Assessing Accuracy of Genotype Imputation in American Indians

Background: Genotype imputation is commonly used in genetic association studies to test untyped variants using information on linkage disequilibrium (LD) with typed markers. Imputing genotypes requires a suitable reference population in which the LD pattern is known, most often one selected from HapMap. However, some populations, such as American Indians, are not represented in HapMap. In the present study, we assessed accuracy of imputation using HapMap reference populations in a genome-wide association study in Pima Indians. Results: Data from six randomly selected chromosomes were used. Genotypes in the study population were masked (either 1 % or 20 % of SNPs available for a given chromosome). The masked genotypes were then imputed using the software Markov Chain Haplotyping Algorithm. Using four HapMap reference populations, average genotype error rates ranged from 7.86 % for Mexican Americans to 22.30 % for Yoruba. In contrast, use of the original Pima Indian data as a reference resulted in an average error rate of 1.73%. Conclusions: Our results suggest that the use of HapMap reference populations results in substantial inaccuracy in the imputation of genotypes in American Indians. A possible solution would be to densely genotype or sequence a referenc