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.
- SourceAvailable from: Desalegn Debelo Serba[Show abstract] [Hide abstract]
ABSTRACT: Switchgrass (Panicum virgatum L.), a warm season, C4, perennial grass, is one of the predominant grass species of the North American tall grass prairies. It is viewed as a high-potential bioenergy feedstock species because it can produce large amounts of lignocellulosic material with relatively few inputs. The objectives of this project were to develop an advanced switchgrass population and use it for the construction of genetic linkage maps and trait characterization. A three-generation, four-founder population was created and a total of 182 progeny of this advanced population were genotyped that included a mixture of self-pollinated and hybrid individuals. The female map integrated both sub-populations and covered 1,629 cM of the switchgrass genome with an average map length of 91 cM per linkage group. The male map of the hybrid progeny covered 1,462 cM with an average map length of 81 cM per linkage group. Average marker density of the female and male maps was 3.9 and 3.5 cM per marker interval, respectively. Based on the parental maps the genome length of switchgrass was estimated to be 1,776 cM and 1,596 cM for the female and male map, respectively. The proportion of the genome within 5 cM of a mapped locus was estimated to be 92% and 93% for the female and male maps, respectively. Thus, the linkage maps have covered most of the switchgrass genome. The assessment of marker transmission ratio distortion found that 26% of the genotyped markers were distorted from either 1:1 or 3:1 ratios expected for segregation of single dose markers in one or both parents respectively. Several regions affected by transmission ratio distortion were found with linkage groups Ib-m and VIIIa-f most affected.G3-Genes Genomes Genetics 03/2014; · 2.51 Impact Factor
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ABSTRACT: Switchgrass (Panicum virgatum) is a polyploid, outcrossing grass species native to North America and has recently been recognized as a potential biofuel feedstock crop. Significant phenotypic variation including ploidy is present across the two primary ecotypes of switchgrass, referred to as upland and lowland switchgrass. The tetraploid switchgrass genome is approximately 1400 Mbp, split between two subgenomes, with significant repetitive sequence content limiting the efficiency of re-sequencing approaches for determining genome diversity. To characterize genetic diversity in upland and lowland switchgrass as a first step in linking genotype to phenotype, we designed an exome capture probe set based on transcript assemblies that represent ~50 Mb of annotated switchgrass exome sequences. We then evaluated and optimized the probe set using solid phase comparative genome hybridization and liquid phase exome capture followed by next generation sequencing. Using the optimized probe set, we assessed variation in the exomes of eight switchgrass genotypes representing tetraploid lowland and octoploid upland cultivars to benchmark our exome capture probe set design. We identified ample variation in the switchgrass genome including 1,395,501 single nucleotide polymorphisms (SNPs), 8,173 putative copy number variants and 3,336 presence/absence variants. While the majority of the SNPs (84%) detected were biallelic, a substantial number were tri-allelic with limited occurrence of tetra-allelic polymorphisms consistent with the heterozygous and polyploid nature of the switchgrass genome. Collectively, these data demonstrate the efficacy of exome capture for discovery of genome variation in a polyploid species with a large, repetitive and heterozygous genome. This article is protected by copyright. All rights reserved.The Plant Journal 06/2014; · 6.82 Impact Factor
- BioEnergy Research 01/2014; · 3.40 Impact Factor
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 gSSR11053121–5Okada 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
aSWG, switchgrass (Panicum virgatum); sorghum, Sorghum bicolor; Millet, foxtail millet (Setaria italica).
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 2March 2012|Genetic Linkage Map of Switchgrass|
Distribution for segregation-distorted loci and the ratio
of linkage phase
Among 94 segregation-distorted loci (SDL), 84.0% (79/94) were
placed on the final framework map, 13.8% (13/94) belonged to
accessory loci, and only 2.1% (2/94) were unmapped. Except for LG
7b and 8a, the other 16 LGs possessed SDL ranging from 3.6% (1/28)
in LG 3a to 38.5% (20/52) in LG 9b (Figure 4). Clustering of SDL with
greater than 4 loci was observed only in the middle part of two LGs,
i.e. 5b and 9b. Remarkably, 10 consecutive SDL were found in LG 9b
(Figure 4). The remaining SDL were randomly scattered over the LGs.
The ratio of coupling to repulsion linkage phase is expected as 1:1
for allopolyploids, and 1:0.25 in autotetraploids (Wu et al. 1992).
Among the 469 linked loci on this framework map, a ratio of
241:228 coupling to repulsion phase linkage were detected, which
conformed to a 1:1 ratio (X2¼ 0.36 , X2(1, 0.05)¼ 5.99), completely
confirming the disomic inheritance reported by Liu and Wu (2011)
and being highly congruent with the results of Okada et al. (2010).
Mapping SSR sequences to foxtail millet genome
Of the plant species with the genome sequenced, foxtail millet is
currently the closest relative of switchgrass, as both species belong to
the Paniceae tribe and share a common ancestor 13 (63) million
years ago (Doust et al. 2009; Brutnell et al. 2010; Li and Brutnell
2011). The blast analysis indicated that of the 453 tested markers,
389 had hits in foxtail genomes. There existed a one-to-one relation-
ship between nine switchgrass homeologous groups and nine foxtail
millet chromosomes (Table 4). A total of 319 of 389 markers from the
switchgrass LGs matched nine corresponding foxtail millet chromo-
somes (Table 4 and Table S4). Two large homeologous groups, 2 and
9 (including 71 and 62 markers, respectively), were anchored in foxtail
millet chromosomes II and IX. In contrast, only 8 markers were on
foxtail millet chromosome VII. The mismatched markers appeared to
scatter at random on foxtail millet chromosomes.
