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Construction of a high-density genetic map using specific-locus amplified fragments in sorghum


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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 F2 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 F2 individuals from 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 the F2 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 marker-assisted breeding. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3430-7) contains supplementary material, which is available to authorized users.
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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
Guisu Ji
, Qingjiang Zhang
, Ruiheng Du
, Peng Lv
, Xue Ma
, Shu Fan
, Suying Li
, Shenglin Hou
Yucui Han
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
individuals from
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
the F
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
marker-assisted breeding.
Keywords: Sorghum bicolor, High-throughput sequencing, SLAF marker, Linkage map construction
* Correspondence:
Equal contributors
Institute of Millet Crops, Hebei Academy of Agricultural & Forestry Sciences/
Hebei Branch of China National Sorghum Improvement Center, Shijiazhuang
050035, China
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 (, 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
( applies to the data made available in this article, unless otherwise stated.
Ji et al. BMC Genomics (2017) 18:51
DOI 10.1186/s12864-016-3430-7
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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) ( 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 34 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) [510]. 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, 1518].
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 [19].
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 [20]. 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 [21]. These
markers have been used for crop genetic analysis such as
sesame, millet, rice and soybean [2226], 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
An F
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
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offspring will have considerable variations which are
good for polymorphic marker screening and linkage
group construction.
Marker identification
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
construction [21].
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
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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).
Map evaluation
Three types of markers were assigned to the genetic
map including 2237 SNP_only,3InDel_only,and6
SNP&InDelmarkers. SNP_onlywas the predomin-
ant marker type accounting for 99.6% of the
markers. InDel_onlymarkers 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-onlymarkers,
one out of 3 InDel-onlyand 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 123318 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
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linkage map for sorghum can serve as a reference map
for cultivated sorghum species and will be useful in
genetic mapping.
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 [27].
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 [28], barley [29]
potato [30], sesame [24], peanut [31] 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 [3]. 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 [19]. 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) [19]. 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 (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
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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
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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
Plant materials
An F
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
individuals were
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
130 F
individuals were phenotyped.
DNA extraction
DNA was extracted from fresh leaf tissue following the
modified CTAB protocol [34]. DNA concentration was
adjusted to be in the range of 50100 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
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-
turers recommendations.
Sequence data grouping and genotyping
Procedures described by Sun et al. [21] 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
PDistorted markers
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
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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
of a
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 [37]. 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 [38]. 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. [40].
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 34 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 [41] and a k-nearest
neighbor algorithm was applied to impute missing geno-
types [42]. Skewed markers were then added into this map
by applying a multipoint method of maximum likelihood
[43]. Map distances were estimated using the Kosambi
mapping function [43].
Additional files
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
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Author details
Institute of Millet Crops, Hebei Academy of Agricultural & Forestry Sciences/
Hebei Branch of China National Sorghum Improvement Center, Shijiazhuang
050035, China.
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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... A lot of works have been done on the association and relationship between quantitative phenotypic data and genetic maps in sorghum, the research areas include the heading days, drought, salt tolerance, cold tolerance in early stage, and resistance to biotic stresses such as greenbug, downy mildew (SDM), parasitic weed Striga, etc. (Ejeta and Knoll, 2007;Knoll and Ejeta, 2008;Shehzad et al., 2009;Bekele et al., 2014;Chopra et al., 2015;Sukumaran et al., 2016;Ji et al., 2017;Kotla et al., 2019;Wang et al., 2020). Among these researches, some genetic loci which control the phenotypic traits have been found and cloning some of these genes is important for our understanding on the heredity of complex agronomic traits in sorghum (Han et al., 2015;Sukumaran et al., 2016;Fujimoto et al., 2018). ...
... Vol:. (1234567890) selected and 2246 effective SLAF markers were finally obtained for mapping the QTLs (Ji et al., 2017). ...
... The high-density genetic map can improve the resolution and accuracy of QTL mapping. The genetic map used in this study as described by Ji et al. (2017). Briefly, a total of 2246 SLAF markers were obtained to construct a high-density genetic linkage map with 2158.1 cM length over the whole genome. ...
