A high-density simple sequence repeat-based genetic linkage map of switchgrass.
ABSTRACT Switchgrass (Panicum virgatum) has been identified as a promising cellulosic biofuel crop in the United States. Construction of a genetic linkage map is fundamental for switchgrass molecular breeding and the elucidation of its genetic mechanisms for economically important traits. In this study, a novel population consisting of 139 selfed progeny of a northern lowland genotype, NL 94 LYE 16X13, was used to construct a linkage map. A total of 2493 simple sequence repeat markers were screened for polymorphism. Of 506 polymorphic loci, 80.8% showed a goodness-of-fit of 1:2:1 segregation ratio. Among 469 linked loci on the framework map, 241 coupling vs. 228 repulsion phase linkages were detected that conformed to a 1:1 ratio, confirming disomic inheritance. A total of 499 loci were mapped to 18 linkage groups (LG), of which the cumulative length was 2085.2 cM, with an average marker interval of 4.2 cM. Nine homeologous LG pairs were identified based on multi-allele markers and comparative genomic analysis. Two clusters of segregation-distorted loci were identified on LG 5b and 9b, respectively. Comparative analysis indicated a one-to-one relationship between nine switchgrass homeologous groups and nine foxtail millet (Setaria italica) chromosomes, suggesting strong homology between the two species. The linkage map derived from selfing a heterozygous parent, instead of two separate maps usually constructed for a cross-fertilized species, provides a new genetic framework to facilitate genomics research, quantitative trait locus (QTL) mapping, and marker-assisted breeding.
A High-Density Simple Sequence Repeat-Based
Genetic Linkage Map of Switchgrass
Linglong Liu, Yanqi Wu,1Yunwen Wang,2and Tim Samuels
Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, Oklahoma 74078
ABSTRACT Switchgrass (Panicum virgatum) has been identified as a promising cellulosic biofuel crop in the
United States. Construction of a genetic linkage map is fundamental for switchgrass molecular breeding and
the elucidation of its genetic mechanisms for economically important traits. In this study, a novel population
consisting of 139 selfed progeny of a northern lowland genotype, NL 94 LYE 16X13, was used to construct
a linkage map. A total of 2493 simple sequence repeat markers were screened for polymorphism. Of 506
polymorphic loci, 80.8% showed a goodness-of-fit of 1:2:1 segregation ratio. Among 469 linked loci on the
framework map, 241 coupling vs. 228 repulsion phase linkages were detected that conformed to a 1:1 ratio,
confirming disomic inheritance. A total of 499 loci were mapped to 18 linkage groups (LG), of which the
cumulative length was 2085.2 cM, with an average marker interval of 4.2 cM. Nine homeologous LG pairs
were identified based on multi-allele markers and comparative genomic analysis. Two clusters of segregation-
distorted loci were identified on LG 5b and 9b, respectively. Comparative analysis indicated a one-to-one
relationship between nine switchgrass homeologous groups and nine foxtail millet (Setaria italica) chromo-
somes, suggesting strong homology between the two species. The linkage map derived from selfing a het-
erozygous parent, instead of two separate maps usually constructed for a cross-fertilized species, provides
a new genetic framework to facilitate genomics research, quantitative trait locus (QTL) mapping, and marker-
Switchgrass (Panicum virgatum L.) is one of the dominant C4 peren-
nial species present in the North American tall grass prairies. Its
natural habitat extends to a larger geographic span between about
15 and 55 degree north latitudes (Hitchcock 1951). According to gross
morphology and habitat preference, switchgrass is classified mainly
into lowland and upland ecotypes (Porter 1966). Lowland plants are
tetraploid (2n = 4x = 36 chromosomes), whereas uplands include both
tetraploid and octoploid plants (2n = 8x = 72) (Hopkins et al. 1996).
