bioGWAS: A Simple and Flexible Tool for Simulating GWAS Datasets

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) identify genetic loci for complex human traits.
• Extracting biological insights from GWAS requires large datasets with known biological ground truth.
• Simulating GWAS data is crucial for developing and validating new analytical methods.

Purpose of the Study:

• To develop bioGWAS, a flexible pipeline for simulating GWAS data.
• To generate simulated genotypes, phenotypes, and GWAS summary statistics.
• To enable simulation of traits with predefined causal variants and molecular pathways.

Main Methods:

• Developed bioGWAS, a pipeline for simulating GWAS data.
• Generated simulated quantitative and binary traits with specified genetic architectures.
• Utilized bioGWAS to recapitulate existing GWAS datasets and benchmark gene set enrichment analysis tools.

Main Results:

• bioGWAS successfully simulates complete GWAS datasets, including genotypes, phenotypes, and summary statistics.
• The pipeline can incorporate predefined causal genetic variants and molecular pathways.
• Demonstrated the utility of bioGWAS in benchmarking gene set enrichment analysis tools.

Conclusions:

• bioGWAS offers a flexible and powerful platform for simulating GWAS data.
• The tool facilitates the development of novel methods for downstream GWAS analysis.
• bioGWAS aids in understanding the genetic basis of complex traits and associated biological processes.