Published November 29, 2016
o r i g i n a l r es e a r c h
SSR Marker Development, Linkage Mapping,
and QTL Analysis for Establishment Rate in
Common Bermudagrass
Yuanwen Guo, Yanqi Wu,* Jeff A. Anderson, Justin Q. Moss, Lan Zhu, and Jinmin Fu
Abstract
Common bermudagrass has been widely used as a major warmseason turf, forage, and soil stabilization grass in the southern
United States. However, codominant marker development,
linkage, and quantitative trait loci (QTL) mapping resources are
limited in the important taxon. Accordingly, the objectives of this
study were to develop simple sequence repeat (SSR) markers,
construct a genetic map, and identify genomic regions associated with establishment rate. Five genomic SSR libraries were
constructed, sequenced, and used in the development of 1003
validated SSR primer pairs (PPs). A linkage map was constructed
using a first-generation selfed population derived from a genotype A12359 (2n = 4x = 36). A total of 249 polymorphic SSR
PPs were mapped to 18 linkage groups (LGs). The total length of
the map is 1094.7 cM, with an average marker interval of 4.3
cM. Ninety-eight out of 252 mapped loci (39%) were found to
be distorted from the Mendelian 1:2:1 segregation ratio. Among
the other 154 nondistorted loci, 88 coupling vs. 66 repulsion
linkage phases were observed to confirm the allopolyploid origin
of the parent. Ground coverage (GCR) phenotypic data in the
establishment stage were collected in two replicated field trials.
Quantitative trait loci mapping identified five genomic regions
significantly related to the trait. The findings of this study provide
valuable genetic tools and resources for genomic research, genetic improvement, and breeding new cultivars in the species.
C
ynodon is taxonomically classified into nine species, and common bermudagrass (C. dactylon var.
dactylon) is the most important because of its wide distribution, enormous variability, and extensive use for
turf, livestock forage, and soil stabilization in the world
(Taliaferro, 1995). Ploidy levels of the taxon range from
diploid (2n = 2x = 18 somatic chromosomes) to hexaploid
(2n = 6x = 54; Harlan et al., 1970; Forbes and Burton,
1963), while tetraploid (2n = 4x = 36) is the prevalent
cytological form (Wu and Anderson, 2011). Common
bermudagrass is a cross-pollinated species enforced
with self-incompatibility (Burton, 1947; Taliaferro and
Lamle, 1997; Tan et al., 2014). The sexual reproduction
behavior dictates that bermudagrass genome is highly
heterozygous at many loci. Therefore, codominant DNA
markers, such as SSR markers, are required to accurately
identify genotypes at individual loci, subsequently useful
for developing genetic maps, identifying clonal cultivars,
analyzing genetic diversity, and using in other genomic
research in common bermudagrass.
The SSR markers or microsatellites are tandemly
repeated sequences with 1 to 6 nucleotides long as one
repeat unit with variable repeating numbers. Simple
Y. Guo and Y.Q. Wu, Dep. of Plant and Soil Sciences, J.A. Anderson and J.Q. Moss, Dep. of Horticulture and Landscape Architecture,
and L. Zhu, Dep. of Statistics, Oklahoma State Univ., Stillwater,
Oklahoma 74078; and J. Fu, Wuhan Botanical Garden, Chinese
Academy of Sciences, Wuhan, Hubei 430074, China. Received 29
July 2016. Accepted 21 Sept. 2016. *Corresponding author (yanqi.
[email protected]). Assigned to Associate Editor David Edwards.
Published in Plant Genome
Volume 9. doi: 10.3835/plantgenome2016.07.0074
© Crop Science Society of America
5585 Guilford Rd., Madison, WI 53711 USA
This is an open access article distributed under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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vol . 10, no . 1 Abbreviations: ADO, allelic dropout; BLAST, Basic Local Alignment
Search Tool; EST, expressed sequence tag; GCR, ground coverage;
GIS, Genetic Identification Services; LB, Lysogeny broth; LG, linkage
group; LOD, log-likelihood of the odds; NCBI, National Center for
Biotechnology Information; PCR, polymerase chain reaction; PKS,
Perkins, OK; PP, primer pair; QTL, quantitative trait locus; RF, recombination frequency; SDRF, single-dose restriction fragment; SSR, simple
sequence repeat; STW, Stillwater, OK.
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sequence repeats have been extensively used in the
identification of inheritance pattern, linkage analysis,
and QTL mapping in multiple crops like sorghum [Sorghum bicolor (L.) Moench], wheat (Triticum aestivum
L.), and pear (Pyrus spp.) as they are codominant, multiallelic, reliable, and based on polymerase chain reaction (PCR; Diwan et al., 2000; Wu and Huang 2006;
Peleg et al., 2008; Wu et al., 2014). Using 9414 Cynodon
dactylon unigenes derived from expressed sequence
tags (ESTs), Kim et al. (2008) reported approximately
1.5% ESTs contained SSRs, which were used to design
95 EST-SSR PPs. Jewell et al. (2010) developed 16 ESTSSR markers by characterizing the EST-SSR primer
sequences of Kim et al. (2008). Harris-Shultz et al.
