title: Impact of genetic similarity on imputation accuracy.
abstract: 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 (F(ST)) originally proposed by Wright and G -statistics (G(ST)) 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.
  
  
  CONCLUSIONS
  Usage of G(ST) or F(ST)-related measures for predicting the optimal reference panel for imputation frameworks relying on a specific reference is highly recommended. A cut-off of G(ST) < 0.01 is recommended to achieve good imputation results for high-frequency variants and small data sets. The linear relationship is less pronounced for low-frequency variants for which we also observed a dependence of imputation accuracy on the number of polymorphic sites in the reference. We also show that the software specific measures MaCH-Rsq and IMPUTE-info must be interpreted with caution if the genetic distance of target and reference population is high.
authors.affiliation: Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107, Leipzig, Germany. roshyara@gmail.com.|LIFE Center (Leipzig Interdisciplinary Research Cluster of Genetic Factors, Phenotypes and Environment), University of Leipzig, Philipp-Rosenthal Strasse 27, 04103, Leipzig, Germany. roshyara@gmail.com.
authors.forename: Nab Raj
authors.initials: NR
authors.lastname: Roshyara
authors.affiliation: Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107, Leipzig, Germany. markus.scholz@imise.uni-leipzig.de.|LIFE Center (Leipzig Interdisciplinary Research Cluster of Genetic Factors, Phenotypes and Environment), University of Leipzig, Philipp-Rosenthal Strasse 27, 04103, Leipzig, Germany. markus.scholz@imise.uni-leipzig.de.
authors.forename: Markus
authors.initials: M
authors.lastname: Scholz
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D019143:Evolution, Molecular; D005828:Genetics, Population; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012984:Software