Assessment of genotype imputation methods.
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.
Biernacka
Joanna M
JM
Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905 USA. biernacka.joanna@mayo.edu.
Tang
Rui
R
Li
Jia
J
McDonnell
Shannon K
SK
Rabe
Kari G
KG
Sinnwell
Jason P
JP
Rider
David N
DN
de Andrade
Mariza
M
Goode
Ellen L
EL
Fridley
Brooke L
BL