Candidate genes for first flower node identified in pepper using combined SLAF-seq and BSA
Candidate genes for first flower node identified in pepper using combined SLAF-seq and BSA
Xiaofen Zhang 0 1 2
Guoyun Wang 0 1 2
Bin Chen 0 1 2
Heshan Du 0 1 2
Fenglan Zhang 0 1 2
Haiying Zhang 0 1 2
Qian Wang 0 2
Sansheng Geng 0 1 2
☯ These authors contributed equally to this work. 0 2
0 R & D Plan (2016YFD0101700), Beijing Innovation Team Building Project (GCTDZJ2014033002) to Xiao Fen Zhang, Innovation Ability Construction Project of Beijing Academy of Agriculture and Forest Sciences (KJCX20170102-14) and National Natural Science Foundation of China (31672157). The funders had no role in study design , data
1 Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China), Ministry of Agriculture , Beijing , P.R. China , 2 College of Horticulture, China Agricultural University , Beijing , P.R. China
2 Editor: Prasanta K. Subudhi, Louisiana State University College of Agriculture , UNITED STATES
First flower node (FFN) is an important trait for evaluating fruit earliness in pepper (Capsicum annuum L.), but the genetic mechanisms that control FFN are still poorly understood. In the present study, we developed 249 F2 plants derived from an intraspecific cross between the inbred pepper lines Z4 and Z5. Thirty plants with the highest FFN and 30 plants with the lowest FFN were chosen and their DNAs were pooled according to phenotype to construct two bulked DNA pools. Specific-locus amplified fragment sequencing (SLAF-seq) was combined with bulked segregant analysis (BSA) to identify candidate regions related to FFN. According to our genetic analysis, the FFN trait is quantitatively inherited. A total of 106,848 high-quality single nucleotide polymorphism (SNP) markers were obtained, and 393 high-quality SNP markers associated with FFN were detected. Ten candidate regions within an interval of 3.98 Mb on chromosome 12 harboring 23 candidate genes were identified as closely correlated with FFN. Five genes (CA12g15130, CA12g15160, CA12g15370, CA12g15360, and CA12g15390) are predicted based on their annotations to be associated with expression of the FFN trait. The present study demonstrates an efficient genetic mapping strategy and lays a good foundation for molecular marker-assisted breeding using SNP markers linked to FFN and for cloning and functional analysis of the key genes controlling FFN.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Pepper (Capsicum annuum L.), Solanaceae, has a sympodial shoot structure with a solitary
flower known as the first flower. During vegetative growth, the pepper shoot apical meristem
(SAM) produces stems and leaves that are arranged in an alternate spiral pattern. The SAM
can later undergo a transition to an inflorescence meristem that subsequently develops into
the first flower at start of the transition from vegetative to reproductive growth [
Therefore, the formation of the first flower is a crucial phase in plant growth that is regulated by a
collection and analysis, decision to publish, or
preparation of the manuscript.
complex network of genes that promote [1±6] or suppress [
] flowering. The
FASCICULATE (FA) and CaJOINTLESS (CaJ) genes control sympodial shoot development in pepper.
FA also stimulates late flowering [
] and CaJ promotes first flower development while
suppressing inflorescence development in pepper [
]. The floral meristem identity genes
CaANANTH, Ca-LEAFY, and Capsicum annuum S promote flower formation [
CaBLIND and CaHAM regulate axillary branching [
]. Additionally, CaJ and CaBLIND are
both epistatic to FA for controlling flowering time and suppressing vegetative growth during
the reproductive phase of pepper [
]. However, the CaRNA-binding protein has a repressive
effect on the flowering time , and Ca-APETALA2, which maps to pepper chromosome 02,
represses flowering in pepper [
]. Although these genes reportedly control the transition
from the vegetative stage to the flowering stage during development in pepper, the molecular
regulatory mechanisms controlling the formation of the first flower in pepper are still poorly
The node at which the first flower develops has been designated as the first flower node
(FFN) in pepper. The position of this node, or its node number, on the primary axis from the
cotyledonary node to the first flower node defines the FFN trait. Pepper species exhibit
extensive natural variation in FFN [
]. Thus, FFN has been an important trait for evaluating
fruit earliness in pepper breeding . In general, earliness in pepper has also been described
in reference to some other agronomic traits such as flowering date or flowering earliness [
], plant height , FFN [
9, 10, 11
], the number of leaves on the primary axis [
number of lateral branches on the primary axis . For example, Liu (2015) found that plants
with lower FFN and moderate plant height exhibited earlier maturity [
]. Furthermore, FFN
is correlated positively with plant height, main stem length, the number of leaves, and the
number of branches [
], which are controlled by quantitative trait loci (QTL). In addition,
FFN is also a primary factor controlling flowering time [
]. In pepper, QTL controlling
flowering date or flowering earliness have been detected on pepper chromosomes 02, 04, and 12
]; QTLs for primary axis length have been identified on pepper chromosomes 02, 03, 09,
and 12 [
]; QTLs for plant height have been detected on pepper chromosomes 02±08 [
]; QTLs for the lateral branch number on the primary axis have been identified on
chromosome 02 [
]; and QTLs for the number of leaves on the main stem have been detected on
all pepper chromosomes except for chromosome 09 [
12, 13, 14, 19
]. In tomato, FFN has been
mapped to tomato chromosomes 02, 03, 05b, 08b, and 11 . However, studies to map QTLs
and identify genes that control the FFN trait in pepper have been limited to date. The
publication of the pepper genome sequence in 2014 [
] should facilitate finely mapping of the FFN
trait in pepper.
Bulked segregant analysis (BSA) [
] is a simplified strategy for identifying molecular
markers tightly linked to a gene. In BSA, a pair of bulked DNA samples is derived by pooling
DNAs from individuals that are grouped according to contrasting extreme phenotypes; these
bulked DNAs are then genotyped. In pepper, loci controlling specific traits have been
identified using a combination of BSA and various molecular markers [16, 23±25]. However, it is
challenging to develop thousands of candidate molecular markers and screen them in the
bulked pools to discover the small subset of markers diagnostic for the target phenotype.
Next-generation sequencing has promoted the development of new strategies to leverage the
advantages of BSA. For example, specific-locus amplified fragment sequencing (SLAF-seq) is a
strategy for discovery of single nucleotide polymorphisms (SNPs) facilitated by
reduced-representation genome sequencing and next-generation sequencing technologies. SLAF-seq is a
rapid, high-throughput, high-accuracy, and cost-effective strategy for large-scale SNP
discovery and genotyping [
]. The combined BSA and SLAF-seq strategy has been used for SNP
discovery in many species including rice [
], cotton [
], Brassica napus [
], and melon [
2 / 18
among others. Integrated BSA and SLAF-seq strategies also have been successfully used for
SNP discovery in pepper to refine the region containing a gene for resistance to Phytophthora
root rot to a 2.57-Mb region [
]. This combined approach has also been used to identify one
major QTL associated with resistance to Cucumber mosaic virus on pepper chromosome 02
]. These studies have demonstrated the efficiency of combined BSA and SLAF-seq as a
strategy for the identification of genes or QTLs linked to a specific trait in plants.
