Impact of genetic similarity on imputation accuracy

Background Genotype imputation is a common technique in genetic research. Genetic similarity between target population and reference dataset is crucial for high-quality results. Although several reference panels are available, it is often not clear which is the most optimal for a particular target dataset to be imputed. Maximizing genetic similarity between study sample and intended reference panels may be the straight forward method for selecting the genetically best-matched reference. However, the impact of genetic similarity on imputation accuracy has not yet been studied in detail. Results We performed a simulation study in 20 ethnic groups obtained from POPRES. High-quality SNPs were masked and re-imputed with MaCH, MaCH-minimac and IMPUTE2 using four different HapMap reference panels (CEU, CHB-JPT, MEX and YRI). Imputation accuracy was assessed by different statistics. Genetic similarity between ethnic groups and reference populations were measured by F -statistics (FST) originally proposed by Wright and G -statistics (GST) introduced by Nei and others. To assess the predictive power of these measures regarding imputation accuracy, we analysed relations between them and corresponding imputation accuracy scores. We found that population genetic distances between homogeneous reference and target populations were strongly linearly correlated with resulting imputation accuracies irrespective of considered distance measure, imputation accuracy measure, missingness and imputation software used. Possible exception was African population. Conclusion Usage of GST or FST-related measures for predicting the optimal reference panel for imputation frameworks relying on a specific reference is highly recommended. A cut-off of GST