R E S E A R C H A R T I C L E Open Access
Construction of a high-density genetic map
using specific-locus amplified fragments in
, Qingjiang Zhang
, Ruiheng Du
, Peng Lv
, Xue Ma
, Shu Fan
, Suying Li
, Shenglin Hou
and Guoqing Liu
Background: Sorghum is mainly used as a human food and beverage source, playing an important role in the
production of ethanol and other bio-industrial products. Thus it is regarded as a model crop for energy plants.
Genetic map construction is the foundation for marker-assisted selection and gene cloning. So far several sorghum
linkage maps have been reported using different kinds of molecular markers. However marker numbers and
chromosome coverage are limited. As a result, it is difficult to get consistent results and the maps are hard to unify.
In the present study, the genomes of 130 individuals consisting an F
population together with their parents were
surveyed using a high-throughput sequencing technique. A high-density linkage map was constructed using
specific-locus amplified fragments (SLAF) markers. This map can provide information and serve as a reference for
effective gene exploration, and for marker assisted-breeding program.
Results: A high-throughput sequencing method was adopted to screen SLAF markers with 130 F
a cross between a grain sorghum variety, J204, and a sweet sorghum variety, Keter. In the present study, 52,928
suitable SLAF markers out of 43,528,021 pair-end reads were chosen to conduct genetic map construction, 12.0% of
which were polymorphic. Among the 6353 polymorphic SLAF markers, 5829 (91.8%) were successfully genotyped in
mapping population. Finally 2246 SLAF markers were obtained to construct a high-density genetic linkage
map. The total distance of linkage map covering all 10 chromosomes was 2158.1 cM. The largest gap on each
chromosome was 10.2 cM on average. The proportion of gaps less than and/or equal to 5.0 cM was averagely
98.1%. The markers on each chromosome ranged from 123 (chromosome 9) to 315 (chromosome 4) with a
mean value of 224.6, the distance between adjacent markers ranged from 0.6 (chromosome 10) to 1.3 cM
(chromosome 9) with an average distance of only 0.98 cM.
Conclusion: A high density sorghum genetic map was constructed in this study. The total length was 2158.
1 cM covering all 10 chromosomes with a total number of 2246 SLAF markers. The construction of this
map can provide detailed information for accurate gene localization and cloning and application of
Keywords: Sorghum bicolor, High-throughput sequencing, SLAF marker, Linkage map construction
* Correspondence: email@example.com
Institute of Millet Crops, Hebei Academy of Agricultural & Forestry Sciences/
Hebei Branch of China National Sorghum Improvement Center, Shijiazhuang
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ji et al. BMC Genomics (2017) 18:51
Sorghum (Sorghum bicolor) is one of the five dominant
crops in the world including corn (Zea mays), wheat
(Triticum aestivum), rice (Oryza sativa) and barley
(Hordeum vulgare) (http://www.fao.org). With the ad-
vantages of high yielding, good adaptability, drought, salt
and alkali tolerance, it is one of the most valuable energy
crops for the future [1, 2]. Sorghum is a typical C
and mainly used as a human food and beverage source.
Sorghum grain is the main ingredient of top-grade
alcohols and its stem can be used as fodder. Both grain
and stem including all plant are stock for bioethanol and
other bio-industrial products.
The genome size of sorghum (750 Mb) is 3–4 times
smaller than corn, thus it was regarded as a diploid
model crop for energy plants like polyploidy sugarcane
and Miscanthus [3, 4]. A genetic map is a foundation for
quantitative and qualitative gene mapping and cloning,
and plays a key role in marker-assisted breeding pro-
gram. High-density genetic maps of sorghum can be
used for genome comparison, useful gene mining and
gene mapping. The genes for disease and insect-resistance,
stress tolerance, sugar concentrations and biological yield
can be identified by comparing homology in different plant
species, and they can also be located on chromosomes by
mapping, which lays a foundation for gene cloning and
application. High-density genetic mapping has great im-
portance in increasing statistical power and precision of
detecting genes and QTLs.
