Analyses and Comparison of Imputation-Based Association Methods

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Analyses and Comparison of Imputation-Based Association Methods (2010)

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by Yu-fang Pei , Lei Zhang , Jian Li , Hong-wen Deng

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BibTeX

@MISC{Pei10analysesand,
    author = {Yu-fang Pei and Lei Zhang and Jian Li and Hong-wen Deng},
    title = {Analyses and Comparison of Imputation-Based Association Methods},
    year = {2010}
}

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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 level; 3). under low LD level, a high imputation accuracy rate cannot guarantee an improvement of power; 4). among methods, MACH2qtl/dat, ProbABEL and SNPTEST perform similarly and they consistently outperform other methods. Our results are helpful in

Keyphrases

imputation-based association method untyped causal snp high ld level mach2qtl dat imputation-based association test additional insight effective imputation-based association test imputation accuracy rate genetic factor ld level comprehensive comparison untyped genotype data popular imputation-based association method low ld level genetic association imputed genotype genotype imputation method real data set power improvement power increase important application

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