Aneuploidy is common in both lowland and upland plants, although
octoploid upland plants have more aneuploidy incidences than tetra-
ploid accessions (Costich et al. 2010). Molecular marker investigations
have revealed enormous genetic diversity within the species (Gunter
et al. 1996; Narasimhamoorthy et al. 2008; Zalapa et al. 2011; Zhang
et al. 2011).
Switchgrass is a tall growing and resilient species. Its genetic
diversity has historically been used for soil conservation, forage
production, game cover, and as an ornamental grass. More recently,
it has been selected as the model herbaceous species for use as
a dedicated bioenergy feedstock crop (McLaughlin and Kszos 2005).
Switchgrass is listed as one of the major biomass energy crops in the
Billion-Ton Update report (U.S. Department of Energy 2011). In
a farm-scale study of switchgrass grown as a biomass energy crop
on marginal cropland, Schmer et al. (2008) reported switchgrass pro-
duces 540% more energy than the energy used for producing its
cellulosic feedstock. They estimated greenhouse gas emissions from
converting switchgrass feedstock to ethanol were 94% lower than that
from gasoline. Switchgrass has received substantial attention and has
the potential to be genetically improved for higher biomass produc-
tion along with other important agronomic traits that can add value to
its use as a biofuel feedstock in breeding programs.
Copyright © 2012 Liu et al.
Manuscript received October 30, 2011; accepted for publication January 16, 2012
This is an open-access article distributed under the terms of the Creative
Commons Attribution Unported License (http://creativecommons.org/licenses/
by/3.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Supporting information is available online at http://www.g3journal.org/lookup/
2Present address: Department of Grassland Science, College of Animal Science
and Technology, China Agricultural University, Beijing 100193, People’s
Republic of China.
1Corresponding author: Department of Plant and Soil Sciences, Oklahoma State
University, 368 Ag Hall, Stillwater, OK 74078-6028.
Volume 2| March 2012|
Switchgrass is a wind pollinated and largely self-incompatible species
(Talbert et al. 1983; Taliaferro et al. 1999; Martinez-Reyna and Vogel
2002). Because of this sexually out-crossing mode of reproduction, all
released cultivars were populations composed of genetically heterozy-
gous individuals. Recently released switchgrass cultivars were primarily
developed using recurrent selection procedures (Vogel et al. 2011).
time period to develop new cultivars. Consequently, genetic gains per
year are relatively low (Vogel and Pedersen 1993). Molecular tools and
genomic information are limited in switchgrass and need to be devel-
oped. These new and quickly evolving technologies have extensive po-
tential if incorporated into and coupled with conventional genetic
improvement and breeding programs for developing superior cultivars.
Molecular markers have been developed to investigate inheritance
in the species and facilitate the construction of genetic linkage maps.
These maps are fundamental for switchgrass breeding through marker-
assisted selection and elucidation of the genetic mechanisms for econo-
mically important traits. The first linkage maps were constructed with
102 restriction fragment length polymorphism (RFLP) single dosage
markers (Missaoui et al. 2005). The markers are distributed in eight
homology groups covering over 400 cM. Developing microsatellites or
simple sequence repeat (SSR) markers, which are tandem repeats of
short (1 to 6 bp) DNA sequences, has gained substantial attention in
switchgrass (Tobias et al. 2005, 2008; Wang et al. 2011). The desirable
features of SSR markers include their easy use, high information con-
tent, codominant inheritance pattern, even distribution along chromo-
somes, reproducibility, and locus specificity (Kashi et al. 1997; Röder
et al. 1998a,b). A pair of genetic maps using SSRs scored as single dos-
age markers has been developed in switchgrass (Okada et al. 2010).
These maps covered, respectively, 1376 and 1645 cM of 18 linkage
groups that are expected to represent the full set for a tetraploid ge-
nome. Okada et al. (2010) reported that the two tetraploid switchgrass
parents had complete or near-complete disomic inheritance.
