Acta Genetica Sinica, February 2006, 33 (2)：152–160
遗 传 学 报
ISSN 0379-4172
Construction of a Microsatellite Linkage Map with Two Sequenced Rice Varieties
ZHANG Qi-Jun 1,3*, YE Shao-Ping1*, LI Jie-Qin1, ZHAO Bing1, LIANG Yong-Shu1, PENG Yong1,
LI Ping1,2,
①
1. Rice Research Institute of Sichuan Agricultural University, Wenjiang 611130,China;
2. Key Laboratory of Ministry of Education of Southwest Crop Genetic Resources and Improvement (Sichuan Agricultural University), Ya’an 625014, China;
3. Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014,China
Abstract: Based on the successful development of new microsatellite markers from the data of two whole-sequenced rice varieties,
japonica variety Nipponbare and indica variety 9311, an F2 population of 90 lines, which was derived from a single cross between
Nipponbare and 9311, was applied to construct a genetic linkage framework map. The map covered 2 455.7 cM of total genomic
length, and consisted of 152 simple sequence repeats (SSRs) loci including 46 pairs of new SSR primers developed by our research
institute. The average genetic distance between two markers was 16.16 cM. In addition, markers RM345 and RM494, which have
not been mapped on the Temnykh’s map et al. (2001) were anchored on the sixth chromosome of this map. We compared this
research with maps of Temnykh et al.(2001) and LAN et al. (2003) regarding the aspects of type and size of population, type and
quantity of markers, and the marker arrangement order on chromosome, etc. Results indicated that the similarity of marker linear
alignment was 93.81% between this map and T-map. Finally, the important significance of using sequenced rice varieties to construct linkage map was also discussed.
Key words: sequenced rice (Oryza sativa L.) varieties; microsatellite marker; genetic linkage map
The development of the construction of rice linkage maps using molecular markers is very rapid. Since
McCouch et al. [1] constructed the first rice molecular
linkage map in 1988, many maps have been published
utilizing different kinds of populations and many types
of molecular markers [2-6]. In the past fifteen years the
most often used marker types were RFLP [5-9] or a mixture of several kinds of markers [10,11]. Recently, with the
implementation and accomplishment of rice genomic
sequence project, more and more reports about constructing rice genetic maps with SSR markers have been
published. Temnykh et al. [12,13] constructed a linkage
map including more than 500 SSR markers with a double haploid (DH) population of IR64/Azucena, LAN et
al. [14] published a SSR linkage map containing 122
markers using a DH population of Pei’ai64s/E32, and
McCouch et al. [15] drew a SSR linkage map including
2 740 markers with the method of electronic polymerase chain reaction (e-PCR) based on the results of
rice genomic sequence project. These maps facilitate
high-resolution genetic mapping and positional cloning
of important genes, allow genetic dissection of quantitative trait loci, assist in local comparisons of synteny,
and provide an ordered scaffold on which complete
physical maps can be assembled [11].
However, among the researches, only few were
completely using SSR markers to construct rice
linkage maps [14,15]. Maybe there were three reasons.
Received: 2004-11-25; Accepted: 2005-05-08
This work was supported by Chinese National Programs for High Technology Research and Development (863 Program) (No.
2003AA212030) and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT 0453).
* Contributed equally to this study
① Corresponding author. E-mail: [email protected]; Tel: +86-28-8272 2497,Fax:+86-28-8272 6875
ZHANG Qi-Jun et al.: Construction of a Microsatellite Linkage Map with Two Sequenced Rice Varieties
First, microsatellites are asymmetrically distributed in
genome[16-18], therefore, when only using SSR markers
to construct rice linkage map, it may make the marker
distribution uneven on the map and some intervals
between two markers are too big. Second, some SSR
primers filtering software have not been applied because
the rice genomic sequence project was just accomplished [19,20]. Third, up to now, only two rice varieties
(Nipponbare and 9311) have been completely sequenced; rice varieties Guangluai-4 and Pei’ai64s
were partly sequenced; and the SSR markers that
were based on the data of these sequenced genomic
production must be validated by other materials or
populations. All of these questions limited the applications of rice SSR markers, especially the new exploited
SSR markers. So, choosing whole-sequenced rice
varieties as mapping parents to construct microsatellite
linkage map may solve these problems.
It is expected to find the common joints between
the map research and the achievement of rice genome
project (RGP) using the sequenced rice varieties as
mapping parents, because it is easy to develop new
molecular markers utilizing the data of rice genomic
sequence project. This provides great prospects for
locating and cloning of genes, especially the quantitative
trait genes, and it can also make some functional
remarks on the current genomic data[21].Hence, the use
of whole-sequenced varieties as mapping parents have
important theoretical significance and potential applied
values but have not been reported in literatures yet.
