Genetic Association Power Calculator
Calculate statistical power for genetic association studies. Determine sample size requirements and detection strength for GWAS and genetic epidemiology research.
Study Parameters
Quick Mode: Uses standard assumptions for rapid power estimation. Perfect for initial study planning.
Combined cases and controls (standard: 1000-5000)
Genetic effect strength (OR = Odds Ratio)
Minor allele frequency in population
Power Analysis Results
Select parameters and click "Calculate Power" to see results
What Is a Genetic Association Power Calculator?
A genetic association power calculator helps determine whether a genetic study is strong enough to detect a real relationship between a gene variant and a trait. Instead of running a costly study and hoping for results, power analysis estimates success before data collection begins.
Statistical power represents the chance of detecting a true genetic association if one exists. This calculator uses simplified statistical models to provide practical guidance for researchers planning genetic association studies.
The tool is particularly valuable for genome-wide association studies (GWAS) and candidate gene studies where sample size and effect size strongly influence study success.
Formula Used in the Calculator
Power Calculation Formula
Power ≈ Φ(Z_effect − Z_α)
Where Z_effect = Standardized effect size
Z_α = Critical value for significance level
Where:
- Z_effect: Standardized genetic effect size (based on OR, MAF, sample size)
- Z_α: Critical value from normal distribution for chosen α level
- Φ: Cumulative normal distribution function
- OR: Odds ratio (genetic effect size)
- MAF: Minor allele frequency
This simplified model provides practical power estimates while remaining statistically sound. For complex study designs, consult with a biostatistician.
Example Power Calculation
GWAS Study Planning Example
| Parameter | Value |
|---|---|
| Sample Size | 1000 (500 cases + 500 controls) |
| Minor Allele Frequency | 0.25 |
| Odds Ratio | 1.5 |
| Significance Level (α) | 0.05 |
| Estimated Power | 82% |
Interpretation
This study has 82% power to detect a true association. This is generally considered adequate power for most genetic studies. The study design appears well-planned for detecting moderate genetic effects.
Final Thoughts
Genetic studies are expensive and time-consuming. A power calculator helps ensure that research efforts are not wasted on underpowered studies. By estimating success probability before data collection, researchers can make informed decisions about study feasibility and design.
This calculator provides practical guidance using established statistical principles. For complex study designs or specific disease models, consultation with a biostatistician is recommended.