The selfed progeny population for mapping
the genome in switchgrass
Due to wind-facilitated cross-pollination and strong genetic self-
incompatibility, switchgrass is an allogamous species and homozygous
inbred lines are unavailable in nature (Talbert et al. 1983). For this
reason, full-sib populations from two heterozygous parents were de-
veloped to construct switchgrass linkage maps (Missaoui et al. 2005;
Okada et al. 2010). The population of Missaoui et al. (2005) was
composed of 85 full-sib progeny from a cross of ‘Alamo’ genotype
AP13 (seed parent) and ‘Summer’ VS 16, whereas the population of
Okada et al. (2010) consisted of 238 full-sib plants derived from
crossing one genotype (seed parent) of “Kanlow” with a selection of
“Alamo.” Because male and female meioses in the full-sib populations
were distinct and independent processes, two separate parental maps
were constructed, one map for the male parent and another for the
female parent (Okada et al. 2010). Recently we identified a self-com-
patible lowland switchgrass genotype NL94. A first (S1) generation
inbred population from selfing NL94 was developed with the assis-
tance of marker-based identification (Liu and Wu 2011). This S1
population is similar to an F2 population derived from selfing a F1
hybrid of a cross between two different inbred lines; therefore, only
one map was constructed instead of two separate (male and female)
maps. In other cross-pollinated species, such as loblolly pine
n Table 2 Segregation of 506 polymorphic loci in the switchgrass
selfed mapping population by chi-square test
aSegregation-distorted loci indicated in parenthesis.
n Table 3 Loci composition and recombination distance of linkage groups
aCalculated parameters with maximum recombination ratio = 0.35 and minimum LOD = 8.0.
bCalculated parameters with maximum recombination ratio = 0.40 and minimum LOD = 3.0.
cLargest gap in whole genome.
Loci on Initial
Total Loci on Final
Gaps .15 cM
per LG (cM)
|L. Liu et al.
Figure 4 A linkage map derived from 139 self-fertilized progeny of tetraploid switchgrass NL 94. Map distances in Kosambi map units (cM) of each linkage group (LG) are shown on the left, and
marker names are shown on the right. The gray segment in LG 6b indicates linkage identified with maximum linkage function at a LOD value of 2.0. The accessory loci, which are nearly
equivalent to the mapped loci based on the maximum linkage function in JoinMap 4 (Van Ooijen 2006), are listed next to mapped loci. The linkage groups were grouped into homeologous
groups based on multi-allele markers (in bold) and connected by dashed lines. The Arabic numeral designation of each homeologous groups (1–9) follows the previously published linkage map
(Okada et al. 2010). The bridge markers with good collinear relationships between two switchgrass maps are underlined, and those commonly used markers but cross-linked with other LGs are
denoted behind the loci with square brackets and their corresponding LGs inside. Note some markers are labeled with both symbols. The markers from sorghum (Sorghum bicolor) and foxtail
millet (Setaria italic) are indicated in italics and species names are added to distinguish them from switchgrass. The dominant markers are indicated with the letter “d” appended to the marker
name. The common markers shared by this map and previous sorghum (abbreviated as “sbi”) or foxtail millet (abbreviated as “Sit”) maps are labeled behind the loci with curly brackets and
their corresponding LGs inside. Segregation-distorted loci (SDL) indicate different significant levels:?P , 0.01,??P , 0.001, and???P , 0.0001.
Volume 2March 2012| Genetic Linkage Map of Switchgrass|
Figure 4 Continued.
|L. Liu et al.
Figure 4 Continued.
Volume 2March 2012|Genetic Linkage Map of Switchgrass|
(Remington et al. 1999), sugarcane (Hoarau et al. 2001; Andru et al.
2011), and grapes (Blasi et al. 2011), S1 progeny have also been used
for constructing linkage maps using SSR, amplified fragment length
polymorphism (AFLP), resistance gene analog (RGA), and target re-
gion amplification polymorphism (TRAP) markers.
Population size is the other important issue in constructing
a linkage map, because it is highly associated with the accuracy of
detecting recombination events. However, the size of the mapping
population is often limited by the genotyping and phenotyping costs.
The population size used in this study was 139 S1 progeny resulting in
278 gametes that were used in the calculation of the recombination
frequencies. Consequently, its mapping accuracy should be much
higher than that used in the reference maps, as each map was
constructed using 238 gametes (238 full-sib F1 individuals, Okada
et al. 2010).
SSR marker polymorphisms
SSRs used as a DNA marker system are advantageous over many
other marker systems and have been widely utilized in many plant
genomic studies (Morgante and Olivieri 1993). In this study, we ex-
haustively screened all available switchgrass SSR markers from various
sources (Tobias et al. 2006, 2008; Okada et al. 2010; Wang et al. 2011)
and found the polymorphic ratio of gSSRs is nearly 2-fold higher than
that of eSSRs (i.e. 28.2% vs. 14.0%). This result is consistent with
previous studies revealing higher polymorphism levels in gSSR than
in eSSR markers (La Rota et al. 2005; Saha et al. 2005). This is because
gSSRs mostly reside in nongenic regions (Varshney et al. 2005), so
more variations can be tolerated than eSSRs. The majority of gSSRs
used here were inherited in a codominant manner, and in most cases,
they are chromosome-specific because only a single locus is amplified
from one of the two homologous chromosomes. This unique marker-
chromosome relationship is a very useful feature in a polyploidy ge-
nome. Similar phenomena were observed in wheat gSSRs (Röder et al.