Using the SSR markers published by Cornell University,
and the SSR primers developped in our laboratory based
on the data of RGP, this research selected an F2 mapping
population derived from a single cross between two
whole-sequenced rice varieties, japonica variety
Nipponbare and indica variety 9311, to construct a
linkage map with 152 SSR markers, and anchored 46
new SSR markers.
1
1. 1
Methods and Materials
Materials and planting
In Oct. 2003, at Linshui county, Hainan Province,
we planted the F2 population derived from the cross
153
Nipponbare (♀) × 9311 (♂) (total 90 lines) and their
parents in the paddy field at a density of 30 cm × 30 cm
with conventional plant management subsequently.
Genomic DNA were isolated from the leaves at the
tiller flourishing stage.
1. 2
Synthesis of SSR primers
The SSR primers in this research partly originated
from the Japanese rice genome project (OSR numbers);
some came from the research results of Cornell
University (RM numbers); and the rest, RP numbers,
were developed by our laboratory, based on the genomic
data of Nipponbare and 9311. All these primers were
synthesized by the Shanghai Bioasia Bio-tech. Co., Ltd.
1. 3
Analysis of SSR
Genomic DNA were extracted from fresh leaves
following the protocol described by Causse et al [2].
PCR total volume was 20 μL including: 2 μL 10 × PCR
buffer, 0.25 mmol/L dNTP, 4 μmol/L primer mixtures,
1 U DNA polymerase, 50-100 ng DNA. PCR was
performed in a PTC200 thermocycler (MJ Research) or
PX2 thermocycler (ThermoHybaid Corporation) as
described by Temnykh et al[13]. The basic profile was:
5 min at 94℃, 35 cycles of 45 s at 94℃, 45 s at 55℃,
1 min at 72℃, and 7 min at 72℃ for final extension.
PCR products were separated on 3% agarose gels and
marker bands were revealed using the EtBr staining
protocol as described by Dieffenbach et al [22].
1. 4
Construction of molecular genetic map
Based on the results of 3% agarose gel electrophoresis, among the Nipponbare × 9311-F2 populations, the
band types identical with mother-Nipponbare’s were
recorded as A, those identical with father-9311’s
recorded as B, those identical with F1 recorded as H,
and those indistinct or lacking band recorded as ―.
MAPMAKER/EXP 3.0[23] was run on a MS-DOS
computer using the Kosambi function. At first the
“anchor” command to anchor two markers per
chromosome based on the linkage map[13] was used,
then the “assign” command to group, and the “compare”
and “ripple” test were used to confirm the marker order
154
遗传学报
as determined by two or multipoint analysis. Markers
with a ripple of LOD>2.0 were integrated into the
framework maps. Finally, using the software of
GENEMAP, the ordered linkage groups were drawn.
2
Results
2. 1
Construction of a microsatellite linkage map
The level of polymorphism between Nipponbare
and 9311 using our synthesized 756 pairs of SSR
primers (including 505 pairs of RM# primers, 7 pairs
of OSR# primers, and 244 pairs of RP# primers) was
33.07% (35.05%, 28.57% and 29.1% for RM#, OSR#
and RP# respectively). This research randomly chose
166 SSR markers which had polymorphism between
Nipponbare and 9311, to construct a framework map,
which covered 2 455.7 cM of the total genomic length,
and included 152 SSR loci (Fig.1). The average genetic distance between two markers was 16.16 cM,
while in the marker-dense regions the nearest markers
were <20 cM apart and were 58% of the total, and
there were 16 regions where the distance between adjacent markers was >30 cM. If we assume that this
map covers the whole rice genome (haploid 4.3 × 108
bp [24]), the markers must be located every 2.83 Mb
on average. Because these markers were evenly distributed on the rice chromosomes, the population and
the new microsatellite map could be used to identify
quantitative trait genes.
Forty-six pairs of new SSR primers (numbered
as RP# series) exploited by our research institute
were anchored on this map (these primer sequences,
chromosome locations and their PCR-product sizes in
Nipponbare and 9311 are presented in Table 1). The
exploitation and application of these new markers
greatly enriched the number of rice microsatellites,
and had important significance to further saturate the
linkage map and gene map, and to improve the molecular marker-assistant selection breeding.