(2010) developed 53 EST-SSR PPs using the same
EST sequences of Kim et al. (2008). Tan et al. (2010)
reported 65 SSRs transferred from Sorghum bicolor to
C. dactylon and C. transvaalensis, and the development of 230 EST SSR PPs using 20,237 Cynodon ESTs
at National Center for Biotechnology Information
(NCBI). Kamps et al. (2011) developed 25 genomic SSR
markers from a genomic library enriched for the CA/
GT repeat motif. Simple sequence repeats are a desirable marker system of choice to construct genetic maps
in common bermudagrass, again due to the expected
heterozygosity in the genome.
Linkage map is among the most important genetic
tools used in contemporary genomic and genetic investigations in crops. Linkage maps have been widely used
to establish marker-trait associations and to indirectly
select for economically important traits. High density
genetic maps are valuable for some reference whole
genome sequence projects (Koning-Boucoiran et al.,
2012; Liu et al., 2012). Linkage maps developed from different populations or resources are necessary to understand genome information and inheritance (Semagn et
al., 2010). However, limited efforts have been made to
construct genetic maps in common bermudagrass. Using
an F1 population between a tetraploid common bermudagrass (2n = 4x = 36) and a diploid African bermudagrass (2n = 2x = 18; C. transvaalensis Burtt-Davy), Bethel
et al. (2006) reported two framework linkage maps of
single-dose restriction fragment (SDRF) markers, one
for each parent. The tetraploid map covers 1837.3 cM
with 155 SDRF markers, while the African diploid map
covers 973.4 cM with 77 markers. The map of common
bermudagrass was estimated to cover about 60% of its
whole genome (Bethel et al. 2006). Harris-Shultz et al.
(2010) mapped additional 35 alleles of SSR markers onto
the tetraploid map of Bethel et al. (2006). But the newer
map spanned just 1055 cM (Harris-Shultz et al., 2010). In
addition to many gaps to be filled in the existing maps,
most mapped markers were detected using restriction
fragment length polymorphism probes based on a DNA
hybridization technique that is rarely used now. We have
developed a first-generation selfed (S1) population from a
tetraploid common bermudagrass, which exhibited disomic inheritance (Guo et al., 2015).
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Vegetative propagation is a common and effective method for commercial production and scientific
research in forage and turf bermudagrass (Busey and
Myers, 1979; Hanna and Anderson, 2008), therefore
establishment rate is one important trait to be evaluated
in field trials from breeder’s selection nurseries to national
tests (i.e., National Turfgrass Evaluation Program; Morris, 2001). Ground coverage is widely used to represent
turfgrass establishment rate during the grow-in stage.
Brosnan and Deputy (2008) indicated through vegetative propagation bermudagrass can rapidly establish and
spread after planting. Richardson et al. (2001) utilized
digital photographs to generate turf GCR through dividing the total number of green pixels by the total number
of pixels in the image taken with a camera mounted on a
light-box. But the genetic determinants for establishment
rate are basically unknown. Quantitative trait loci mapping has been widely used in identifying genome regions
associated with phenotype variation in major crops in
the last two decades. Quantitative trait loci mapping for
turfgrass has mainly been focused on disease and stress
tolerance, and reproductive development related traits in
ryegrass (Lolium perenne L.), zoysiagrass (Zoysia japonica
Steud.), and St. Augustinegrass [Stenotaphrum secundatum (Walter) Kuntze; Forster et al., 2004; Ding et al.,
2010; Jessup et al., 2011; Mulkey et al., 2014]. To date, no
QTL mapping for establishment rate in bermudagrass has
been reported. Accordingly, the objectives of this study
were to develop SSR markers, construct a genetic map,
and identify genomic regions associated with establishment rate in common bermudagrass.
Materials and Methods
Plant Materials and Field Trials
Common bermudagrass ‘Zebra’ (2n = 4x = 36), a variegated F1 plant found in a C. dactylon population
(Johnston and Taliaferro, 1975), was selected to isolate
genomic DNA for constructing SSR enriched libraries. Zebra DNA was also used in the initial screening of
designed SSR PPs. A first-generation selfed progeny population from a common bermudagrass genotype A12359
(2n = 4x = 36; Wu and Huang, 2006) was used for linkage map construction. This population was selected for
this linkage mapping study because it exhibited a lower
amount of distortion compared with a selfed progeny
population of Zebra in an SSR marker segregation analysis as recently described by Guo et al. (2015). Within the
selfed population of A12359, 130 selfed progenies were
randomly selected and used for genetic map construction
and QTL analysis.
In May 2014, two field trials of the mapping population were established in a randomized complete block
design with three replications at Cow Creek Bottom,
Stillwater, OK (STW) and Cimarron Valley Research
Station, Perkins, OK (PKS). Soil types for STW and PKS
were Easpur loam or Ashport silty clay loam, and Teller
fine sandy loam respectively. The spacing between two
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neighboring plants in a row and between adjacent rows
was 1.5 m. Base fertilizer 17–5–5 (N–P–K) was applied at
a rate of 448 kg ha–1 before transplanting. Immediately
after transplanting, plants of the mapping population
into the trials in STW on 14 May and PKS on 21 May,
Ronstar 50WSP [3-(2,4-dichloro-5-(1-methylethaxy)
phenyl)-5-(1,1-dimethylethy)-1,3,4-oxadizol-2(3H)one] was applied at both sites at 2.24 kg, a.i. ha–1.
