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Updated: Jul 16, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
GenABEL: an R library for genome-wide association analysis
Yurii S Aulchenko1, Stephan Ripke, Aaron Isaacs
1Department of Epidemiology and Biostatistics, Erasmus MC Rotterdam, Postbus 2040, 3000 CA Rotterdam, The Netherlands. i.aoultchenko@erasmusmc.nl
This study introduces GenABEL, an R library for genome-wide association (GWA) analysis. GenABEL efficiently handles GWA data, performs quality control, and tests single nucleotide polymorphism associations, making GWA analysis accessible on standard computers.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genetics
Background:
- Genome-Wide Association (GWA) studies are crucial for identifying genetic variants associated with traits.
- Effective computational tools are needed for analyzing large-scale GWA datasets.
- The R programming environment is widely used in statistical genetics.
Purpose of the Study:
- To introduce GenABEL, a novel R library designed for comprehensive GWA analysis.
- To provide efficient tools for data handling, quality control, and association testing in GWA studies.
- To facilitate the analysis of GWA data on standard desktop computing environments.
Main Methods:
- Implementation of efficient data storage and management for GWA data.
- Development of rapid procedures for genetic data quality control.
- Integration of association testing for single nucleotide polymorphisms (SNPs) with binary or quantitative traits.
- Provision of visualization tools and interfaces to standard R statistical and graphical packages.
Main Results:
- GenABEL offers robust capabilities for GWA data analysis.
- The library supports quality control, association testing, and result visualization.
- Performance was validated using simulated and real GWA datasets.
- The software enables GWA analysis on desktop computers.
Conclusions:
- GenABEL provides a comprehensive and efficient solution for GWA analysis.
- The library simplifies complex genetic data analysis tasks.
- Accessibility on desktop computers broadens the utility of GWA studies.
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