Thus, in the present study we have used a combined BSA and SLAF-seq strategy to identify
genomic regions linked to the FFN trait in DNAs pooled from distinct FFN phenotypes in an
F2 population derived from a cross between inbred parental lines Z4 and Z5 in pepper. Our
objectives were to: 1) investigate the mode of inheritance of the FFN trait in pepper; 2) identify
the genomic regions correlated with variation in FFN; and 3) identify candidate genes and
SNP markers linked to the FFN trait in pepper.
Materials and methods
The first flower node was measured as the number of nodes on the primary axis from the node
of the cotyledon to that of the first flower. An F2 mapping population comprising 249 plants
was derived from a cross between the Capsicum annuum pepper lines Z4 and Z5, which were
bred in Beijing Vegetable Research Center of the Beijing Academy of Agriculture and Forestry
Sciences. Plants from line Z5 tend to be tall and have a high FFN (i.e., 19 nodes), while those
from line Z4 tend to be of medium height with a low FFN (i.e., 9 nodes). Parental and F2 plants
were grown in the greenhouse at the Beijing Vegetable Research Center of the Beijing
Academy of Agriculture and Forestry Sciences in Beijing, China.
Genetic analysis of the FFN trait
Phenotypic data were statistically analyzed using Microsoft Excel (Microsoft Office, Microsoft,
2003) and data were plotted using SigmaPlot 10.0 (SPSS Inc., Chicago, IL).
DNA extraction and construction of DNA pools
Total genomic DNA was isolated from young leaves of the both parental lines and F2 plants
using the cetyl trimethyl ammonium bromide (CTAB) method [
]. Two DNA pools were
constructed by separately pooling an equal amount of DNA from each of 30 extreme high FFN
plants (H-pool) or 30 extreme low FFN plants (L-pool) identified in the F2 population.
Construction of SLAF libraries and high-throughput sequencing
The genome of Capsicum annuum cv. `Criollo de Morelos 334' (CM334) (http://peppergenome.
snu.ac.kr/download.php, version 1.55) was used as the reference genome in the present study.
Genomic DNAs from both parents and the H-pool and the L-pool were digested with the
restriction enzyme HaeIII (New England BioLabs, Ipswich, USA) after optimizing restriction
enzyme digestion completeness to obtain even genome coverage. Single-nucleotide A
overhangs were polished from these DNA fragments using Klenow fragment (New England
BioLabs), and fragments were then ligated to dual-index sequencing adaptors [
fragments were then amplified by PCR, purified, pooled, and screened to construct the SLAF
library. Details of the processes for SLAF library construction and screening were performed as
described in Sun et al. (2013) [
]. Target DNA fragments of sizes in the range of 414±514 bp
were selected as SLAFs from the quality-tested library and prepared for paired-end sequencing
on an Illumina HighSeq 2500 platform (Illumina, Inc., San Diego, CA, USA) at Beijing
3 / 18
Biomarker Technologies Corporation in Beijing, China (http://www.biomarker.com.cn). To
check the reliability and validity of sequencing and screening processes, the genome of rice
(Oryza sativa L. japonica, http://rice.plantbiology.msu.edu/, version 7.0) was selected as a
control to undergo in parallel the same library construction and sequencing processes as performed
for the pepper mapping population.
Data analysis for SLAF-seq
Raw reads were filtered for quality and trimmed to remove adaptors, and then sequence
quality was assessed based on sequencing quality scores and guanine-cytosine (GC) content [
The proportion of sequencing quality scores Q30 in the four libraries was >80% (A quality
score of Q30 indicates a 0.1% error rate or 99.9% sequence accuracy.). High-quality reads were
mapped onto the pepper reference genome using BWA software [
]. We clustered all
pairedend reads that had perfect index reads according to sequence similarity among both parents
and the two pooled libraries using blast [
]. Sequences with >90% identity were grouped into
a single SLAF locus (or SLAF tag).
Identification of high-quality SNPs
Single-nucleotide polymorphisms (SNPs) were detected primarily using GATK software [
Using clean reads mapped onto the reference genome, local realignments were conducted, and
SNPs were detected using GATK software as described by https://www.broadinstitute.org/
gatk/guide/best-practices.php. To ensure the accuracy of SNPs identified using GATK,
SAMtools software also was used to detect SNPs [
]. The intersection of SNPs that were detected
using both GATK and SAMtools software was designated as final SNPs for further analysis. In
addition, the localization (e.g., upstream, downstream, or intergenic regions) of SNPs, and the
coding effects (e.g., synonymous or non-synonymous mutation) of SNPs were annotated
using SnpEff software [
] based on gene model annotations at the pepper reference genome
databases (http://peppergenome.snu.ac.kr/download.php, version 1.55). Before performing
association analysis, SNPs were filtered using the following criteria: SNPs with multiple alleles
were filtered out; SNPs with sequencing depths of less than 4× in each pool or parent were
excluded; SNPs with the same genotypes among pools were removed; and SNPs with recessive
alleles that were not inherited from parents with recessive genotypes in pools were filtered out.
Ultimately, a collection of high-quality SNP markers was obtained for use in association
Association mapping was performed using either Euclidean distance (ED) [
] or the
SNPindex algorithm [
] separately. ED between the allele frequencies at each SNP in the
Lpool and H-pool was calculated as in Hill et al. (2013) using the equation 
AL pool AH pool2
CL pool CH pool2
GL pool GH pool2
TL pool TH pool2;
where each letter (A, C, G, T) indicates the frequency of its corresponding DNA nucleotide.
ED values were then squared to decrease the effects of noise and increase the effects of large
ED measurements based on distance measurements raised to a power (EDx). Data were then
fitted using Loess regression [
]. In addition, the threshold for the significance of
markertrait associations was set at 1% of the biggest Loess-fitted values. The genomic regions at which
the Loess-fitted values exceeded the threshold were then designated as candidate regions
related to FFN in pepper.
4 / 18
The SNP-index algorithm is valuable for finding significant differences in the frequencies
of genotypes between DNA pools. The frequency of genotypes in the SNP-index algorithm is
denoted by SLAF depth [
]. The SNP-index is calculated as the proportion of the depth of
Lpool or H-pool derived from the female parent relative to the two parental depths, and then
the ΔSNP-index is defined by subtracting the H-pool SNP-index from the L-pool SNP-index.
Therefore, the ΔSNP-index is equal to 0 if the SNP-index of the L-pool is equal to that of the
H-pool. If the ΔSNP-index value equals 1, one genotype is associated almost entirely with the
high FFN pool, and the associated SNPs are therefore linked closely to the high FFN
phenotype. On the contrary, if the ΔSNP-index value equals -1, the associated SNPs are linked to the
low FFN phenotype. The confidence coefficient of the ΔSNP-index was calculated, and all
ΔSNP-index values were fitted using SNPNUM [
]. FFN-related regions were identified
when the fitted values of markers were above the threshold at the 99% of confidence interval.
