Causeway: a pipeline for genome-wide effector gene screening with Mendelian Randomization and colocalization.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2025-05-29 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf110
Julia A de Amorim, João Vitor F Cavalcante, Diego Marques-Coelho, Rodrigo J S Dalmolin, Vasiliki Lagou
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引用次数: 0

Abstract

Summary: The integration of quantitative trait loci and disease genome-wide association studies for pinpointing candidate causal genes is a computationally demanding task accompanied by pitfalls related to the methods used. To address these issues, we introduce Causeway, a novel Nextflow pipeline for performing summary statistics-based two sample Mendelian Randomization for causal gene prioritization. The pipeline executes sensitivity and colocalization analyses for interrogation of findings providing robust results. The tool is designed to run tasks in a computationally efficient way even in low-resource environments, such as a personal computer. Furthermore, it can scale to web servers and high-performance computing clusters.

Availability and implementation: The source code of Causeway is available at GitHub https://github.com/juliaapolonio/Causeway, while the documentation and instructions to run the vignette at https://juliaapolonio.github.io/Causeway/.

Causeway:用孟德尔随机化和共定位筛选全基因组效应基因的管道。
摘要:整合数量性状位点和疾病全基因组关联研究以精确定位候选致病基因是一项计算要求很高的任务,同时伴随着与所使用方法相关的陷阱。为了解决这些问题,我们引入了Causeway,这是一种新型的Nextflow管道,用于执行基于摘要统计的两样本孟德尔随机化,以确定因果基因的优先级。该管道执行敏感性和共定位分析,以询问提供可靠结果的发现。该工具旨在即使在资源匮乏的环境(如个人计算机)中也能以计算效率高的方式运行任务。此外,它还可以扩展到web服务器和高性能计算集群。可用性和实现:Causeway的源代码可在GitHub https://github.com/juliaapolonio/Causeway上获得,而运行小视频的文档和说明可在https://juliaapolonio.github.io/Causeway/上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.60
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