Genetic map construction of sorghum began in 1990s.
The early linkage maps of sorghum were constructed
mainly by using labor-intensive or dominant markers such
as RFLP (Restriction fragment length polymorphism), AFLP
(Amplified fragment length polymorphism) and RAPD
(Random amplified polymorphic DNA) [5–10]. These maps
have played important role in sorghum gene (QTL) map-
ping, comparative genomics and genetics studies. However,
these genetic marker systems have limited marker numbers,
dominant expression, and not repeatable in different maps.
More informative marker types can effectively overcome
the disadvantages mentioned above are required. Due to
the quick development of sequencing and genotyping tech-
nologies, simple sequence repeat (SSR) with features of
high reproducibility, co-dominant inheritance, multi-allelic
dominant markers for constructing linkage maps. SSR
markers were first used for polymorphism detecting and
linkage group identification [11, 12], then were used to
construct sorghum genetic maps with the development of a
large amount of SSR markers [13, 14]. Several linkage maps
with SSR markers or mainly based on SSR markers have
been developed and have been using in sorghum gene
(QTL) mapping, genome evolution, molecular genetics and
marker-assisted breeding [4, 15–18].
However, the above technologies such as RAPD, RFLP,
AFLP and SSR to determine genetic fingerprints have
limitations to cover full genome which requires the iden-
tification of a large number of polymorphic markers.
With these technologies this is a step by step approach
that is labor intensive and plagued by process variation.
Diversity Arrays Technology (DArT) was initially used
to detect a large number of genetic differences between
plant and animal varieties. Recently this technology was
introduced for sorghum map construction. DArT
markers were integrated into a sorghum consensus map
which consisted of a total of 1997 markers mapped to
2029 unique loci (1190 DArT loci and 839 other loci)
spanning 1603.5 cM and with an average marker density
of 1 marker/0.79 cM .
Great progress has been made in the sequencing tech-
nologies and bioinformatics at an exponentially reduced
cost, which led to a revolution in the field of genotyping
technologies. Restriction associated DNA sequencing
(RAD-seq) and genotyping by sequencing (GBS) have
emerged as powerful genotyping platforms, which are
capable of identifying, sequencing, and genotyping thou-
sands of markers across almost any genome of interest
and number of individuals in a population . The next
generation sequencing can directly determine differences
in DNA sequence with high accuracy, thus it has been
widely used for plant and animal genetic analysis. SLAF
(specific-locus amplified fragments) markers, which has
been used for genetic investigation, have the properties
of being present in large amount, being evenly dis-
tributed and avoiding repeated sequences . These
markers have been used for crop genetic analysis such as
sesame, millet, rice and soybean [22–26], especially in
the applications of high-density genetic map construc-
tion and functional genes verification. Exploiting this
approach to scan the whole sorghum genome has great
importance for high density marker development and
gene mining for sorghum breeding.
The purpose of this study is to construct a high-density
linkage map with SNPs through next generation sequen-
cing technology. The map can provide information and
serve as a reference for effective gene exploration and lay
a foundation for marker assisted breeding. Further, it can
benefit the development of biological energy resources.
Parents and F
population for map construction
population consisting of 130 individuals from a
cross of Keter × J204 was used for genetic map construc-
tion. The maternal parent is a sweet sorghum variety
and the paternal parent is a grain sorghum variety.
There have been great differences in phenotypic char-
acters between the two parents, such as plant height,
heading time, seed coat color, etc. Therefore, their
Ji et al. BMC Genomics (2017) 18:51 Page 2 of 10
offspring will have considerable variations which are
good for polymorphic marker screening and linkage
The SLAF number and sequencing depth identified in
the parents and their offspring were plotted in Fig. 1a.
The SLAF marker number in paternal and maternal par-
ents was 44,895 and 42,100, respectively. The sequen-
cing depth on average was 16.8-fold in paternal parent
and 12.9-fold in maternal parent. The SLAF numbers
for each F
individual ranged from 26,737 to 39,291 with
an average of 33,445.1. The sequencing depth shifted
from 2.2 to 3.7-fold with an average of 2.8-fold (Fig. 1b).
