Environmental factor dependent maximum likelihood method for association study targeted to personalized medicine.
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.
Tsunoda
T
T
SNP Research Center, Institute of Chemical and Physical Research (RIKEN), Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato, Tokyo 108-8639, Japan. tatsu@ims.u-tokyo.ac.jp
Yamada
R
R
Tanaka
T
T
Ohnishi
Y
Y
Kamatani
N
N
D000465:Algorithms; D016022:Case-Control Studies; D019295:Computational Biology; D004777:Environment; D005260:Female; D020869:Gene Expression Profiling; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008957:Models, Genetic; D009203:Myocardial Infarction / Q000209:etiology / Q000235:genetics; D010641:Phenotype; D011110:Polymorphism, Genetic; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors