[gwasfilter: an R script to filter genome-wide association study]

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWASs) are crucial for identifying genetic variants associated with traits.
• The GWAS Catalog is a valuable resource but requires efficient filtering methods for large datasets.
• Manual filtering of GWAS data can be time-consuming and prone to inconsistencies.

Purpose of the Study:

• To develop an automated R script for filtering GWAS data from the GWAS Catalog.
• To enhance the efficiency and accuracy of selecting relevant GWAS studies.
• To provide a user-friendly tool for researchers working with large-scale genetic association data.

Main Methods:

• Established selection principles for GWAS filtering based on prior research.
• Abstracted manual filtering processes into standardized algorithms.
• Developed and rigorously tested the R script 'gwasfilter.R' with multiple functions.

Main Results:

• The gwasfilter.R script filters GWASs in six distinct steps.
• Key filtering criteria include study replication, sample size, and population ethnicity.
• The script processes single-trait GWAS data in under one second, demonstrating high efficiency.

Conclusions:

• The gwasfilter.R script offers a user-friendly, efficient, and standardized method for flexible GWAS filtering.
• This tool streamlines the process of accessing and analyzing GWAS data.
• The source code is publicly available for community use and further development.