Summary: Post-GWAS studies using the results from large consortium meta-analysis often need to correctly take care of the overlapping sample issue. The gold standard approach for resolving this issue is to reperform the GWAS or meta-analysis excluding the overlapped participants. However, such approach is time-consuming and, sometimes, restricted by the available data. deMeta provides a user friendly and computationally efficient command-line implementation for removing the effect of a contributing sub-study to a consortium from the meta-analysis results. Only the summary statistics of the meta-analysis the sub-study to be removed are required. In addition, deMeta can generate contrasting Manhattan and quantile-quantile plots for users to visualize the impact of the sub-study on the meta-analysis results. Availability and Implementation: The python source code, examples and documentations of deMeta are publicly available at https://github.com/Computational-NeuroGenetics/deMeta-beta .

中文翻译：

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deMeta：从全基因组关联研究（GWAS）的荟萃分析中删除子研究

简介：使用大型联合体荟萃分析的结果进行的GWAS后研究通常需要正确处理重叠的样本问题。解决此问题的金标准方法是执行GWAS或荟萃分析，排除重叠的参与者。但是，这种方法很耗时，并且有时受到可用数据的限制。deMeta提供了一种用户友好且计算效率高的命令行实施方式，用于从meta分析结果中消除对财团的贡献子研究的影响。只需要要删除的子研究的荟萃分析的摘要统计量。此外，deMeta可以为用户生成曼哈顿图和分位数图的对比图，以使子研究对荟萃分析结果的影响可视化。可用性和实施​​：