The Relationship between Imputation Error and Statistical Power in Genetic Association Studies in Diverse Populations

Genotype-imputation methods provide an essential technique for high-resolution genome-wide association (GWA) studies with millions of single-nucleotide polymorphisms. For optimal design and interpretation of imputation-based GWA studies, it is important to understand the connection between imputation error and power to detect associations at imputed markers. Here, using a 2 x 3 chi-square test, we describe a relationship between genotype-imputation error rates and the sample-size inflation required for achieving statistical power at an imputed marker equal to that obtained if genotypes at the marker were known with certainty. Surprisingly, typical imputation error rates (similar to 2%-6%) lead to a large increase in the required sample size (similar to 10%-60%), and in some African populations whose genotypes are particularly difficult to impute, the required sample-size increase is as high as similar to 30%-150%. In most populations, each 1% increase in imputation error leads to an increase of similar to 5%-13% in the sample size required for maintaining power. These results imply that in GWA sample-size calculations investigators will need to account for a potentially considerable loss of power from even low levels of imputation error and that development of additional genomic resources that decrease imputation error will translate into substantial reduction in the sample sizes needed for imputation-based detection of the variants that underlie complex human diseases.