Finally, ED, SNP-index, and ΔSNP-index values were plotted and the intersections between
the candidate regions for FFN that were identified using the ED and SNP-index methods were
designated as the final candidate FFN-related regions. A circular graph representing the
distribution of chromosomes, genes, SNPs, ED values, and ΔSNP-index values was then plotted
using CIRCOS 0.66 software (http://circos.ca/).
Annotation of candidate genes
To verify the predicted genes in the target region, we compared these candidate genes to the
CM334 reference genome using blast. Putative functions of candidate genes were predicted
based on sequence alignments with annotated genes in the databases at Swiss-Prot (http://
www.uniprot.org/), the Gene Ontology (GO, http://www.geneontology.org/), the Cluster of
Orthologous Groups of proteins (COG, http://www.ncbi.nlm.nih.gov/COG/), the Kyoto
Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/), and the NCBI
nonredundant protein database (NR, ftp://ftp.ncbi.nih.gov/blast/db/) using blastp with default
Genetic analysis of the FFN trait
An F2 population comprising 249 plants developed from the Z4 × Z5 cross was used to
investigate the inheritance of FFN, which ranged in value from 8 to 20 (Fig 1). The continuous
phenotypic distribution and transgressive segregation of FFN in the F2 population in Fig 1
suggests that FFN is quantitatively inherited, and predicts that the FFN phenotype is likely
controlled bysome major genes and some minor genes.
Evaluation of the SLAF library
HaeIII was chosen as the restriction enzyme for SLAF library construction based on
preliminary experiments to identify a restriction enzyme that produced SLAFs evenly distributed on
the pepper reference genome (S1A Fig). The SLAF libraries were then evaluated to verify the
accuracy of sequencing compared with the rice genome control. First, the sequence reads of
the rice genome control were compared with those of the pepper reference genome using
BWA software [
]. The percentage of mapped paired-end reads in the pepper genome was
80.12% (Table 1), which is a typical efficiency for mapped paired-end reads from a SLAF
library. Second, the efficiency of enzyme digestion is an important index for revaluating the
likelihood of successful SLAF experiments. More efficient restriction enzyme digestion results
in more successful SLAF experiments. In the present study, the efficiency of restriction enzyme
5 / 18
Fig 1. The phenotypic distribution of the first flower node (FFN) trait in the F2 population and inbred pepper lines Z4 and
digestion was 91.45% (Table 1), indicating that restriction enzyme digestion was adequate for
constructing the SLAF libraries. Finally, average read length of SLAF was determined based on
the insert size distribution of mapped paired-end reads in the rice genome. When the distance
between reads at the ends of SLAFs was within 1 Kb, the integrity of SLAFs was greater than
0.1 and sequencing depth was greater than 3×, SLAFs of 414±514 bp in length were used for
further analyses (S2 Fig).
Sequence data analysis and SNP identification
After constructing SLAF libraries and performing high-throughput sequencing, a total of
153,821,146 raw reads were obtained with an average read length of 100 bp (Table 2). After
L-pool, the DNA pool from plants with the lowest first flower node phenotype; H-pool, the DNA pool from plants with the highest first flower node phenotype;
Proportion of mapped reads (%), clean reads mapped to the pepper reference genome as a percentage of the total clean reads; Q30, a quality score of 30 indicates 0.1%
error rate or 99.9% sequence accuracy; GC, guanine-cytosine content; SLAF, specific-locus amplified fragment; SNP, single-nucleotide polymorphism.
filtering reads, 76.24 Mb of clean reads and 15.25 Gb of clean bases remained. The average
proportion of reads with Q30 scores was 92.11% and average GC content was 39.17%, indicating
that most bases were of high quality. Further, BWA software was used to map clean reads to
the pepper reference genome [
]. The proportion of clean reads that could be mapped to the
reference genome was >88.81%, which also reflects sequencing accuracy (Table 2). In total,
cluster analysis identified 492,259 SLAFs (S1 Table) that were distributed evenly on the
chromosomes of the pepper reference genome (S1B Fig). The highest number of SLAFs occurred
on pepper chromosome 03, while the lowest number of SLAFs occurred on chromosome 08.
There were 429,868 SLAFs in Z4, 420,488 in Z5, 476,558 in the L-pool, and 477,116 in the
Hpool. Average sequencing depths were 36.92× in the parental libraries, 32.36× in the L-pool,
and 36.57× in the H-pool (Table 2).
A total of 1,001,405 SNPs were identified using GATK [
] and SAMtools software [
and the properties of total SNPs were showed in S2 Table. The number of SNPs on each
chromosome (shown in S1 Table) ranged from 21,371 on chromosome 08 to 150,965 on
chromosome 11. The sequence coverage of the pepper genome was more than 0.042 when calculated
across all markers. The distribution of SNPs on each pepper chromosome is shown in S1C Fig.
After filtering, a total of 106,848 of high-quality SNPs remained as useful markers for
association analysis to identify candidate regions associated with FFN in pepper.
Association analysis based on Euclidean distance
ED values were calculated for total of 106,848 high-quality SNP markers to identify genomic
regions and markers associated with FFN. The ED value for each pair of SNPs was calculated.
ED values were squared to decrease the effects of noise, and association values were then fitted
by Loess regression [
]. The marker-trait association threshold was set to 0.31based on the
1% of the biggest Loess-fitted values. A graph was generated for the association values based
on ED for each chromosome (Fig 2). Only one candidate region related to FFN for which the
Loess-fitted value was above the threshold was detected on chromosome 12. In this region, we
identified an interval from 196,328,926 to 210,751,601 bp containing 125 genes, among which
two genes contained non-synonymous SNPs (Table 3). There were also 1,069 high-quality
SNP markers within these candidate regions.
Association analysis based on SNP-indices
The SNP-indices for 106,848 SNP markers were calculated, and graphs of SNP-index values
were drawn for the H-pool (Fig 3A) and L-pool (Fig 3B) as plots of average SNP-index values
against each sliding-window position in the CM334 genome assembly. The ΔSNP-index was
determined by subtracting the SNP-index of the L-pool from that of the H-pool, and was
7 / 18
Fig 2. Graph of Euclidean distance-based association values between SNPs on each chromosome. The x-axis represents the 12 pepper
chromosomes, and the y-axis represents the association value based on Euclidean distance. The colored dots represent the association values based on
Euclidean distance at each SNP location. The red dashed line and black line represent association threshold and Loess-fitted values, respectively. Higher
association values based on Euclidean distance indicate stronger association between a SNP and first flower node (FFN).
plotted (Fig 3C). Ten candidate regions for control of the FFN phenotype were identified on
chromosome 12 by examining the ΔSNP-index plot and identifying the fitted values of SNP
markers that were above the threshold at the 99% confidence interval. Although we did not
identify any candidate genes within the five FFN candidate regions, in five other regions we
identified one gene within the interval from 199,120,000 to 199,293,531 bp, one gene
within the interval from 199,970,974 to 200,210,994 bp, one gene within the interval from
200,211,315 to 200,281,965 bp, 12 genes within the interval from 199,293,569 to 199,952,426
8 / 18
Fig 3. Graphs of the SNP-index of the H-pool (A), the L-pool (B), and the ΔSNP-index values (C) for association analysis. The x-axis and y-axis
indicate the 12 pepper chromosomes and the SNP index, separately. The black line represents the fitted SNP-index or ΔSNP-index. The red, blue, or
green line indicates the threshold for association with FFN at the 99%, 95%, or 90% confidence interval, respectively.
bp, and eight genes within the interval from 201,055,482 to 203,828,248 bp. A total of 393 SNP
markers were detected within those candidate regions within a 3.98 Mb interval (Table 3).
