Protocol for using GRPath to identify putative gene regulation paths in complex human diseases

Xi Xi; Haochen Li; Lei Wei; Xuegong Zhang

doi:10.1016/j.xpro.2022.101831

Protocol for using GRPath to identify putative gene regulation paths in complex human diseases

STAR Protoc. 2022 Nov 9;3(4):101831. doi: 10.1016/j.xpro.2022.101831. eCollection 2022 Dec 16.

Authors

Xi Xi¹, Haochen Li², Lei Wei³, Xuegong Zhang⁴

Affiliations

¹ MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: xix19@mails.tsinghua.edu.cn.
² School of Medicine, Tsinghua University, Beijing 100084, China.
³ MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, China.
⁴ MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, China; School of Medicine, Tsinghua University, Beijing 100084, China. Electronic address: zhangxg@tsinghua.edu.cn.

Abstract

Unfolding the "black-box" associations between genotype and phenotype is essential for understanding the molecular mechanisms of complex human diseases. Here, we describe the use of GRPath to uncover putative causal paths (pcPaths) from genetic variants to disease phenotypes. GRPath takes multiple omics data and summary statistics as input and identifies pcPaths that link the putative causal region (pcRegion), putative causal variant (pcVariant), putative causal gene (pcGene), noteworthy cell type, and disease phenotype. For complete details on the use and execution of this protocol, please refer to Xi et al. (2022).¹.

Keywords: Bioinformatics; Gene Expression; Health Sciences; RNAseq; Single Cell; Systems biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Causality
Gene Expression Regulation*
Genome-Wide Association Study*
Genotype
Humans
Phenotype