Full-text available
Specific-locus amplified fragments (SLAF) is widely used to identify QTLs in crops. In our study, 11 agronomic traits were evaluated including plant height (PH), number of stem nodes (NSN), stem diameter (SD), panicle neck length (PNL), panicle length (PL), fresh stem weight (FSW), stem juice weight (SJW), Brix, panicle weight (PW), 100-grain weight (HGW) and number of days to heading (HD). After genome-wide SLAF marker analysis, 12 QTLs of 7 traits were obtained and located on five chromosomes. QTLs of plant height (qPH1, qPH2, qPH3) were detected on Chr 6 and Chr 9, with the phenotypic variations explaining (PVE) 27.13–29.02%. Two QTLs of fresh stem weight (qFSW1, qFSW2) were also detected on Chr 6 and Chr 9, which explained 27.10% and 16.08% of the phenotype variations, respectively. Besides, QTLs of Brix (qBRIX1, qBRIX2) were located on Chr 7 (PVE 11.70%) and Chr 9 (PVE 7.23%). qHD1 and qHD2 were mapped on Chr 1(PVP 31.56%) and Chr 6 (PVP 17.65%). Moreover, QTLs of both number of stem nodes (qNSN) and stem juice weight (qSJW) were detected on Chr 6, with PVE value 17.63% and 37.68%, respectively. The QTL controlling panicle neck length (qPNL) was located on Chr 3 and it explained 14.25% of the phenotype variation. Overall, the QTLs identified in this study can be to increase our understanding of the inheritance of the agronomic traits and gene cloning in sorghum.
... Sorghum genome is 3-4 times smaller than that of maize, and is considered the model plant for polyploid sugarcane and diploid crops [40]. The sorghum-sudangrass hybrid genome was compared to the sorghum genome by Yang et al. [41] and Jin et al. [29], who demonstrated that it is feasible to develop SNP markers for sorghum-sudangrass hybrid using the Sorghum genome. ...
... The high-density genetic maps have been widely used in QTL mapping, fine mapping of important traits, and candidate gene prediction in weed-rice [51], wheat [52], maize [53], rape [54], cotton [55], and others. In addition, high-density genetic maps can also be served as a platform for genome assembly or studing the collinearity between related species and the structural variation of hybrid genomes [40]. ...
Full-text available
The sorghum-sudangrass hybrid is a vital annual gramineous herbage. Few reports exist on its ultra-high-density genetic map. In this study, we sought to create an ultra-high-density genetic linkage map for this hybrid to strengthen its functional genomics research and genetic breeding. We used 150 sorghum-sudangrass hybrid F2 individuals and their parents (scattered ear sorghum and red hull sudangrass) for high-throughput sequencing on the basis of whole genome resequencing. In total, 1,180.66 Gb of data were collected. After identification, filtration for integrity, and partial segregation, over 5,656 single nucleotide polymorphism markers of high quality were detected. An ultra-high-density genetic linkage map was constructed using these data. The markers covered approximately 2,192.84 cM of the map with average marker intervals of 0.39 cM. The length ranged from 115.39 cM to 264.04 cM for the 10 linkage groups. Currently, this represents the first genetic linkage map of this size, number of molecular markers, density, and coverage for sorghum-sudangrass hybrid. The findings of this study provide valuable genome-level information on species evolution and comparative genomics analysis and lay the foundation for further research on quantitative trait loci fine mapping and gene cloning and marker-assisted breeding of important traits in sorghum-sudangrass hybrids.
... The usage of BTx623 streamlines the process of integrating the consensus genetic map generated in this work with the publicly accessible physical map sequence. Three additional mapping populations with a varied parental line were employed in this analysis, including the F2 population used by Ji et al. (2017), the RIL population used by Lopez et al. (2017) and Phuong et al. (2019). ...
... The final consensus map allowed us to map more markers than any individual map, acquire a more comprehensive coverage of the sorghum genome, and to complete multiple gaps in previously published maps (Ji et al. 2017;Lopez et al. 2017;Kong et al. 2018;Phuong et al. 2019). Apart from the fact that the sequence of markers was consistent across individual component maps, excellent agreement in the total distances between common marker pairs was discovered throughout the component maps utilized in this investigation using a different ratio approach Hu et al. 2021). ...
