Environmental Factor Dependent Maximum Likelihood Method for Association Study Targeted to Personalized Medicine

Tatsuhiko Tsunoda[1](tatsu@ims.u-tokyo.ac.jp)
Ryo Yamada[1](ryamada-tky@umin.ac.jp)
Toshihiro Tanaka[1](toshitan@ims.u-tokyo.ac.jp)
Yozo Ohnishi[1](ohnishi@ims.u-tokyo.ac.jp)
Naoyuki Kamatani[2](kamatani@ior.twmu.ac.jp)

[1]SNP Research Center, Institute of Chemical and PhysicalResearch (RIKEN),
Institute of Medical Science, University of Tokyo,4-6-1 Shirokanedai, Minato,
Tokyo 108-8639, Japan
[2]Institute of Reumatology, Tokyo Women's Medical University,10-22 Kawada-cho,
Shinjuku-ku, Tokyo 162-0054, Japan

Abstract

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.

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