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.

基因型插补是全基因组关联研究中不可或缺的工具，它有助于荟萃分析、提高功效并实现精细绘图。随着全基因组序列数据集的可用性不断增加，研究人员可以使用多种参考面板选择来进行基因型插补。原则上，将所有测序的全基因组组合成一个大面板将提供最佳的插补性能，但由于隐私限制，这通常很麻烦或不可能。在这里，我们描述元插补，一种允许使用不同参考面板生成的插补结果组合成共识插补数据集的方法。我们的元插补方法需要对现有插补工具的输出进行微小的更改，以产生必要的输入，然后使用针对每个个体和基因组片段定制的动态估计权重进行组合。在我们检查的场景中，该方法始终优于使用单个参考面板的插补，并且达到与使用组合参考面板的插补相当的准确性。