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Updated: Aug 21, 2025

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

Xi Xi1, Haochen Li2, Lei Wei1

  • 1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, China.

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|November 17, 2022
PubMed
Summary
This summary is machine-generated.

GRPath uncovers causal pathways linking genetic variants to disease phenotypes. This tool integrates multi-omics data to identify key genetic factors and cell types involved in complex human diseases.

Keywords:
BioinformaticsGene ExpressionHealth SciencesRNAseqSingle CellSystems biology

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Area of Science:

  • Genetics
  • Systems Biology
  • Computational Biology

Background:

  • Understanding genotype-phenotype associations is crucial for deciphering complex human diseases.
  • The
  • black-box
  • nature of these links impedes mechanistic insights.

Purpose of the Study:

  • To introduce GRPath, a novel computational tool for identifying putative causal paths (pcPaths) from genetic variants to disease phenotypes.
  • To enable the exploration of molecular mechanisms underlying complex diseases by linking genetic variations to observable traits.

Main Methods:

  • GRPath integrates multiple omics datasets and summary statistics.
  • The tool identifies pcPaths by connecting putative causal regions (pcRegions), variants (pcVariants), genes (pcGenes), cell types, and disease phenotypes.

Main Results:

  • GRPath successfully uncovers putative causal pathways from genetic variants to disease phenotypes.
  • The method links genetic variations to specific cell types and disease outcomes.

Conclusions:

  • GRPath provides a framework for dissecting the genetic architecture of complex diseases.
  • This approach facilitates the discovery of molecular mechanisms and potential therapeutic targets.