Full-text available
Satrio RD, Nikmah IA, Fendiyanto MH, Pratami MP, Awwanah M, Sari NIP, Farah N, Nurhadiyanta. 2022. Construction of an ultra-high-density consensus genetic map and analysis of recombination rate variation in Sorghum bicolor. Asian J Agric 6: 47-54. Sorghum is one of the most widely grown cereal crops on a global scale. A consensus map is a method for combining genetic information from multiple populations, and it is an effective way to increase genome coverage and marker density. This study constructed a consensus map by combining publicly available marker data from four mapping populations. A total of 3449 non-redundant polymorphic markers at the nucleotide level were used to construct a single consensus map on 10 sorghum chromosomes. This study generated an ultra-high-density sorghum consensus map consisting of a large number of markers spanning 1571.68 cM and averaging one marker per 0.46 cM. Due to the high density of the markers, it is only 0.06% of the markers had an interval greater than 5 cM. The rates of local recombination were estimated using a set of all markers genetic and physical positions along each of the 10 chromosomes. The analysis of the recombination rate on 10 sorghum chromosomes revealed that it decreased as the centromere position was getting closer. The consensus map generated in this study can be used to integrate information related to sorghum genetic resources and QTLs to the genome sequence, thereby accelerating the discovery of novel potential genes in sorghum.
... Distorted markers are frequently reported in oyster species, such as C. virginica (Yu and Guo, 2003), O. edulis (Lallias et al., 2007), C. gigas , indicating that the genotype frequency deviates from the typical Mendelian ratio (Ji et al., 2017). In this study, all distorted markers were retained for mapping because we were interested in studying the distribution pattern of segregation distortions in C. hongkongensis genome. ...
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Hong Kong oyster Crassostrea hongkongensis is an important aquaculture species in the coastal areas of southern China. Because of its high demand and reasonable price, the production of Cohnella hongkongensis has increased drastically. However, the aquaculture of C. hongkongensis has also encountered some problems, such as seasonal mass mortality and reduction of availability and quality of wild seeds. Genetic linkage map is an effective tool for analyzing economically important traits. In this study, a consensus genetic map of C. hongkongensis was constructed by using microsatellite markers of F1 family. The consensus linkage map contained 104 loci, with a span of 653.9 cM and an average resolution of 6.9 cM. The estimated coverage of consensus linkage map was 85%. We identified 10 linkage groups, which were consistent with the haploid chromosome number of the species. The linkage map of male C. hongkongensis comprised 57 markers with a span of 467.6 cM, while the linkage map of female C. hongkongensis comprised 72 markers with a span of 570.9 cM. The average recombination ratio between males and females was 1:1.2. This map is vital for future QTL mapping framework of C. hongkongensis to perform marker assisted selection. Moreover, compared with di-nucleotide microsatellite markers, tri-nucleotide microsatellite markers have significantly stronger correlation with functional genes and cross-amplification success. We also found that the application potential of tri-nucleotide microsatellite markers in molecular-marker assisted selection gene mapping, and comparative linkage mapping was greater than that of di-nucleotide markers.
... The final map exhibited a density of 23.23 SNP per cM (Punnuri et al. 2016). Ji et al. (2017) constructed high-density linkage map using specific-locus-amplified fragment (SLAF) markers in sorghum. The linkage map of 2158.1 cM covering 10 chromosomes was developed using 2246 SLAF markers. ...
Millets serve as the important staple food in arid and semiarid regions of Africa, India, and Southern Asia. Despite several nutritive properties and health benefits, most of the millets remained as the neglected crops until the twenty‐first century. The genomic data for several millets are obscure. Presently, crop improvement programs involve both phenotypic selection and marker‐assisted selection. With the advancement in next‐generation sequencing (NGS) technology, a lot of prominences were given for the development of informative genetic and genomic resources. This is facilitated by the sequencing methods such as genotyping‐by‐sequencing (GBS), restriction site‐associated DNA (RAD) sequencing, and whole‐genome resequencing (WGRS). GBS is a novel application of NGS used for the identification and genotyping of single‐nucleotide polymorphisms (SNPs) in genomes and populations. Several reports are available in the past decade describing the effective use of GBS in implementing GWAS, genomic diversity study, genetic linkage analysis, molecular marker discovery, and GS in large‐scale breeding programs in certain millets such as foxtail millet, pearl millet, sorghum, and finger millet. However, studies have been carried out on a very limited scale among other millets. Thus, there is a huge scope for researchers to study the genomes of the millets. Such studies will certainly help in crop improvement programs in the upcoming decades.
