GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) are crucial for understanding genetic contributions to disease susceptibility and severity.
• Existing computational tools for GWAS often lack comprehensive data integration capabilities.
• There is a need for advanced informatics solutions to integrate diverse data sources in GWAS.

Purpose of the Study:

• To develop and present GWAS Analyzer, a database-driven web tool for integrating multiple data types in GWAS.
• To provide functionalities for data exploration, analysis, and filtering of association results.
• To demonstrate the utility of GWAS Analyzer in a real-world GWAS scenario.

Main Methods:

• Developed a web-based, database-driven tool named GWAS Analyzer.
• Integrated genotype, phenotype, association analysis results, and genomic annotations from public resources.
• Implemented features for data browsing, phenotypic exploration, and filtering of association results.
• Utilized the tool in a genome-wide association study of human in vitro susceptibility to bacterial infection.

Main Results:

• GWAS Analyzer successfully managed large genotype and phenotype datasets.
• The tool facilitated analysis of phenotypic variation and heritability.
• It enabled the generation of refined candidate single nucleotide polymorphisms (SNPs).
• A specific SNP was identified and experimentally validated to be associated with increased cell death in Salmonella-infected HapMap cell lines.

Conclusions:

• GWAS Analyzer is an effective informatics solution for integrating and analyzing diverse data in GWAS.
• The tool aids in identifying significant genetic associations and refining candidate SNPs.
• Experimental validation confirmed the utility of GWAS Analyzer in discovering disease-associated genetic variants.