Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be discovered, but these data are usually provided for various variant sets. Thus imputing these summary statistics of different variant sets into a common reference panel for meta-analyses is impossible using traditional genotype imputation methods. Here we develop a fast and accurate P-value imputation (FAPI) method that utilizes summary statistics of common variants only. Its computational cost is linear with the number of untyped variants and has similar accuracy compared with IMPUTE2 with prephasing, one of the leading methods in genotype imputation. In addition, based on the FAPI idea, we develop a metric to detect abnormal association at a variant and showed that it had a significantly greater power compared with LD-PAC, a method that quantifies the evidence of spurious associations based on likelihood ratio. Our method is implemented in a user-friendly software tool, which is available at http://statgenpro.psychiatry.hku.hk/fapi.