However, none of the identified genes carried non-synonymous SNPs.
Final candidate regions tightly associated with the FFN trait in pepper were identified using
a combination of ED and SNP-index association analysis. The same candidate regions were
identified by ED and SNP-index association analyses within the interval from 0.00091 to 2.77
Mb on chromosome 12 that harbors 23 genes.
Visualization of the combined results of SLAF-seq and BSA
We used a strategy combining SLAF-seq and BSA to identify genomic regions, SNPs, and
genes associated with FFN. All of the results of these approaches are visualized in the circular
graph shown in S3 Fig. The circles in this graph, from the outermost to the innermost,
9 / 18
represent the 12 pepper chromosomes, the distribution of genes on the pepper chromosomes,
SNP density, ED values, and ΔSNP-index values, respectively.
Annotated SNP markers and genes within the candidate region
We identified a total of 393 high-quality SNP markers within the candidate regions (Table 3).
There were 58 high-quality SNP markers within the interval from 199,293,569 to 199,952,426
bp, and three high-quality SNP markers within each of the intervals from 198,792,663 to
198,793,573 bp, from 200,326,950 to 200,359,194 bp, and from 200,629,900 to 200,638,585 bp,
respectively (Table 3). The localizations and coding effects of SNPs in these candidate regions
were annotated using SnpEff software [
]. There were 3, 588, and 3 SNPs between genes in
the Z4 and Z5 genomes in their upstream, intergenic, and downstream regions, respectively.
We also identified 4, 418, and 1 SNPs between the H-pool and the L-pool in the upstream,
intergenic, and downstream regions of genes, respectively (S3 Table). However, no
non-synonymous SNPs were identified in the candidate regions among parental lines and both pools
(S3 Table). Thus, more than 98% of SNP markers were located in intergenic regions.
We predicted that 23 protein-coding genes (Table 4), including nine genes with no
annotations in the public databases, are be located within the 10 candidate regions for FFN on pepper
chromosome 12, based on the current annotation of the CM334 pepper reference genome. We
found annotations for 12, 5, 1, and 12 genes in the candidate regions in the GO, COG, KEGG,
10 / 18
and Swiss-Prot databases, respectively. Some genes were annotated with more than one term
in different domains, and could thus be categorized into two or more functional categories (S4
Fig). GO term enrichment analysis of predicted genes yielded functional assignments of 45
genes to the `cellular component' domain, 12 genes to the `molecular function' domain, and
43 genes to the `biological process domain (S4 Fig). As shown in S4 Fig, 11 genes in the `cellular
component' domain are associated with the GO term `cell part' (GO:0044464); four genes
in the `molecular function' domain are associated with the GO term `catalytic activity'
(GO:0003824), four genes in the `molecular function' domain are associated with `transporter
activity' (GO:0005215), four genes in the `molecular function' domain are associated with
`binding' (GO:0005488); and 10 genes are associated with the GO term `cellular process'
(GO:0009987) in the `biological process' domain. COG analysis predicted five genes, among
which the functions of CA12g15090, CA12g15100, and CA12g15320 were related to
`transcription', `general function prediction only', and `inorganic ion transport and metabolism',
respectively. The function of CA12g15130 was related to `intracellular trafficking, secretion, and
vesicular transport', and that of CA12g15370 was associated with `posttranslational
modification, protein turnover, and chaperones' (Table 4; Fig 4). KEGG analysis identified a match for
only one gene (CA12g15130), a homolog of SEC22 in Arabidopsis thaliana that encodes a 25.3
kDa vesicle transport protein that takes part in both the `phagosome' pathway (S5 Fig) and the
`SNARE interactions in the vesicular transport' pathway (S6 Fig). Five of these 23 candidate
genes might be related to first flower node development based on our current annotation, but
their functions must be further studied. CA12g15130 plays an important role in vesicle
trafficking from the endoplasmic reticulum to the Golgi complex, and is highly expressed in
Arabidopsis flowers [
]. CA12g15360, which encodes the pentatricopeptide repeat-containing
protein At2g40720, was predicted to take part in xylem and phloem pattern formation
Fig 4. Functional classification of candidate genes at the Cluster of Orthologous Groups of proteins database.
11 / 18
according to its GO annotation. The RING-H2 finger protein ATL52 (CA12g15370) might be
involved in the protein `ubiquitination' pathway. Hu et al. (2003) reported that ATL52 is
expressed in Arabidopsis flowers [
]. Additionally, CA12g15390 matched a purine permease
1, which is highly expressed in leaves, stems, and flowers, but not in roots [
]. This protein
has been predicted to function as a transporter for nucleotides, nucleosides, and their
derivatives . CA12g15160 is homologous to At1g48120 from Arabidopsis, which encodes a serine/
threonine-protein phosphatase 7 long form homolog that is expressed in the SAM, root tips,
hydathodes, leaf vasculature, and mature flowers [
]. Because the stem and leaves arise from
the SAM, this gene might be important for the development of the first flower node, as it
might be required to maintain cell division activity in meristematic cells. These five predicted
genes that are expressed in flowers, xylem and phloem, stem, and the SAM [43±47] participate
in many important biological processes in relevant tissues and organs and might play
important roles in the FFN trait in pepper. The molecular functions, if any, of these candidate genes
in the control of FFN need to be further examined.
SLAF-seq and BSA has been successfully used in many gene mining research
SLAF-seq, a recently developed strategy for SNP discovery and large-scale genotyping [
has been applied successfully for construction of linkage maps and analysis of QTL in many
species [48±52]. Compared with earlier methods of marker development (e.g., restriction
fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified
fragment length polymorphism (AFLP), and simple sequence repeat (SSR), SLAF-seq has
several advantages including high accuracy, high throughput, efficiency, and cost effectiveness for
SNP discovery and large-scale genotyping [
]. SLAF-seq takes advantage of large amounts of
sequence data to develop the SLAF markers, improve marker development, and increase
coverage of entire genomes [
]. In addition, BSA is an efficient way to identify markers that are
specific for a trait of interest. We combined the SLAF-seq and BSA techniques to identify the
genomic regions associated with the FFN trait in pepper. Previous studies have combined the
SLAF-seq and BSA approaches successfully in many organisms [27±32]. For example, Xu et al.
