Genotyping-by-Sequencing Enhances Genetic Diversity Analysis of Crested Wheatgrass Agropyron cristatum (L.) Gaertn - MDPI
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International Journal of
Molecular Sciences
Article
Genotyping-by-Sequencing Enhances Genetic
Diversity Analysis of Crested Wheatgrass
[Agropyron cristatum (L.) Gaertn.]
Kiran Baral 1 , Bruce Coulman 1 , Bill Biligetu 1, * ID
and Yong-Bi Fu 2, * ID
1 Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8,
Canada; kiran.baral@usask.ca (K.B.); bruce.coulman@usask.ca (B.C.)
2 Plant Gene Resources of Canada, Saskatoon Research and Development Centre, Agriculture and Agri-Food
Canada, 107 Science Place, Saskatoon, SK S7N 0X2, Canada
* Correspondence: bill.biligetu@usask.ca (B.B.); yong-bi.fu@agr.gc.ca (Y.-B.F.);
Tel.: +1-306-966-4007 (B.B.); +1-306-385-9298 (Y.-B.F.)
Received: 3 August 2018; Accepted: 28 August 2018; Published: 31 August 2018
Abstract: Molecular characterization of unsequenced plant species with complex genomes is now
possible by genotyping-by-sequencing (GBS) using recent next generation sequencing technologies.
This study represents the first use of GBS application to sample genome-wide variants of crested
wheatgrass [Agropyron cristatum (L.) Gaertn.] and assess the genetic diversity present in 192 genotypes
from 12 tetraploid lines. Bioinformatic analysis identified 45,507 single nucleotide polymorphism
(SNP) markers in this outcrossing grass species. The model-based Bayesian analysis revealed four
major clusters of the samples assayed. The diversity analysis revealed 15.8% of SNP variation residing
among the 12 lines, and 12.1% SNP variation present among four genetic clusters identified by the
Bayesian analysis. The principal coordinates analysis and dendrogram were able to distinguish
four lines of Asian origin from Canadian cultivars and breeding lines. These results serve as a
valuable resource for understanding genetic variability, and will aid in the genetic improvement of
this outcrossing polyploid grass species for forage production. These findings illustrate the potential
of GBS application in the characterization of non-model polyploid plants with complex genomes.
Keywords: genotyping-by-sequencing; Agropyron; genetic diversity; genetic structure; SNP
1. Introduction
Genotyping-by-sequencing (GBS) is a powerful genomic approach for identification of genetic
variation on a genome-wide scale for genetic diversity analysis of non-model plants [1–3]. This
approach produces high-density, low-cost genotypic information without the requirement for a
reference genome sequence [4]. The detailed GBS approach in plant diversity analysis is described
in Peterson et al. [3]. In brief, the GBS analysis involves five major steps: (1) genome complexity
reduction with restriction enzyme; (2) barcoding the seared genomic DNAs with indexed adaptors;
(3) high-throughput sequencing of barcoded DNA fragments; (4) identification of genetic variants
through a bioinformatics analysis of de-multiplexed reads; and (5) a genetic diversity analysis of
sequenced samples based on sample-by-variant matrix. The GBS application, despite being a powerful
approach, has certain limitations, including many missing data points, uneven genome coverage,
complex bioinformatics, and issues related to polyploidy [5–8]. To overcome these limitations,
a GBS-based pipeline, called Haplotag, was developed by Tinker et al. [9], which can generate tag-level
haplotype and single nucleotide polymorphism (SNP) data for polyploid organisms. This approach has
been successfully applied in the study of diploid and polyploid genomes in oat (Avena sativa) [10–12]
and genetic diversity analysis of northern wheatgrass (Elymus lanceolatus ssp. Lanceolatus) [13].
Int. J. Mol. Sci. 2018, 19, 2587; doi:10.3390/ijms19092587 www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2018, 19, 2587 2 of 13
Crested wheatgrass [CWG; Agropyron cristatum (L.) Gaertn.] is one of the perennial species of
the genus Agropyron that comprises 10–15 species in a polyploid series of diploid (2n = 2x = 14),
tetraploid (2n = 4x = 28) and hexaploid (2n = 6x = 42) forms with the P genome [14,15]. Agropyron
species are native to temperate-frigid grassland and sandy soils of Eurasia [14,16,17], and were first
introduced to Canada in 1911 [16]. CWG is the most important commercial species of the crested
wheatgrass complex in Canadian grasslands [18]. It is characterized by an extensive root system,
making it drought tolerant and winter hardy. CWG is considered an important pasture grass for
early spring grazing, providing highly palatable and nutritious forage [19]. This species is easy to
establish, has strong competitive ability, tolerates insect predation, provides high forage yield, and
can be managed for multiple harvests in a season [16,19,20]. It performs well on marginal lands and
semi-desert environments to moist moderately saline soils [19,20]. Due to these features, this species
can be used for land reclamation of abandoned croplands, burnt and degraded areas, as well as in
erosion control [21]. It has persisted as a high yielding species compared to native forage species, even
in 20- to 40-year-old pastures, despite heavy grazing and trampling [19,22]. In addition, CWG is also
known to possess traits of interest, including disease resistance, tolerance to abiotic stress, and high
yield, which have been utilized in wheat and barley breeding [23–27]. The palatability and nutrient
content of CWG declines after anthesis, and it becomes less desirable for summer grazing [19]. Thus,
a goal of present CWG breeding programs is to develop later maturing cultivars that would maintain
nutritive value into the summer grazing season. Development of high forage-quality, late-maturing
CWG cultivars is limited by the relatively long varietal development process, few studies to assess
genetic variability of the germplasm, and lack of an effective marker system for marker-assisted
and/or genomic selection/breeding. Recent RNA-seq studies in CWG have identified flowering time
related genes and flowering related differentially expressed genes [28,29]. This emphasizes the need
for genetic diversity studies of CWG for the management and utilization of proper genetic resources in
a breeding program as exogamous perennial forage species are often morphologically comparable,
though they are genetically highly heterogeneous and heterozygous [30,31]. An adequate level of
genetic diversity is crucial for both germplasm adaptation and the long-term sustainability of plant
communities [32].
