PYPE:研究者驱动的生物库规模分析中的全表型关联和孟德尔随机化管道

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Taykhoom Dalal, Chirag J. Patel
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引用次数: 0

摘要

全表型关联研究(Phenome-wide association studies,PheWASs)是记录基因型与多种表型之间关系的一种方法,有助于发现尚未探索的基因型与表型之间的关联(称为多效性)。其次,孟德尔随机化(MR)可以通过比较一对表型的遗传结构,对其因果关系做出说明。因此,同时实现 PheWAS 和 MR 自动化的方法可以加强生物库规模的分析,通过提供全面的端到端工具来推动科学发现,从而避免对多种工具的需求。为此,我们推出了PYPE,一种用于运行、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还能生成各种可视化结果,并可用于识别附近的基因和显著关联的功能性后果。最后,PYPE 还能在各种因果效应建模情况下使用 MR 识别表型之间可能存在的因果关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis

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.

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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
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