Marker-assisted selection is more efficient when molecular maps
are well saturated, as high-density maps provide increased opportu-
nities for detecting polymorphic markers in genomic regions of
interest. Linkage maps developed using different genetic backgrounds
are needed to better understand inheritance in the species. Linkage
maps constructed from different populations will enhance the
understanding of the genome structure and gene interaction (Semagn
et al. 2010). Two full-sib F1 populations were used to construct pre-
viously published maps (Missaoui et al. 2005; Okada et al. 2010). The
F1 full-sib populations were made by crossing two selected heterozy-
gous parental plants.
NL94 LYE 16x13 (abbreviated as NL94) is a self-compatible genotype
selected from a breeding nursery of the OSU northern lowland pop-
ulation formed using Kanlow and other germplasm (Liu and Wu 2011).
Selfing NL94 and other self-compatible lowland plants can produce
promising inbred lines that offer numerous advantages in breeding,
especially for utilizing heterosis by crossing selected heterotic inbreds.
We have developed an inbred population from NL94 (Liu and Wu
2011). Accordingly, the major objective of this study was to construct
a more saturated SSR-based linkage map using the inbred progeny
population. This map would provide a new genetic framework to study
associations of molecular markers and agronomic traits of interest.
MATERIALS AND METHODS
Plant materials and the selfed mapping population
The mapping population was recently described by Liu and Wu
(2011). Basically, the mapping population consisted of 139 individuals
randomly selected from 279 inbreds derived by selfing NL94, which
was identified as a typical tetraploid lowland ecotype (2n = 4X = 36)
based on the detection of the 49 bp deletion in trnL-UAA intron (see
supporting information, Figure S1), a special marker for switchgrass
classification between lowland and upland (Missaoui et al. 2006). The
decision to use 139 progeny was dictated by our genotype-detecting
equipment, which enables the organization of the entire mapping
population in two 66-well plates plus a small marker screening panel
including seven individuals and the parent.
DNA isolation and PCR amplification
The genomic DNA for NL94 and its progeny plants was respectively
isolated from healthy leaf tissues using the CTAB method (Doyle and
Doyle 1990), with minor modifications as described by Liu and Wu
(2011). To avoid allele dropout due to poor DNA quality, DNA samples
with smear bands of A260/A280 less than 1.8 were extracted de novo.
The working solutions were diluted to 10 ng/ml as PCR templates.
SSR markers were amplified using selected primer pairs (PP;
described in the next section) on Biosystems 2720 thermal cyclers
(Applied Biosystems, CA), using the PCR reaction conditions as
described by Wang et al. (2011). PCR products were separated using
6.5% KB plus polyacrylamide gel solution on a LI-COR 4300 DNA
Analyzer (LI-COR Biosciences, Lincoln, NE). The band sizes of these
amplified fragments of SSR markers were determined using Saga
Generation 2 software, version 3.3 (LI-COR Biosciences).
SSR markers and genotyping analysis
A total of 2288 switchgrass SSR primer pairs were assembled from
previous publications (Tobias et al. 2006, 2008; Okada et al. 2010;
Wang et al. 2011). They were compared to each other using a special-
ized blast program called bl2seq in NCBI (www.ncbi.nlm.nih.gov/
BLAST/) designed to exclude redundancy. Non-redundant markers
were then selected for polymorphism. In addition, 354 sorghum (Sor-
ghum bicolor) SSRs from Wu and Huang (2006) were tested for their
transferability in switchgrass varieties “Cave-in-rock” and “Alamo.”
Of 189 foxtail millet (Setaria italica) nonredundant SSRs, 80 were
taken from Jia et al. (2009) (a primer pair “b255” was excluded from
their primer list due to the same primer sequences with “b225”), and
the remaining 109 were kindly provided by Dr. A. Doust (Botany
Department, Oklahoma State University).
