechanisms underlying the anthocyanin biosynthesis in pepper
fruit. Until now, A locus is the only one locus to be identified to
control the expression of early genes in the pathway, which encoded a
CaMYB that cannot control the expression of PAL, C4H, and 4CL
[[243]10]. The AN2 has been thought the most likely candidate gene for
the pepper A locus [[244]15]. Whether there are genes other than CaMYB
that can control the expression of early structural genes (PAL, C4H,
and 4CL), and other transcription factors like bHLH and WD40 that can
control the anthocyanin accumulation as the candidate genes for bHLH
and WD40 have not been found in pepper so far. Besides, Deshpande
(1933) also postulated the presence of a second locus other than A
locus to explain the variation of anthocyanin [[245]74]. In this study,
we sought to identify the candidate genes involved in anthocyanin
accumulation and variation including A locus in pepper fruit. We
identified a total of 126 genes in the candidate regions, and 12 of
which, annotations indicate, could be related to anthocyanin
accumulation. Future studies to isolate and functional testing other
genes will likely be aided by the study of these candidate genes. Our
results showed that CA10g04060, CA10g03640, CA10g0376, CA10g03880,
CA10g12640, CA10g12650, and CA10g12890 are related to structural genes
that might play important roles in pepper fruit anthocyanin
biosynthesis. Among these, CA10g04060 and CA10g12890 were homologous to
4CL2 from Nicotiana tabacum and CYP76A2 from Solanum melongena,
respectively, which have been separately related to 4CL and F3´5´H. Shi
and Xie (2010) also found that the expression of 4CL2 increased during
anthocyanin biosynthesis [[246]50]. CA10g03640 and CA10g03760, which
encode 5AT and 3AT respectively, were both predicted to function in
acylation of anthocyanin. CA10g03880, CA10g12640, and CA10g12650,
homologs of 3GGT, UGT73C3, and UGT73C5 respectively, have all been
annotated as predicted glucosyltransferases. Further, the genes
encoding the anthocyanin biosynthetic enzymes are likely also
transcriptionally regulated in pepper. We also identified homologs of
MYB39 (CA10g03650) and APL (CA10g12810), which belong to the MYB TF
family and could be involved in regulation of anthocyanin metabolism
based on the GO annotation. Additionally, we may answer the question
rasied by Zhang et al. (2015) that MYB39 might take part in
phenylpropanoid pathway [[247]75]. Zhou et al. (2016) also reported
that APL took part in phenylpropanoid and flavonoid biosynthesis
pathways [[248]52]. CA10g03650 and CA10g12810 were different from A
locus because A locus was homologous to tomato ANT1 and petunia AN2
[[249]15]. The two genes (CA10g03650 and CA10g12810) might be detected
as a second or third gene other than A locus to explain the synthesis
of anthocyanin, which was consistent with the postulation of Deshpande
(1933) [[250]74]. Although previous studies have showed that the method
of BSA combined with SLAF-seq in the study was
accurate[[251]29,[252]30,[253]32,[254]33,[255]34,[256]54], A locus
wasn’t identified most possibly because the plant material used in this
study was different from that of Borovsky et al. (2004) [[257]15]. At
the same time, the function of two genes in anthocyanin metabolism and
the relationship between two genes with CaMYB need to be further
verified. COG annotation indicated that CA10g12710 is a likely
WD40-repeat protein that might regulate the expression of anthocyanin.
Finally, CA10g03810 is related to a gene involved in accumulation of
anthocyanin in tissues exposed to ultraviolet light, and a SNP
resulting in a non-synonymous mutation in CA10g12840 was found. All
above annotated genes lay a good foundation for the understanding of
the genetic regulatory mechanisms of the anthocyanin accumulation in
pepper fruit, and allow us to be cloned to further analyze the function
of these genes that influence anthocyanin accumulation in this species,
but not all genes are identified due to the limitation of BSA and large
pepper genome (3.48Gb) as previous study mentioned [[258]54]. Although
these genes are homologous to genes that are related to anthocyanin
biosynthesis and accumulation, or contain non-synonymous SNPs in
pepper, direct evidence as to whether these candidate genes control
anthocyanin accumulation in pepper has not been found, but could be
revealed by future analyses of the functions of these candidate genes.
