Genome-Wide Association Mapping Using a Bayesian Mixture Model for Plant Height in Oryza sativa

Authors : Burak KARACAÖREN

Pages : 41-46

View : 13 | Download : 5

Publication Date : 2016-07-30

Article Type : Research Paper

Abstract :Genotypic and phenotypic data could be used to predict inheritance of complex traits for plant breeding in genome wide association mapping studies insert ignore into journalissuearticles values(GWAS);. In GWAS using a single marker model may leads to suboptimal use of genotypic datasets. Alternatively, using whole genome, a Bayesian mixture model may cluster markers into predefined classes. We used 413 diverse accessions of Oryza sativa with 36900 Single Nucleotide Polymorphisms insert ignore into journalissuearticles values(SNPs); markers for plant height. We assumed different genetic architectures for the phenotype. We estimated genotypic heritability as 0.61. Bayesian mixture model detected 144, 446, 54 SNPs with explanatory levels of 0.0001, 0.001 and 0.01 respectively. Chromosome 1 insert ignore into journalissuearticles values(n=109);, and 3 insert ignore into journalissuearticles values(n=85); had the highest explanatory genetic variances as 23% and 19%, respectively. Correlation between genomic predicted observations and actual observations was found to be 0.94. Since GWAS are mostly based on only one replication as was also the case in this study; results need to be confirmed by independent validation experiments.
Keywords : Genome wide association mapping studies, Bayesian mixture model, Single Nucleotide Polymorphisms Markers