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The most challenging strategy for analyzing genome-wide polymorphisms and/or expression profiles is to solve multi-factor causal-relationship simultaneously. As the first step, we propose a framework of association study using maximum likelihood method that simultaneously handles genetic polymorphisms and epi-genetic information, e.g. environmental factors. We evaluate the theory by applying it to genotyped data of myocardial infarction (MI) patients.

Environmental factor dependent maximum likelihood method for association study targeted to personalized medicine.