The markers were initially screened for informative segregation
using a small screening panel. Polymorphic SSRs were used to genotype
the first DNA panel of 66 individuals, and then those having stable,
heritable, and reproducible markers were genotyped on the second
panel of other 66 individuals. At last, the information from the small
screening panel and the two 66-well panels was collected to represent
the entire mapping population. The markers with greater than 10%
missing data were genotyped again from those samples that did not
have data in the previous genotyping runs.
All codominant markers were scored using the same segregation
pattern (,hkxhk.: locus heterozygous in the parent, two alleles).
SSR-amplified fragments were encoded as “hh” (only one upper
band), “hk” (two bands), and “kk” (only one lower band). For dom-
inant loci, “h-” was scored for presence, and “–” for absence. In both
scenarios, “u” was recorded as missing data; this included unclear or
ambiguous bands. If a marker produced multiple bands with the same
segregation profile but with different sizes, only two main bands were
recorded as segregating alleles and the other bands were omitted as
redundant information. However, in addition to these main bands, if
those markers produced stable secondary bands with different
| L. Liu et al.
segregation profiles from the main bands, i.e. multi-allele markers,
they were separately encoded by primer name, and the band size in
the base pairs was used as a suffix for differentiating between them. All
gel bands were manually scored by two independent people. Raw
genotyping data are given in File S1.
Segregation and linkage analysis
Several segregation ratios are possible in the selfed progeny derived
from a tetraploid plant with two segregating bands (Table S1). The
goodness-of-fit between observed and expected Mendelian ratios was
analyzed for each marker locus using a x2test built in JoinMap 4.0
(Van Ooijen 2006). Markers that deviated from the theoretical
expected ratios were considered distorted and were marked to indicate
different significance levels (?P , 0.01,??P , 0.001, and???P ,
Linkage analysis was performed using JoinMap 4.0, and the
outcross pollinated (CP) full-sib family was used as the population
type, which enabled the analysis of a self-pollinated population
derived from a heterozygous parent. The linkage map was constructed
in two steps. Initially, loci were grouped into linkage groups using the
following parameters: the independence test log-likelihood of the odds
(LOD) score $ 8.0, maximum-likelihood (ML) mapping module
(Stam 1993; Jansen et al. 2001), Kosambi’s mapping function
(Kosambi 1944), maximum recombination (REC) frequency ¼ 0.35,
goodness-of-fit Jump threshold for removal loci ¼ 5.0, ripple ¼ 1, and
third round ¼ yes. After grouping, loci within linkage groups were
ordered using the regression mapping algorithm (Stam 1993). The
linkage groups established from the third round of analysis formed
the initial framework map. Then four to six loci distributed evenly on
the framework map were fixed and the calculation parameter was
changed to a LOD score $ 3.0 and a maximum REC frequency ¼
0.40. This step allowed us to assign some of the otherwise ungrouped
loci on the already established linkage groups. Two independent link-
age groups were accepted as linked if a marker on the end of one
group showed a cross linkage to another marker on a second group
through the “maximum linkages” function of JoinMap 4.0. In addi-
tion, those unmapped markers that showed weak links with mapped
loci at the maximum linkage parameter threshold of 2.0 were listed
next to mapped loci as accessory loci and formed the final linkage
map. Markers showing segregation distortion were included in the
final map if their presence did not alter surrounding marker order
in a given linkage group. For any markers with an estimated position
of less than 0 cM, their position was set as 0 cM, and the positions of
other markers on the same linkage group were adjusted accordingly
(Beldade et al. 2009). To compare the collinearity between the initial
and final maps, the same markers and their individual distances on
both maps were arrayed in Microsoft Excel 2007, and a function
“correlate” was conducted to obtain a correlation coefficient.
Linkage groups (LG) were identified to be homeologous if they
shared common SSR markers. Linkage groups were numbered based
on the comparison with published linkage maps (Okada et al. 2010).
The designation of subgenome “a” or “b” for each LG in this study
was given according to the named subgenome of a corresponding LG,
which shared more markers than its alternate LG (Okada et al. 2010).
