SeminarsIntroduction to a high-dimensional statistical analysis framework with application to discovery of gene-environment interactions in a GWAS Data
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Environment has long been known to play an important part in disease aetiology. However, not many genome-wide association studies (GWAS) take environmental factors into consideration. There is also a need for new methods to identify the gene–environment interactions. In this talk, we proposed a method incorporating an influence measure that captured pure gene–environment effect. We found that pure gene–age interaction has a stronger association than the genetic effect alone for systolic blood pressure. We also provide evidence that age might have a non-linear effect on genetic association. The result suggests that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially that of age.
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Inchi Hu
2013-03-29
14:30:00 - 16:00:00
103 , Mathematics Research Center Building (ori. New Math. Bldg.)
Environment has long been known to play an important part in disease aetiology. However, not many genome-wide association studies (GWAS) take environmental factors into consideration. There is also a need for new methods to identify the gene–environment interactions. In this talk, we proposed a method incorporating an influence measure that captured pure gene–environment effect. We found that pure gene–age interaction has a stronger association than the genetic effect alone for systolic blood pressure. We also provide evidence that age might have a non-linear effect on genetic association. The result suggests that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially that of age.