Dalal Taykhoom, Patel Chirag J
Harvard Medical School Department of Biomedical Informatics, Boston, MA 02115, USA.
Patterns (N Y). 2024 May 1;5(6):100982. doi: 10.1016/j.patter.2024.100982. eCollection 2024 Jun 14.
Phenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian randomization (MR) can be harnessed to make causal statements about a pair of phenotypes by comparing their genetic architecture. Thus, approaches that automate both PheWASs and MR can enhance biobank-scale analyses, circumventing the need for multiple tools by providing a comprehensive, end-to-end tool to drive scientific discovery. To this end, we present PYPE, a Python pipeline for running, visualizing, and interpreting PheWASs. PYPE utilizes input genotype or phenotype files to automatically estimate associations between the chosen independent variables and phenotypes. PYPE can also produce a variety of visualizations and can be used to identify nearby genes and functional consequences of significant associations. Finally, PYPE can identify possible causal relationships between phenotypes using MR under a variety of causal effect modeling scenarios.
全表型组关联研究（PheWAS）是记录基因型与多种表型之间关系的一种方式，有助于发现未被探索的基因型-表型关联（即多效性）。其次，孟德尔随机化（MR）可通过比较一对表型的遗传结构来做出因果推断。因此，能够自动化执行PheWAS和MR的方法可以加强生物样本库规模的分析，通过提供一个全面的端到端工具来推动科学发现，从而避免使用多种工具。为此，我们展示了PYPE，这是一个用于运行、可视化和解释PheWAS的Python管道。PYPE利用输入的基因型或表型文件自动估计所选自变量与表型之间的关联。PYPE还可以生成各种可视化结果，并可用于识别附近的基因以及显著关联的功能后果。最后，PYPE可以在各种因果效应建模场景下，使用MR识别表型之间可能的因果关系。