For a LG (i.e. 7b), on which only four gSSR markers mapped and no
bridge markers were found, the original clone sequences harboring the
four gSSR markers (Wang et al. 2011) were blasted against sorghum
genome in GRAMENE (http://www.gramene.org/) with “near-exact
matches” set as the search sensitivity parameter. Thus, using the sor-
ghum genome sequence as a tool, LG 7b was identified and compared
with the reference switchgrass maps of Okada et al. (2010).
The linkage phase of each locus on the final framework map was
obtained from JoinMap 4.0, which automatically determined the
coupling and repulsion phase types during the estimation of the
recombination frequencies. Chi-square testing for the ratio of coupling
to repulsion linkage phase was conducted with the online software of
To search for the locations of the mapped SSR loci on foxtail millet
chromosomes, foxtail millet genome sequence Phytozome v7.0 (http://
www.phytozome.net/foxtailmillet.php, accessed on March 31, 2011)
was used for alignment with the switchgrass mapped marker sequen-
ces. The parameters for BlastN program were as follows: Expected (E)
threshold = 10, comparison matrix = Blosum62, alignments to show =
100, allow gaps = yes, filter = yes. The output was parsed manually to
identify those significant hits with the lowest e-value and the position
of each query sequence. Only the final framework markers were eval-
uated, and all others were omitted.
Determination of nonredundant PCR markers
and polymorphism screening
Of the 2288 switchgrass gSSR and EST SSR (eSSR) markers from
different sources (Tobias et al. 2006, 2008; Okada et al. 2010; Wang
et al. 2011), 19 were determined to be redundant PPs (see Table S2),
in addition to the 4 redundant PPs reported previously (Wang et al.
2011). The resultant 2265 nonredundant SSR markers from switch-
grass were screened for polymorphisms. Polymorphic markers were
preliminarily identified if they showed segregation in the small panel
of eight genotypes (Figure 1). Of 1105 switchgrass gSSRs, 377 were
determined to be polymorphic. After amplifying them on a panel of
66 individuals, 7 gave monomorphic amplifications (no segregation)
and 58 produced unclear amplifications, resulting in difficulties in
band scoring and subsequently discarded. The remaining 312
(28.2%) were used for the linkage map construction. Of the 1160
switchgrass eSSRs, 210 showed polymorphisms in the small screening
panel. Later, 48 were further discarded due to their unclear amplifi-
cations, and the remaining 162 (14%) eSSRs were used for genotyping
the entire population.
To explore which marker types give more information for the
linkage map construction, the relationship between polymorphism,
repeat, and motif type were further analyzed. Of the 2265 non-
redundant SSR markers, 1022 were of dinucleotide type, 924 were
trinucleotide, 244 were compound, and the remaining 75 were
tetranucleotide, pentanucleotide, hexanucleotide, or unknown repeat
type. Their polymorphic rates were as high as 25.8% (63/244) for the
compounds, followed by 25.1% (257/1022) for dinucleotide repeats,
16.7% (155/924) for trinucleotide, and 9.3% (7/75) belonging to other
Among all switchgrass SSRs tested, 2221 had known motif types.
Of them, GA/AG/TC/CT occurred at the highest frequency of 23.9%
(531/2221), followed by CA/AC/GT/TG with 17.5% (388/2221),
CCG/GCC/CGC/CGG/GGC/GCG with 16.9% [376/2221, most of
them from ESTs developed by Tobias et al. (2008)], CAG/GCA/AGC/
CTG/TGC/GCT with 15.1% (336/2221), and AAG/GAA/AGA/CTT/
TTC/TCT with 10.5% (233/2221). The frequencies of motif type were
almost consistent with marker polymorphisms. GA/TC was the most
abundant motif type with the highest polymorphic frequency of
36.4%, followed by the motif CA/TG with 17.1% (Figure 2).
Of the 354 sorghum SSRs, 39 (11.0%) amplified clear and scorable
bands in switchgrass, indicating their transferability across the species.
