SPS: A Simulation Tool for Calculating Power of Set-Based Genetic Association Tests

Area of Science:

• Genetics
• Biostatistics
• Computational Biology

Background:

• Set-based association tests are crucial for identifying complex disease risk loci.
• Existing tools lack comprehensive power estimation capabilities for study design.

Purpose of the Study:

• To develop a user-friendly simulation tool, SPS, for estimating the statistical power of various set-based tests.
• To facilitate efficient study design for complex disease genetic research.

Main Methods:

• Developed a simulation tool (SPS) with a graphical interface for parameter setting and result visualization.
• Incorporated advanced functions: real genotype loading, set-based meta-analysis, and parallel simulations.
• Integrated SPS into the KGG software as a functional module.

Main Results:

• SPS accurately estimates statistical power for multiple set-based tests.
• Proof-of-principle examples show average power estimation time under 3 seconds.
• The tool supports complex genetic architectures and meta-analyses.

Conclusions:

• SPS provides a comprehensive and user-friendly solution for power estimation in set-based association studies.
• This tool will aid researchers in designing more effective genetic studies for complex diseases.
• SPS is freely available, promoting wider adoption in genetic research.