1998a). Although previous studies indicated eSSRs are prone to accu-
mulate in gene-rich regions and affect the coverage of linkage maps
(Pinto et al. 2006), in this study, mapped eSSRs were interspersed
along the whole linkage map, mostly between the gSSRs, and no
obvious clusters were observed. It is further noteworthy that 80.6%
of SSRs (2010 of 2493 tested markers) amplified monomorphic bands
in the parent NL94 of this mapping population, indicating the parent
has a high level of homozygosity. Higher polymorphisms of the same
gSSR and eSSRs markers were reported between switchgrass varieties
(Wang et al. 2011; Tobias et al. 2008). We observed a higher frequency
of polymorphism in dinucleotide microsatellites (GA- and CA-motifs)
than in trinucleotide microsatellites (CCG-, CAG-, and AAG-). This is
in agreement with the previous results obtained in switchgrass (Wang
et al. 2011) and other grasses, such as tall fescue (Festuca arundinacea)
(Hirata et al. 2006; Saha et al. 2006), timothy (Phleum pretense) (Cai
et al. 2003), perennial ryegrass (Lolium perenne) (Jones et al. 2001),
and zoysiagrass (Zoysia spp.) (Cai et al. 2005). Trinucleotide motifs
could stably reside in the target regions and suppress frameshift muta-
tions and variations (Varshney et al. 2005), and dimeric repeats have
been found in the untranslated regions of many species (Morgante
et al. 2002; Jung et al. 2005). On the basis of the higher occurrence and
polymorphism, dinucleotide SSRs could be preferentially selected for
linkage map development in the future.
We observed that sorghum and foxtail millet gSSR markers had
a marker transferability of 11% and 54%, respectively, which mirrored
the respective divergence distance of the two species from switchgrass.
Because the whole-genome sequence (although not 100%) of foxtail
millet is publicly available now and has a higher transferability to
switchgrass than sorghum, it provided a valuable resource to develop
new molecular markers for constructing LGs of switchgrass, a plant
with limited sequence information.
Genetic linkage map
Estimates of the genome length and map distance between markers
are important for the characterization of gene effects, integration of
genetic and physical maps, and evaluation of map coverage. Several
factors can affect the accuracy of LG construction. Among them,
genotyping errors are a common factor that inflates the map length,
especially when multiple bands (alleles) of the markers are scored in
a polyploidy species (Pompanon et al. 2005). Errors can be divided
into two distinct types: type I is allelic dropout, in which one allele of
a heterozygote randomly fails in PCR amplification, and type II is false
alleles, in which the true allele is mistakenly genotyped due to low
quality of DNA, resulting in PCR or electrophoresis artifacts or scor-
ing errors in reading and recording data (Broquet and Petit 2004). To
ensure the quality of the linkage map, a number of measures were
taken as follows. First, high-quality DNA samples were extracted using
a stringent CTAB method (Doyle and Doyle 1990), and each sample
was measured for A260/280 value to be ?1.8 and analyzed in agarose
gel electrophoresis to show only one bright band in a high molecular
weight area. Second, the scoring system of codominant markers was
n Table 4 Positioning of switchgrass mapped loci in the genome of foxtail millet (Setaria italica)
Foxtail Millet Chromosomea
aBased on the personal communication with Dr. A. Doust (Botany Department, Oklahoma State University), the nine chromosomes of foxtail millet corresponded to
the first nine assembled scaffolds, which represented 98.9% of the whole-genome sequence of foxtail millet released in Phytozome v7.0 (www.phytozome.net/
bTotal number of loci showing one-to-one relationship between switchgrass linkage groups and foxtail millet chromosomes are indicated in parenthesis.
cOverall mean of percentage.
|L. Liu et al.
utilized rather than the single dose dominant markers previously used
by Okada et al. (2010) and Missaoui et al. (2005). We found most
polymorphic markers showed mainly two reproducible alleles in par-
ent NL94. This result is similar with previous studies where the mean
number of amplicons per individual was 2.18 (Tobias et al. 2008). The
segregation of all markers in progeny was scored as only one pattern
(,hkxhk.), instead of five types (,abxcd., ,efxeg., ,lmxll.,
,nnxnp., and ,hkxhk.) ranging from two to four alleles in
a full-sib population. The fewer alleles recorded, the fewer chances
that an allele dropout could occur, thus increasing the accuracy of
genotyping. The codominant markers used here provided more in-
formation for linkage analysis than do dominant ones (Wu et al.
2002). Third, all marker data were scored independently by two peo-
ple to reduce errors. Those markers with spurious, unstable, or re-
dundant bands were either repeatedly tested or excluded (accounting
for about 10% of total data) from the final map calculations. Four, we
initially started linkage calculations using the markers with high fidel-
ity to construct a framework map with LOD to 8.0. Those markers
were fixed to allow more markers to be added on the linkage map by
reducing the LOD threshold to 3.0 (except for 2.0 to link two seg-
ments of LG 6b). This strategy has been successfully used by previous
studies (Wu and Huang 2006; Okada et al. 2010) and guaranteed both
the accuracy and high coverage of the final full map.
