Professor: Yoichiro KamataniOur laboratory aims to reveal complex trait genomics by applying statistical genetics approach with statistical learning to human genome and -omics data set. A “trait” is a characteristic of an organism and characterizes an individual. It includes appearance, personality, laboratory tests, and diseases. A trait which cannot be defined by a few genes is called complex trait. Most of the complex trait exhibits heritability, and such traits are composed by multiple genetic factors and also environmental factors. We and several researchers employed genome-wide association study (GWAS) approach for complex trait analysis and identified more than tens or hundreds of susceptibility variants, and they are mostly located in the active gene regulatory region. Furthermore, we found that some of the complex traits are genetically closely correlated with each other. We suppose this might mean that differences in genomic sequence lead to individual complex trait as a result of the diversity of the general cellular response to various environmental factors. We think that this cellular response might be described as a non-linear network on the basis of genome. Now we move to the next phase of complex trait genomics, and will perform integrative analysis for rare variants and -omics data. To detect genetic effects of common variants, GWAS, a linear model, worked very well. However, for these latest biological data sets we think non-linear model is required, and our aim is to apply statistical learning on the basis of statistical genetics, to create a novel analysis of complex traits. Also, students will learn how to make use of complex trait genomics at clinical medicine or the society in the future.
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