2. 2
A comparison between this and other researches
Recently, a distinct progress made in the rice
microsatellite linkage mapping was the research of
Acta Genetica Sinica
Vol.33 No.2 2006
Temnykh et al [13]. They constructed a more than 500
SSR markers linkage map using the DH population of
IR64/Azucena. Primarily we compared this research
with their research (marked as ‘T-map’). In addition,
we compared this research with LAN’s et al. [14], who
drew a 122 SSR markers linkage map with the DH
population of Pei’ai64s/E32 (marked as ‘L-map’). The
main results are as follows:
(1) Type and size of population: T-map used a
DH population of 96 lines, L-map also a DH
population of 86 lines, while in this research an F2
population of 90 lines were used. Therefore, the type
of our population was different from the former two,
and the population size straddled the middle of their
populations.
(2) Type and quantity of markers: T-map had
more than 500 SSRs and 145 RFLP markers; L-map
only had 122 RM# SSR markers; while in this research
106 RM# and 46 RP# markers were used. Compared
with T-map, there were two new additional RM#
primers in this map (RM345 and RM494, their polymorphized fragment size in Nipponbare were 155 bp
and 203 bp, in 9311 were 168 bp and 176 bp respectively, and both of them were anchored on chromosome 6).
(3) Compared with T-map, this map had 104 pairs
of same RM# primers. Besides 7 SSR markers anchored
on different linkage groups, the identity reached to
93.27%. Twelve pairs of RM# primers were located on
different positions in the chromsome linear alignment
among the 97 RM# primers, and the similarity of linear
alignment was 93.81%. This indicated that our constructed linkage map was correct and reliable.
(4) Though the number of markers and the
genomic length covered by this map were larger than
that of the L-map, there were still more gaps in our map
than in L-map, 8 versus 1, respectively. Furthermore,
there were some distant regions between two markers,
such as on chromosome 3, the genetic distance of
RM168-RM565 is 46.3 cM, and RM570-RM535 is
45.4 cM. Accounting for the gap formation, possibly
there are three reasons. First, the population parents
are different; indica variety PA64S and E32 have
the japonica consanguinity. However, in our research,
ZHANG Qi-Jun et al.: Construction of a Microsatellite Linkage Map with Two Sequenced Rice Varieties
Fig. 1
The SSR linkage map with Nipponbare × 9311-F2 population
155
156
Table 1
Name
遗传学报
Acta Genetica Sinica
Vol.33 No.2 2006
The RP# microsatellite markers anchored on this map
RP12
Forward-primer sequence
(5′
3′)
gtcgttggtcgtcgttgg
Reverse-primer sequence
(5′
3′)
acgttactcccaccttccc
RP14
gcagtgaggtaggacgagtc
aggaggaggagaggagacag
Size in 9311/ Nipponbare（bp）
Chromosome
178/249
8
179/197
3
RP53
gccatcttggattaggattagg
atcaaccaccatgtgtactatc
173/161
8
RP54
acagcagcagacagcaac
tacaacacgtacacgcctg
169/150
2
RP57
gtcgctaatctgtgtattgtac
ggttgtaatggaggtgaactc
180/168
3
RP62
caccacgcagtttgacgac
gcgtggttgagtcagtgtg
151/135
9
RP68
ccactctgtagccactgtaag
gacgatcaaggcggaaataaag
123/135
4
RP105
cgtgctcctcttcgtcaag
cggtactcgaaacggagag
171/155
9
RP119
acctacaacaagataagcgtac
atgatgacgacgatgaagaag
210/179
1
RP128
ccatgcggcgtgtatatcg
gaggaccaacaagtgcgac
158/149
1
RP129
ccgtatccgattcctgttgg
ttgtcgtcgtcgtggtaac
169/208
12
RP135
cgtgataatctcctctccttgc
ctgtagcgagatccacaatgc
155/238
3
RP145
gtgcttgcattatcggttgatc
ctgctgctcctgctctacc
158/219
1
RP151
tctatcagccgaacacactttc
gacggacggagaaggcag
149/177
4
RP162
ggaaggagaggaggaggagac
acctgacctgaccacctgag
165/138
6
RP164
tgatgattgaccaacctgc
cagaatcacgagcacaagtc
168/433
12
RP165
tgcttcttcggtggtgtgg
tgcgggagtcaatcggatg
179/151
11
RP166
gccaagtttatgtatcggtctg
gcggcttatgatgatgatgatg
117/158
8
RP171
cctcgtgttgtgttgtgcc
accgaaagtggagatggatcag
153/206
5
RP174
tactcgtcactcactcactcag
tgaccatttacactgcgtttcc
152/476
5
RP178
atagtggtgtagcaaataggag
attgtcacgcactataggttc
175/995
2
RP185
gaaagaaattccagcccatcc
gactgtccacttgacttcattc
151/113
3
RP188
tgtggattgttgacctggttc