Volume 2March 2012|Genetic Linkage Map of Switchgrass|
In the small screening panel, 6 sorghum SSRs showed polymorphisms
but only 1 produced clear and heritable bands and was used for the
map construction. The 189 nonredundant foxtail millet SSRs were
screened, and 102 (54.0%) showed expected bands and were scorable.
Of them, 8 markers were validated to be effective for genotyping the
entire mapping population. Together, 483 unambiguous polymorphic
SSR markers, including 312 gSSR, 162 eSSR, 1 sorghum SSR, and 8
foxtail millet SSR markers, were identified to have effective poly-
morphisms in the population. The average number of segregated
alleles per polymorphic PP was 2.03, with a range of 1 to 6 (Table 1).
Inheritance of markers
The typical segregation of SSR markers was scored as “hh,” “hk,” or
“kk” in the mapping population (Figure 3), and only three loci pro-
duced from sww-2097, sww-1678, and PVAAG-3053/3054 were
scored as dominant. Of 503 codominant polymorphic loci, 81.3%
(408/503) had a goodness-of-fit of 1:2:1 segregation ratio in the x2
test (P ¼ 0.05); the remaining 94 (18.7%) loci demonstrated distorted
segregation, i.e. deviating from the Mendelian ratio (Table 2). Of the 3
dominant loci, 2 (sww-1678 and PVAAG-3053/3054) showed a 3:1
ratio and 1 (sww-2097 with presence:absence ratio ¼ 80:58) deviated
Linkage map construction
Under the highly stringent conditions with a minimum LOD score of
8.0 and maximum REC value of 0.35, an initial framework map was
constructed with 360 loci (Table 3). Then the framework order was
fixed to allow the positioning of an additional 109 loci with
the minimum LOD ¼ 3.0 and maximum REC ¼ 0.4. [This value is
the maximum detectable recombination frequency for our population
size of 139, according to the calculating equation of Wu et al. (1992)].
A lower LOD score of 2.0 was set for joining two separated groups in
LG 6b because two of the markers resided in the end of each LG
(sww-1969 and sww-1889) and showed a cross link with an REC
frequency of 0.38. Thus, a total of 469 loci from 453 SSR PPs were
ordered and placed on the final framework map with 18 LGs, the
complete set expected for a tetraploid switchgrass genome (Figure
4). In addition, 30 accessory loci were assigned to likely positions
on the map (Figure 4). Only 7 (1.4%) of 506 loci were not grouped
or placed on the final map. Excellent correlation between the initial
Figure 1 A gel image of screening SSR primer pairs for polymorphism and reliability on a panel of NL94 (first lane from left side per panel) and
seven selfed progeny. Polymorphic and segregated markers are indicated in boxes. The first and last lanes are DNA ladder 50–350 size standards
(LI-COR Biosciences, Lincoln, NE).
Figure 2 Distribution frequency and polymorphism of switchgrass
SSRs based on motif types.
|L. Liu et al.
and final maps was observed by comparing their orders (r ¼ 0.9873,
P , 0.01; see Figure S2).
Including accessory loci, the percentage of polymorphic markers
mapped was 98.6% (499/506). The number of loci per LG varied from
4 (LG 7b) to 52 (LG 9b). The total length of the map was 2085.2 cM,
and the average distance between two adjacent markers was 4.2 cM
(Table 3 and Figure 4). The length of the LGs varied from 3.8 (LG 7b)
to 162.5 cM (LG 6b), with an average of 115.8 cM. The marker loci
were not evenly distributed across LGs, and consequently, some LGs
(LG 2a, 2b, 3b, 5b, and 9b) were denser with the clustering of markers
than others. Twenty-three gaps with each $ 15.0 cM, a distance suit-
able for QTL analysis and marker-assisted application (Beckmann and
Soller 1983), remained and collectively spanned 339.6 cM (Table 3).
