Page 56 - RUT Publisher

Life Science Research and Sustainable Development ISBN: 978-98-84663-33-9

association is effective but may notbe sufficient to control the confounding effects (Zhao et al.,
2007). Yu et al. (2005)introduced a mixed-modelapproach to control the population structure and
the genetic relatedness among inbreds. Similar to othermixed-model-based methods, a random
effect toestimate the fraction of the phenotypic variation,which can be explained by genome-wide
correlations,is included by assuming that the phenotypiccovariance between individuals is
proportional to theirrelative relatedness or kinship. Relative relatedness isestimated by using
genome-wide marker data (the Kmatrix of pairwise kinship coefficients). In addition tothis
random effect, a fixed effect by using thepopulation assignments produced by theSTRUCTURE
algorithm (the Q matrix), was includedas a fixed effect in the model. The Q and K seem tocapture
different features of the confoundingpopulation structure. However, Zhao et al. (2007)found that
Q was not required in most cases if K wascomputed by using a method different from the oneused
by Yu et al. (2005). AM has been proven to be an efficient method inrice using low- and high-
density markers.
The bestexamples were presented by Huang et al. (2010) and Zhao et al. (2011). Huang et
al. (2010) used whole-genome sequencing to identify singlenucleotide polymorphisms (SNPs) for
association analysis, whereas Zhao et al. (2011) used Affymatrix chips with 44 100 SNPs. Huang
et al. (2010) utilized517 rice landraces and about 3.6 million SNPs toanalyze marker-trait
association for 14 agronomictraits. They identified a total of 37 significantassociation signals.
Association signals for six traitswere located close to previously known genes, whichwere
identified by using mutants or in studies ofrecombinant populations. They later reported
ongenome-wide association studies of flowering timeand grain yield traits by using 950
worldwide varietiesand detected and identified 32 new loci responsible forflowering time and 10
grain-related traits (Huang et al.,2010). Zhao et al. (2011) applied association analysisto 413 diverse
accessions of O. sativa from 82countries for 34 traits. They found SNPs associatedwith panicle
length at 31.7 Mb to 31.7 Mb onchromosome 1 and for amylose content and floweringtime at 4.2
Mb to 4.6 Mb on chromosome 6.
References:
Abe, Y., Mieda, K., Ando, T., Kono, I., Yano, M., Kitano, H. and Iwasaki, Y., 2010. The SMALL
AND ROUND SEED1 (SRS1/DEP2) gene is involved in the regulation of seed size in rice.
Genes & genetic systems, 85(5): 327-339.
Agrama, H., Eizenga, G. and Yan, W., 2007.Association mapping of yield and its components in
rice cultivars. Molecular Breeding, 19(4): 341-356.
Andersen, J.R. and Lübberstedt, T., 2003.Functional markers in plants. Trends in plant science,
8(11): 554-560.
Ashikari, M. and Matsuoka, M., 2006.Identification, isolation and pyramiding of quantitative trait
loci for rice breeding. Trends in plant science, 11(7): 344-350.
Ashikari, M., Sakakibara, H., Lin, S., Yamamoto, T., Takashi, T., Nishimura, A., Angeles, E.R.,
Qian, Q., Kitano, H. and Matsuoka, M., 2005. Cytokinin oxidase regulates rice grain
production. Science, 309(5735): 741-745.
Ashikari, M., Wu, J., Yano, M., Sasaki, T. and Yoshimura, A., 1999. Rice gibberellin-insensitive
dwarf mutant gene Dwarf 1 encodes the α-subunit of GTP-binding protein. Proceedings
of the National Academy of Sciences, 96(18): 10284-10289.
Bai, X., Luo, L., Yan, W., Kovi, M.R., Zhan, W. and Xing, Y., 2010. Genetic dissection of rice grain
shape using a recombinant inbred line population derived from two contrasting parents
and fine mapping a pleiotropic quantitative trait locus qGL7. BMC genetics, 11(1): 1.

https://jesjalna.org/Zoology-Publications/index.html 48 Department of Zoology, J. E. S. College, Jalna