Alleys (60 cm) were cleaned up by repeated applications of Roundup [glyphosate: isopropylamine salt of
N-(phosphonomethyl) glycine; Monsanto, St. Louis, MO]
at 2.24 kg a.i. ha–1, as necessary.
Genomic DNA Libraries Construction and
SSR Marker Development
One genomic DNA sample of Zebra bermudagrass was
extracted from approximately 1.5 g healthy leaf tissues of
one potted plant. Plant leaves were removed and placed
immediately on ice and brought back to the lab and
stored at –80oC until needed for genomic DNA extraction. The tissue sample was ground in liquid N, and then
subjected to extracting DNA using IBI Maxi Prep kit
(IBI Scientific, IA) according to a manufacture-provided
protocol. Approximately 100 mg of genomic DNA from
Zebra was sent to Genetic Identification Services (GIS,
Chatsworth, CA) for the construction of five SSR libraries enriched for (CA)n, (GA)n, (ATG)n, (AAC)n, and
(CAG)n. Each of the five libraries received from GIS was
dispensed into 50 mL aliquots and placed at –80oC (Jones
et al., 2002; Wang et al., 2011).
Clones were recovered by mixing 10 mL of each library
with 30 mL of SOC broth (super optimal broth with catabolite repression) and spread out onto Lysogeny broth–ampicillin (LB–AMP; 100 mg/mL) X-GAL (80 mg/mL) IPTG (50
mM) plates for blue-white screening. Plates were incubated
overnight at 37°C without light and immediately placed in
4°C for at least two additional hours to enhance blue color.
White colonies were picked with sterilized toothpicks and
placed into 96 well blocks, each well containing 1 mL of LB
broth plus AMP (100 mg mL–1). Two controls were placed
in wells H11 and H12 in each of eight blocks for each
library. The no-colony well was used as a negative check
and the other one containing a blue colony (no insert),
as a positive check. Blocks were covered with breathable
film and grown overnight (18 h) at 37°C with shaking at
250 rpm. Glycerol stocks of each block were made by mixing 50 mL of 30% glycerol and 50 mL of overnight culture,
placed in a 96-well culture plate, and sealed with aluminum tape and stored at –80°C. The remaining solution
in the blocks was spun down in a centrifuge at a speed of
2500 × g for 7 min. The solution in each well was decanted,
and the culture blocks were covered with aluminum tape
and sent to the Oklahoma State University Recombinant
DNA/Protein Resource Facility (OSU Core Facility) for
plasmid preparation and sequencing.
DNA sequencing on recombinant clones was performed using ABI BigDye Terminator v1.1 Sequencing
Kit and analyzed on an ABI Model 3730 DNA Analyzer
(Applied Biosystems) at the OSU Core Facility. DNA
sequence information was analyzed for quality control
using the Sequence Scanner v.1.0 (Applied Biosystems
Inc., CA). After vector sequences were trimmed at the
OSU Core Facility and main sequences were placed in a
fasta file for further analysis.
The fasta file containing all trimmed sequences
was analyzed with the SSR Locator program of da Maia
et al. (2008). This program identified SSR sequences
and designed PPs. The parameters for the SSR primer
design were default with changes as follows: 5-di-, tri-,
tetranucleotide; 4-penta-, hexanucleotide; 3-hepta-,
octa-, nona-, and decanucleotide. The defaults for the
primer design were chosen as follows: amplicon size
140 to 350 bp; GC clamp 0; primer size range 18 to 22
bases; optimum primer size 20; melting temp (Tm) 55 to
61°C with an optimum of 59°C; G/C content 45 to 50%;
start and end point automatic; and end stability at 250.
Individual forward primers designed by SSR Locator
were modified by adding a M13 sequence (5¢-CACGACGTTGTAAAACGAC-3¢; Integrated DNA Technologies)
to the 5¢ end of forward primer sequence. The trimmed
fasta files of all sequences found to contain SSRs were
further analyzed with the CAP3 program of Huang and
Madan (1999) to form contigs and singletons to eliminate all redundant clones.
To prevent any redundancy with already published bermudagrass SSR primers, a comparison was
performed between the forward and reverse genomic
DNA primers of Kamps et al. (2011) and the trimmed
sequences from the Zebra genomic libraries. Each set of
sequences were compared by a specialized NCBI BLAST
(Basic Local Alignment Search Tool) program bl2seq
for aligning two (or more) sequences together. This specialized BLAST program was optimized for highly similar sequences (megaBLAST) per NCBI default parameters with the exception that the word size algorithmic
parameter was changed from 28 to 16 due to the size of
the primers (20–21 bp).