(2016) combined SLAF-seq and BSA to map resistance to Phytophthora root rot to pepper
chromosome 10 [
]. Guo et al. (2017) have also identified one major QTL linked to Cucumber
mosaic virus resistance in the physical interval from 152.87 to 153.20 Mb on chromosome 02
in pepper using this integrated strategy [
We detected 10 FFN candidate regions efficiently by using SAF-seq combined with BSA
We obtained a total of 76.24 Mb of clean reads from 153.82 Mb of raw reads with an average
read length of 100 bp, and evaluated sequencing depth and sequencing quality scores. For
successful SLAF-seq, Sun et al. (2013) suggested that sequencing depth should be greater than 6×
and quality scores should not be lower than Q30 [
]. The restriction enzyme digestion
efficiency, sequencing depths, Q30 scores, and percentages of mapped paired-end reads achieved
in our study demonstrate that the construction and sequencing of our SLAF library were
sufficiently efficient, accurate, and high-quality.
SNPs are important tools for molecular genetic analysis, due to their high frequency, wide
], high polymorphism, and ability to reveal fine-scale genetic variation .
Chen et al. (2015) showed that SNP markers are much more densely distributed than are SSR
12 / 18
or other markers [
]. In total 1,001,405 SNPs were obtained in this study, and more than
21,371 SNPs were mapped to the chromosomes of pepper, covering the entire pepper genome
more densely than in the map published by Cheng et al. (2016) [
]. The SNP coverage (more
than 0.042) in our study was much higher than that in Chen et al. (2015) (~0.00208) [
provides adequate marker density and increases the accuracy of candidate gene identification.
A total of 106,848 high-quality SNP markers were used for association analysis to identify
candidate regions closely associated with FFN in pepper. To improve the accuracy of FFN
candidate region identification, we overlapped results from ED association analysis and SNP-index
association analysis, which accurately and quantitatively evaluated parental allele frequencies
and the inheritance of parental alleles by F2 progenies [
]. Although the criterions for the
threshold set in ED analysis and SNP-index analysis were different, candidate regions
identified in ED analysis were almost in coincidence with that in SNP-index analysis, revealing the
accuracy for FFN candidate region identification. As the threshold at the 99% of confidence
interval was used in SNP-index analysis, the results obtained in SNP-index analysis would be
more accuracy than that in ED analysis. Therefore, the final candidate regions were then
narrowed down to a 3.98-Mb interval on chromosome 12 harboring 393 high-quality SNP
markers. Annotations for SNP markers revealed that these SNP markers, 98% of which were located
in intergenic regions, were useful for refining the mapping of a gene related to FFN to a smaller
Since the CM334 reference genome was released in 2014, it has been available for
comparisons of high-throughput sequencing results from other crosses to determine the distribution
of DNA polymorphisms across the pepper genome [
]. FFN, an important criterion of fruit
earliness in pepper, is quantitatively inherited [
]. When FFN is lower and plants are shorter,
fruits develop earlier [
]. We mapped the FFN trait to 10 candidate regions on pepper
chromosome 12 in the intervals from 198,792,663 to 198,793,573 bp, from 199,120,000 to
199,293,531 bp, from 199,293,569 to 199,952,426 bp, from 199,970,974 to 200,210,994 bp,
from 200,211,315 to 200,281,965 bp, from 200,326,950 to 200,359,194 bp, from 200,629,900 to
200,638,585 bp, from 201,010,194 to 201,036,653 bp, from 201,044,155 to 201,044,589 bp, and
from 201,055,482 to 203,828,248 bp. These intervals ranged from 0.00091 to 2.77 Mb in length.
In previous studies, QTL for length of the primary axis and leaf number on the primary axis
have also been mapped to chromosome 12 [
]. Additionally, FFN is an important factor
related to flowering time [
], which has also been mapped to chromosome 12 [
FFN is positively correlated with plant height, primary axis length, the number of leaves, and
the number of branches [
] in pepper, loci in addition to FFN on chromosome 12 are likely
important for early vegetative development, which is consistent with the results of Alimi et al.
]. However, Alimi et al. (2013) mapped leaf number on the primary axis to a region
different than that related to FFN at 23.1 cM on chromosome 12 [
]. We conclude that the
3.98-Mb interval associated with FFN on pepper chromosome 12 is a strong candidate region
for FFN in pepper that could contain the gene(s) controlling this trait.
We identified 5 candidate genes correlated to FFN in pepper
Among the 23 candidate genes for which annotations were found in the GO, COG, KEGG,
and Swiss-Prot databases, some might be related to FFN, and should be useful for future gene
isolation and functional testing studies. S4, S5 and S6 Figs, Fig 4 and Table 4 show details of
the annotations from the GO, COG, KEGG and Swiss-Prot databases of the 23 candidate
genes identified in this study that can be related to FFN. The transition of the SAM from
vegetative to reproductive growth [
] reveals its importance in the development of the first
flower. In previous studies, FA, CaJ, Ca-ANANTH, Ca-LEAFY, Capsicum annuum S, and
13 / 18
CaBLIND were found to promote flower development [1±5], while the CaRNA-binding
protein and Ca-APETALA2 were found to suppress flower formation [
]. Tan et al. (2015)
annotated three homologs of Arabidopsis APETALA2 and CLF [
] as related to leaf number
on the primary axis in pepper, which might also be related to FFN. FFN is not only relevant to
flowering, but also to plant growth and development, including plant height, primary axis
length, lateral branch number, and leaf number on the primary axis [
]. Based on
annotations in our study, we have identified five candidate genes correlated to FFN in pepper that
are homologous to flower or stem development-related genes in Arabidopsis. CA12g15160,
which is homologous to Arabidopsis At1g48120 and encodes a serine/threonine-protein
phosphatase 7 long form homolog, is particularly interesting because it is closely related to
development of the SAM . Due to the importance of the SAM in the development of the first
flower, the gene CA12g15160 is an important candidate gene for FFN in pepper. Additionally,
CA12g15390, CA12g15370, and CA12g15130 are also highly expressed in Arabidopsis flowers
[43±46], and the GO annotation for CA12g15360 indicates that it could be related to xylem
and phloem development. The protein encoded by CA12g15130 might take part in both the
`phagosome' pathway and the `SNARE interactions in the vesicular transport' pathway.
Although these genes that were annotated based on the reference genome are homologous to
genes related to flower or stem development in Arabidopsis, none of previously known
flowering genes have been found within the candidate regions. On the one hand, the constructed
reference genome is still not perfect enough to find all the genes controlling pepper flowering due
to the large pepper genome (3.48 Gb) [
]. On the other hand, BSA used in the study can be
extended to identify a few major genes that controlling quantitative traits, but it is not useful to
the analysis of minor genes [
]. Additionally, we still lack direct evidence that these genes
control the FFN trait of pepper. Future functional analyses of these candidate genes could
reveal whether they play any parts in the control of the FFN trait in pepper.