Attempts have been made to assess genetic variability within and among the genus Agropyron
using molecular markers like amplified fragment length polymorphism (AFLP) [18] and simple
sequence repeat (SSR) markers [31,33,34]. The revealed variabilities have allowed for better
understanding of the extent of diversity present in the genus. However, these marker systems
are unable to provide high resolution of genetic diversity and population structure information to
understand the ancestry and microevolution of the populations. Research is needed to assess molecular
characteristics of CWG for plant breeding. The molecular characterization is now more feasible than
before with the advanced sequencing technology and reduced cost to acquire informative markers
such as SNPs in non-model polyploid CWG plants. Recent GBS studies in polyploid plants [10,13]
demonstrate the likelihood that GBS will unveil genetic variability on a genome-wide scale in CWG
plants, and characterize CWG germplasm for breeding and genetic research.
This study was conducted with the objective to apply GBS in combination with the Universal
Network Enabled Analysis Kit (UNEAK) [35] and the Haplotag pipelines to (1) identify genome-wide
SNP markers; (2) assess the genetic diversity present in 12 lines of A. cristatum; and (3) assess whether
the GBS application is useful in the genetic diversity analysis of complex polyploid plants.
2. Results
2.1. SNP Discovery and Characterization
The Miseq run of 192 genotypes from 12 CWG lines (Table 1) generated approximately 87.8 million
raw forward (R1) sequence reads of 250 bp. The number of raw forward sequence reads per sample
ranged from 190,606 to 775,160 with an average of 457,279. Combined UNEAK and Haplotag analysis
IJMS 2018, 9, x FOR PEER REVIEW 3 of 13
Int. J. Mol. at
analysis Sci.the
2018, 19, 2587
20%, 30%, 40%, and 50% level of missing data generated 227; 1,884; 10,738; and 45,507 3 of 13
SNPs, respectively across the 192 genotypes. In addition, this analysis also generated many
metagenomic
at the 20%, 30%, files associated
40%, and 50%withlevelthe SNP discovery,
of missing which are
data generated 227;described and and
1,884; 10,738; accessible
45,507 in the
SNPs,
online Supplementary Materials. The distribution of the minor allele frequency in 45,507
respectively across the 192 genotypes. In addition, this analysis also generated many metagenomic files SNPs’ data
ranged
associated from 0.025
with thetoSNP
0.5, and exhibited
discovery, a steady
which decline ofand
are described minor alleles in
accessible with
theincreased occurrence of
online Supplementary
frequencies
Materials. The distribution of the minor allele frequency in 45,507 SNPs’ data ranged frompercentages
from 0.075 to 0.5 (Figure 1A). Likewise, there were more SNPs at the higher 0.025 to 0.5,
of
and missing
exhibiteddataa (Figure 1B). of minor alleles with increased occurrence of frequencies from 0.075 to
steady decline
0.5 (Figure 1A). Likewise, there were more SNPs at the higher percentages of missing data (Figure 1B).
Table 1. List of the 12 crested wheatgrass (A. cristatum) lines used in the study.
Table 1. List aof the 12 crested wheatgrass (A. cristatum) lines used in the study.
Alternative
Lines CN Number Origin Type
Identification a
Kirk CN108662 PI 536010
Alternative Canada Cultivar
Lines CN Number a Origin Type
AC-Goliath CN108673 Identification a Canada Cultivar
NewKirk
Kirk CN108662 FOR552
PI 536010 Canada
Canada Cultivar
Cultivar
Vysokij 9
AC-Goliath CN30995
CN108673 PI 370654 Siberia, Former Soviet
CanadaUnion, Omsk region Genebank
Cultivar line
Karabalykskij
NewKirk 202 CN31068 PI FOR552
326204 Kazakhstan, FormerCanada
Soviet Union, Kustanai region Genebank
Cultivar line
PGR 168309
Vysokij CN43478
CN30995 PI 370654 Kazakhstan
Siberia, Former Soviet Union, Omsk region Genebank
Genebank line
line
Karabalykskij
S8959E 202 CN31068 PI 326204
FOR917 Kazakhstan, FormerSiberia/Canada
Soviet Union, Kustanai region Genebank
Breedingline
line
PGR
S949116830 CN43478 S9491 Kazakhstan
Canada Genebank
Breedingline
line
S8959E
S9514 FOR917
S9514 Siberia/Canada
Canada Breeding line
Breeding line
S9491
S9516 S9491
S9516 Canada
Canada Breeding line
Breeding line
S9514 S9514 Canada Breeding line
S9544 S9544 Canada Breeding line
S9516 S9516 Canada Breeding line
S9556 S9556 Canada Breeding line
S9544 S9544 Canada Breeding line
a CN number
S9556 is the line identification in Plant Gene Resources of Canada, Agriculture, and
S9556 Canada Agri-Food
Breeding line
a CN
Canada (AAFC),
numberwhile
is the the
linealternative identifications,
identification includingofFOR
in Plant Gene Resources or S, Agriculture,
Canada, are from theand
joint forage breeding
Agri-Food Canada
(AAFC), while
program of the the alternative
University identifications, and
of Saskatchewan including
AAFC,FOR or S,PIare
and isfrom
fromtheplant
joint forage breeding
inventory program
book, of
National
the University of Saskatchewan and AAFC, and PI is from plant inventory book, National Germplasm Resources
Germplasm Resources
Laboratory, USA. Laboratory, USA.