... These two similar, reduced-representation, genome-wide resequencing methods are capable of identifying, sequencing and genotyping thousands of markers across the genome at low cost in large populations, making them highly suitable for genome-wide analyses of complex traits. In a more targeted approach, Ji et al. (2017) implemented genome-wide specific-locus amplified fragments (SLAF) markers, which are highly abundant and evenly distributed across the genome and thus facilitate the scanning of the sorghum genome for gene mining. The main advantage of NGS-based genotyping platforms compared with arrays is their lack of ascertainment bias in the markers assayed, improving their potential for discovery of novel variants of interest for trait improvement. ...
Full-text available
This book contains 29 chapters focusing on wheat, maize and sorghum molecular breeding. It aims to contribute the latest understandings of the molecular and genetic bases of abiotic stress tolerance, yield and quality improvement of wheat, maize and sorghum to develop strategies for improving abiotic stress tolerance that will lead to enhance productivity and better utilization of natural resources to ensure food security through modern breeding.
... Recently, a high genetic density map was published by Ji et al. [40], where specific length amplified fragment markers (SLAFs) were utilized. This map was based on a F 2 population of 130 individuals originated from a cross between a grain sorghum variety, J204, and a sweet sorghum variety, Keter. ...
Full-text available
Sorghum is one of the main cereal crops, its consumption is large, since it provides grain, fiber and biofuel. Likewise, its genome, with only 10 diploid chromosomes, makes it an attractive model for research and genetic improvement. Sorghum is the most studied C4 plant of its genus; several lines have been developed under three main characteristics: grain, forage and sugar biomass. Compared to other crops, sweet sorghum possesses high levels of highly fermentable sugars in the stem. Also, it has the ability of producing high production yields in marginal lands. These characteristics make it and attractive crop for the generation of biofuels. Molecular markers associated to several resistances and tolerances to biotic and abiotic factors have been described in literature. These allow the development of high-density linkage maps, which, along with the rising availability of sorghum genomes, will accelerate the identification of markers and the integration of the complete genome sequence. This will facilitate the selection of traits related to biofuels and the marker-assisted genetic improvement. Most of the information presented in this review is focused in Sorghum bicolor (L.) Moench. However, from the bioenergetics perspective, it is limited to sweet sorghum, which represents a promising opportunity for further studies.
... Therefore, it has become possible to use SNP markers to construct a high-quality, high-density genetic linkage map (HDGM) in G. barbadense. HDGMs of multiple species have been successfully constructed using resequencing technology [34][35][36][37]. ...
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Background: Resistance to Fusarium wilt (FW) is of great significance for increasing the yield of Gossypium barbadense. Most published genetic studies on G. barbadense focus on yield and fiber quality traits, while there are few reports on resistance to FW. Results: To understand the genetic basis of cotton resistance to FW, this study used 110 recombinant inbred lines (RILs) of G. barbadense obtained from the parental materials Xinhai 14 and 06-146, and Nannong was used to construct a high-density genetic linkage map. The high-density genetic map was based on the resequencing of 933,845 single-nucleotide polymorphism (SNP) markers, and 3627 bins covering 2483.17 cM were finally obtained. The collinearity matched the physical map. A total of 9 QTLs for FW resistance were identified, each QTL explained 4.27-14.92% of the observed phenotypic variation, and qFW-Dt3-1 was identified in at least two environments. According to gene annotation information from multiple databases, promoter homeopathic elements and transcriptome data, 10 candidate genes were screened in a stable QTL interval. qRT-PCR analysis showed that the GOBAR_DD06292 gene was differentially expressed in the roots of the two parents under FW stress and exhibited the same expression trend in the G. barbadense resource materials. Conclusions: These results indicate the importance of the GOBAR_DD06292 gene in FW resistance in G. barbadense and lay a molecular foundation for the analysis of the molecular mechanism of FW in G. barbadense.
... The specific length amplified fragment sequencing (SLAFseq) strategy, a combination of locus-specific amplification and high-throughput sequencing, has been adopted in substantial studies that have demonstrated its high efficiency and the accuracy of the generated markers (Yan et al. 2015). Recently, the SLAF-seq technology has been used successfully to develop a large number of SLAF markers and for the construction of high-density genetic maps for many plants, including soybean, red sage, grape, sunflower, sorghum, and pea (Ji et al. 2017;Li et al. 2014;Luo et al. 2016;Zhou et al. 2018). ...