Among the 52,928 (Additional file 1) qualified SLAF
markers, 6353 were polymorphic with a polymorphism
rate of only 12.0% (Table 1). Of the 6353 polymorphic
SLAF markers, 5829 (91.8%) were classified into eight
segregation patterns (Fig. 2). Among them 5093 (87.4%)
markers fell into segregation pattern aa × bb. Because in-
dividuals in the F
population which was obtained by
selfing the F
of a cross between two fully homozygous
parents showed this genotype, only the aa × bb segrega-
tion pattern in the F
population was used to construct
the genetic map. Finally 2,246 (Additional file 2) markers
were assigned onto linkage groups.
The average sequencing depths were 29.3-fold in the
parents and 3.3-fold in the offsprings on linked markers
(Table 2). This integrity and depth of markers were
enough to guarantee the accuracy for genetic map
Linkage map construction
All the 2246 assigned markers were grouped to 10 chromo-
somes, the linear alignments of markers on chromosomes
were built by the genetic distances between adjacent
Fig. 1 The SLAF number and the sequencing-depth in the parents and F
individuals. aNumber of SLAF markers. bSequencing-depth of SLAF
markers. The X-axis in (a) and (b) indicates individuals including maternal parent (Keter, designated as 1), paternal parent (J204, designated as 2)
and 130 individuals from the F
population. The Y-axis indicates the number of reads in (a) and the sequencing-depth in (b)
Ji et al. BMC Genomics (2017) 18:51 Page 3 of 10
markers. Finally the 2246 markers were assigned onto the
genetic map with a total length of 2158.1 cM and average
distance between markers of 0.98 cM. The degree of link-
age between markers was reflected by Gap less than and/or
equal to 5.0 cM (Gap < = 5) ranging between 96.3% and
100.0% with an average value of 98.1%. The largest
gap on chromosome 7 is 15.7 cM. On average 224.6
markers were assigned on each chromosome with a
length of 215.8 cM (Table 3).
Among the 2246 markers, 315 were assigned on
chromosome 4 which was the largest in the ten chromo-
somes. The total length was 300.4 cM with an average
distance of only 0.96 cM between adjacent markers. A
large gap of 13.7 cM was located between 245.6 to
259.3 cM, the gap < = 5 ratio was 98.1%. The fewest
markers (123) were on chromosome 9, which was
152.2 cM in length with an average distance of 1.3 cM
between adjacent markers. A large gap of 4.3 cM was
located at the end of the chromosome. The gap < = 5
ratio was 100.0% which indicates the good quality of
marker assignment (Table 3).
Three types of markers were assigned to the genetic
map including 2237 ‘SNP_only’,3‘InDel_only’,and6
‘SNP&InDel’markers. ‘SNP_only’was the predomin-
ant marker type accounting for 99.6% of the
markers. ‘InDel_only’markers were assigned on chro-
mosomes 1, 5 and 6, respectively. While 6 ‘SNP&InDel’
markers were assigned on chromosomes 2, 3, 4 and 6,
respectively (Table 3).
Of the all 2237 SNP markers, most were transition
type SNPs with R (G/A) and Y (T/C) types accounting
for 32.8% and 32.7%, respectively. The other four SNP
types were transversions including S (G/C), M (A/C), K
(G/T), and W (A/T) with percentages of 9.1, 8.4, 8.3 and
8.7 of all SNPs, respectively (Table 4).
Markers that showed significant (χ
,p< 0.05) segrega-
tion distortion (1192 in total) were finally assigned onto
the map (Fig. 3) and most of them were clustered at the
two ends of chromosomes and some located at chromo-
some centers such as chromosome 4 (Table 5, Fig. 3).