A high collinearity between our map and the previously published
switchgrass maps of crossed populations (Okada et al. 2010) was
observed, excepting the following small discrepancies: seven inver-
sions in marker order between our map and the reference maps
(Figure S3). Except for 1 mismatched foxtail millet marker, the other
7 markers from foxtail millet (Jia et al. 2009) and 1 marker from
sorghum (Wu and Huang 2006) matched their corresponding foxtail
millet and sorghum linkage maps, respectively (Figure 4). The local
rearrangement in some LGs could be real due to their universality in
plant genomes (Paterson et al. 1996) or to genotyping errors in either
of the populations. After all, errors can never be completely avoided in
linkage mapping (Johnson and Haydon 2007). When a genetic map is
constructed, it is assumed that marker crossovers during meiosis oc-
cur at random intervals along chromosomes (Lichten and Goldman
1995). In this study, although we used all available markers, only 4
markers were mapped on LG 7b. In contrast, LG 7b is one of the
linkage groups with the highest marker density in a previous study
(Okada et al. 2010). Of the 63 markers mapped on LG 7b by Okada
et al. (2010), 57 markers were monomorphic and 6 markers produced
unscorable bands in our population, indicating the very high homo-
zygosity level of the NL 94 chromosome corresponding to LG 7b.
Genome structure of switchgrass
The genome structure of switchgrass was described in previous
studies, but no consistent conclusions were drawn (Missaoui et al.
2005; Okada et al. 2010; Saski et al. 2011), i.e. allotetraploid vs. auto-
tetraploid. Allotetraploids have two diverged subgenomes and show
the same inheritance as diploids. In this study, we speculate NL 94
switchgrass is an allotetraploid genotype because of the evidence de-
scribed as follows. First, all 503 codominant markers had disomic
inheritance, although 18.7% showed segregation distortion. Second,
the ratio of coupling to repulsion linkage was 1:1, which is consistent
with the disomic inheritance mode in polyploidy map construction
(Wu et al. 1992; Qu and Hancock 2001). Third, among 476 mapped
SSR markers, only 20 (4.2%) amplified more than two bands and
mapped on homeologous LGs, indicating that the two subgenomes
are significantly different from each other. The allotetraploid-like ge-
nome structure of the tetraploid switchgrass uncovered by this study
and the two recent molecular marker investigations (Okada et al.
2010; Liu and Wu 2011) agree well with the published cytological
observations of the bivalent pairing behavior of meiotic chromosomes
(Barnett and Carver 1967; Martinez-Reyna et al. 2001; Young et al.
2010). However, because diploid wild ancestors of switchgrass are
unknown and there are many examples of autotetraploids that
through time have undergone “diploidization” leading to disomic in-
heritance (see review by Wolfe 2001), much more sequence data are
needed to address this issue of allopolyploidy confidently.
In this study, we successfully identified six homeologous LGs using
the 20 multi-allele markers. Attempts were made to find more multi-
allele markers, but most of those multi-allele primer pairs were error-
prone during band scoring or unstable for the entire population. To
avoid eroding the quality of the linkage map, we were cautious when
selecting these markers. Approximately 20% the eSSR markers
amplified alleles in two subgenomes of switchgrass in previous maps
and identified nine complete homeologous LGs (Okada et al. 2010).
Although higher numbers of multi-allele SSRs were reported in their
study, Okada et al. (2010) strongly believed that the subgenomes of
switchgrass were distantly related. Through the bridge markers and
comparative mapping between ours and published maps (Okada et al.
2010), nine complete homeologous LGs were identified in this study.
Numerous examples of segregation distortion have been reported in
many crop species, including barley (Hordeum vulgare) (Graner et al.
1991; Devaux et al. 1995), rice (Oryza sativa)(Causse et al. 1994; Xu
et al. 1997), maize (Zea mays) (Wendel et al. 1987; Lu et al. 2002), and
wheat (Triticum aestivum) (Blanco et al. 2004; Quarrie et al. 2005). In
this study, segregation distortion was observed for 18.7% of the total
marker loci analyzed. This is slightly higher than that of the full-sib
switchgrass population with a value of up to 14% (Okada et al. 2010).
Because a selfed population was used here, the result is consistent with
previous reports that showed selfed populations had a higher tendency
for segregation distortions due to inbreeding depression (Andru et al.
2011; Charlesworth and Willis 2009).
Two SDL clusters were identified on LG 5b and 9b in this study
(Figure 4). Previous studies indicated that segregation distortion was
an indication of the linkage between molecular markers and distorting
factors (such as recessive lethal genes and incompatible alleles) (Lyttle
1991). If the linkage was tight, they usually had similar segregation
patterns and the skewed markers would appear to be clustered
(Jenczewski et al. 1997). These two SDL clusters were not found in
other studies, suggesting they are population-specific. Similarly, in
sugarcane and grape S1 maps (Hoarau et al. 2001; Blasi et al. 2011),
some distorted markers were also clustered together.
