Logistic Regression Model of Binary Disease Trait for Case-Control Study Considering Interactions between SNPs and Environments.

LM 02

Reiichiro Nakamichi 1 Seiya Imoto 1 Satoru Miyano

1 Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó. Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó. Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó.

1 Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

Introduction

Single nucleotide polymorphisms (SNPs) are the most frequent genomic variations. A large number of SNPs have been developed, and genome-wide association study has been proposed to identify complex trait loci.

Case-control study is a popular way of the association study because of easiness in gathering data. Traits are often controlled by multiple genes and environments. However, since the contribution of each genetic and environmental effect is relatively small, it is difficult to detect these interactions by testing each locus separately, and we need to construct a statistic model considering multiple SNPs, environmental effects and their interactions simultaneously.

In this paper, we propose a combination of logistic regression and genetic algorithm for the association study of the binary disease trait. We use a logistic regression model to describe the relation of multiple SNPs, environments and the binary trait.

To construct an accurate prediction rule for binary trait, we adopted Akaike information criterion (AIC) to find the most effective set of SNPs and environments. Since the number of combinations of SNPs and environments is huge, we propose the use of the genetic algorithm for choosing the optimal SNPs and environments in the sense of AIC.

Keywords:

binary disease, case-control study, SNP, logistic regression, genetic algorithm