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Manhattan Harvester and Cropper: a system for GWAS peak detection
Toomas Haller1, Tõnis Tasa2, Andres Metspalu3
1Estonian Genome Center, Institute of Genomics, University of Tartu, 23b Riia Street, 51010, Tartu, Estonia. Toomas.Haller@ut.ee.
BMC Bioinformatics
|January 13, 2019
Summary
Genome-Wide Association Studies (GWAS) analysis is enhanced by new tools. Manhattan Harvester and Cropper automate the detection and visualization of significant genomic regions, improving research efficiency.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genome-Wide Association Studies (GWAS) analysis generates vast datasets, making manual identification of significant genomic regions from Manhattan Plots challenging.
- Current methods for selecting interesting regions in GWAS are time-consuming and not scalable for thousands of phenotypes.
Purpose of the Study:
- To develop automated tools for detecting and evaluating significant genomic regions in GWAS data.
- To provide researchers with efficient methods for analyzing large-scale GWAS output.
Main Methods:
- Development of Manhattan Harvester for automated peak extraction from GWAS summary files.
- Implementation of algorithms for computing peak characteristics and a quality score.
- Creation of Cropper, a graphical tool for inspecting, cropping, and subsetting Manhattan Plot regions.
Main Results:
- Manhattan Harvester successfully extracts and quantifies significant peaks from GWAS data.
- A quality score model was developed to evaluate peaks comparable to human researcher assessment.
- Cropper provides visualization and validation of regions identified by Manhattan Harvester.
Conclusions:
- The developed tools, Manhattan Harvester and Cropper, address the need for automated GWAS output screening.
- These tools enable efficient batch processing and in-depth analysis of GWAS results.
- Both tools are open-source and freely accessible to the research community.
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