Comparative genome relationships have been established among rice,
foxtail millet, sugar cane, sorghum, pearl millet, maize, and the Triti-
ceae cereals (wheat, barley, rye, and oats) (Gale and Devos 1998). As
an emerging biofuel crop with a relatively short breeding history,
switchgrass had the only reported relationship with the sorghum ge-
nome, in which nine homeologous groups of switchgrass corre-
sponded to 10 sorghum chromosomes (Okada et al. 2010). Here we
utilized the newly released foxtail millet sequence and revealed that
about 80% of switchgrass markers were located in the foxtail millet
chromosomes. This result directly indicated the strong one-to-one
relationship between the chromosomes of switchgrass and foxtail mil-
let. The corresponding genomic relationship among switchgrass, fox-
tail millet, and sorghum is consistent with a previously integrated
grass comparative map (Devos and Gale 1997). The high genetic
Volume 2March 2012|Genetic Linkage Map of Switchgrass|
similarity between switchgrass and foxtail millet is not surprising
when considering their close relationship, with both belonging to
the same Paniceae tribe and the high collinearity among grasses
reported previously (Devos and Gale 1997, 2000). However, mis-
matched markers still existed. These might have been derived from
chromosomal rearrangements occurring after the respective speciation
of both switchgrass and foxtail millet. Due to its small stature, rapid
life cycle, inbred pollination, and prolific seed production, foxtail mil-
let is a desirable candidate to be used as a reference model for switch-
grass genetic and functional genomics investigation.
Genomics and breeding implications
Switchgrass has large, highly heterozygous polyploidy genomes that
hinder the effort of whole-genome sequencing. Dihaploid lines were
identified in switchgrass (Young et al. 2010) and seem to be helpful for
simplifying sequence assembly but are unsuitable for entire genome
sequencing due to their high sterility and instability (Young et al.
2010). A diploid relative of switchgrass (i.e. Panicum hallii) (2n =
2x = 18) is being sequenced at the Joint Genome Institute (JGI,
http://www.jgi.doe.gov/genome-projects/); however, it has only one
set of chromosomes, and therefore, it may not be effective for defining
two subgenomes of switchgrass. Here we constructed a high-density
genetic map derived from selfing a single genotype NL94. A total of
476 SSR markers were mapped in more than 2000 cM genome length
with average marker distance of 4.2 cM. The present map provides an
excellent tool for distinguishing switchgrass subgenomes. Through
continuing to self switchgrass plants like NL94, inbred lines are being
produced in our lab. These could be used as the starting material for
Based on our preliminary observations in the field, the selfed
progeny of NL94 showed significant segregation for biomass and its
associated traits, such as plant height, circumference of plant base,
tiller number, and spring growth vigor. It would be much easier to
perform a QTL analysis using a single map than two maps from a full-
sib population. Our S1 mapping population should have some
features of F2 populations, including the estimation of both additive
and dominant effects (Carbonell et al. 1993).
In conclusion, this study developed a complete genetic map of 18
linkage groups in an inbred lowland switchgrass population using
mostly codominant SSR markers. This map is the longest and most
dense one constructed to date. Two clusters of segregation-distorted
loci were found in the switchgrass genome. A one-to-one relationship
between nine switchgrass homeologous groups and nine foxtail millet
chromosomes was established. This linkage map should provide a new
genetic framework to facilitate genomics research, quantitative trait
locus (QTL) mapping, and marker-assisted selection.
We would like to thank the following funding sources and individuals
for sponsoring and helping in this research: National Science
Foundation Award EPS 0814361; Oklahoma Agricultural Experiment
Station; Andrew Doust (Botany Department, Oklahoma State Uni-
versity) for sharing foxtail millet primer and sequence information;
Christian M. Tobias (Western Regional Research Center, USDA–
ARS) for his helpful discussion in the construction of the linkage
map; Mrs. Janelle Malone for editorial input; and lab members, in-
cluding Yiwen Xiang, Candace Pitts, Elke Grether, Gary Williams,
Sharon Williams, Pu Feng, Chengcheng Tan, James Todd, Shiva
Makaju, and Shuiyi Lu. The genome nucleotide sequence of foxtail
millet for blast analysis was generated by J.G.I. and has not yet been
Andru, S., Y. B. Pan, S. Thongthawee, D. M. Burner, and C. A. Kimbeng,
2011Genetic analysis of the sugarcane (Saccharum spp.) cultivar ’LCP
85–384’. I. Linkage mapping using AFLP, SSR, and TRAP markers.
Theor. Appl. Genet. 123: 77–93.
Barnett, F. L., and R. F. Carver, 1967
switchgrass, Panicum virgatum L. Crop Sci. 7: 301–304.
Beckmann, J. S., and M. Soller, 1983
morphisms in genetic improvement: methodologies, mapping and costs.
Theor. Appl. Genet. 67: 35–43.
Beldade, P., S. V. Saenko, N. Pul, and A. D. Long, 2009
linkage map for Bicyclus anynana butterflies allows for a comprehensive
analysis of synteny with the lepidopteran reference genome. PLoS Genet.
Blanco, A., R. Simeone, A. Cenci, A. Gadaleta, O. A. Tanzarella et al.,
2004Extension of the “Messapia x dicoccoides” linkage map of Triti-
cum turgidum (L.). Thell. Cell Mol. Biol. Lett. 9: 529–541.
Blasi, P., S. Blanc, S. Wiedemann-Merdinoglu, E. Prado, E. H. Rühl et al.,
2011Construction of a reference linkage map of Vitis amurensis and
genetic mapping of Rpv8, a locus conferring resistance to grapevine
downy mildew. Theor. Appl. Genet. 123: 43–53.
Broquet, T., and E. Petit, 2004Quantifying genotyping errors in noninva-
sive population genetics. Mol. Ecol. 13: 3601–3608.