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Soybean mosaic virus (SMV) is one of the most harmful pathogens in soybeans, causing large yield losses. Therefore, it is very important to breed resistant soybean cultivars, but first we need to understand the genetics of soybean. Genetic maps are efficient tools in plant genetics and molecular-assisted breeding. In this study, a large-scale survey of single nucleotide polymorphisms (SNPs) using the specific length amplified fragment sequencing (SLAF-seq) technique was used to construct the fine gene mapping for two soybean cultivars. The cultivar Tianlong No. 1 (TL1) is resistant to the SC9 strain, but Zhexiandou No. 4 (ZXD4) is susceptible to this strain. All F1 individuals showed the susceptible phenotype for strain SC9. Among the 425 F2 plants derived from F1 individuals, 309 had the susceptible phenotype and 116 were resistant (χ2 = 1.1929, P = 0.725). The results indicated that TL1 harbors a recessive gene underlying resistance to strain SC9. A total of 28.91 million sequencing reads were obtained, with average sequencing depths of over 17-fold for TL1 and ZXD4 and 63-fold for the F2 offspring. In summary, 144,754 high-quality SLAFs were obtained, of which 30,578 were polymorphic. In all, 8738 SNPs were selected to construct the fine gene mapping with an average genetic distance of 1.30 cM between adjacent markers. Approximately 148 target genes were mapped in the intervening genomic region of chromosome 2. The results of this study will not only provide a fine gene mapping for SMV resistance research but will also serve as a robust tool for the molecular breeding of soybean.
Yellow rust is an important destructive fungal disease caused by Puccinia striiformis in small grain cereals, and the prevalent Chinese yellow race CYR34 has recently become widespread in China. To detect quantitative trait loci (QTLs) responsible for resistance to CYR34 in triticale (×Triticosecale Wittm.), 520 F2 plants derived from the cross between cv. Gannong No. 1 (susceptible parent) and cv. Gannong No. 2 (resistant parent) were used as mapping population. Fourteen inter-simple sequence repeat (ISSR) markers were used for constructing the linkage map. The obtained results indicated that 92 loci have been mapped on seven linkage groups (LG1-LG7). The total map length was 542.9 cM with an average of 6.95 cM per marker. Six QTLs (qdr1, qdr3, qdr4, qdr5-1, qdr5-2, and qdr6) related to the resistance to CYR34 have been detected. The contribution of these QTLs varied from 5.1% to 11.2%. Moreover, qdr5-1 was the main QTL responsible for CYR34 resistance.
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Exponential reduction in sequencing costs with advances in Next Generation Sequencing (NGS) technologies has led to rapid developments in the field of genotyping technologies. Genome complexity reduction methods such as Restriction Associated DNA sequencing (RAD-seq) and Genotyping by sequencing (GBS) has emerged as powerful genotyping platform which are capable of discovering, sequencing and genotyping not hundreds but thousands of markers across almost any genome of interest, but also number of individuals in a population in a single and simple experiment. GBS currently usage low coverage sequencing protocol backed by power of NGS for genotyping large populations and more precise association of genotype and phenotype. Few potential drawbacks of GBS are large propotion of missing data points due to low coverage of sequencing, management and analysis of large amount of sequence data. But with further increase in sequencing output, availability of more reference genomes and developments in field of bioinformatics will further empower this techniques. However flexible, rapid and low cost GBS makes it an excellent tool for many applications and to address many questions of plant breeding and genetics. This review summarizes the family of GBS approaches and its potential to hold a genome wide genotyping platform.
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High-density genetic map is a valuable tool for fine mapping locus controlling a specific trait especially for perennial woody plants. In this study, we firstly constructed a high-density genetic map of mei (Prunus mume) using SLAF markers, developed by specific locus amplified fragment sequencing (SLAF-seq). The linkage map contains 8,007 markers, with a mean marker distance of 0.195 cM, making it the densest genetic map for the genus Prunus. Though weeping trees are used worldwide as landscape plants, little is known about weeping controlling gene(s) (Pl). To test the utility of the high-density genetic map, we did fine-scale mapping of this important ornamental trait. In total, three statistic methods were performed progressively based on the result of inheritance analysis. Quantitative trait loci (QTL) analysis initially revealed that a locus on linkage group 7 was strongly responsible for weeping trait. Mutmap-like strategy and extreme linkage analysis were then applied to fine map this locus within 1.14 cM. Bioinformatics analysis of the locus identified some candidate genes. The successful localization of weeping trait strongly indicates that the high-density map constructed using SLAF markers is a worthy reference for mapping important traits for woody plants. © The Author 2015. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.
