Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Genetic Analysis Workshop 16

Open Access Proceedings

Assessment of genotype imputation methods

Joanna M Biernacka, Rui Tang, Jia Li, Shannon K McDonnell, Kari G Rabe, Jason P Sinnwell, David N Rider, Mariza de Andrade, Ellen L Goode and Brooke L Fridley*

Author Affiliations

Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905 USA

For all author emails, please log on.

BMC Proceedings 2009, 3(Suppl 7):S5  doi:

Published: 15 December 2009

Abstract

Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE. We compared the methods' imputation error rates and performance of association tests using the imputed data, in the context of imputing completely untyped markers as well as imputing missing genotypes to combine two datasets genotyped at different sets of markers. As expected, all methods performed better for single-nucleotide polymorphisms (SNPs) in high linkage disequilibrium with genotyped SNPs. However, MACH and IMPUTE generated lower imputation error rates than fastPHASE and PLINK. Association tests based on allele "dosage" from MACH and tests based on the posterior probabilities from IMPUTE provided results closest to those based on complete data. However, in both situations, none of the imputation-based tests provide the same level of evidence of association as the complete data at SNPs strongly associated with disease.