JSM 2005 - Toronto

Abstract #303352

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 277
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #303352
Title: Multiple Imputation Method for SNP Typing Data in Linkage Disequilibrium Mapping of Polygenic Traits
Author(s): Yasunori Sato*+ and Hidekazu Ando and Akihiro Hirakawa and Hideki Suganami and Chikuma Hamada and Isao Yoshimura and Teruhiko Yoshida and Kimio Yoshimura
Companies: Tokyo University of Science and Tokyo University of Science and Tokyo University of Science and Tokyo University of Science and Tokyo University of Science and Tokyo University of Science and National Cancer Center Research Institute and National Cancer Center Research Institute
Address: 1 3 Kagurazaka Shinjukuku, Tokyo, 162-8601, Japan
Keywords: single nucleotide polymorphism ; multiple imputation ; missing data ; polygenic diseases
Abstract:

Single nucleotide polymorphism (SNP) typing technology has rapidly progressed and offers several very high throughput. However, it is difficult to obtain the data without missing values. Almost all statistical packages exclude observations with a missing covariate to perform a complete case analysis. This leads to a reduction of the statistical power and may lead to biased results. Therefore, missing data should not be ignored. Multiple imputation is a statistical technique for analyzing incomplete data. It consists of imputation, analysis, and pooling. It will result in valid statistical inferences by properly reflecting the uncertainty due to missing values under a certain condition. In this study, we propose a method for imputing missing data in association studies using linkage disequilibrium between SNPs. We evaluated the performance of the proposed method by simulation study, which showed the proposed method can give more accurate estimates than those of complete case analysis, especially for moderate to high missing rates (10-30%). The precision of parameter estimate did not highly depend on missing patterns.

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Revised March 2005