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Background Quantitative trait locus (QTL) mapping is an efficient approach to discover the genetic architecture underlying complex quantitative traits. However, the low density of molecular markers in genetic maps has limited the efficiency and accuracy of QTL mapping. In this study, specific length amplified fragment sequencing (SLAF-seq), a new high-throughput strategy for large-scale SNP discovery and genotyping based on next generation sequencing (NGS), was employed to construct a high-density soybean genetic map using recombinant inbred lines (RILs, Luheidou2 x Nanhuizao, F5:8). With this map, the consistent QTLs for isoflavone content across various environments were identified. Results In total, 23 Gb of data containing 87,604,858 pair-end reads were obtained. The average coverage for each SLAF marker was 11.20-fold for the female parent, 12.51-fold for the male parent, and an average of 3.98-fold for individual RILs. Among the 116,216 high-quality SLAFs obtained, 9,948 were polymorphic. The final map consisted of 5,785 SLAFs on 20 linkage groups (LGs) and spanned 2,255.18 cM in genome size with an average distance of 0.43 cM between adjacent markers. Comparative genomic analysis revealed a relatively high collinearity of 20 LGs with the soybean reference genome. Based on this map, 41 QTLs were identified that contributed to the isoflavone content. The high efficiency and accuracy of this map were evidenced by the discovery of genes encoding isoflavone biosynthetic enzymes within these loci. Moreover, 11 of these 41 QTLs (including six novel loci) were associated with isoflavone content across multiple environments. One of them, qIF20-2, contributed to a majority of isoflavone components across various environments and explained a high amount of phenotypic variance (8.7% - 35.3%). This represents a novel major QTL underlying isoflavone content across various environments in soybean. Conclusions Herein, we reported a high-density genetic map for soybean. This map exhibited high resolution and accuracy. It will facilitate the identification of genes and QTLs underlying essential agronomic traits in soybean. The novel major QTL for isoflavone content is useful not only for further study on the genetic basis of isoflavone accumulation, but also for marker-assisted selection (MAS) in soybean breeding in the future.
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Soybean is an important oil seed crop, but very few high-density genetic maps have been published for this species. Specific length amplified fragment sequencing (SLAF-seq) is a recently developed high-resolution strategy for large scale de novo discovery and genotyping of single nucleotide polymorphisms. SLAF-seq was employed in this study to obtain sufficient markers to construct a high-density genetic map for soybean. In total, 33.10 Gb of data containing 171,001,333 paired-end reads were obtained after preprocessing. The average sequencing depth was 42.29 in the Dongnong594, 56.63 in the Charleston, and 3.92 in each progeny. In total, 164,197 high-quality SLAFs were detected, of which 12,577 SLAFs were polymorphic, and 5,308 of the polymorphic markers met the requirements for use in constructing a genetic map. The final map included 5,308 markers on 20 linkage groups and was 2,655.68 cM in length, with an average distance of 0.5 cM between adjacent markers. To our knowledge, this map has the shortest average distance of adjacent markers for soybean. We report here a high-density genetic map for soybean. The map was constructed using a recombinant inbred line population and the SLAF-seq approach, which allowed the efficient development of a large number of polymorphic markers in a short time. Results of this study will not only provide a platform for gene/quantitative trait loci fine mapping, but will also serve as a reference for molecular breeding of soybean.