Linkage Map Construction
A total of 810 SSR PPs developed from the small-insert
genomic DNA libraries described above were screened
for polymorphism using a small panel of DNA samples
consisting of two A12359 replicates and its six selfed
progenies. Respective genomic DNA of A12359 and
its progeny plants was extracted following the CTAB
method of Doyle with minor modifications (Doyle,
1990), then DNA concentration was quantified using
a NanoDrop ND-1000 spectrophotometer (Nanodrop
Products), and each DNA sample was diluted to 10 ng/
mL, which was prepared for PCR. Then PCR was performed to amplify SSR markers in a Biosystems 2720
Thermal Cycler (Applied Biosystems Inc., CA) with the
reaction conditions as described by Guo et al. (2015).
The PCR products were separated on a 4300 LI-COR
DNA Analyzer (LI-COR Biosciences, Lincoln, NE) for
collecting genotypic data of SSR markers in the progeny
guo et al .: developing genomic tools in bermudagrass 3
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population. Polymorphic markers were tested to amplify
one DNA plate encompassing two replicates of the parent
and single samples of 62 progenies, and those producing stable and heritable bands were used to genotype the
other DNA plate encompassing two parent replicates and
additional 62 selfed progeny samples.
Allelic data for each polymorphic SSR PP of the 130
progeny were recorded into an Excel worksheet. The
segregation type <hk × hk> was used for scoring alleles
amplified with each polymorphic SSR PP because only
two alleles existed at most loci in the parent and segregated in the progeny population. For a heterozygous
locus of the parent with two alleles, h and k, if only
one upper band was amplified in a progeny sample, the
band pattern was scored as “hh”; if only one lower band
in a progeny it was scored as “kk”; and, if two bands
amplified, it was scored as “hk”; missing genotype (no
band showing up for a sample) was encoded as “--”. For
these SSR PPs, each producing stable but segregating
four bands, termed multiallele markers, two sets of loci
were recorded and analyzed separately on the basis of
their distinguishing allele sizes and segregation patterns (Liu et al., 2012).
Chi-square test was performed to examine the
Mendelian segregation ratio (i.e., 1:2:1) using Joinmap
4.0 (Van Ooijen, 2006a). Markers distorted from the
expected segregation ratio were marked as *, **, and ***
in the linkage map described below for P < 0.01, P <
0.001, and P < 0.0001, respectively, to denote different
significance levels (Liu et al., 2012). Linkage map was
constructed using the same software in which “cross
pollinator” was used as type code for the selfed population (Van Ooijen, 2006a). According to Van Ooijen
(2006a), it was recommended to start with a higher
log-likelihood of the odds (LOD) value indicating a
relatively stringent level to construct a map, although
it may lead to a map with more LGs than the actual
number, and then LOD value with reduced stringency
was decreased to regroup the map properly. Therefore,
initially, a minimum of LOD value of 10.0 was used
to group loci, and other linkage parameters was set
as follows: Show weak linkages with a recombination
frequency (RF) larger than 0.45, or a LOD smaller than
0.05; Show strong linkages with an RF smaller than
0.01, or a LOD larger than 10; Show linkages as suspected if RF estimates are larger than 0.5; Number of
maximum linkages to show per locus = 2; Determine
linkage phases using pairs with a LOD > 1; Show heterogeneity tests with P-values < 0.1. Regression mapping algorithm was used with parameters: Use linkages
with an RF smaller than 0.49, and a LOD value larger
than 0.1; Goodness-of-fit jump threshold for removal of
loci = 5; Number of added loci after which to perform a
ripple = 1. Kosambi’s mapping function was performed,
and yes for third round, and map was printed for each
round. A minimum LOD value of 3 was considered as
appropriate for the acceptance of two loci linkage (Bandaranayake and Kearsey, 2005). Accordingly, the lowest
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Table 1. Five simple sequence repeat (SSR) enriched
libraries and data collected during the development of
SSR markers.
Library
Clones
sequenced
SSRs
identified
PPs
designed†
766
766
766
766
766
766
726
951
491
947
727
3842
602
689
447
837
521
3096
A (CA)n
B (GA)n
C (ATG)n
D (AAC)n
E (CAG)n
Total
Nonredundant SSR markers
SSR PPs
validated
314
460
118
131
288
1311
283
385
74
64
197
1003
† PP, primer pair.
LOD value was set to 3.0 to regroup the map, which
allowed us to link two independent LGs if one marker
on one group could show a cross linkage with another
marker on the other LG (Liu et al., 2012).
Phenotypic Data Collection and QTL Analysis
Digital photographs were taken on 3 July for the trial in
STW and 27 Aug. 2014 for the trial in PKS with a digital
camera mounted on a light box. Bermudagrass GCR was
determined using Assess 2.0 (Image Analysis Software
for Plant Disease Quantification) for each plot. Phenotypic data was analyzed using SAS 9.4 (SAS Institute,
2014). SAS/MEANS procedure was conducted for mean
values and associated standard deviations for the parent
and selfed population in each location, and SAS/MIXED
was used for ANOVA.