In the present study, we combined the SLAF-seq technology and BSA to successfully identify
genomic regions involved in control of the FFN trait in pepper (Capsicum annuum lines Z4
and Z5). This method proved efficient for mapping genes related to the FFN trait in the
reference genome (Capsicum annuum line CM334). Ten candidate regions within a 3.98-Mb
interval on chromosome 12 were detected and associated with expression of the FFN trait. Five out
of 23 candidate genes, in particular CA12g15160, were chosen for further analysis based on
their annotations. The functions of the genes correlated to FFN will be further examined in
future studies using transformation and mutation approaches.
S1 Fig. SLAFs distributed on chromosomes of the pepper reference genome in the prelimi
nary restriction enzyme digestion experiment (A), and SLAFs (B) and SNPs (C)
distributed on chromosomes of samples. The x-axis and y-axis represent the length and sequence of
each chromosome, respectively. Each yellow bar indicates a chromosome that is divided into
1-Mb intervals and the black line indicates SLAF or SNP.
S2 Fig. Distribution of mapped pair-end reads in the rice genome.
S3 Fig. Circular graphic results from analysis of genome sequence variants and combined
SLAF-seq and BSA association analyses in the parental lines and two bulked DNA pools.
14 / 18
The first circle represents the 12 pepper chromosomes. The second circle represents the genes
distributed along the pepper chromosomes. The third circle represents the SNP density
distribution. The fourth circle represents the distribution of Euclidean distance values. The fifth
circle represents the distribution of ΔSNP-index values. Data were graphed using the Circos
S4 Fig. Functional classification of candidate genes via Gene Ontology term analysis.
S5 Fig. The annotated `phagosome' pathway associated with candidate genes in the candi
date regions. Blue boxes represent all of the known enzymes that participate in the
`phagosome' pathway and the red box indicates the enzyme associated with the annotated match to
the candidate gene.
S6 Fig. Annotated `SNARE interactions in the vesicular transport' pathway associated with
candidate genes in the candidate regions. Blue boxes represent all of the known enzymes that
participate in the `SNARE interactions in the vesicular transport' pathway and the red
box indicates the enzyme associated with the annotated match to the candidate gene.
S1 Table. Distribution of SLAFs and SNPs on each chromosome of Capsicum annuum
lines Z4 and Z5.
S2 Table. The properties of total SNPs identified in samples.
S3 Table. Annotation of SNP markers in candidate region for the parents and pools using association analysis based Euclidean distance or SNP-index.
Data curation: Heshan Du.
Funding acquisition: Fenglan Zhang.
Project administration: Bin Chen, Fenglan Zhang, Haiying Zhang, Qian Wang.
Supervision: Haiying Zhang, Qian Wang, Sansheng Geng.
Writing ± original draft: Xiaofen Zhang, Guoyun Wang.
Writing ± review & editing: Xiaofen Zhang, Sansheng Geng.
15 / 18
16 / 18
17 / 18
1. Cohen O , Borovsky Y , David-Schwartz R , Paran I. CaJOINTLESS is a MADS-box gene involved in suppression of vegetative growth in all shoot meristems in pepper . J Exp Bot . 2012 ; 63 ( 13 ): 4947 ± 4957 . https://doi.org/10.1093/jxb/ers172 PMID: 22859675
2. Cohen O , Borovsky Y , David-Schwartz R , Paran I. Capsicum annuum S (CaS) promotes reproductive transition and is required for flower formation in pepper (Capsicum annuum) . New Phytol . 2014 ; 202 ( 3 ): 1014 ± 1023 . PMID: 24716519
3. Lippman ZB , Cohen O , Alvarez JP , Abu-Abied M , Pekker I , Paran I , et al. The making of a compound inflorescence in tomato and related nightshades . PLoS Biol . 2008 ; 6 ( 11 ): e288. https://doi.org/10.1371/ journal.pbio. 0060288 PMID: 19018664
4. Elitzur T , Nahum H , Borovsky Y , Pekker I , Eshed Y , Paran I. Co-ordinated regulation of flowering time, plant architecture and growth by FASCICULATE: The pepper orthologue of SELF PRUNING . J Exp Bot . 2009 ; 60 ( 3 ): 869 ± 880 . https://doi.org/10.1093/jxb/ern334 PMID: 19174461
5. Jeifetz D , David-Schwartz R , Borovsky Y , Paran I. CaBLIND regulates axillary meristem initiation and transition to flowering in pepper . Planta . 2011 ; 234 ( 6 ): 1227 ± 1236 . https://doi.org/10.1007/s00425- 011 -1479-8 PMID: 21773792
6. David-Schwartz R , Borovsky Y , Zemach H , Paran I. CaHAM is autoregulated and regulates CaSTM expression and is required for shoot apical meristem organization in pepper . Plant Sci . 2013 ; 203 ± 204 : 8± 16 . https://doi.org/10.1016/j.plantsci. 2012 . 12 .011 PMID: 23415323
7. Borovsky Y , Sharma VK , Verbakel H , Paran I. CaAP2 transcription factor is a candidate gene for a flowering repressor and a candidate for controlling natural variation of flowering time in Capsicum annuum . Theor Appl Genet . 2015 ; 128 ( 6 ): 1073 ± 1082 . https://doi.org/10.1007/s00122-015 -2491-3 PMID: 25748116
8. Kim HM , Lee JH , Ah-Young K , Park SH , Ma SH , Lee S , et al. Heterologous expression of an RNA-binding protein affects flowering time as well as other developmental processes in Solanaceae . Mol Breeding . 2016 ; 36 ( 6 ): 71 . https://doi.org/10.1007/s11032-016-0494-7
9. Liu Z , Shen L , Yang Y , Cao Z. Genetic diversity and correlation analysis of main botanical traits of chili pepper genetic resources . Agric Biotechnol . 2015 ; 4 ( 4 ): 18 ± 22 .
10. Abu NE , Uguru MI , Obi IU . Genotypic stability and correlation among quantitative characters in genotypes of aromatic pepper grown over years . Afr J Biotechnol . 2013 ; 12 ( 20 ): 2792 ± 2801 .
11. Chen X , Chen J , Fang R , Cheng Z , Wang S . Inheritance of the node for first flower in pepper (Capsicum annuum L.) Acta Horticulturae Sinica . 2006 ; 33 ( 1 ): 152 ± 154 . Chinese .
12. Barchi L , Lefebvre V , Sage-Palloix A , Lanteri S , Palloix A . QTL analysis of plant development and fruit traits in pepper and performance of selective phenotyping . Theoret Appl Genet . 2009 ; 118 ( 6 ): 1157 ± 71 . https://doi.org/10.1007/s00122-009 -0970-0 PMID: 19219599
13. Mimura Y , Minamiyama Y , Sano H , Hirai M . Mapping for axillary shooting, flowering date, primary axis length, and number of leaves in pepper (Capsicum annuum) . J Jpn Soc Hortic Sci . 2010 ; 79 ( 1 ): 56 ± 63 .
