Abstract:
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Understanding the genetic basis of biological traits is critical for improving the yield and quality of staple crops. Recently, genome-wide association studies (GWAS) have become prevalent tools for exploring important effects of genetic variants, typically the single nucleotide polymorphisms (SNPs), on biological traits. For detecting all non-negligible SNP main effects and SNP-SNP interactions, it is rather challenging to conduct an exhaustive scan, primarily due to the large number of SNPs considered. In this talk, I will introduce a new statistical method for whole-genome association and interaction studies. A noteworthy feature of the proposed method is that once the phenotypic variance is quantified, it can be repeatedly used throughout the whole-genome data analysis, then the computational cost is reduced significantly. I will show some analysis results on an Asian rice (Oryza sativa) genome dataset to demonstrate that the proposed method is a promising alternative.
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