More than half (53.1%) of the assigned markers showed
significant (p< 0.05) segregation distortion which distrib-
uted on each chromosome. The largest chromosome
(chr. 4) had the highest percentage of segregation distor-
tion markers (15.8%) and the smallest chromosome
(Chr. 9) had the lowest percentage of segregation distor-
tion markers (6.0%). All the distorted markers clustered
into 98 segregation distortion regions (SDRs) which
distributed on each chromosome. Similarly 14 SDRs
were found on chr 4 and 6 on chr 9 (Table 5). Among
the three different marker types assigned to the final
map, no one marker type was observed to show a particu-
lar tendency for skewness. Besides ‘SNP-only’markers,
one out of 3 ‘InDel-only’and 4 out of 6 ‘SNP&InDel’
markers showed segregation distortion, respectively.
In the present study an F
mapping population from a
cross between a sweet and a grain sorghum variety was
employed to construct a sorghum linkage map. The
great character variations between the two parents bene-
fited the marker polymorphism discovery. The high-
throughput sequencing technology used in the present
study has greatly enhanced the identification and guar-
anteed the quantity and quality of markers. Therefore a
high density genetic map was successfully constructed.
Some existing sorghum maps are unsatisfactory for gene
identification because of lacking adequate markers from
the whole genome, and broken chromosome segments.
In the present study a dense genetic map was generated
in which the whole sorghum genome sequence was
surveyed, high quality markers were identified and uni-
formly distributed on 10 chromosomes. Each chromo-
some contains 123–318 markers and its length ranged
from 132.8 to 300.4 cM. This high density SNP-based
Table 1 SLAF marker identification
Type SLAF number Ratio (%)
Polymorphisms 6353 12.0
Non-polymorphisms 46575 88.0
Total 52928 100.0
Fig. 2 The marker numbers in different segregation patterns. The
X-axis indicates different segregation patterns. The Y-axis indicates
the SLAF number in each pattern
Table 2 The sequencing depth of assigned makers in the
parents and F
Name Marker number Total depth Average depth
Keter 2246 59620 26.5
J204 2246 71689 31.9
Offspring 1971 7296 3.3
Ji et al. BMC Genomics (2017) 18:51 Page 4 of 10
linkage map for sorghum can serve as a reference map
for cultivated sorghum species and will be useful in
DNA marker distribution is not random with some
clear marker-dense regions and some marker deserts. In
the present map, marker deserts (gaps) were observed
with varied sizes. Most (97.0%) of the gaps on every link-
age group are less than and/or equal to 5.0 cM. In total
only 11 gaps larger than 5.0 cM were detected in all
chromosomes except chr 9 suggesting that such gaps are
not restricted to a particular chromosome. Gaps larger
than 10.0 cM were found on chromosomes 1, 4, 6 and 7.
The longest one was 15.7 cM on the distal end of chr 7.
The presence of these gaps may have negative effects on
the application of mapped DNA markers, for example,
genomic regions that lack DNA markers will make
detection of quantitative trait loci (QTL) difficult .
Therefore, more comparable markers between different
sorghum maps are needed to fill in the gaps to obtain a
more complete coverage of the sorghum genome.
Segregation distortion is a common phenomenon in
which the genotypic frequency of a marker deviates from
a typical Mendelian ratio. Previous studies have showed
that a large number of segregation distortions and SDRs
occur in many species, such as maize , barley 
potato , sesame , peanut  and sorghum [3,
19]. The genetic basis of segregation distortion is still
under debate, and gametophyte and/or zygotic selection
and chromosomal rearrangements may be the main
cause of this phenomenon . However, some studies
found segregation distortion in a non-random and con-
sistent distribution pattern suggested that distorted seg-
regation is due to the elimination of gametes or zygotes
by a lethal factor located in a neighboring region of the
marker . On a sorghum consensus map, chr 1 has
the highest proportion of chromosomal regions associ-
ated with skewed segregation (67%). Two other chromo-
somes (chr 4 and chr 8) also have over 50% of the
chromosomal regions associated with skewed segrega-
tion (51.6% and 54.1%, respectively) . In the present
study, an F
mapping population was employed to con-
struct a linkage map, among the 2246 assigned markers,
1192 markers (53.1%) showed significant segregation
distortion. All the skewed markers clustered into segre-
gation distortion regions. Although it is not exactly the
same, chr 1 has the highest proportion (71.5%) of
skewed markers and chr 4 has the biggest number (14)
of SDRs in this final map, which indicates that there
may be similar mechanism of skewed segregation
phenomenon between the two studies. Further, studies
have proved that the presence of segregation distortion
markers will not affect the use of linkage maps for appli-
cations such as QTL mapping [32, 33].
