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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
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PAPA: a flexible tool for identifying pleiotropic pathways using genome-wide association study summaries
Yan Wen1, Wenyu Wang1, Xiong Guo1
1Key Laboratory of Trace Elements and Endemic Diseases of Ministry of Health, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Bioinformatics (Oxford, England)
|November 17, 2015
Summary
Pleiotropy is common in complex diseases. We developed PAPA, a novel tool for identifying pleiotropic pathways using multiple genome-wide association study (GWAS) results, aiding obesity research.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Pleiotropy, where genes influence multiple traits, is prevalent in complex disease genetics.
- Existing tools lack the capability to analyze pleiotropic pathways using multiple genome-wide association study (GWAS) summary data.
Purpose of the Study:
- To introduce PAPA, a flexible software tool designed for pleiotropic pathway analysis.
- To enable the identification of shared genetic pathways across different complex diseases using multiple GWAS datasets.
Main Methods:
- PAPA utilizes summary statistics from multiple GWAS.
- The tool performs pleiotropic pathway analysis to identify shared genetic influences.
Main Results:
- PAPA successfully identified significant pleiotropic pathways.
- Validation using GWAS data for body mass index and waist-hip ratio confirmed PAPA's performance.
- Identified pathways are implicated in the development of obesity.
Conclusions:
- PAPA provides a novel and flexible approach for pleiotropic pathway analysis.
- The tool facilitates the discovery of genetic links between complex diseases.
- PAPA aids in understanding the genetic architecture of obesity and related conditions.
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