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Genotype imputation is a process of estimating missing ge-notypes from the haplotype or genotype reference panel. It can effectively boost the power of detecting single nucleotide polymorphisms (SNPs) in genome-wide association studies, integrate multi-studies for meta-analysis, and be applied in fine-mapping studies. The performance of genotype imputation is affected by many factors, including software, reference selection, sample size, and SNP density/sequencing coverage. A systematical evaluation of the imputation performance of current popular software will benefit future studies. Here, we evaluate imputation performances of Beagle4. 1, IMPUTE2, MACH+ Minimac3, and SHAPEIT2+ IM-PUTE2 using test samples of East Asian ancestry and references of the 1000 Genomes Project. The result indicated the accuracy of IMPUTE2 (99.18%) is slightly higher than that of the others (Beagle4. 1: 98.94 …