Figure 1.
Figure The minor
1. The minor allele
allele frequency
frequency distribution
distribution (A)
(A) and
and the
the frequency
frequency of
of missing
missing data
data (B)
(B) for
for 45,507
45,507
SNP markers in 192 genotypes of 12 crested wheatgrass lines.
SNP markers in 192 genotypes of 12 crested wheatgrass lines.
2.2. Genetic
2.2. Genetic Structure
Structure and
and Relationship
Relationship
genetic structure
The genetic structureestimated
estimatedforfor192
192genotypes
genotypesfromfrom1212CWG
CWG lines
lines without
without consideration
consideration of
of prior population information in the STRUCTURE [36] analysis revealed four
prior population information in the STRUCTURE [36] analysis revealed four optimal clusters (Figure optimal clusters
(Figure
2A) with2A) withsupport
strong strong support from change
from change in LnP(K)
in LnP(K) variance variance
(Figure(Figure
2B) and2B) theand the largest
largest delta Kdelta
valueK
value (Figure
(Figure 2C). Cluster
2C). Cluster 1 (red1 (red in color)
in color) consisted
consisted of 17
of 17 genotypes
genotypes (16(16
fromfromVysokij
Vysokij99and
and one
one from
S8959E). Cluster 2 (green in color) had 22 genotypes (16 from S9491 and 6 from S9514). Cluster 3 (blue
in color) was the largest cluster, with 95 genotypes from seven lines. Cluster 4 (yellow in color), with
58 genotypes from five lines, was the second largest cluster. The neighbor-joining
neighbor-joining (NJ) (NJ) tree was in
agreement with clusters obtained from the STRUCTURE analysis (Figure 3). However, However, there existed
some discrepancies,
discrepancies, asassome
somemembers
membersofofcluster
cluster44(yellow
(yellowinincolor)
color)were
were spread
spreadinto cluster
into 2 (green
cluster in
2 (green
color)
in and
color) cluster
and 3 (blue
cluster in color).
3 (blue in color).
IJMS
Int. J. 2018, 9, x2018,
Mol. Sci. FOR 19,
PEER REVIEW
2587 44 of
of 13
13
IJMS 2018, 9, x FOR PEER REVIEW 4 of 13
Figure
Figure
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Figure 3. Genetic relationship
0.02
of 192 genotypes of the 12 crested wheatgrass lines as revealed by
neighbor-joining clustering with the 45,507 SNP markers. Each genotype is numbered after its line
Figure
Figure 3.
label.3. Genetic
Genetic
Each relationship
a genotypeof
relationship
node for ofis192
192 genotypes
genotypes
represented of colored
of
with the 12
the 12 crested
crested
circle wheatgrass
wheatgrass
followed lines as
lines
by genotype asname.
revealed
revealed
Red, byby
neighbor-joining
neighbor-joining clustering
green, blue, andclustering with the
with
yellow represent theplants
45,507
45,507 SNP
inSNP markers.
markers.
Clusters Each
1, 2, 3,Each genotype
and genotype
4, inferredisisfrom
numbered
numbered after its
after
the STRUCTURE its line
line
label.
label. Each node
Each(Figure
analysis for
node for a genotype
a genotype
2A), is represented with colored circle followed by genotype
is represented with colored circle followed by genotype name. Red,
respectively. name. Red,
green, blue,
green, blue, and
and yellow
yellow represent
represent plants
plants in
in Clusters
Clusters 1,
1, 2,
2, 3,
3, and
and 4,4, inferred
inferred from
from the
the STRUCTURE
STRUCTURE
analysis (Figure 2A), respectively.
analysis (Figure 2A), respectively.Int. J. Mol. Sci. 2018, 19, 2587 5 of 13
IJMS 2018, 9, x FOR PEER REVIEW 5 of 13
The principal
The principalcoordinates
coordinates analysis
analysis (PCoA)
(PCoA) revealed
revealed that
that the the relationship
genetic genetic relationship of 192
of 192 genotypes
genotypes (Figure 4A) was not in accordance to the Bayesian inferences
(Figure 4A) was not in accordance to the Bayesian inferences from the STRUCTURE analysis. from the STRUCTURE
analysis.
The TheII,
clusters clusters
III, andII,IV
III,identified
and IV identified by the Bayesian
by the Bayesian inferences inferences
appearedappeared
to overlapto and
overlap and
became
became undistinguishable with PCoA. However, the PCoA plot was able to distinguish
undistinguishable with PCoA. However, the PCoA plot was able to distinguish four lines Karabalykskij four lines
Karabalykskij
202 202 (fromPGR
(from Kazakhstan), Kazakhstan),
16,830 (from PGR 16,830 (from
Kazakhstan), Kazakhstan),
Vysokij Vysokijand
9 (from Russia) 9 (from Russia)
S8,959E and
(selected
S8,959E
from (selected
Vysokij from
9) from Vysokij
the rest of 9)thefrom
linesthe rest of4B).