Software MapQTL 6.0 (Van Ooijen, 2006b) was used
to perform QTL analysis for GCR with three text files
including locus genotype, quantitative data, and genetic
map files. Mean values of GCR over three replications
for each genotype were used for separate QTL mapping
analyses by each location (Dong et al., 2015), and one
joint QTL mapping was analyzed using mean values of
GCR across two locations (Zhang et al., 2014). Regression
algorithms were used in QTL analysis, interval mapping was performed initially to determine the presence
of a segregating QTL with mapping step size 1.0, and a
permutation test was used to decide genome-wide significance LOD threshold at 95% confidence level when the
number of permutation was set as 1000. Using selective
cofactors which were QTLs found in the initial analysis,
multiple-QTL model mapping was reiterated until stable
cofactors were obtained (Makaju, 2014).
Results
SSR Marker Development
For each of the five SSR enriched libraries, 766 clones were
sequenced, resulting in collectively 3830 sequences available for SSR marker development (Table 1). SSR Locator
identified 3842 SSRs in the 3830 sequences since some
sequences containing more than one SSR motif. For example, 951 SSR fragments were identified from 766 sequences
in Library B. Similarly, 947 SSR sequences were recognized
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Fig. 1. A gel image of genotyping an SSR marker CDGA5-1465/1466 in the parent A12359 and its 62 selfed progenies. The band
pattern of A12359 (P) was scored as “hk,” and an individual progeny band pattern was coded as “hh,” “kk,” or “hk” if one upper
band, one lower band, or two bands were amplified, respectively, and a missing lane was labeled as “–”.
in Library D. From the SSR containing sequences, 3096
SSR PPs were designed. Excluding redundant sequences,
1311 SSR PPs were unique. Higher redundant SSRs were
identified in ATG- and AAC-enriched libraries than in
AC-, AG-, and CAG-enriched libraries (Table 1). Library
B, enriched for (GA)n repeat had the highest nonredundant rate of 67%. The PCR amplifications of the 1311 SSR
PPs on Zebra DNA showed that 1003 PPs were effective
in the amplification of target bands (Table 1). Among the
five libraries, efficiency was different in the development of
effective SSR markers. Library B was the most efficient and
contributed 385 effective SSRs, while Library C was the
least, contributing 74 SSR PPs.
Among the 1003 unique and effective SSR markers (shown in Supplemental Table S1), 731 were of
perfect types, and the remaining 272 were compound
types based on the repeat type and structure. The largest number (278) of a single perfect type, (AG/TC)n
or (GA/CT)n, was observed in Library B, which was
expected as the library was enriched for the perfect
core sequence. Following the number of (AG/TC)n or
(GA/CT)n SSR repeat motifs, there were 145 (CA/GT)
n or (AC/TG)n motifs found in Library A. Collectively,
most (712 of 731) of the perfect SSRs in the five libraries
belonged to the expected target repeat types. However,
98 SSRs of AAC/TTG or GTT/CAA or ACA/TGT were
developed in Library E, enriched for CAG repeats;
while only 20 markers were in Library D, enriched for
AAC core sequence. Among the compound SSR markers, majority sequences contained the target repeats. Of
the five libraries, the predominant motifs of SSR markers were dinucleotide repeats (AC/TG, CA/GT, GA/CT,
AG/TC, and related compounds).
respectively (Table 2). The average LG length was 60.8
cM, and the mean number of loci each group was 14.
Six multiallelic PPs, which simultaneously amplified
one locus on each subgenome, were detected during the
gel scoring process. Among the six multiallelic PPs, four
were identified to link three pairs of homeologous LGs
while the loci amplified by other two SSRs were not linked
to other markers on the map. Simple sequence repeat
CDGA4-1301/1302 was found to bridge LG 1 and LG 6,
CDCA5-469/470 and CDGA5-1455/1456 were mapped to
associate LG 7 and LG 8, and CDAAC7-2693/2694 to connect LGs 15 and 16 as homeologous pairs.
Genetic Linkage Map
ANOVA Results and QTLs Detection
Out of 810 SSR PPs screened in the small panel of two parent replicates and six progenies, 260 were polymorphic and
then selected for genotyping the whole progeny population.
The 260 polymorphic SSRs amplified a total of 266 loci,
since six SSR PPs amplified four alleles of two loci each,
and the other 254 PPs generated two alleles each. A gel
image of the parent (A12359) and 62 S1 progenies amplified
with one SSR amplifying two alleles is given in Fig. 1.
Two-hundred-fifty-two loci were assigned to 18 LGs
that constituted a genetic map for the common bermudagrass plant A12359, while 14 loci were unlinked
(Fig. 2a, 2b, 2c). The total length of the map was 1094.7
cM, and two adjacent markers average distance was 4.3
cM. The largest LG (18) and shortest LG (6) was 122.3
and 12.7 cM in length, and contained 38 and 4 loci,
There was substantial variation in GCR among genotypes within the selfed population. Genotype, location, and block had significant effects for the trait with
the probability values of <0.0001, 0.0033, and <0.0001,
respectively, except for genotype by location interaction
with a probability of 0.1415. Means values (%) and associated standard deviations for GCR in STW were 43.27 ±
6.97 (parent) and 38.48 ± 17.39 (selfed population), while
in PKS the values (%) were 63.66 ± 6.14 and 60.00 ± 28.07
for the parent and selfed population.
