Page 12 - Molecular Plant Breeding

Molecular Plant Breeding 2010, Vol.1 No.1
http://mpb.sophiapublisher.com
Page 9 of 10
panicles whose filled number are more than five, (8)
spikelet density (SD)- the mean value of grain number
per centimeter in five spikelets. (9) grain yield per
panicle (GY)-weight of filled grains per panicle, and
(10) grain yield per plant (GYP)-weight of filled
grains per plant.
3.4 Marker genotype analyses
Gnomic DNA was extracted from fresh leaves (one
month age seedling) sampled from ten plants each line.
The collected leaves were bulked by using the protocol
of Li et al (2006).SSR primers were synthesized based
on public SSR information (Chen, et al., 1997;
Temnykh et al., 2000). A volume of 10 μl reaction
mixture consists of 2 ng/μl of template DNA, 1 μmol/L
primers, 1 μl of 10 mmol/L dNTPs, 50 mmol/L KCl,
10 mmol/L Tris-HCl (PH 9.0), 1.5 mmol/L MgCl2, and
0.75 unit Taq polymerase. PCR Amplification was
performed with the following steps: predenaturing at
94℃ for 5min, followed by 35 cycles of 94℃ for 30 s,
55℃ for 1min, and 72℃ for 2 min, and last step is 8 min
at 72℃. The amplified products were separated on 6%
polyacrylamide denaturing gels. Linkage maps were
constructed by using 117 SSR markers and the order
and distance of the markers for each group was
determined based on two published SSR maps
(Temnykh et al., 2001; McCouch et al., 2002).
3.5 Statistical analysis
Phenotypic data were statistically analyzed by using
recognized SAS version (SAS 8.2) (Cary et al., 1992).
The normal distribution of phenotypic data was
verified by the shapiro-wilk test at level of α=0.01.
Some traits need to 1og conversion or square-root
transformation for their normal distribution. Pearson
correlation coefficient was calculated among
quantitative phenotypic traits. Linkage map was
constructed with Kosambi Function by using
MAPMARKER (Ver.3.3) (Lander et al., 1987). Linkage
groups were assigned based on the rice maps
previously developed by Temnykh et al. (2000).
Composite interval mapping (CIM) was employed to
detect QTL LOD peaks (>2.0). by using QTL
Cartographer (Ver.2.5), (Wang et al., 2007) The parameter
settings for CIM were model 6; forward and backward
stepwise regression with threshold of P<0.05 to
select cofactors; window-size 2 cM walking speed
along chromosomes; We used a reset likelihood ratio
(LR) threshold of 9.22 to detect significant QTLs.
Probability of a QTL locus was represented with a LR
score where LR=-2 ln (L1/L 0) and where L0 represents
the probability of an association between the marker
and the trait and L1 represents the alternate hypothesis
of no association. LOD and LR are related by the
formula LOD=0.2172LR. The positions of the
significant QTL were given for the maximum LR
value within the region under analysis. The
phenotypic variance controlled by a given QTL was
determined by its determination coefficient (R2 ), while
the phenotypic variance controlled by all the markers
in the regression model was represented by a second
determination coefficient (TR 2) as defined by the
software program (Blair et al., 2006).
3.6 QTL nomenclature
QTLs were named by following the instruction of
McCouch et al., (1997). Two or three letters
abbreviated from the trait name position behind italic
q letter, then follow hyphen and arabic figure of rice
chromosome code where the QTL is found and add
additional figure for the site number at the same locus.
For example, qSN -6 -2 stands for QTL of the seed
number trait mapped on chromosome 6 that is the
second site at this locus.
Authors’contributions
ZBJ and YYQ carried out the trait phenotyping, analyzed the QTL data and
drafted the manuscript. YC and DJP worked on the trait phenotyping,
helped with the analyses and wrote substantial parts of the paper. ZLF and
JYC obtained and analyzed the QTL data and was involved in the writing.
CL conceived the overall study, performed the experiment designs and took
part to the data analysis and to the writing. All authors read and approved
the final manuscript.
Acknowledgements
We thank Lisa Yu (Department of Biochemistry, University of Toronto,
Toronto, Ontario, Canada M5S 1A8) for critical reading and formatting of
the manuscript, and X.K.Wang (Department of Plant Genetic and
Breeding and State Key Laboratory of Agro biotechnology, China
Agricultural University) for help and advice on the experiment .The
research was supported by The National Key Technology R&D Program
(NO. 2006BAD13B01 -11), Fund for Basic Scientific and Technical
Supporting Program of Guangdong Province (2006B60101021) ,The Key
Basic Project of Scientific and Technical Supporting Program of Guangdong
Academy of Agricultural Sciences (07 -basis -04).
References
Aluko G., Martinez C., Tohme J., Castano C., Bergman C., and Oard J. H.,
2004, QTL mapping of grain quality traits from the interspecific cross
Oryza sativa×O. glaberrima, Theor. Appl .Genet, 109: 630-639