(Figure the We
lines (Figure
also 4B). We
observed linesalso observed
S9,516, S9,544lines
andS9,516,
S9,556
from cluster 3 (blue in color from the model-based Bayesian analysis) were more dispersed than more
S9,544 and S9,556 from cluster 3 (blue in color from the model-based Bayesian analysis) were other
dispersed than other breeding lines and cultivars, likely indicating the larger genetic
breeding lines and cultivars, likely indicating the larger genetic diversity present in those breeding diversity present
in those
lines breeding
(Figure 4B). lines (Figure 4B).
Figure 4. Genetic
Genetic relationship
relationship of
of 192
192 genotypes
genotypes ofof the
the 12 crested wheatgrass lines as revealed by
principal coordinates analysis (PCoA) with the 45,507 SNP markers. Two panels are identical, but in
left panel
the left panel(A)
(A)each
eachgenotype
genotype is is labelled
labelled with
with colored
colored circles
circles representing
representing the clusters
the clusters obtained
obtained from
from
the the STRUCTURE
STRUCTURE analysis,
analysis, whilewhile the panel
the right right panel (B) labels
(B) labels genotypes
genotypes for 12for 12 lines.
lines.
2.3. Genetic
2.3. Genetic Differentiation
Differentiation
The analysis
The analysis of molecular variance
of molecular variance (AMOVA) revealed that
(AMOVA) revealed that most
most of
of the
the SNP
SNP variations
variations were
were
present within the lines (84.2%), while much smaller variations reside among lines
present within the lines (84.2%), while much smaller variations reside among lines (15.8%) or among(15.8%) or among
the four
the four Bayesian
Bayesian clusters
clusters (12.07%)
(12.07%) (Table 2). Line-specific
(Table 2). Line-specific FFst
st was
was also
also estimated
estimated fromfrom AMOVA
AMOVA for for
each line as the weighted variation among individual plants within a line to observe
each line as the weighted variation among individual plants within a line to observe the extent of the extent of
inbreeding. They were obtained in the range of 0.56 (in line S9491) to 0.64 (in the cultivar
inbreeding. They were obtained in the range of 0.56 (in line S9491) to 0.64 (in the cultivar Kirk) with Kirk) with
mean of
mean of0.60
0.60(Figure
(Figure 5B).
5B). TheThe pairwise
pairwise genetic
genetic distance
distance amongamong the 12
the 12 lines lines from
ranged ranged from
0.055 0.055
(between
(between AC-Goliath and S9544) to 0.32 (between Karabalykskij 202 and S9491) with
AC-Goliath and S9544) to 0.32 (between Karabalykskij 202 and S9491) with an average distance of 0.15. an average
distance of 0.15.
Table 2. Results of the analysis of molecular variance for two models of genetic structure (12 lines and
Tableclusters
four 2. Results
fromofthe
the STRUCTURE
analysis of molecular
analysis)variance
based on for45,507
two models of genetic structure (12 lines and
SNP markers.
four clusters from the STRUCTURE analysis) based on 45,507 SNP markers.
Model/Source of Variation df Sum of Squares Variance Explained Variance (%) a
Sum of
Model/Source
12 lines of Variation df Variance Explained Variance (%)a
Squares
Among lines
12 lines 11 101,048.8 246.0 15.8
Within lines 372 488,598.0 1313.4 84.2
Among lines 11 101,048.8 246.0 15.8
Four clusters from STRUCTURE
Within lines 372 488,598.0 1313.4 84.2
Among clusters 3 54,736.5 193.3 12.1
Four clusters
Within clusters from STRUCTURE 380 534,910.3 1407.7 87.9
Among clusters a 3 54,736.5 193.3 12.1
These variances were statistically significant from zero at P < 0.0001.
Within clusters 380 534,910.3 1407.7 87.9
a These variances were statistically significant from zero at P < 0.0001.Int. J. Mol. Sci. 2018, 19, 2587 6 of 13
IJMS 2018, 9, x FOR PEER REVIEW 6 of 13
IJMS 2018, 9, x FOR PEER REVIEW 6 of 13
Figure 5. Genetic diversity and genetic relationships of the 12 crested wheatgrass lines. Left panel (A)
Figure 5. Genetic diversity and genetic relationships of the 12 crested wheatgrass lines. Left panel (A)
shows their genetic relationship in the unweighted pair group method with arithmetic mean
shows their
shows theirgenetic
genetic relationship
relationship in unweighted
in the the unweighted pair method
pair group group method with arithmetic
with arithmetic mean
mean (UPGMA)
(UPGMA) dendrogram based on the Phi statistics obtained from the AMOVA. The right panel (B)
dendrogram based on the
(UPGMA) dendrogram Phi statistics
based on the Phiobtained from
statistics the AMOVA.
obtained The
from the right panel
AMOVA. The(B) displays
right panel the
(B)
displays the line-specific Fst values for the 12 lines.
line-specific F values for
displays the line-specific
st the 12 lines.
Fst values for the 12 lines.