Five QTLs affecting bermudagrass GCR were identified in this population. They were located on LGs 1, 8,
13, 16, and 18 with explained phenotypic variation ranging from 6.5 to 14.9% each (Table 3). Four QTLs were
identified in the trial at STW, one peak close to marker
Markers Clustering, Segregation Distortion,
and Linkage Phase Ratio
The SSR markers were distributed nonevenly across
the 18 LGs in the common bermudagrass genome, and
marker distribution with >5 loci per 10 cM was identified as marker clustering. Clustering of SSR markers
was observed in many LGs, including LG 1, 3, 7, 8,
11, 13, 15, 16, and 18, while the phenomenon was not
apparent on other LGs (Fig. 2). Severe segregation distortion was observed in 98 out of 252 (39%) mapped
loci in the selfed population. Distorted markers were
clustered in LGs 2, 5, 6, 10, 15, 16, and 18, and more
severely, all markers on LGs 2 and 6 were distorted.
Among the 154 nondistorted markers, the number of
SSRs with coupling linkage phase and repulsion phase
was 88 and 66, respectively. Therefore, the ratio of coupling to repulsion phase SSRs was consistent with 1:1
ratio (P = 0.076), which confirmed the allopolyploid
origin for the common bermudagrass genotype (Guo et
al., 2015).
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Fig. 2. A genetic map of 18 linkage groups (LGs): (a) 1 to 6, (b) 7 to 12, and (c) 13 to 18 developed from 130 selfed progenies of a
common bermudagrass A12359. The left side of each LG shows map distance (cM), and right side presents simple sequence repeat
(SSR) names. Homologous groups were determined by multiallele markers (in bold) and connected by solid lines.
CDCA5-469/470_260 on LG 8 explained 7.7% of GCR
variation. One region located between CDCA5-463/464
and CDGA1-807/808 on LG 13 accounting for 14.9% of
the variation. The QTL around SSR CDGA5-1459/1460
localized on LG 16 explained 7.0% of the phenotypic
variance. The fourth QTL were detected on LG 18 near
marker CDGA8-1783/1784, accounting for 6.5% of the
variation. In PKS, one peak was identified on LG 18
close to SSR CDGA8-1783/1784, explaining 8.7% of the
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phenotypic variation. This QTL was located in a similar
interval as the QTL found in the trial at STW. Across the
two locations, joint data analysis recognized three QTLs,
including one close to CDGA8-1807/1807 on LG 1, one
between CDGA5-1391/1392 and CDGA1-807/808 on LG
13, and one near SSR CDGA8-1783/1784 on LG 18, with
each explaining 8.1, 9.9, and 11.6% of the phenotypic
variation, respectively. Among the five identified QTLs,
four had positive values of additive effects, and one had
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Table 2. Linkage group (LG) assignment, marker numbers, LG length, and marker density in a common bermudagrass genetic map.
Linkage
group
LG 1
LG 2
LG 3
LG 4
LG 5
LG 6
LG 7
LG 8
LG 9
LG 10
LG 11
LG 12
LG 13
LG 14
LG 15
LG 16
LG 17
LG 18
Total markers
Length
cM/marker
Gaps (>10 cM)
19
5
15
7
11
4
26
14
6
12
6
15
14
9
21
18
12
38
cM
53.2
15.2
54.6
78.6
80.1
12.7
48.7
40.9
50.6
52.5
17.2
60.0
57.1
61.2
105.2
91.6
93.0
122.3
2.8
3.0
3.6
11.2
7.3
3.2
1.9
2.9
8.4
4.4
2.9
4
4.1
6.8
5.0
5.1
7.8
3.2
0
0
3
3
3
1
1
1
3
2
1
2
2
3
3
3
5
3
negative value of additive effects (Table 3). LOD profiles
of major QTLs detected are shown in Fig. 3.
Discussion
Development and Characterization
of SSR Markers
Heterozygous loci of common bermudagrass necessitate
codominant markers to fully reveal their genotypes. This
research project generated the largest set of more than
1,000 validated SSR PPs in common bermudagrass. Tan et
al. (2010) developed 230 SSR PPs from bermudagrass EST
sequences and demonstrated 65 SSR PPs were transferable from sorghum to bermudagrass. Harris-Shultz et al.
(2010, 2011), Jewell et al. (2010), and Kamps et al. (2011)
also reported the development of EST and genomic SSR
PPs. The SSR PPs should be highly valuable for genotyping clonal cultivars (Wang et al., 2011; Harris-Shultz et al.,
2011; Fang et al. 2015), constructing genetic maps (HarrisShultz et al., 2010; and this report), mapping QTL for
important agronomic traits, and more in-depth genomic
research, including full genome sequencing, in the future.
