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Updated: Jul 17, 2026
05:01
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
snp.plotter: an R-based SNP/haplotype association and linkage disequilibrium plotting package
Augustin Luna1, Kristin K Nicodemus
1GCAP/CBDB, NIMH/NIH, 10 Center Drive, Room 4S-235, Bethesda, MD 20814, USA.
Bioinformatics (Oxford, England)
|January 20, 2007
Summary
This study introduces snp.plotter, an R package for visualizing genetic association study results. It integrates P-value and linkage disequilibrium (LD) plots for clearer interpretation of genetic data.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genetics
Background:
- Genetic association studies are crucial for identifying disease-related genes.
- Visualizing results, including P-values and linkage disequilibrium (LD), is essential for interpretation.
- Existing tools may lack comprehensive visualization options for integrated genetic data.
Purpose of the Study:
- To develop a user-friendly R package, snp.plotter, for high-quality visualization of genetic association study results.
- To enable simultaneous plotting of association P-values and LD metrics (r2 or D').
- To offer flexible customization of plot aesthetics and data representation.
Main Methods:
- Development of an R package named snp.plotter.
- Implementation of features for plotting P-values and LD.
- Inclusion of options for X-axis scaling (equal spacing or physical map).
- Support for plotting single nucleotide polymorphism (SNP) and haplotype data.
- Capability to plot multiple result sets concurrently.
Main Results:
- snp.plotter generates high-quality plots integrating association and LD information.
- The package allows users to choose between r2 or D' for LD metric display.
- Customization options for labels, colors, symbols, and LD heatmap schemes are available.
- Both single SNP and haplotype data can be visualized effectively.
Conclusions:
- snp.plotter provides a simple and appealing graphic method for presenting genetic association and LD data.
- The package enhances the interpretability of genetic association study findings.
- It serves as a valuable tool for researchers in statistical genetics and bioinformatics.
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