The dendrogram based on AMOVA showed the grouping of the 12 CWG lines into three
The dendrogram
The dendrogram basedbased on AMOVA showed the the grouping
grouping of of the
the 12
12 CWG lines
lines into
into three
three
genetically distinct clusters at on
the AMOVA
Phi statistic showed
of 0.08 or more (Figure 5A). TheCWG
dendrogram grouped
genetically
genetically distinct
distinct clusters at
clustersand the
at the Phi statistic
Phi statistic of 0.08 or
of 0.08 or more (Figure
more The
(Figure 5A). The dendrogram
5A).distinct
The dendrogram grouped
grouped
the lines from Kazakhstan Russia in one distinct cluster. second cluster consisted of
the
the lines
lines from
from Kazakhstan
Kazakhstan and
and Russia
Russia in
in one
one distinct
distinct cluster.
cluster. The
The second
second distinct
distinct cluster
cluster consisted
consisted of
of
the single line S9491. The largest of all is the third cluster, with seven lines consisting of cultivars and
the single
the single line
line S9491.
S9491. The
The largest
largest of
of all
all is
is the
the third
third cluster, with seven
cluster, with seven lines
lines consisting
consisting of
of cultivars
cultivars and
and
breeding lines from Canada.
breeding lines from Canada.
breeding lines from Canada.
2.4. Effects of Missing Data on Diversity Analysis
2.4. Effects of Missing Data on Diversity Analysis
The optimal numbers of genetic clusters inferred from STRUCTURE analyses with respect to the
The optimal numbers of genetic clusters inferred from STRUCTURE analyses with respect to the
extent of missing data from M20%, M30%, M40%, and M50% datasets provided 4, 6, 6, and 4 optimal
extent of missing data from M20%, M30%, M40%, and M50% datasets providedprovided 4, 6, 6, and 4 optimal
clusters, respectively (Figure 6A). Comparing the proportions of SNP variance residing among the
clusters, respectively (Figure 6A). Comparing the proportions of SNP variance residing among the
12 lines inferred from the AMOVA analysis showed 24.6%, 20.3%, 17.8%, and 15.8% for M20%, M30%,
12 lines inferred from the AMOVA
12 analysis showed
AMOVA analysis showed 24.6%,
24.6%, 20.3%, 17.8%, and 15.8% for M20%, M30%,
M30%,
M40%, and M50%, respectively (Figure 6B).
respectively (Figure
M40%, and M50%, respectively (Figure 6B).
6B).
Figure
Figure 6.6. The impact of missing SNP data on the inferences of STRUCTURE
STRUCTURE and and AMOVA analysis.
Figure 6. The impact of missing SNP data on the inferences of STRUCTURE and AMOVA analysis.
The
The left
left panel
panel (A)
(A) shows
shows the
the four
four optimal
optimal clusters
clusters obtained
obtained from
from the
the STRUCTURE
STRUCTURE analyses
analyses at
at the
the
The left panel (A) shows the four optimal clusters obtained from the STRUCTURE analyses at the
missing level of M20% and M50%, and six clusters at M30% and M40%. The right panel
missing level of M20% and M50%, and six clusters at M30% and M40%. The right panel (B) shows the (B) shows the
missing level of M20% and M50%, and six clusters at M30% and M40%. The right panel (B) shows the
SNP
SNP variances, ranging from 24.6 to 15.78%, inferred from AMOVA AMOVA analyses
analyses residing
residing among
among 12
12 lines
lines
SNP variances, ranging from 24.6 to 15.78%, inferred from AMOVA analyses residing among 12 lines
at
at the
the increasing
increasing level
level of
of missing
missing values
values from
fromM20%
M20%to toM50%,
M50%,respectively.
respectively.
at the increasing level of missing values from M20% to M50%, respectively.
3. Discussion
3. Discussion
This study utilized the gd-GBS application, in combination with Haplotag pipeline, for the first
This study utilized the gd-GBS application, in combination with Haplotag pipeline, for the first
time in CWG, to generate a data matrix of 192 genotypes × 45,507 SNP markers, and captured
time in CWG, to generate a data matrix of 192 genotypes × 45,507 SNP markers, and capturedInt. J. Mol. Sci. 2018, 19, 2587 7 of 13
3. Discussion
This study utilized the gd-GBS application, in combination with Haplotag pipeline, for the
first time in CWG, to generate a data matrix of 192 genotypes × 45,507 SNP markers, and captured
genome-wide genetic variants to evaluate the genetic diversity present in tetraploid CWG. The diversity
analysis revealed 15.8% of SNP variation residing among the 12 lines and the model-based Bayesian
analysis identified four major clusters of the assayed samples. These research outputs are not only
useful for understanding the genetic diversity of CWG and for its breeding, but also are encouraging
for molecular characterization of non-model polyploid plants.