There were 120 common markers shared by this and the previous
maps of Okada et al. (2010). Except for three local rearrangements on
LG 2b and four on 9a, good collinearity of marker orders along 18
LGs was observed through 102 bridge SSR markers (Figure S3). The
LGs, therefore, were named according to the previous maps (Okada
et al. 2010) for consistency (Figure 4). The maximum number of
bridge markers within each pair of corresponding LGs ranged from
3 to 16 (see Table S3). The designations of subgenome (“a” or “b”)
were assigned to each LG based on the identification of bridge
markers with previous maps (Okada et al. 2010). Among the remain-
ing 18 common markers, 15 (83.3%) resided on their corresponding
homeologous LGs based on the reference map information (Okada
et al. 2010) (Table S3), and the other 3 were distributed in nonho-
meologous LGs (Figure 4). One short LG was formed with 4 marker
loci, of which none were mapped in the Okada et al. (2010) map. Blast
analysis indicated 2 of the 4 mapped markers (i.e. PVCAG-2491/2492
and PVCAG-2163/2164) had hits on the bottom of sorghum chro-
mosome 6 with the E-values of 2.2e-03 and 2.8e-05, respectively (Fig-
ure S3). Thus, it was named “LG 7b” as the sorghum chromosome 6
corresponds to switchgrass LG 7, and LG 7a was identified based on
the bridge markers with the previous maps (Okada et al. 2010).
A polymorphic sorghum SSR (Xtxp-46), which resided on the end
of sorghum LG Sbi01 (Wu and Huang 2006), was mapped on LG
9a (Figure 4). Except for a foxtail millet SSR (p58) which resided on
an accessory locus of LG 1b, the other seven polymorphic foxtail
millet SSRs were distributed on six different LGs, i.e. MPGD25
on LG 2a, b255 on LG 3a, MPGD19 on LG 5b, MPGD17 on LG
7a, b159 on LG 8b, and both b171 and p44 on LG 9b (Figure 4). A
comparison of shared markers between this study and a published
foxtail millet map (Jia et al. 2009) indicated that, except for one mis-
match where a marker (Millet-b159) was expected on LG 6a (or LG
6b) but was actually mapped on LG 8b, the other seven markers
showed consistent correspondence of LGs between switchgrass and
foxtail millet (Figure 4).
A total of 20 multi-allele PPs were used to determine the
homeologous LGs. The majority of these PPs (18 of 20) amplified
one locus on each subgenome, and the remaining 2 PPs produced
three different loci each. Six homeologous LG pairs (LG 1a to 1b, 2a to
2b, 4a to 4b, 6a to 6b, 8a to 8b, and 9a to 9b) were identified based on
12 shared PPs (Figure 4). The result was consistent with the LG
naming system described by Okada et al. (2010). The other three
homeologous LGs were identified based on bridge markers with the
previously published maps (Okada et al. 2010).
n Table 1 Amplification, polymorphism, and mean number of segregated alleles in NL94 switchgrass
Mean Number of
Alleles in NL94
SWG gSSR11053121–5 Okada et al. (2010); Wang
et al. (2011)
Tobias et al. (2006, 2008);
Okada et al. (2010)
Wu and Huang (2006)
Jia et al. (2009); Dr. A. Doust
SWG eSSR 1160 1621–6360341 2.09
aSWG, switchgrass (Panicum virgatum); sorghum, Sorghum bicolor; Millet, foxtail millet (Setaria italica).
2493483 1048 9912.03 (Ave)
Figure 3 A gel image of geno-
typing an SSR marker PVGA-
1963/1964 in the parent NL 94
(P) and 65 selfed progeny. In-
dividual genotypes were scored
as homozygous (hh or kk) or
and last lanes are DNA ladder
50–350 size standards (LI-COR
Biosciences, Lincoln, NE).
Volume 2 March 2012| Genetic Linkage Map of Switchgrass|