U-PASS

U-PASS performs unified power analysis for genetic association studies of binary qualitative traits by quantifying asymptotic power limits across common association tests and mapping the trade-off between risk allele frequency and odds ratio to support prospective design and retrospective assessment of single-SNP associations.

Key Features:

• Unified framework: Implements power analysis across multiple common association tests for binary qualitative traits to provide model-invariant and test-independent comparisons.
• Asymptotic power limits: Quantifies shared asymptotic power limits of association tests to characterize theoretical performance under large-sample assumptions.
• Risk allele frequency versus odds ratio visualization: Maps the trade-off between risk allele frequency and odds ratio to illustrate their joint impact on statistical power.
• Finite-sample applicability: Addresses limitations of asymptotic calculations for finite samples to improve relevance to studies with limited sample sizes.
• Single-SNP test guidelines: Provides specific guidelines for conducting and interpreting single-SNP-based association tests.
• Retrospective assessment: Enables statistical evaluation of previously reported single-SNP associations to assess their reported power and validity.

Scientific Applications:

• Prospective study design: Inform case-control and other association study designs by estimating required sample sizes and detectable effect sizes using risk allele frequency and odds ratio relationships.
• Genetic epidemiology analyses: Support interpretation of power and limitations in studies of binary qualitative traits across different association tests.
• Retrospective validation: Assess the statistical plausibility and power of reported single-SNP associations from the literature.

Methodology:

Implements asymptotic power calculations across common association tests for binary qualitative traits, quantifies shared asymptotic power limits, visualizes risk allele frequency versus odds ratio, accounts for finite-sample applicability, provides guidelines for single-SNP tests, and is implemented using R Shiny.