Genomic approaches such as high-throughput se-
quencing and large-scale genotyping technologies have
been used in genetic linkage mapping. The SLAF-seq
method provided significant advantages to generate
enough polymorphic markers for high-density genetic
map construction. The high density map is sufficient
to ensure adequate polymorphic marker coverage in
regions of interest and can be used as a reference
map for sorghum genetic studies.
Table 3 Map information based on high quality SLAFs obtained from population sequencing
Linkage groups Marker types and numbers Total distance (cM) Average distance
between markers (cM)
Gap < =5 (%)
Total SNP only InDel only SNP & InDel
1 200 199 1 0 238.7 1.20 14.1 97.5
2 292 290 0 2 287.9 0.99 8.5 99.0
3 250 249 0 1 286.7 1.15 7.4 98.4
4 315 314 0 1 300.4 0.96 13.7 98.1
5 218 217 1 0 183.7 0.85 9.4 97.2
6 216 213 1 2 175.0 0.81 10.7 98.1
7 217 217 0 0 266.3 1.23 15.7 96.3
8 189 189 0 0 134.6 0.72 8.6 98.9
9 123 123 0 0 152.2 1.25 4.3 100.0
10 226 226 0 0 132.8 0.59 9.7 97. 8
Total 2246 2237 3 6 2158.1 0.98 10.2 98.1
Table 4 Different SNP types in the linkage group
SNP types Number Ratio (%)
R(G/A) 915 32.8
Y(T/C) 914 32.7
S(G/C) 255 9.1
W(A/T) 243 8.7
K(G/T) 231 8.3
M(A/C) 234 8.4
Total 2792 100.0
Ji et al. BMC Genomics (2017) 18:51 Page 5 of 10
Fig. 3 Ten linkage groups of sorghum from a cross of Keter × J204. SLAF marker names and their locations are listed on the right and left sides of
the axis. Segregation distortion markers on the map are highlighted in green
Ji et al. BMC Genomics (2017) 18:51 Page 6 of 10
A high density sorghum map was constructed in this
study by employing SLAF markers developed from high-
throughput sequencing technology. The total map length
is 2158.1 cM covering sorghum 10 chromosomes with a
total of 2246 SLAF markers. The construction of this
map can provide detailed information for gene
localization, cloning and application of marker-assisted
mapping population derived from a cross of sweet
sorghum Keter and grain sorghum J204 (a variation line
of J14859 from the USA) was employed to construct a
linkage map. The parents and F
planted in the Experiment Station, Institute of Millet
Crops, Shijiazhuang, China in the year 2012, the heading
date was recorded and the heads were bagged prior to
anthesis to prevent out crossing contamination and
allowed to self-fertilize. Five plants of each parent and
individuals were phenotyped.
DNA was extracted from fresh leaf tissue following the
modified CTAB protocol . DNA concentration was
adjusted to be in the range of 50–100 ng μl
SLAF library construction and high-throughput
An improved SLAF-seq strategy was used in this study.
Firstly, sorghum genome was used as reference to design
the experiments for marker discovery by simulating in
silico, different enzymes were adopted to produce a lot
of markers. Next, predesigned scheme was used to con-
struct the SLAF library. Enzyme MseI (New England
Biolabs, NEB, (USA)) was adopted for the F
After digested the genomic DNA, a single nucleotide (A)
overhang was added to the digested fragments using
Klenow Fragment (3´ →5´ exon) (NEB) and dATP at
37 °C. Duplex tag-labeled sequencing adapters (PAGE-
purified, Life Technologies, USA) were then ligated to
the A-tailed fragments using T
DNA ligase. Diluted
restriction-ligation DNA samples were used to performe
the polymerase chain reaction (PCR): dNTP, Q5 high-
fidelity DNA polymerase and PCR primers (Forward
primer: 5’-AATGATACGGCGACCACCGA-3’, reverse
primer: 5’-CAAGCAGAAGACGGCATACG-3’) (PAGE-
purified, Life Technologies). Then purified and pooled
the PCR products by agencourt AMPure XP beads
(Beckman Coulter, High Wycombe, UK). 2% agarose gel
electrophoresis was used to separate pooled samples.