Supporting information
S1 Fig. The anthocyanin biosynthesis pathway in pepper.
(TIF)
[259]Click here for additional data file.^ (1.1MB, tif)
S2 Fig. Distribution of SLAFs on the chromosomes of the pepper CM334
reference genome in the restriction enzyme digestion experiment.
The x-axis and y-axis represent chromosome length and chromosome
number, respectively. The distance between two adjacent yellow bars
indicates 1 Mb on the chromosome, and black lines indicate SLAFs or
SNPs.
(TIF)
[260]Click here for additional data file.^ (2.1MB, tif)
S3 Fig. The distribution of paired-end reads of the rice SLAF control
mapped to the rice genome.
Fragments between 364 and 414 bp in size were chosen.
(TIF)
[261]Click here for additional data file.^ (166.6KB, tif)
S4 Fig. Circular graph of sequence variants detected by BSA in pepper.
The first to fifth circles in the graph represent, in order, the 12
chromosomes of pepper, gene distribution, SNP density, Euclidean
distance values, and ΔSNP-index values related to anthocyanin
accumulation.
(TIF)
[262]Click here for additional data file.^ (1.4MB, tif)
S5 Fig. GO term enrichment analysis of 24 candidate genes related to
anthocyanin content according to functional categories in the cellular
component, molecular function and biological process domains.
(TIF)
[263]Click here for additional data file.^ (501.9KB, tif)
S6 Fig. Directed acyclic graphs of enriched cellular component domain
GO terms in the candidate region associated with anthocyanin
accumulation.
Each enriched GO term is shown, and the box indicates the 10
most-enriched terms. A detailed description of each GO term and the
significance of its enrichment are shown in the box or ellipse.
Different colors represent different degrees of significance of
enrichment: darker colors indicate greater significance.
(PDF)
[264]Click here for additional data file.^ (53.4KB, pdf)
S7 Fig. Directed acyclic graphs of enriched GO terms from the molecular
function domain in the candidate region for anthocyanin accumulation.
Each enriched GO term is shown, and the box indicates the 10
most-enriched terms. A detailed description of each GO term and the
significance of its enrichment are shown in the box or ellipse.
Different colors represent different degrees of significance of
enrichment: darker colors indicate greater significance.
(PDF)
[265]Click here for additional data file.^ (37KB, pdf)
S8 Fig. Directed acyclic graphs of enriched biological process domain
GO terms in the candidate region for anthocyanin accumulation.
Each enriched GO term is shown, and the box indicates the 10
most-enriched terms. A detailed description of each GO term and the
significance of its enrichment are shown in the box or ellipse.
Different colors represent different degrees of significance of
enrichment: darker colors indicate greater significance.
(PDF)
[266]Click here for additional data file.^ (140.5KB, pdf)
S1 Table. Anthocyanin concentrations in parental lines and high- and
low-anthocyanin pools.
(XLSX)
[267]Click here for additional data file.^ (11.8KB, xlsx)
S2 Table. The distribution of SLAFs and SNPs on each chromosome of
pepper.
(DOCX)
[268]Click here for additional data file.^ (13.3KB, docx)
S3 Table. The properties of all SNPs identified between high- and
low-anthocyanin lines and pools.
(XLSX)
[269]Click here for additional data file.^ (72.7MB, xlsx)
S4 Table. Annotation of SNP markers in the candidate region for
high-and low- anthocyanin parents and pools by both Euclidean distance
and SNP-index association analysis.
(DOCX)
[270]Click here for additional data file.^ (14.2KB, docx)
S5 Table. Annotation for 126 candidate genes for anthocyanin
accumulation.
(XLSX)
[271]Click here for additional data file.^ (40.3KB, xlsx)
S6 Table. Pathway enrichment analysis via KEGG for candidate genes in
pepper.
(DOCX)
[272]Click here for additional data file.^ (13KB, docx)
Data Availability
All relevant data are within the paper and its Supporting Information
files.
Funding Statement
Construction Program of Science and Technology Innovation Capability of
Beijing Academy of Agriculture and Forestry ScienceS (KJCX2018042),
Innovation Ability Construction Project of Beijing Academy of
Agriculture and Forestry Sciences (KJCX20170102-14) and National Key R
& D Plan (2016YFD0101700).
References