Brutnell, T. P., L. Wang, K. Swartwood, A. Goldschmidt, D. Jackson et al.,
2010Setaria viridis: a model for C4 photosynthesis. Plant Cell 22:
Cai, H. W., N. Yuyama, H. Tamaki, and A. Yoshizawa, 2003
characterization of simple sequence repeat markers in the hexaploid
forage grass timothy (Phleum pretense L.). Theor. Appl. Genet. 107: 1337–
Cai, H. W., M. Inoue, N. Yuyama, W. Takahashi, M. Hirata et al., 2005
characterization and mapping of simple sequence repeat markers in zoysia-
grass (Zoysia spp.). Theor. Appl. Genet. 112: 158–166.
Carbonell, E. A., M. J. Asins, M. Baselga, E. Balansard, and T. M. Gerig,
1993Power studies in the estimation of genetic parameters of quanti-
tative trait loci for backcross and doubled haploid populations. Theor.
Appl. Genet. 86: 411–416.
Causse, M. A., T. M. Fulton, Y. G. Cho, S. N. Ahn, J. Chunwongse et al.,
1994Saturated molecular map of the rice genome based on an inter-
specific backcross population. Genetics 138: 1251–1274.
Charlesworth, D., and J. H. Willis, 2009
pression. Nat. Rev. Genet. 10: 783–796.
Costich, D. E., B. Friebe, M. J. Sheehan, M. C. Casler, and E. S. Buckler,
2010Genome-size variation in switchgrass (Panicum virgatum): flow
cytometry and cytology reveal rampant aneuploidy. The Plant Genome 3:
Devaux, P., A. Kilian, and A. Kleinhofs, 1995
barley genome with male and female recombination-derived, doubled
haploid populations. Mol. Gen. Genet. 249: 600–608.
Devos, K. M., and M. D. Gale, 1997
Plant Mol. Biol. 35: 3–15.
Devos, K. M., and M. D. Gale, 2000Genome relationships: the grass model
in current research. Plant Cell 12: 637–646.
Doust, A. N., E. A. Kellogg, K. M. Devos, and J. F. Bennetzen, 2009
millet, a sequence driven grass model system. Plant Physiol. 149: 137–141.
Doyle, J. J., and J. K. Doyle, 1990Isolation of plant DNA from fresh tissue.
Focus 12: 13–15.
Gale, M. D., and K. M. Devos, 1998
years. Science 282: 656–659.
Graner, A., A. Jahoor, J. Schondelmaier, H. Siedler, K. Pillen et al., 1991
struction of an RFLP map of barley. Theor. Appl. Genet. 83: 250–256.
Gunter, L. E., G. A. Tuskan, and S. D. Wullschleger, 1996
populations of switchgrass based on RAPD markers. Crop Sci. 36: 1017–
Hitchcock, A. S., and A. Chase, 1951
States. United States Department of Agriculture. Miscellaneous Publica-
tion No. 200. U.S. Government Printing Office, Washington.
Meiosis and pollen stainability in
Restriction fragment length poly-
The genetics of inbreeding de-
Comparative mapping of the
Comparative genetics in the grasses.
Plant comparative genetics after 10
Manual of the Grasses of the United
| L. Liu et al.
Hirata, M., H. W. Cai, M. Inoue, N. Yuyama, Y. Miura et al., 2006
ment of simple sequence repeat (SSR) markers and construction of an
SSR-based linkage map in Italian ryegrass (Lolium multiflorum Lam.). Theor.
Appl. Genet. 113: 270–279.
Hoarau, J. Y., B. Offmann, A. D’Hont, A. M. Risterucci, D. Roques et al.,
2001Genetic dissection of a modern sugarcane cultivar (Saccharum spp.).
I. Genome mapping with AFLP markers. Theor. Appl. Genet. 103: 84–97.
Hopkins, A. A., C. M. Taliaferro, C. D. Murphy, and D. Christian,
1996Chromosome number and nuclear DNA content of several
switchgrass populations. Crop Sci. 36: 1192–1195.
Jansen, J., A. G. De Jong, and J. W. Van Ooijen, 2001
genetic linkage maps. Theor. Appl. Genet. 102: 1113–1122.
Jenczewski, E., M. Gherandi, I. Bonnin, J. M. Prosperi, I. Oliveri et al.,
1997 Insight on segregation distortions in two intraspecific crosses be-
tween annual species of Medicago (Leguminosae). Theor. Appl. Genet. 94:
Jia, X., Z. Zhang, Y. Liu, C. Zhang, Y. Shi et al., 2009
genetic mapping of SSR markers in foxtail millet. [Setaria italica (L.)
P. Beauv.] Theor. Appl. Genet. 118: 821–829.
Johnson, P. C. D., and D. T. Haydon, 2007
of allelic dropout and false allele error rates from microsatellite genotypes
in the absence of reference data. Genetics 175: 827–842.
Jones, E. S., M. P. Dupal, R. Kolliker, M. C. Drayton, and J. W. Forster,
2001Development and characterization of simple sequence repeat
(SSR) markers for perennial ryegrass (Lolium perenne L.). Theor. Appl.
Genet. 102: 405–415.
Jung, S., A. Abbott, C. Jesudurai, J. Tomkins, and D. Main, 2005
type, distribution and annotation of simple sequence repeats in Rosaceae
ESTs. Funct. Integr. Genomics 5: 136–143.
Kashi, Y., D. King, and M. Soller, 1997
of quantitative genetic variation. Trends Genet. 13: 74–78.
Kosambi, D. D., 1944The estimation of map distances from recombination
values. Ann. Eugen. 12: 172–175.
La Rota, M., R. V. Kantety, J. K. Yu, and M. E. Sorrells, 2005
distribution and frequencies of genomic and EST-derived microsatellite
markers in rice, wheat, and barley. BMC Genomics 6: 23.
