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Genotype imputation is an integral tool in genome-wide association studies, in which it facilitates meta-analysis, increases power, and enables fine-mapping. With the increasing availability of whole-genome-sequence datasets, investigators have access to a multitude of reference-panel choices for genotype imputation. In principle, combining all sequenced whole genomes into a single large panel would provide the best imputation performance, but this is often cumbersome or impossible due to privacy restrictions. Here, we describe meta-imputation, a method that allows imputation results generated using different reference panels to be combined into a consensus imputed dataset. Our meta-imputation method requires small changes to the output of existing imputation tools to produce necessary inputs, which are then combined using dynamically estimated weights that are tailored to each individual and genome segment. In the scenarios we examined, the method consistently outperforms imputation using a single reference panel and achieves accuracy comparable to imputation using a combined reference panel.