The revealed patterns of genetic diversity are interesting. First, the model-based Bayesian
approach in the STRUCTURE identified four major clusters of the assayed genotypes, while
the distance-based approaches like PCoA and UPGMA identified three major clusters; however,
the neighbor-joining analysis was in accordance with the result from STRUCTURE analysis. Following
the pedigree of the assayed genotypes (Table S1), we could infer that the model-based Bayesian
analysis and neighbor-joining analysis were able to genetically infer population substructure—an
outcome of probable processes such as genetic drift, migration, mutation, and selection—more
distinctly than distance-based approaches. Results also showed most of the genotypes grouped
together within their lines, revealing that different lines were distinct. The STRUCTURE analysis
(Figure 2A), neighbor-joining analysis (Figure 3), PCoA (Figure 4B), and UPGMA dendrogram
(Figure 5A) revealed the genetic distinctness of lines Karabalykskij 202, PGR 16830, S8959E, and
Vysokij 9. S8959E is a breeding line in the Saskatoon program, but it is a selection from Russian
genebank line Vysokij 9. Although it has been recurrently selected for vigorous growth and plant type,
it has not been interpollinated with any other lines, explaining its distinctness from other Canadian
cultivars/breeding lines. However, STRUCTURE revealed all genotypes, except one (S8959E-14;
Figure 2A) from line S8959E, showing high affinity with the line from Kazakhstan. This is also
supported by UPGMA clustering (Figure 5A), while neighbor-joining analysis revealed the relatedness
of lines from Russia. These findings will serve as valuable information for the genetic improvement of
CWG for forage production.
Our analysis showed high within-line genetic variation (Table 2) of assayed CWG lines, which is
in agreement with studies on highly outcrossing species [37]. Overall, our genetic diversity results
are in accordance with diversity studies of CWG reported by Mellish et al. [18] using AFLP markers
and Che et al. [31] and Che et al. [33,34] using SSR markers. The somewhat higher among population
variation (15.8%) observed in the present study may partly be due to narrower genetic base of eight
of the breeding lines/cultivars relative to the three genebank lines and one line of Russian origin
(S8959E). Most of the Canadian cultivars and breeding lines shared one or more common parents in
their genetic background (Table S1), and they have gone through many cycles of recurrent selection
for vigor and yield. Thus, there has probably been a slight reduction in heterozygosity as indicated
by the generally higher inbreeding coefficients (Figure 5B). The distinctness of the lines S8959E,
Vysokij 9, Karabalykskij 202, and PGR 16830 can be attributed to their Asian origin and absence of
interpollination with Canadian cultivars/lines and selection under Canadian conditions, except for
the recurrent selection of line S8959E, mentioned above. Thus, the cultivars/breeding lines likely have
reduced the within-line variation, while diverging more from the unselected Asian lines, explaining
some increase of the among-line variation. Further research is needed on the utilization of the genetic
variability of these lines with focus on morpho-physiological studies, adaptation, and their utilization
in breeding programs. Likewise, the distinctness of the line S9491 in the UPGMA analysis (Figure 5A)
is attributed to its synthesis from seven different lines/cultivars from breeding programs in Saskatoon
and Logan, Utah, USA. The line S9514 was directly selected from S9491, which explains why these two
lines clustered (green cluster) together in the STRUCTURE analysis (Figure 2) and neighbor-joining
analysis (Figure 3). However, the Canadian cultivar “Kirk” developed partly from a plant introduction
from a botanical garden in Finland (University of Turku) in 1968 showed shared pedigree with some or
all of the Kazakhstan lines based on model-based Bayesian clustering (Figure 2A) and neighbor-joiningInt. J. Mol. Sci. 2018, 19, 2587 8 of 13
analysis (Figure 3). While the origin of the plant introduction from the University of Turku remains
unknown, it can be reasoned that this original introduction may have common genetic background
with some of the Kazakhstan lines based on Bayesian clustering.
It was observed that the extent of reduction in heterozygosity, as explained by Fst , was more in
cultivars than most of the breeding lines. Two cultivars “AC-Goliath” and “Kirk” had lower diversity as
indicated by higher inbreeding coefficient (Fst values) (Figure 5B), perhaps because of being synthesized
from the interpollination of fewer genotype than many of the breeding lines. Also, most of the breeding
lines included cultivars “Kirk”, “AC-Goliath”, and other sources, in their pedigrees. The cultivar
“Newkirk” was selected from progenies of crosses between “Kirk” and “AC-Goliath”. However,
the inbreeding coefficient of “Newkirk” was lower than the parental cultivars, indicating a higher
level of heterozygosity. The three breeding lines S9516, S9544, and S9556 showed high within-line
genetic diversity according to greater dispersal of these lines on PCoA (Figure 4B), higher within line
variation (92.2%) as explained by a separate AMOVA, and lower line-specific Fst (Figure 5B). This
greater genetic diversity could be attributed to inclusion of diverse germplasm sources during their
synthesis (Table S1). The high within-line variability suggests that there is sufficient genetic variation in
all lines in this study to make progress from selection. Inclusion of germplasm from the Asian lines in
the breeding program to interpollinate with Canadian cultivars/breeding lines will increase diversity.
Our gd-GBS application has identified thousands of genome-wide SNP markers to assess the
extent of genetic diversity in the non-model polyploid CWG with no prior genomic information.
These results demonstrated the technical feasibility and effectiveness of GBS to sample genome-wide
genetic variability in other perennial grass species with complex genomes. High resolution plant
genetic diversity analysis, with 45,000 SNP markers spread over a genome, is more informative
than with relatively few markers, like AFLP and SSR used in previous studies [1,12,18,38–40]. Also,
the experimental cost for sampling genome-wide variants in this study was roughly $12,000, suggesting
the feasibility of a wider application of GBS to characterize other perennial polyploid grass species.
The results of the present study, along with those published in northern wheatgrass and wild oat [12,13],
demonstrate the utility of GBS in molecular characterization of non-model plants with complex ploidy
and genetic structures.