14. Tan S , Cheng J , Zhang L , Qin C , Nong D , Li W , et al. Construction of an interspecific genetic map based on InDel and SSR for mapping the QTLs affecting the initiation of flower primordia in pepper (Capsicum spp .). PLoS One . 2015 ; 10 ( 3 ): e0119389. https://doi.org/10.1371/journal.pone. 0119389 PMID: 25781878
15. Han K , Jeong H , Yang H , Kang S , Kwon J , Kim S , et al. An ultra-high-density bin map facilitates highthroughput QTL mapping of horticultural traits in pepper (Capsicum annuum) . DNA Res . 2016 ; 23 ( 2 ): 81 ± 91 . https://doi.org/10.1093/dnares/dsv038 PMID: 26744365
16. Truong HH , Duthion C . Time of flowering of pea (Pisum sativum L.) as a function of leaf appearance rate and node of first flower . Ann Bot . 1993 ; 72 ( 2 ): 133 ± 142 .
17. Ben-Chaim A , Paran I , Grube RC , Jahn M , van Wijk R , Peleman J . QTL mapping of fruit-related traits in pepper (Capsicum annuum) . Theoret Appl Genet . 2001 ; 102 ( 6 ±7): 1016 ± 1028 .
18. Dwivedi N , Kumar R , Paliwal R , Kumar U , Kumar S , Singh M , et al. QTL mapping for important horticultural traits in pepper (Capsicum annuum L.) . J Plant Biochem Biotech . 2015 ; 24 ( 2 ): 154 ± 160 .
19. Alimi NA , Bink MC , Dieleman JA , MagaÂn JJ , Wubs AM , Palloix A , et al. Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper . Theoret Appl Genet . 2013 ; 126 ( 10 ): 2597 ± 2625 . https://doi.org/10.1007/s00122-013 -2160-3 PMID: 23903631
20. Liu Y , Zhou X , Zhang J , Li H , Zhuang T , Yang R , et al. Bayesian analysis of interacting quantitative trait loci (QTL) for yield traits in tomato . Afr J Biotechnol . 2011 ; 10 ( 63 ): 13719 ± 13723 .
21. Kim S , Park M , Yeom SI , Kim YM , Lee JM , Lee HA , et al. Genome sequence of the hot pepper provides insights into the evolution of pungency in Capsicum species . Nat Genet . 2014 ; 46 : 270 ± 278 . https://doi. org/10.1038/ng.2877 PMID: 24441736
22. Michelmore RW , Paran I , Kesseli RV . Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations . Proc Natl Acad Sci USA . 1991 ; 88 ( 21 ): 9828 ± 9832 . PMID: 1682921
23. Caranta C , Thabuis A , Palloix A . Development of a CAPS marker for the Pvr4 locus: A tool for pyramiding potyvirus resistance genes in pepper . Genome . 1999 ; 42 ( 6 ): 1111 ± 1116 . PMID: 10659777
24. Lee J , Yoon JB , Han J , Lee WP , Kim SH , Park HG . Three AFLP markers tightly linked to the genic male sterility MS3 gene in chili pepper (Capsicum annuum L.) and conversion to a CAPS marker . Euphytica . 2010 ; 173 ( 1 ): 55 ± 61 . https://doi.org/10.1007/s10681-009-0107-1
25. Lee H , An HJ , Kim H , Harn CH , Yang DC , Choi SH , et al. Development of a high resolution melting (HRM) marker linked to genic male sterility in Capsicum annuum L . Plant Breeding. 2012 ; 131 ( 3 ): 444 ± 448 . https://doi.org/10.1111/j.1439- 0523 . 2012 . 01956 .x
26. Sun X , Liu D , Zhang X , Li W , Liu H , Hong W , et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing . PLoS One . 2013 ; 8 ( 3 ): e58700. https://doi.org/10.1371/journal.pone. 0058700 PMID: 23527008
27. Xu F , Sun X , Chen Y , Huang Y , Tong C , Bao J . Rapid identification of major QTLs associated with rice grain weight and their utilization . PLoS One . 2015 ; 10 ( 3 ): e122206. https://doi.org/10.1371/journal. pone. 0122206 PMID: 25815721
28. Zhang Z , Li J , Muhammad J , Cai J , Jia F , Shi Y , et al. High resolution consensus mapping of quantitative trait loci for fiber strength, length and micronaire on chromosome 25 of the upland cotton (Gossypium hirsutum L.) . PLoS One . 2015b ; 10 ( 8 ): e0135430. https://doi.org/10.1371/journal.pone. 0135430 PMID: 26262992
29. Geng X , Jiang C , Yang J , Wang L , Wu X , Wei W. Rapid identification of candidate genes for seed weight using the SLAF-seq method in Brassica napus . PLoS One . 2016 ; 11 ( 1 ): e0147580. https://doi. org/10.1371/journal.pone. 0147580 PMID: 26824525
30. Zhang H , Yi H , Wu M , Zhang Y , Zhang X , Li M , et al. Mapping the flavor contributing traits on "Fengwei melon" (Cucumis melo L.) chromosomes using parent resequencing and super bulked-segregant analysis . PLoS One . 2016 ; 11 ( 2 ): e0148150. https://doi.org/10.1371/journal.pone. 0148150 PMID: 26840947
31. Xu X , Chao J , Cheng X . Mapping of a novel race specific resistance gene to Phytophthora root rot of pepper (Capsicum annuum) using bulked segregant analysis combined with specific length amplified fragment sequencing strategy . PLoS One . 2016 ; 11 ( 3 ): e151401. https://doi.org/10.1371/journal.pone. 0151401 PMID: 26992080
32. Guo G , Wang S , Liu J , Pan B , Diao W , Ge W , et al. Rapid identification of QTLs underlying resistance to cucumber mosaic virus in pepper (Capsicum frutescens) . Theoret Appl Genet . 2017 ; 130 ( 1 ): 41 ± 52 . https://doi.org/10.1007/s00122-016 -2790-3 PMID: 27650192
33. Fulton TM , Chunwongse J , Tanksley SD . Microprep protocol for extraction of DNA from tomato and other herbaceous plants . Plant Mol Biol Rep . 1995 ; 13 : 207 ± 209 .
