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GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits
Sheng Feng1, Shengchu Wang, Chia-Cheng Chen
1Deaprtment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710, USA. sheng.feng@duke.edu
BMC Genetics
|January 25, 2011
Summary
Calculating statistical power for genome-wide association (GWA) studies with quantitative traits is now easier with GWAPower software. This tool directly uses heritability, simplifying power calculations for genetic research.
Area of Science:
- Genetics
- Statistical genetics
- Bioinformatics
Background:
- Statistical power calculation is crucial for genome-wide association (GWA) studies.
- Traditional methods require summary statistics (mean, variance) not directly applicable to heritability in genetic studies.
- Heritability, representing genetic contribution to phenotypic variation, is difficult to translate into standard statistical parameters.
Purpose of the Study:
- To develop a user-friendly software package for calculating statistical power in GWA studies.
- To address the challenge of using heritability directly in power calculations for quantitative traits.
- To provide a practical tool for researchers designing genetic association studies.
Main Methods:
- Presents GWAPower, a statistical software package for GWA study power calculation.
- Utilizes one-degree-of-freedom genetic models to directly incorporate heritability.
- Avoids the need for approximating the non-centrality parameter of the F-distribution.
Main Results:
- GWAPower enables direct use of heritability for power calculations without approximation.
- The software allows adjustments for covariates and linkage disequilibrium.
- Demonstrates the application of GWAPower with an illustrative example.
Conclusions:
- GWAPower is a free, user-friendly software for calculating statistical power in GWA studies.
- The software is specifically designed for quantitative traits where genetic effect is defined by heritability.
- Available for download, facilitating its adoption in genetic research.
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