4. Materials and Methods
4.1. Plant Materials
The study material comprised 12 tetraploid CWG lines consisting of six breeding lines, three
cultivars, and three genebank accessions (Table 1). These accessions were acquired from USDA-ARS
plant germplasm system, Plant Gene Resources of Canada (PGRC), and the joint forage breeding
program of the University of Saskatchewan and Agriculture and Agri-Food Canada (AAFC). For ease
of interpretation, all the acquired material will be referred to as lines, rather than accessions, in this
study. Seeds of each line were grown for six weeks in the greenhouse at the Saskatoon Research and
Development Centre, AAFC, under the following growth conditions: 16 h photoperiod at 22 ◦ C and
8 h dark at 16 ◦ C. Young leaf tissues were collected from 16 randomly selected plants for each of the
lines and stored at −80 ◦ C prior to DNA extraction. A total of 192 genotypes from the 12 tetraploid
lines, listed in Table 1, were used for bioinformatics and genetic diversity analyses.
4.2. Genotyping-by-Sequencing
For each of the 192 genotypes, DNA was extracted from 0.1 g finely ground tissue following the
protocols of NucleoSpin® Plant II Kit (Macherey-Nagel, Bethlehem, PA, USA), and was eluted in a
1.5 mL Eppendorf tube with Elution Buffer. NanoDrop 8000 (Thermo Fisher Scientific, Waltham, MT,
USA) was used to measure the quality of the DNA by comparing the 260 and 280 nm absorptions. DNA
samples were further quantified through the Quant-iTTM PicoGreen®dsDNA assay kit (Invitrogen,
Carisbad, CA, USA) and diluted to 60 ng/µL with 1× TE buffer prior to sequencing analysis.Int. J. Mol. Sci. 2018, 19, 2587 9 of 13
A genetic diversity-focused GBS (gd-GBS) protocol by Peterson et al. [3] was used for the
preparation of multiplexed GBS libraries. In brief, for each library, 200 ng purified genomic DNA was
first digested with the restriction enzyme combination PstI and MspI (New England Biolabs, Whitby,
ON, Canada). Ligation of customized adapters onto the 50 and 30 ends of the restriction fragments by
T4 ligase was subsequently carried out. Then, the ligation fragments were purified by an AMPure XP
kit (Beckman Coulter, Brea, CA, USA). Following the purification, Illumina TruSeq HT multiplexing
primers were added through PCR amplification. The amplicon fragments were further quantified,
concentrated, and pooled to form 4 subgroups of 12 samples each. The samples in the subgroups were
pre-selected using a Pippin Prep instrument (Sage Science, Beverly, MA, USA) for an insert size range
of 250–450 bp, before pooling the samples into a library. Each pooled library was diluted to 6 pM, and
denatured with 5% of sequencing-ready Illumina PhiX Library Control (Illumina, San Diego, CA, USA)
that can serve for calibration. Sequencing was completed using an Illumina MiSeq Instrument with
paired-ends of 250 bp in length. MiSeq runs generated 384 FASTQ sequence files from 192 genotypes
of 12 lines (one forward and one reverse for each of 192 genotypes). All the raw pair-end sequencing
data in FASTQ format were deposited into the National Center for Biotechnology Information (NCBI)
Sequence Read Archive (SRA) with accession number SRP115373 as part of the larger sequencing effort
to enhance crested wheatgrass breeding [41]. The sequencing information for all 192 assayed samples
is described in the online Supplementary Material, Section A.
4.3. Bioinformatics Analysis
Bioinformatic analysis began with sequence (FASTQ) data cleaning, using Trimmomatic version
0.36 [42] to remove any sequenced-through Illumina adapters, low quality sequence (sliding window
of 10 bases, average Phred of 20), and fragments under 64 bases long.
As the UNEAK-GBS pipeline [35] only considers sequences of 64 bp (after barcode removal) with
an intact 5-base PstI residue (TGCAG) at the beginning, each FASTQ file of 250 bp was first split into
three fragment sets with a custom Perl script fastq184CutandCode-Pst.pl. The first set comprised the first
64 bases with the PstI residual restriction site, and the next two sets each with 59 base portions and an
added 5-base PstI residue. The script also provided an arbitrary barcode sequence (CATCAT) at the
start of each sequence fragment, since the UNEAK pipeline expects to deconvolute barcoded sequence
reads which are not already separated by sample. The three 70-base-long fragments formed, thereafter,
were independent, as their relationship was not preserved. Each fragment set was recognized by the
UNEAK-GBS pipeline [35], and was passed into UNEAK as an independent dataset.
Each fragment set (70 bases long) was analyzed with UNEAK and the Haplotag pipelines [9],
resulting in the analysis of a total of 177 bases of genetic sequence. Online Supplementary Material,
Section B, describes the procedures to run UNEAK. Two types of meta data files—a single mergedAll.txt
(all tags observed more than 10 times) and a set of individual tagCount files (one per sample) needed
for the Haplotag pipeline—were generated from the UNEAK run.
Haplotag was run with the parameters and filtering threshold settings described in the HTinput.txt
file, and generated a matrix of samples by SNP loci (online Supplementary Material, Section B). A set
of tag-level haplotypes (“HTgenos”) are first generated by Haplotag, followed by a set of SNP data
derived from these haplotypes (“HTSNPgenos”). These two data types are technically redundant,
so choosing one of them relies on the implementation and preference of software. In the present study,
most (97.5%) haplotypes were found to contain only a single SNP; thus, we decided to analyze the
SNP dataset for simplicity and compatibility with downstream analysis software.
