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Genetic Epidemiology 35 : 102110 (2011) A Comparison of Approaches to Account for Uncertainty
 

Summary: Genetic Epidemiology 35 : 102110 (2011)
A Comparison of Approaches to Account for Uncertainty
in Analysis of Imputed Genotypes
Jin Zheng,1
Yun Li,1
Gonc-alo R. Abecasis,1
and Paul Scheet1,2
1
Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
2
Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas
The availability of extensively genotyped reference samples, such as ``The HapMap'' and 1,000 Genomes Project reference
panels, together with advances in statistical methodology, have allowed for the imputation of genotypes at single nucleotide
polymorphism (SNP) markers that are untyped in a cohort or case-control study. These imputation procedures facilitate the
interpretation and meta-analyses of genome-wide association studies. A natural question when implementing these
procedures concerns how best to take into account uncertainty in imputed genotypes. Here we compare the performance of
the following three strategies: least-squares regression on the ``best-guess'' imputed genotype; regression on the expected
genotype score or ``dosage''; and mixture regression models that more fully incorporate posterior probabilities of genotypes
at untyped SNPs. Using simulation, we considered a range of sample sizes, minor allele frequencies, and imputation
accuracies to compare the performance of the different methods under various genetic models. The mixture models

  

Source: Abecasis, Goncalo - Department of Biostatistics, University of Michigan

 

Collections: Biology and Medicine; Mathematics