gtggagaggaggaatgagagg
180/222
3
RP190
ttcaccaactgagcaacataag
catgattcacggctaacacg
146/171
7
RP192
ctgtacgacacgcagcac
accacagtccacccgtttc
135/174
6
RP194
acctcttctgctcttcttcctc
aacattggcacaggcatacg
286/254
3
RP207
ggaggtctctaccagcgatg
gaggctcttgttgacaggttg
180/147
4
RP217
tcgagagcgtttataggatacc
gcactacaagtatgtggatacc
179/208
11
RP222
cgagcgtgcgttagcttg
ggacgtacagaatttgcgaatc
144/80
11
RP230
acttgtctccctaaccttcttg
cctcaatgtttgctaccttgc
146/176
8
RP245
cctgacatgcttaatcgaactg
gagaagaagaagaggaggaagg
136/173
5
RP249
cgctgttcatcctcacatatcg
acctgtcgtacctgccaatc
150/188
6
RP252
actgggctccttaatcttaatg
gtttggattggtttgtagtgag
154/370
10
RP257
tcaccaggagagttggctag
ggtgaagatgctgtgcttgg
160/98
12
RP258
aacctgtttacccatgtagttc
aattagccactctgtttgtctg
139/99
6
RP288
cgccgtgaccacatctatatc
atatggcaaggttggatcagtc
125/168
4
RP296
gcacacactctctgactctc
gatacaccaacaccacatcttg
162/191
7
RP297
tgtggacggataagctggtag
tatctgttgctggcattctgag
161/277
12
RP298
ctgctagtcccattgtacttc
tctttattgattgattggtgcg
167/206
6
RP299
ggcgtgtgctcagaatcatc
cgcttcaacgactttatgcttg
174/223
5
RP305
ggcacatcctaactcacattac
atccattgtacatgtcatctgc
171/210
10
RP313
caccgtgatctgactgactg
ggaggaggacgaccgaatc
180/114
5
RP320
ggacatcaaactctatgaatgc
gcaggtactcgtcatcaag
164/114
7
RP329
aggcgacgacactacactatc
cgacgacgtgtggagtagg
135/105
5
RP360
ataggtccttagagccacttag
cttggtggatatggatgatgag
151/193
1
RP363
gcttcacgctggctactg
aataccttgagttggagtctgg
174/226
11
ZHANG Qi-Jun et al.: Construction of a Microsatellite Linkage Map with Two Sequenced Rice Varieties
9311 is a classical indica variety while Nipponbare is
a classical japonica variety. Second, the RM# marker
selection in this research was random whereas RP#
markers in this research and microsatillites in L-map
selection were not. Third, the seperation methods
were also different. In L-map, PCR products were
seperated by 4% polyacrylamide denaturing gels
(PAG) and marker bands were revealed by the silver
staining protocol. However, in this research, PCR
products were seperated by 3% agarose gels and
marker bands were revealed by the EtBr staining
method. Apparently, the resolution power and the
polymorphism detection of 4% PAG is higher than
those of 3% agarose gels.
3
Discussion
Microsatellites are tandemly arranged repeats of
short DNA motifs (1–6 bp in length) that frequently
exhibit variation in the number of repeats at a locus.
Because of their abundance and inherent potential for
variation, these SSRs have become a valuable source
of genetic markers [13]. There are many advantages in
using SSR markers to construct linkage map for the
molecular marker-assistant selection breeding over
other molecular markers [16,17]. Most of the SSR
markers, like RFLP markers, are co-dominant, which
can be used to distinguish the hybrids. Compared to
RFLP markers, however, SSR marker is easy to handle, avoiding restriction enzyme incision and hybridization procedures, and just general PCR steps are
needed [18,25].
Along with the accomplishment of rice genomic
sequence project and the application of a series of
marker selecting software, the numbers of markers have
been greatly enriched. For example, McCouch et al.[15]
constructed a SSR linkage map including 2 740 markers with the method of electronic polymerase chain
reaction (e-PCR) based on the data of RGP. There are
about 3 000 pairs of SSR primers identified on the
network at present. In our laboratory, about 14 000
pairs of SSR primers were filtered out using the SSR
primers filtering software, which had polymorphisms
between Nipponbare and 9311. With these SSR
157
primers, the average genetic distance between two
markers is about 0.05 cM (corresponding to 10 kb
genomic DNA length). This can provide a good
foundation for a saturation genetic linkage map of
rice, even for a regional physical map, and it is also
suitable for gene mapping and cloning. Moreover, in
our research, part of RP# markers were integrated
into the linkage map, indicating that our SSR primer
filtering software was correct.