Li, P., and T. P. Brutnell, 2011Setaria viridis and Setaria italica, model
genetic systems for the Panicoid grasses. J. Exp. Bot. 62: 3031–3037.
Lichten, M., and A. S. Goldman, 1995
Annu. Rev. Genet. 29: 423–444.
Liu, L. L., and Y. Q. Wu, 2011Identification of a selfing compatible ge-
notype and mode of inheritance in switchgrass. Bioenergy Res. (in press)
Lu, H., J. Romero-Severson, and R. Bernardo, 2002
associated with segregation distortion in maize. Theor. Appl. Genet. 105:
Lyttle, T. W., 1991Segregation distorters. Annu. Rev. Genet. 25: 511–557.
Martinez-Reyna, J. M., K. P. Vogel, C. Caha, and D. J. Lee, 2001
stability, chloroplast DNA polymorphisms, and morphological traits of
upland · lowland switchgrass reciprocal hybrids. Crop Sci. 41: 1579–1583.
Martinez-Reyna, J. M., and K. P. Vogel, 2002
switchgrass. Crop Sci. 42: 1800–1805.
McLaughlin, S. B., and L. A. Kszos, 2005
(Panicum virgatum) as a bioenergy feedstock in the United States. Bio-
mass Bioenergy 28: 515–535.
Missaoui, A. M., A. H. Paterson, and J. H. Bouton, 2005
genomic organization in switchgrass (Panicum virgatum L.) using DNA
markers. Theor. Appl. Genet. 110: 1372–1383.
Missaoui, A. M., A. H. Paterson, and J. H. Bouton, 2006
for the classification of switchgrass (Panicum virgatum L.) germplasm
and to assess genetic diversity in three synthetic switchgrass populations.
Genet. Resour. Crop Evol. 53: 1291–1302.
Morgante, M., and A. M. Olivieri, 1993
markers in plant genetics. Plant J. 3: 175–182.
Morgante, M., M. Hanafey, and W. Powell, 2002
erentially associated with non-repetitive DNA in plant genomes. Nat.
Genet. 30: 194–200.
Simple sequence repeats as a source
Meiotic recombination hotspots.
Incompatibility systems in
Development of switchgrass
PCR-amplified microsatellites as
Microsatellites are pref-
Narasimhamoorthy, B., M. C. Saha, B. Swaller, and J. H. Bouton, 2008
diversity in switchgrass collections assessed by EST-SSR markers. Bioenergy
Res. 1: 136–146.
Okada, M., C. Lanzatella, M. C. Saha, J. Bouton, R. Wu et al., 2010
switchgrass genetic maps reveal subgenome collinearity, preferential pairing
and multilocus interactions. Genetics 185: 745–760.
Paterson, A. H., T. H. Lan, K. P. Reischmann, C. Chang, Y. R. Lin et al.,
1996 Toward a unified genetic map of higher plants, transcending the
monocot-dicot divergence. Nat. Genet. 14: 380–382.
Pinto, L. R., K. M. Oliveira, T. Marconi, A. A. F. Garcia, E. C. Ulian et al.,
2006Characterization of novel sugarcane expressed sequence tag mi-
crosatellites and their comparison with genomic SSRs. Plant Breed. 125:
Pompanon, F., A. Bonin, E. Bellemain, and P. Taberlet, 2005
errors: causes, consequences and solutions. Nat. Rev. Genet. 6: 847–859.
Porter, C. L., 1966An analysis of variation between upland and lowland
switchgrass, Panicum virgatum L., in central Oklahoma. Ecology 47: 980–992.
Preacher, K. J., 2001Calculation for the chi-square test: an interactive
calculation tool for chi-square tests of goodness of fit and independence
[Computer software]. Available at http://quantpsy.org.
Qu, L., and J. F. Hancock, 2001Detecting and mapping repulsion-phase
linkage in polyploids with polysomic inheritance. Theor. Appl. Genet.
Quarrie, S. A., A. Steed, C. Calestani, A. Semikhodskii, C. Lebreton et al.,
2005A high-density genetic map of hexaploid wheat (Triticum aesti-
vum L.) from the cross Chinese Spring · SQ1 and its use to compare
QTLs for grain yield across a range of environments. Theor. Appl. Genet.
Remington, D. L., R. W. Whetten, B. H. Liu, and D. M. O’Malley,
1999Construction of an AFLP genetic map with nearly complete ge-
nome coverage in Pinus taeda. Theor. Appl. Genet. 98: 1279–1292.
Röder, M. S., V. Korzun, K. Wandehake, J. Planschke, M. H. Tixier et al.,
1998aA microsatellite map of wheat. Genetics 149: 2007–2023.
Röder, M. S., V. Korzun, B. S. Gill, and M. W. Ganal, 1998b
mapping of microsatellite markers in wheat. Genome 41: 278–283.
Saha, M. C., R. Mian, J. C. Zwonitzer, K. Chekhovskiy, and A. A. Hopkins,
2005An SSR- and AFLP-based genetic linkage map of tall fescue
(Festuca arundinacea Schreb.). Theor. Appl. Genet. 110: 323–336.
Saha, M. C., J. D. Cooper, M. A. Rouf Mian, K. Chekhovskiy, and G. D. May,
2006Tall fescue genomic SSR markers: development and transferability
across multiple grass species. Theor. Appl. Genet. 113: 1449–1458.
