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Updated: May 19, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

SNPWay: streamlined SNP-to-function and pathway over-representation analysis.

Bryan Queme¹, Ayaan Kakkar², Anushya Muruganujan¹

¹Division of Biostatistics and Health Data Science, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90089, USA.

Biorxiv : the Preprint Server for Biology

|May 18, 2026

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SNPWay simplifies post-GWAS analysis by integrating variant annotation and pathway over-representation testing into a single workflow. This tool enhances reproducibility and accessibility for genetic interpretation and functional hypothesis generation.

Area of Science:

Genomics and Bioinformatics
Statistical Genetics
Computational Biology

Background:

Interpreting Genome-Wide Association Study (GWAS) results requires translating variant data into functional and pathway hypotheses.
Current methods involve integrating multiple bioinformatics tools, increasing complexity and reducing reproducibility.
This integration burden limits end-to-end analysis to researchers with specialized bioinformatics support.

Purpose of the Study:

To present SNPWay, a unified web server and R package for streamlined SNP-to-function and pathway over-representation analysis.
To reduce the integration burden and enhance reproducibility in post-GWAS interpretation.
To provide an accessible tool for researchers to generate functional hypotheses from genetic variants.

Main Methods:

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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SNPWay accepts various input formats including rsIDs, VCF files, and genomic coordinates (hg19/GRCh37).
It integrates multiple annotation tools (ANNOVAR, SnpEff, VEP) via Annotation Query (AnnoQ) for comprehensive SNP-to-gene mapping, including noncoding variants using PEREGRINE enhancer-gene links.
Aggregated gene lists are analyzed using PANTHER for pathway and functional over-representation testing.

Main Results:

SNPWay provides an end-to-end workflow for SNP-to-function and pathway analysis in a single, standardized process.
The tool generates over-represented pathways and functional categories with direct links for interactive exploration.
It supports multiple gene models (Ensembl, RefSeq) and incorporates regulatory information for noncoding variants.

Conclusions:

SNPWay offers a user-friendly and reproducible solution for post-GWAS functional interpretation.
The tool democratizes access to pathway analysis for a wider range of researchers.
Its modular design allows for future extensibility and incorporation of new analysis methods.