34. Kozich JJ , Westcott SL , Baxter NT , Highlander SK , Schloss PD . Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform . Appl Environ Microbiol . 2013 ; 79 ( 17 ): 5112 ± 5120 . https://doi.org/10.1128/AEM.01043-13 PMID: 23793624
35. Li H , Durbin R . Fast and accurate short read alignment with Burrows±Wheeler transform . Bioinformatics . 2009 ; 25 ( 14 ): 1754 ± 1760 . https://doi.org/10.1093/bioinformatics/btp324 PMID: 19451168
36. Kent WJ . BLAT±the BLAST-like alignment tool . Genome Res . 2002 ; 12 ( 4 ): 656 ± 664 . https://doi.org/ 10.1101/gr.229202 PMID: 11932250
37. Hill JT , Demarest BL , Bisgrove BW , Gorsi B , Su YC , Yost HJ . MMAPPR: mutation mapping analysis pipeline for pooled RNA-seq . Genome Res . 2013 ; 23 ( 4 ): 687 ± 697 . https://doi.org/10.1101/gr.146936. 112 PMID: 23299975
38. Li H , Handsaker B , Wysoker A , Fennell T , Ruan J , Homer N , et al. The Sequence Alignment/Map (SAM) format and SAMtools . Bioinformatics. 2009 ; 25 ( 16 ): 2078 ± 2079 . https://doi.org/10.1093/ bioinformatics/btp352 PMID: 19505943
39. Cingolani P , Platts A , Wang LL , Coon M , Nguyen T , Wang L , et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3 . Fly (Austin). 2012 ; 6 ( 2 ): 80 ± 92 . https://doi.org/10.4161/fly.19695 PMID: 22728672
40. Takagi H , Yoshida K , Kosugi S , Natsume S , Mitsuoka C , Uemura A , et al. QTL-seq: Rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations . Plant J . 2013 ; 74 ( 1 ): 174 ± 183 . https://doi.org/10.1111/tpj.12105 PMID: 23289725
41. Fekih R , Takagi H , Tamiru M , Abe A , Natsume S , Yaegashi H , et al. MutMap+: Genetic mapping and mutant identification without crossing in rice . PLoS One . 2013 ; 8 ( 7 ): e0068529. https://doi.org/10.1371/ journal.pone. 0068529 PMID: 23874658
42. Abe A , Kosugi S , Yoshida K , Natsume S , Takagi H , Kanzaki H , et al. Genome sequencing reveals agronomically important loci in rice using MutMap . Nat Biotechnol . 2012 ; 30 ( 2 ): 174 ± 178 . https://doi.org/10. 1038/nbt.2095 PMID: 22267009
43. El-Kasmi F , Pacher T , Strompen G , Stierhof Y-D , MuÈller LM , Koncz C , et al. Arabidopsis SNARE protein SEC22 is essential for gametophyte development and maintenance of Golgi-stack integrity . Plant J . 2011 ; 66 ( 2 ): 268 ± 279 . https://doi.org/10.1111/j. 1365 - 313X . 2011 . 04487 . x PMID : 21205036
44. Hu W , Wang Y , Bowers C , Ma H. Isolation, sequence analysis, and expression studies of florally expressed cDNAs in Arabidopsis . Plant Mol Biol . 2003 ; 53 ( 4 ): 545 ± 563 . https://doi.org/10.1023/B: PLAN. 0000019063 .18097.62 PMID: 15010618
45. Gillissen B , Burkle L , Andre B , Kuhn C , Rentsch D , Brandl B , et al. A new family of high-affinity transporters for adenine, cytosine, and purine derivatives in Arabidopsis . Plant Cell . 2000 ; 12 ( 2 ): 291 ± 300 . PMID: 10662864
46. BuÈrkle L , Cedzich A , Doepke C , Stransky H , Okumoto S , Gillissen B , et al. Transport of cytokinins mediated by purine transporters of the PUP family expressed in phloem, hydathodes, and pollen of Arabidopsis . Plant J. 2003 ; 34 ( 1 ): 13 ± 26 . PMID: 12662305
47. UÈ hlken C , Horvath B , Stadler R , Sauer N , Weingartner M. MAIN-LIKE1 is a crucial factor for correct cell division and differentiation in Arabidopsis thaliana . Plant J . 2014 ; 78 ( 1 ): 107 ± 120 . https://doi.org/10. 1111/tpj.12455 PMID: 24635680
48. Qi Z , Huang L , Zhu R , Xin D , Liu C , Han X , et al. A high-density genetic map for soybean based on specific length amplified fragment sequencing . PLoS One . 2014 ; 9 ( 8 ): e104871. https://doi.org/10.1371/ journal.pone. 0104871 PMID: 25118194
49. Zhang J , Zhang QX , Cheng TR , Yang WR , Pan HT , Zhong JJ , et al. High±density genetic map construction and identification of a locus controlling weeping trait in an ornamental woody plant (Prunus mume Sieb . et Zucc). DNA Res . 2015a ; 22 ( 3 ): 183 ± 191 . https://doi.org/10.1093/dnares/dsv003 PMID: 25776277
50. Yu S , Su T , Zhi S , Zhang F , Wang W , Zhang D , et al. Construction of a sequence-based bin map and mapping of QTLs for downy mildew resistance at four developmental stages in Chinese cabbage (Brassica rapa L. ssp . pekinensis). Mol Breeding . 2016 ; 36 ( 4 ): 1± 12 .
51. Zhao Z , Gu H , Sheng X , Yu H , Wang J , Huang L , et al. Genome-wide single-nucleotide polymorphisms discovery and high-density genetic map construction in cauliflower using specific-locus amplified fragment sequencing . Front Plant Sci . 2016 ; 7 : 334 . https://doi.org/10.3389/fpls. 2016 .00334 PMID: 27047515 .
52. Zhang Z , Shang H , Shi Y , Huang L , Li J , Ge Q , et al. Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to quantitative trait loci (QTL) analysis for boll weight in upland cotton (Gossypium hirsutum .). BMC Plant Biol . 2016 ; 16 ( 1 ): 79 . https://doi.org/10.1186/s12870-016 -0741-4 PMID: 27067834
53. Chen X , Li XM , Zhang B , Xu JS , Wu ZK , Wang B , et al. Detection and genotyping of restriction fragment associated polymorphisms in polyploid crops with a pseudo-reference sequence: a case study in allotetraploid Brassica napus . BMC Genomics . 2013 ; 14 : 346 . https://doi.org/10.1186/ 1471 -2164-14-346 PMID: 23706002
54. Chen W , Yao J , Chu L , Yuan Z , Li Y , Zhang Y. Genetic mapping of the nulliplex-branch gene (gb_nb1) in cotton using next-generation sequencing . Theoret Appl Genet . 2015 ; 128 ( 3 ): 539 ± 547 . https://doi. org/10.1007/s00122-014 -2452-2 PMID: 25575840
55. Suh Y , Vijg J. SNP discovery in associating genetic variation with human disease phenotypes . Mutat Res . 2005 ; 573 ( 1 ±2): 41 ± 53 . https://doi.org/10.1016/j.mrfmmm. 2005 . 01 .005 PMID: 15829236
56. Cheng J , Qin C , Tang X , Zhou H , Hu Y , Zhao Z , et al. Development of a SNP array and its application to genetic mapping and diversity assessment in pepper (Capsicum spp .). Sci Rep-UK . 2016 ; 6 , 33293. https://doi.org/10.1038/srep33293
57. Lu H , Lin T , Klein J , Wang S , Qi J , Zhou Q , et al. QTL-seq identifies an early flowering QTL located near flowering locus T in cucumber . Theoret Appl Genet . 2014 ; 127 ( 7 ): 1491 ± 1499 . https://doi.org/10.1007/ s00122-014-2313-z PMID: 24845123
58. Korol A , Frenkel Z , Cohen L , Lipkin E , Soller M. Fractioned DNA pooling: a new cost-effective strategy for fine mapping of quantitative trait loci . Genetics . 2007 ; 176 ( 4 ): 2611 ± 2623 . https://doi.org/10.1534/ genetics.106.070011 PMID: 17603122