We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Assessment of genotype imputation methods.
- Authors
Biernacka, Joanna M.; Rui Tang; Jia Li; McDonnell, Shannon K.; Rabe, Kari G.; Sinnwell, Jason P.; Rider, David N.; De Andrade, Mariza; Goode, Ellen L.; Fridley, Brooke L.
- 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 singlenucleotide 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.
- Subjects
RHEUMATOID arthritis; GENETIC polymorphisms; AUTOIMMUNE diseases; NUCLEOTIDES; MEDICAL genetics; RHEUMATISM
- Publication
BMC Proceedings, 2009, Vol 3, p1
- ISSN
1753-6561
- Publication type
Article
- DOI
10.1186/1753-6561-3-S7-S5