Saski, C. A., Z. Li, F. A. Feltus, and H. Luo, 2011
switchgrass: a BAC library and comparative analysis of because genomic
regions harboring bioenergy traits. BMC Genomics 12: 369.
Schmer, M. R., R. B. Mitchell, and R. K. Perrin, 2008
losic ethanol from switchgrass. Proc. Nat. Acad. Sci. USA 1050: 464–469.
Semagn, K., Å. Bjørnstad, and Y. Xu, 2010
quantitative traits in crops. Electron. J. Biotechnol. 13: 1–45.
Stam, P., 1993Construction of integrated genetic linkage maps by means of
a new computer package: JOINMAP. Plant J. 3: 739–744.
Talbert, L. E., D. H. Timothy, J. C. Burns, J. O. Rawlings, and R. H. Moll,
1983Estimates of genetic parameters in switchgrass. Crop Sci. 23: 725–728.
Taliaferro, C. M., K. P. Vogel, J. H. Bouton, S. B. McLaughlin, and G. A.
Tuskan, 1999Reproductive characteristics and breeding improvement
potential of switchgrass, pp. 147–153 in Biomass, a Growth Opportunity
in Green Energy and Value-added Products—Proceedings of the 4th Bio-
mass Conference of the Americas, edited by R. Overend and E. Chornet.
Elsevier Sciences, Oxford, UK.
Tobias, C., P. Twigg, D. Hayden, K. Vogel, R. Mitchell et al., 2005
of expressed sequence tags and the identification of associated short
tandem repeats in switchgrass. Theor. Appl. Genet. 111: 956–964.
Tobias, C. M., D. M. Hyden, P. Twigg, and G. Sarath, 2006
satellite markers derived from EST sequences of switchgrass (Panicum
virgatum L.). Mol. Ecol. Notes 6: 185–187.
Tobias, C. M., G. Sarath, P. Twigg, E. Lindquist, J. Pangilinan et al.,
2008Comparative genomics in switchgrass using 61,585 high-quality
expressed sequence tags. Plant Genome 1: 2.
New genomic resources for
Net energy of cellu-
The genetic dissection of
Volume 2March 2012|Genetic Linkage Map of Switchgrass|
U.S. Department of Energy, 2011
a Bioenergy and Bioproducts Industry, led by R. D. Perlack and B. J. Stokes.
ORNL/TM-2011/224. Oak Ridge National Laboratory, Oak Ridge, TN.
Van Ooijen, J. W., 2006JoinMap 4, Software for the Calculation of Genetic
Linkage Maps in Experimental Populations. Kyazma BV, Wageningen,
Varshney, R. K., A. Graner, and M. E. Sorrells, 2005
markers in plants: features and applications. Trends Biotechnol. 23: 48–55.
Vogel, K. P., and J. F. Pedersen, 1993
perennial grasses. Plant Breed. Rev. 11: 251–274.
Vogel, K. P., G. Sarath, A. J. Saathoff, and R. B. Mitchell, 2011
pp. 341–380 in Energy Crops, edited by N. Halford and A. Karp. The
Royal Society of Chemistry, Cambridge, UK.
Wang, Y. W., T. D. Samuels, and Y. Q. Wu, 2011
genomic SSR markers in switchgrass. Theor. Appl. Genet. 122: 677–686.
Wendel, J. F., M. D. Edwards, and C. W. Stuber, 1987
genetic control of preferential fertilization in maize. Heredity 58: 297–302.
Wolfe, K. H., 2001Yesterday’s polyploidization and mystery of diploid-
ization. Nat. Rev. Genet. 2: 333–341.
Wu, K. K., W. Burnquist, M. E. Sorrells, T. L. Tew, P. H. Moore et al.,
1992The detection and estimation of linkage in polyploids using single-
dose restriction fragments. Theor. Appl. Genet. 83: 294–300.
U.S. Billion-Ton Update: Biomass Supply for
Breeding systems for cross-pollinated
Development of 1,030
Evidence for multilocus
Wu, R., C. X. Ma, I. Painter, and Z. B. Zeng, 2002
likelihood estimation of linkage and linkage phases in outcrossing species.
Popul Biol. 61: 349–363.
Wu, Y., and Y. Huang, 2006An SSR genetic map of Sorghum bicolor (L.)
Moench and its comparison to a published genetic map. Genome 50: 84–89.
Xu, Y., L. Zhu, J. Xiao, N. Huang, and S. R. McCouch, 1997
regions associated with segregation distortion of molecular markers in F2,
backcross, doubled haploid and recombinant inbred populations of rice
(Oryza sativa L.). Mol. Gen. Genet. 253: 535–545.
Young, H. A., B. J. Hernlem, A. L. Anderton, C. Lanzatella-Craig, and C. M.
Tobias, 2010Dihaploid stocks for switchgrass isolated by a screening
approach. BioEnergy Res. 3: 305–313.
Zalapa, J. E., D. L. Price, S. M. Kaeppler, C. M. Tobias, M. Okada et al.,
2011Hierarchical classification of switchgrass genotypes using SSR and
chloroplast sequences: ecotypes, ploidies, gene pools, and cultivars. Theor.
Appl. Genet. 122: 805–817.
Zhang, Y., J. E. Zalapa, A. R. Jakubowski, D. L. Price, A. Acharya et al.,
2011Post-glacial evolution of Panicum virgatum: centers of diversity
and gene pools revealed by SSR markers and cpDNA sequences. Genetica
Communicating editor: K. Dawe
|L. Liu et al.