The usefulness of genetic maps largely depends
on their density. For a genetic map, if the loci of
markers are adequately dense and their distribution is
correspondingly even, its applicable value will be high.
In this research randomly chosen 166 SSR primers
were used and 152 loci were anchored. Compared with
the T-map, there were 9 markers anchored on different
linkage groups in this map.Supposedly, the reasons
are as follows: First, it is related to the different
parent, type and size of the population, because same
microsatellite motif may have different sites in
different rice varieties [13]; Second, microsatellites are
asymmetrically distributed in genome[16-18], and when
analyzing data, the software of MAPMAKER/EXP 3.0
itself has some errors [23]. Third, in this map only SSR
markers were used, but in T-map there were
additional 145 RFLP markers. All these factors may
affect the linkage genetic distance among markers.
Therefore, we are now continuing to further saturate
the linkage map, increasing the lines of population
and adding the numbers of markers.
There are many advantages of using sequenced
rice varieties to construct a genetic map, as they are
mainly representing the trait analysis combined with
the genetic map. It maybe a convenient, fast and high
efficient way to locate, clone and functionally remark
plant genes (specially the quantitative trait genes)
based on existing genomic sequenced data[21], and
there are some successful examples of QTL cloning
[26-32]
. In particular, there is no need to construct high
density genetic map when only one QTL was detected
within an interval. Compared with some functionally
known genes and their interval bioinformative
analysis, itis relatively easy to get the canditate gene,
then to validate the function of canditate gene by
158
co-complementary testing, and finally to clone this
QTL [21]. According to the above-mentioned approach
in one QTL detection, different traits controlled by
varied QTL intervals can be revealed, and we may
achieve a higher efficiency. In our research, an
attempt following this method was also made and
resulted in some significant outcomes (will be
reported by other papers).
Another function of using sequenced rice
varieties to construct linkage map and analyze QTLs
is that some unknown functional genomic regions
could be noted. Analyzing the QTL mapping
complexion on the network of Gramene, it was easy
to find that the existing natural mutating genes were
not included in the mapped QTL intervals. In other
words, it may be a fast and highly efficient way to
remark some function of unknown genes using
sequenced rice varieties to construct linkage map and
analyze QTLs [21]. Most of the crop traits are quantitative in nature, which are controlled by polygene, so
it is very important for crop improvement to map
QTLs. We may lose sight of some other contributing
genes, especially the micro-effect genes, because the
traditional genetic dissection of genes is mainly
aimed at the quality trait genes. After finding one trait
controlled by one or more chromosome intervals, we
can analyze these chromosome intervals with the
database of sequenced rice varieties, and may remark
some annotations on these chromosome intervals
combining the related function-known genes of this
kind of trait [21].
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利用两个测序水稻品种构建微卫星连锁图谱
张启军 1,3 *，叶少平 1 *，李杰勤 1，赵 兵 1，梁永书 1，彭 勇 1，李 平 1,2
1. 四川农业大学水稻研究所，温江 611130；
2. 西南作物基因资源与遗传改良教育部重点实验室，雅安 625014；
3. 江苏省农业科学院粮食作物研究所，南京 210014
摘 要：利用已完成基因组测序的两个水稻品种日本晴和 9311 的数据库成功开发出水稻微卫星新标记，并利用由 90 个单
株组成的日本晴¯9311 F2 作图群体，构建了一张包含 152 个 SSR 标记位点、覆盖基因组总长度 2 455.7 cM 的连锁图谱，
有 46 个 SSR 新标记为自主开发，该图谱标记间的平均遗传距离为 16.16 cM；并将未能在 Temnykh 等人（2001）构建的图
谱上定位的微卫星标记 RM345 和 RM494 定位在第 6 染色体上。通过与 Temnykh 等人（2001）和兰涛等人（2003）所构建
的图谱从作图群体的类型和大小、标记的类型和数量、标记在染色体上的线性排列顺序等几个方面进行比较，所绘制的图
谱其标记在染色体线性排列上与 Temnykh 等人绘制的图谱具有很高的一致性，达 93.81%。
关键词：测序水稻品种；微卫星标记；遗传连锁图谱
作者简介：张启军（1973-），男，重庆开县人，博士，研究方向：水稻基因组与遗传与育种