Took the fragments ranged from 380 to 410 base pairs
(with indexes and adaptors) in size from the gel and
excised and purified using a QIAquick gel extraction
kit (Qiagen, Hilden, Germany). After diluted, the
pair-end sequencing (Each end 125 bp) was per-
formed on an Illumina HiSeq 2500 system (Illumina,
Inc; San Diego, CA, USA) according to the manufac-
Sequence data grouping and genotyping
Procedures described by Sun et al.  was adopted to
SLAF marker identification and genotyping. After the
low-quality reads (quality score < 20e) were filtered out,
the SLAF pair-end reads with clear index information
were clustered based on sequence similarity (BLAT)
[35, 36] (−tileSize = 10 –step Size = 5). Sequences with
over 95% identity were grouped in one SLAF locus.
Single nucleotide polymorphism (SNP) loci of each
SLAF locus were then detected between parents, and
SLAFs with more than 3 SNPs were filtered out
firstly. Alleles were defined in each SLAF using the
minor allele frequency (MAF) evaluation.
Table 5 Description on segregation distortion markers
LGs Total marker Segregation distortion marker χ
on each LG (%)
No. % No. %
1 200 8.9 132 11.1 20.644 0.005 71.5 12
2 292 13.0 112 9.4 15.147 0.008 38.4 13
3 250 11.1 156 13.1 19.837 0.007 62.0 13
4 315 14.0 188 15.8 19.416 0.006 59.7 14
5 218 9.7 140 11.7 20.009 0.006 64.2 7
6 216 9.6 74 6.2 18.813 0.007 34.3 7
7 217 9.7 131 11.0 21.687 0.006 60.4 10
8 189 8.4 101 8.5 21.148 0.004 53.4 8
9 123 5.5 72 6.0 19.949 0.005 58.5 6
10 226 10.1 86 7.2 20.354 0.007 44.3 5
Total 2246 1192 98
Ji et al. BMC Genomics (2017) 18:51 Page 7 of 10
Groups containing more than four tags were filtered out
as repetitive SLAFs for a diploid species like sorghum that
one locus contains at most four SLAF tags. The low-depth
filtered out and the SLAFs with 2, 3, or 4 tags were identi-
fied as polymorphic SLAFs which were the potential
markers. Polymorphic markers were classified into eight
segregation patterns (ab × cd, ef × eg, hk × hk, lm × ll, nn ×
np, aa × bb, ab × cc and cc × ab). Because individuals in the
population which was obtained by selfing the F
cross between two fully homozygous parents showed segre-
gation pattern aa × bb, SLAF markers showing other segre-
gation patterns which caused by parental heterozygosity
were unsuited genotypes and filtered out for mapping. The
SLAFs with low integrity percentage and seriously segrega-
tion distortion were filtered out too. Then SLAF markers
which segregation patterns were aa × bb was used only for
linkage construction. The average sequence depth of SLAF
markers were greater than 20-fold in parents and 3-fold
greater in progeny, the integrity percentage both in the
progeny and in the parents were 80% above.
Bayesian method was used to score the genotype and
ensure its quality. First, the coverage of each allele and the
number of single nucleotide polymorphism were used to
calculate the posteriori conditional probability. Next, the
qualified markers for subsequent analysis were selected
from the probability translated from genotyping quality
score . Low-quality markers and the worse marker or
individual were deleted during the dynamic process, the
process stopped when the average genotype quality scores
of all SLAF markers reached the cutoff value.