In the technical aspects for the development of SSR
markers, the redundancy rate in the five libraries ranged
from 33 to 84%, which were higher than other crop species, including tea tree [Melaleuca alternifolia (Maiden
& Betche) Cheel; 24%] and perennial ryegrass (16.0%;
Rossetto et al., 1999; Jones et al., 2001). Redundant
sequences were most found in the same libraries because
of clone duplication (Cai et al., 2005). A total of 1003
(76.5%) PPs provided effective amplification products
Table 3. Detected quantitative trait loci (QTLs)
associated with ground coverage in a common
bermudagrass population.†
LG
LOD
peak
Position of
LOD peak
STW
8
3.25
cM
27.599
STW
13
6.04
20.431
STW
STW
PKS
STW + PKS
STW + PKS
16
18
18
1
13
2.99
2.77
2.66
2.8
3.49
91.638
0
4
32.12
12.307
STW + PKS
18
3.6
Location‡
4
Marker region
CDAAC7-2575/2676 to
CDCA5-469/470_260
CDCA5-463/464 to
CDGA1-807/808
CDGA5-1459/1460
CDGA8-1783/1784
CDGA8-1783/1784
CDGA8-1807/1808
CDGA5-1391/1392 to
CDGA1-807/808
CDGA8-1783/1784
PVE
A
%
7.7
3.12
14.9
7.04
7
6.5
8.7
8.1
9.9
–2.78
3
4.94
3.64
4.73
11.6
4.69
† A, the additive effects contributed by QTL; LG, linkage group; LOD, log-likelihood of the odds, PVE,
phenotypic variance explained.
‡ PKS, Cimarron Valley Research Station, Perkins, OK; STW, Cow Creek Bottom, Stillwater, OK.
with expected band size at a similar amplification efficiency rate with other reported forage and turf species,
including perennial ryegrass (81.0%), and zoysiagrass
(91%; Jones et al., 2001; Tsuruta et al., 2005). Our results
of core sequence types were consistent with the report of
Cardle et al. (2000), indicating (AT/TA)n was the most
common dinucleotide repeat motif among plant genomic
sequences, followed by GA/CT and CA/GT. However,
AT/TA was not usually used in SSR markers due to its
self-complementary nature (Cai et al., 2005).
Genetic Linkage Map
The mapping population for this investigation was selected
from one of two populations used for our previous inheritance mode study in common bermudagrass (Guo et
al., 2015). Since the S1 progenies from Zebra had severe
segregation distortion that would negatively affect linkage analysis by biased recombination fractions and spurious linkages (Liu et al., 2012), we selected S1 progenies of
A12359 (2n = 4x = 36) with less segregation distortion for
the genetic map construction. This is the first common
bermudagrass genetic map completely based on SSR markers. Because of the various advantages of SSR markers
including reproducibility, locus specificity, hypervariability, and especially transferability among different species
(Xu, 2010), our common bermudagrass linkage map will
be valuable for comparative genomic study with other
grasses or crops. In Fig. 2a, LG 1 was much longer than its
homeologous pair LG 6, indicating the latter LG may contain homozygous regions which cannot be mapped. Likely,
the genetic length of the whole genome is underestimated
as a result of homozygous regions in the genome.
In order to improve the accuracy of our linkage
map, several considerations were made in the process of
developing the linkage map. First, population size was
one of the most important factors affecting genetic map
guo et al .: developing genomic tools in bermudagrass 7
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Fig. 3. Two common quantitative trait loci (QTLs) identified on linkage groups (a) 13 and (b) 18 in multiple analyses. The 95% significant
threshold value for declaring a QTL is indicated as a horizontal solid line.
construction, and a total number of 260 gametes were
included in our data analysis, which should lead to a
higher quality as compared with the previous bermudagrass maps (113 gametes in a bermudagrass framework
linkage map of Bethel et al., 2006, and 118 gametes for
linkage mapping by Harris-Shultz et al., 2010). Second,
allelic dropout (ADO: one allele of a heterozygous locus
could not be amplified during a PCR) will increase
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genotyping error rate which could not be avoided by laboratory procedures (Broquet and Petit, 2004); two alleles
at each locus should reduce ADO. In our study, only coding type <hk × hk> was used to genotype the progeny
individuals instead of other patterns (<ab × cd>, <ef ×
eg>, <lm × ll>, and <nn × np>) which could increase map
accuracy because of only two coding alleles at each locus
(Liu et al., 2012). According to Pawlowsky-Glahn and
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vol . 10, no . 1
Buccianti (2011), missing value <10% was considered to be
acceptable. The highest missing value each SSR PP was 8
(less than 13) in this study, indicating the map accuracy.
