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Molecular Plant Breeding 2010, Vol.1 No.5
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seeds using the CTAB method (Saghai-Maroof et al.
1984) with small modification(Using chloroform:octanol
(24:1) extraction for only one time). A total of 116
markers including 76 SSR and 40 EST-SSR markers,
which amplify the expected fragments, were chosen
from thousands of markers for the analysis (data not
shown). Based on the consensus map Ta-SSR-2004
(Somers et al. 2004), 31 markers were located on
chromosome 4A, the other markers were distributed
across the rest wheat chromosomes. All of those
markers were selected and synthesized according to
the information available in the GrainGenes database
. PCR was carried out
in a final reaction volume of 20
μ
L containing 6
μ
L
template DNA, 2
μ
L 10×buffer buffer, 0.5
μ
L 2 units/
μ
L Taq DNA polymerase, 0.4
μ
L 10mmol dNTPs, and
4
μ
L
1.25μM
primers. The PCR were performed 35 or
45 cycles at 94
˚C for 45 s, at
different annealing
temperature (50
˚C to 65˚C
for different primer sets)
for 45 s and 72
˚C for 90 s, and a final extension step
at 72
˚C for 10 min.
The PCR products were separated
by electrophoresis in a 6% polyacrylamide gel.
3.3 Marker polymorphism and Population structure
The software of
PowerMarker V3.25
(Liu and Muse
2005) was used to calculate allele number, allele
frequency, gene diversity, polymorphism information
content (PIC) and gene frequency. Rare alleles (with
frequency<5%) in the population were treated as
missing data for population structure, linkage
disequilibrium analysis and association analysis
(
Breseghello
and Sorrells, 2006).
Eight-nine unlinked marker loci, distributed over all
the wheat chromosomes, were chosen to assess the
population structure of the collection of 103
accessions using the model-based (Bayesian clustering)
method implemented in the software of
Structure v2.2
(Pritchard et al. 2000a, 2000b;
http://pritch.bsd.uchic-
ago.edu/structure.html
). The number of subgroups (K)
was set from 2 to 12 based on models characterized by
admixture and correlated allele frequencies. For each
K, five runs were performed separately, 100,000
iterations and a burn-in period of 100,000 were carried
out for each run. A value of
K
was selected when the
estimate of
InPr(X|K)
peaked in the range of 2 to 12
sub-populations.
3.4 Linkage disequilibrium
Linkage disequilibrium between all pairs of loci was
evaluated using the software
TASSEL v2.0.1
(
Bradbury
et al., 2007; http:/
by setting 1000
permutations. The LD parameter r
2
among loci, which
is the squared correlation coefficient between two loci
and summarizes both mutational and recombination
history, were calculated separately for unlinked loci
on different chromosomes and for linked loci on the
same chromosome (unlinked r
2
and syntenic r
2
,
respectively). The loci were considered to be in
significant LD if
P
< 0.001. The LD decay scatterplots
of syntenic r
2
against genetic distance on chromosomes
4A was drawn using
PowerMarker V3.25
. LD decay
was calculated according to the method described by
Breseghello
and Sorrels (2006).
3.5 Marker-trait associations
Association between markers and traits was calculated
using a mixed linear model (MLM) method in
TASSEL v2.0.1
(Yu et al. 2006). The population
structure matrix (Q) obtained from the
Structure
software as described above and the relative kinship
matrix (K matrix) derived from the unlinked marker
data estimated by
TASSEL v2.0.1
were combined to
covariate in the association tests to reduce false
positive rate. The significant marker-trait associations
were declared by P
≤0.01
and the magnitude of the
QTL effects were evaluated by R
2
-marker.
Authors' contributions
The author conducted the major part of this study including
experimental design, extraction of the DNA, SSR and EST
analysis, data analysis and manuscript preparation. Lixin Wang
participated in experimental design and preliminary analysis of
data. Ji Yao participated in SSR and EST analysis. Yonglian
Zheng and Changping Zhao participated in the development of
the project, experimental design, and manuscript preparation.
All authors read and approved the final manuscript
Acknowledgement
This research was supported by ‘‘863’’ program from the
Chinese Ministry of Science and Technology (No.
2006AA100102 and No. 2009AA101102), Beijing Agricultural
Breeding Research Platform
ІІ
(No. D08070500690801), and
948 Ministry of Agriculture project (No. 2009-Z4).
References
Abdurakhmonov I.Y., and Abdukarimov A., 2008, Application of
association mapping to understanding the genetic diversity of plant