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analyses and comparison of imputation-based association methodsimputation-based协会方法的分析和比较
Analyses and Comparison of Imputation-Based
Association Methods
1,2 1,2 2 1,2,3
Yu-Fang Pei , Lei Zhang , Jian Li , Hong-Wen Deng *
1 Key Laboratory of Biomedical Information Engineering, Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi’an Jiaotong
University, Xi’an, People’s Republic of China, 2 School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, United States of America, 3 Center of System
Biomedical Sciences, Shanghai University of Science and Technology, Shanghai, People’s Republic of China
Abstract
Genotype imputation methods have become increasingly popular for recovering untyped genotype data. An important
application with imputed genotypes is to test genetic association for diseases. Imputation-based association test can
provide additional insight beyond what is provided by testing on typed tagging SNPs only. A variety of effective
imputation-based association tests have been proposed. However, their performances are affected by a variety of genetic
factors, which have not been well studied. In this study, using both simulated and real data sets, we investigated the effects
of LD, MAF of untyped causal SNP and imputation accuracy rate on the performances of seven popular imputation-based
association methods, including MACH2qtl/dat, SNPTEST, ProbABEL, Beagle, Plink, BIMBAM and SNPMStat. We also aimed to
provide a comprehensive comparison among methods. Results show that: 1). imputation-based association tests can boost
signals and improve power under medium and high LD levels, with the power improvement increasing with strengthening
LD level; 2) the power increases with higher MAF of untyped causal SNPs under medium to high LD le
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