Segregation Distortion
Segregation distortion, a common genetic phenomenon,
has been reported in both outcrossing and selfing crops,
including rice (Oryza sativa L.; Xu et al., 1997; Habu et
al., 2015), maize (Zea mays L.; Lu et al., 2002; Xu et al.,
2013), soybean [Glycine max (L.) Merr.; Baumbach et al.,
2012], barley (Hordeum vulgare L.; Cistue et al., 2005),
and also some perennial herbages like alfalfa (Medicago
sativa L.; Li et al., 2011), ryegrass (Bert et al., 1999), and
switchgrass (Panicum virgatum L.; Missaoui et al., 2005;
Liu et al., 2012). Harris-Shultz et al. (2010) reported
severe segregation distortion in 11 out of 75 (15%) ESTSSR alleles in a triploid bermudagrass population derived
from a cross of T89 (2n = 4x = 36) and T574 (2n = 2x =
18). Because a selfed population has a higher tendency
for segregation distortion (Liu et al., 2012), a higher rate
(39%) of segregation distortion was observed in this population as compared with the previous study by HarrisShultz et al. (2010). Segregation distortion will result in
linkage disequilibrium, and the enhancer alleles will be
found in coupling phase and suppressor alleles in repulsion phase (Lyttle, 1991). However, we still preferred to
keep those distorted markers as suggested by Van Ooijen
(2006a) that distorted markers could be better understood if used in map construction.
Allopolyploid Origin of Common Bermudagrass
According to Wu et al. (1992), the ratio of coupling to
repulsion linkage phase was used to detect allopolyploid
or autopolyploid, in which a ratio of 1:1 was expected for
an allopolyploid and 1:0.25 or 1:0 for an autopolyploid.
We observed a ratio of 88:66 (coupling versus repulsion),
and the chi-square value for the observed and expected
1:1 ratio was 3.143, smaller than the critical value 3.841 (df
= 1, a = 0.05). This chi-square test confirmed the bivalent
preferential chromosome pairing behavior during meiosis (Wu et al., 1992), which further confirmed disomic
inheritance pattern for common bermudagrass reported
by Guo et al. (2015). Compared with switchgrass, another
allopolyploid grass species, in which 12 multiallele
markers of 499 SSRs tested (2.4%) were mapped in both
subgenomes by Liu et al. (2012), we observed that six out
of 260 SSRs (2.3%) were multiallelic, further indicating
the allopolyploid origin rather than autopolyploid in the
common bermudagrass genotype.
Detection of QTLs for Ground Coverage
in Establishment
Establishment rate (measured as GCR) during the turf
forming stages or post damage recovery is an important
trait for sod production or divot repair on golf courses,
or damage recovery on sports fields. Our study identified
common bermudagrass genomic regions associated with
GCR during establishment, and clearly indicated common
bermudagrass GCR was controlled by multiple genetic
loci and was significantly affected by environments.
Among the five QTLs affecting GCR detected in this
study, individual QTLs appeared to show different sensitivities to environments (Paterson et al., 1991). One close
to CDGA8-1783/1784 SSR on LG 18 were identified in all
three analyses using data from STW, PKS, and combined
data of the two locations, and this QTL explained 6.5 to
11.6% of the phenotypic variation in the two field trials.
One QTL between CDCA5-463/464 and CDGA1-807/808
on LG13 were detected in STW and then confirmed in
the joint analysis, but not in the trial at PKS. Three other
QTLs were more sensitive than others, including two
on LGs 8 and 16 only found in the trial at STW, and one
genomic region in LG 1 only identified in the joint analysis. These unstable QTLs likely result from QTL ´ microenvironment interactions, wherein QTL phenotypic effect
is affected by environmental factors including soil type,
water, light, and temperature (Johnson et al., 2012).
The five QTLs identified in this study together
explained nearly half of the observed phenotypic variation of GCR. The remaining variation could be derived
from other sources, including environment plus error
term, QTLs with effects too small to be detected, QTL ´
QTL interactions, and interaction of progeny individuals
with environmental variation (Paterson et al., 1991).
Conclusions
Common bermudagrass is an important perennial grass.
More than 1000 SSR PPs, the largest set of codominant
DNA markers, were developed in this study. Using an S1
progeny population, we reported the first genetic map of
18 LGs based on codominant SSR markers in common
bermudagrass. The mapping phase ratio of one coupling
to one repulsion in this population firmly indicated the
allopolyploid origin of the tetraploid bermudagrass.
This study identified five QTLs associated with GCR in
the establishment stage in two field trials. One genomic
region on LG 18 was stable in the testing environments.
The developed SSR PPs, genetic map, and QTLs associated with GCR have high potential to be used for various
genetic applications and hopefully in accelerating bermudagrass breeding.
Supplemental Information Available
Supplemental information is included with this article.
Supplemental Table S1. Marker ID, repeat motif,
forward and reverse primer sequence, and predicted size
information for the effective 1003 SSRs.
Acknowledgments
We thank Drs. Tim Samuels and Tilin Fang for technical support and
Mrs. Pu Feng for greenhouse assistance, and we appreciate the help
from our previous and current lab members (Linglong Liu, Lie Yang,
Chengcheng Tan, Shuiyi Lu, Laxman Adhikari, Hongxu Dong, Shiva
Makaju, Dan Chang, and John Baker). The research was funded by USDA
NIFA SCRI Award 2010-51181-21064, US Golf Association, China Natural
Science Foundation Grant 31428021, Oklahoma Agricultural Experiment
Station, and China Scholarship Council.
guo et al .: developing genomic tools in bermudagrass 9
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