The following criteria was adopted to filter the high-
quality SLAF markers for the genetic mapping. 1) The aver-
age sequence depths should be more then 3-fold in each
progeny and more than 29-fold in the parents. 2) Markers
with more than 30% missing data were filtered. 3) The chi-
square test was performed to examine the segregation
distortion. Markers with significant segregation distortion
(p< 0.05) were initially excluded from the map construction
and were then added later as accessory markers.
Linkage map construction
According to the locations on the genome, marker loci
were partitioned primarily into linkage groups (LGs).
Markers with MLOD scores < 5 were filtered, and then,
the modified logarithm of odds (MLOD) scores between
markers were calculated to further confirm the robustness
of markers for each LGs. To ensure efficient construction
of the high-density and high-quality map, a newly de-
veloped high map strategy was utilized to order the
SLAF markers and correct genotyping errors within
LGs . Firstly, recombinant frequencies and LOD scores
were calculated by two-point analysis, which were applied
to infer linkage phases. Then, enhanced Gibbs sampling,
spatial sampling and simulated annealing algorithms were
combined to conduct an iterative process of marker order-
ing [38, 39]. Summation of adjacent recombination frac-
tions was calculated as illustrated by Liu et al. .
While a number of successive steps, the annealing sys-
tem continued until the newly generated map order is
rejected. Blocked Gibbs sampling was employed to esti-
mate multipoint recombination frequencies of the par-
ents after the optimal map order of sample markers
were obtained. The updated recombination frequencies
were used to integrate the parental maps and optimize
the map order in the next cycle of simulated annealing.
Once a stable map order was obtained after 3–4 cycles,
the next map building would be turned round. The
unmapped markers was selected and added to the previ-
ous sample. The mapping algorithm repeats until all the
markers were mapped appropriately. The error correction
strategy of SMOOTH was then conducted according to
parental contribution of genotypes  and a k-nearest
neighbor algorithm was applied to impute missing geno-
types . Skewed markers were then added into this map
by applying a multipoint method of maximum likelihood
. Map distances were estimated using the Kosambi
mapping function .
Additional file 1: Details of assigned marker sequences. (TXT 1116 kb)
Additional file 2: Details of F
individual genotypes. (GENOTYPE 896 kb)
AFLP: Amplified fragment length polymorphism; DArT: Diversity arrays
technology; InDel: Insert and deletion; LOD: Logarithm of odds;
PCR: Polymerase chain reaction; QTL: Quantitative trait locus; RAPD: Random
amplified polymorphic DNA; RFLP: Restriction fragment length
polymorphism; RIL: Recombinant inbred line; SLAF: Specific length amplified
fragments; SNP: Single nucleotide polymorphisms
This project was supported by Hebei Natural Science Foundation
(C2012301002); Research Funds in Technology and Development of Hebei
Academy Agricultural & Forestry Sciences (A2015030201) and Earmarked
Fund for China Agricultural Research System.
The study design and data analysis were supported by the Hebei Natural
Science Foundation (C2012301002). The data interpretation was supported
by Earmarked Fund for China Agricultural Research System. The manuscript
writing was supported by Research Funds in Technology and Development
of Hebei Academy Agricultural & Forestry Sciences (A2015030201).
Availability of data and materials
All the data supporting the findings is contained within the manuscript.
GJ and GL designed the project and wrote the manuscript. QZ and RH
collected the plant materials and carried out the experiment. PL, YH and XM
carried out the DNA extraction and the laboratory work, SF performed the
high-throughput sequencing and data analysis. SL and SH assisted
with the high-throughput sequencing. All authors read and approved
the final manuscript.
Ji et al. BMC Genomics (2017) 18:51 Page 8 of 10
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Institute of Millet Crops, Hebei Academy of Agricultural & Forestry Sciences/
Hebei Branch of China National Sorghum Improvement Center, Shijiazhuang
Institute of Cereal and Oil Crops, Hebei Academy of
Agricultural & Forestry Sciences, Shijiazhuang 050035, China.
Technologies Corporation, Beijing 101300, China.
Received: 30 August 2016 Accepted: 16 December 2016
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