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Linkage maps enable the study of important biological questions. The construction of high-density linkage maps appears more feasible since the advent of next-generation sequencing (NGS), which eases SNP discovery and high-throughput genotyping of large population. However, the marker number explosion and genotyping errors from NGS data challenge the computational efficiency and linkage map quality of linkage study methods. Here we report the HighMap method for constructing high-density linkage maps from NGS data. HighMap employs an iterative ordering and error correction strategy based on a k-nearest neighbor algorithm and a Monte Carlo multipoint maximum likelihood algorithm. Simulation study shows HighMap can create a linkage map with three times as many markers as ordering-only methods while offering more accurate marker orders and stable genetic distances. Using HighMap, we constructed a common carp linkage map with 10,004 markers. The singleton rate was less than one-ninth of that generated by JoinMap4.1. Its total map distance was 5,908 cM, consistent with reports on low-density maps. HighMap is an efficient method for constructing high-density, high-quality linkage maps from high-throughput population NGS data. It will facilitate genome assembling, comparative genomic analysis, and QTL studies. HighMap is available at
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Background Cultivated peanut, or groundnut (Arachis hypogaea L.), is an important oilseed crop with an allotetraploid genome (AABB, 2n = 4x = 40). In recent years, many efforts have been made to construct linkage maps in cultivated peanut, but almost all of these maps were constructed using low-throughput molecular markers, and most show a low density, directly influencing the value of their applications. With advances in next-generation sequencing (NGS) technology, the construction of high-density genetic maps has become more achievable in a cost-effective and rapid manner. The objective of this study was to establish a high-density single nucleotide polymorphism (SNP)-based genetic map for cultivated peanut by analyzing next-generation double-digest restriction-site-associated DNA sequencing (ddRADseq) reads. Results We constructed reduced representation libraries (RRLs) for two A. hypogaea lines and 166 of their recombinant inbred line (RIL) progenies using the ddRADseq technique. Approximately 175 gigabases of data containing 952,679,665 paired-end reads were obtained following Solexa sequencing. Mining this dataset, 53,257 SNPs were detected between the parents, of which 14,663 SNPs were also detected in the population, and 1,765 of the obtained polymorphic markers met the requirements for use in the construction of a genetic map. Among 50 randomly selected in silico SNPs, 47 were able to be successfully validated. One linkage map was constructed, which was comprised of 1,685 marker loci, including 1,621 SNPs and 64 simple sequence repeat (SSR) markers. The map displayed a distribution of the markers into 20 linkage groups (LGs A01–A10 and B01–B10), spanning a distance of 1,446.7 cM. The alignment of the LGs from this map was shown in comparison with a previously integrated consensus map from peanut. Conclusions This study showed that the ddRAD library combined with NGS allowed the rapid discovery of a large number of SNPs in the cultivated peanut. The first high density SNP-based linkage map for A. hypogaea was generated that can serve as a reference map for cultivated Arachis species and will be useful in genetic mapping. Our results contribute to the available molecular marker resources and to the assembly of a reference genome sequence for the peanut. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-351) contains supplementary material, which is available to authorized users.
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Enhancing drought tolerance without yield decrease has been a great challenge in crop improvement. Here, we report the Arabidopsis (Arabidopsis thaliana) homodomain-leucine zipper transcription factor Enhanced Drought Tolerance/HOMEODOMAIN GLABROUS11 (EDT1/HDG11) was able to confer drought tolerance and increase grain yield in transgenic rice (Oryza sativa) plants. The improved drought tolerance was associated with a more extensive root system, reduced stomatal density, and higher water use efficiency. The transgenic rice plants also had higher levels of abscisic acid, proline, soluble sugar, and reactive oxygen species-scavenging enzyme activities during stress treatments. The increased grain yield of the transgenic rice was contributed by improved seed setting, larger panicle, and more tillers as well as increased photosynthetic capacity. Digital gene expression analysis indicated that AOEDT1/HDG11 had a significant influence on gene expression profile in rice, which was consistent with the observed phenotypes of transgenic rice plants. Our study shows that AtEDT1/HDG11 can improve both stress tolerance and grain yield in rice, demonstrating the efficacy of AtEDT1/HDG11 in crop improvement.
The genetic map is a tool to quantify the distance between genes on a chromosome, based on the observed frequency of crossovers during cell division.
Sorghum, an African grass related to sugar cane and maize, is grown for food, feed, fibre and fuel. We present an initial analysis of the approximately 730-megabase Sorghum bicolor (L.) Moench genome, placing approximately 98% of genes in their chromosomal context using whole-genome shotgun sequence validated by genetic, physical and syntenic information. Genetic recombination is largely confined to about one-third of the sorghum genome with gene order and density similar to those of rice. Retrotransposon accumulation in recombinationally recalcitrant heterochromatin explains the approximately 75% larger genome size of sorghum compared with rice. Although gene and repetitive DNA distributions have been preserved since palaeopolyploidization approximately 70 million years ago, most duplicated gene sets lost one member before the sorghum-rice divergence. Concerted evolution makes one duplicated chromosomal segment appear to be only a few million years old. About 24% of genes are grass-specific and 7% are sorghum-specific. Recent gene and microRNA duplications may contribute to sorghum's drought tolerance.