The character by Taxa (CbyT) program supplied by N. Tinker was used to generate a filtered
SNP file. In brief, Haplotag generated three separate “HTSNPGenos” files, which were merged before
running CbyT. The “minimum presence” value in CbyT was set to 80%, 70%, 60%, and 50% for 20%,
30%, 40%, and 50% missing data, respectively. A SNP-by-sample matrix in the output files was used in
further analyses. Additional descriptions of the SNP data matrix and the custom Perl and Shell scripts
are available in the online Supplementary Material, Section A. Analyses from FASTQ file separation toInt. J. Mol. Sci. 2018, 19, 2587 10 of 13
SNP generation were conducted using Microsoft Windows 7 64-bit OS with an Intel (R) Xeon (R) CPU
E5-2623 v3 @ 3.00 GHz (8 threads) and 32 GB RAM.
4.4. Genetic Diversity Analysis
The diversity analysis was based on 45,507 SNP markers, with 50% or less missing values in 192
genotypes from 12 CWG lines. Data analysis began with calculation of the minor allele frequency and
the extent of missing SNP data with Microsoft Excel® . Thereafter, diversity analyses at the individual
and line levels were carried out.
Three types of diversity analysis were performed at individual genotype level. First, genetic
structure of 192 CWG genotypes was examined using a model-based Bayesian method implemented
in the program STRUCTURE version 2.2.3 [36,43]. Linux server with 60 core parallel computing
was used to run the STRUCTURE program, where each population subgroup (K = 1–9) was run 20
times, using an admixture model with 10,000 replicates each for burn-in and during the analysis.
Based on (1) a plot of likelihood of these models, (2) the rate of change in the second derivative
(∆K) between successive K values [44], and (3) the consistency of group configuration across 20 runs,
the final population subgroups were determined. For a given population subgroup (K) with 20 runs,
the run having the highest likelihood value was chosen to assign the posterior membership coefficients
to each sample. These posterior membership coefficients were used to create a graphical bar plot.
The size and formation of each optimal cluster with respect to population were evaluated. Second,
a neighbor-joining (NJ) analysis of the 192 genotypes was conducted using MEGA version 7.0.14 [45]
based on the dissimilarity matrix obtained from R routine AveDissR [46,47], and a radiation tree was
displayed. Third, a PCoA of all 192 genotypes was also done using the R routine AveDissR [46,47] to
assess genetic distinctness and redundancy, and to assess the genotype associations, plots of the first
two resulting principal components were generated. For comparison, the resulting NJ trees and PCoA
plots were individually labeled for the inferred structures.
Genetic variation present among the 12 lines was evaluated with AMOVA using Arlequin
version 3.5 [48] on 45,507 markers. In addition, the pairwise genetic distances were computed and
line-specific Fst values (inbreeding coefficient) for each line [49] were generated to infer the reduction
in heterozygosity. To inspect the genetic variation among the clusters identified from the STRUCTURE
analysis, additional AMOVA was performed. Unweighted pair group method, with arithmetic mean
(UPGMA) dendrogram based on pairwise genetic distances among the 12 lines obtained from AMOVA,
were generated using MEGA version 7.0.14 [45], to evaluate line differentiation and distinctness.
To estimate the influence of missing SNP data on the genetic diversity analysis, four datasets of 272;
1884; 10,738; and 45,507 SNPs representing 20%, 30%, 40%, and 50% of missing SNPs (M20%, M30%,
M40%, and M50%) were attained for the 192 genotypes, respectively. For each dataset, the among-line
variance from AMOVA and the optimal number of genetic clusters from STRUCTURE were obtained
and compared among the four datasets of varying percentages of missing data.
5. Conclusions
With the application of GBS, it has been possible to generate 45,507 SNP markers for a diversity
analysis of crested wheatgrass. The variation residing among these 12 lines of CWG was found to
be 15.8%. Further analysis grouped the assayed samples into four genetic clusters, and revealed the
genetic distinctness of two cultivars each from Kazakhstan and Russia, respectively. These results can
enhance parental selection for increased genetic variation and improved offspring performance in
crested wheatgrass breeding. The findings in this study can also aid in the application of GBS in the
characterization of non-model plants with complex genomes.
Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/9/
2587/s1.Int. J. Mol. Sci. 2018, 19, 2587 11 of 13
Author Contributions: Y.-B.F., B.B. and B.C. conceived the project; Y.-B.F. designed research; B.C. prepared the
study material; Y-B.F. conducted sequencing; K.B. and Y.-B.F. performed data analysis; K.B. wrote the manuscript;
B.C., Y.-B.F. and B.B. made revisions to the manuscript. All authors read and approved the final manuscript.
Funding: The work was financially supported by the Beef Cattle Research Council of Canada and Agriculture
and Agri-Food Canada (AAFC) Growing Forward 2 Funds (FGR.08.13).
Acknowledgments: The author would like to thank Gregory Peterson and Carolee Horbach for their technical
assistance; Isobel Parkin for the access to and the use of the Illumina MiSeq instrument; Compute Canada and
Westgrid for providing the high-performance computing service as well as of their technical support; and Helen
Booker, Bunyamin Tar’an and two anonymous journal reviewers for their helpful comments on the early version
of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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