PIAGE

PIAGE estimates statistical power and required sample sizes for detecting genetic main effects, environmental marginal effects, and gene–environment (GxE) interactions in indirect 1:1 matched case–control genetic association studies where disease-causing variants are unobserved and inferred via markers in linkage disequilibrium (LD).

Key Features:

• Power and sample size estimation: Calculates sample size requirements and statistical power for detecting genetic main effects, environmental effects, and GxE interactions in matched case–control settings.
• Gene–environment interaction analysis: Provides power estimation for detecting GxE effects and for joint tests combining genetic marginal and GxE effects.
• Indirect marker modeling: Accounts for scenarios where true disease variants are not directly genotyped but inferred through markers in LD with disease loci.
• Study-variable consideration: Evaluates the influence of true genetic main effects, disease allele frequencies, matching between marker and disease allele frequencies, LD between loci, prevalence of environmental exposures, and magnitude of interactions on power and sample size.
• Algorithmic evaluation: Incorporates an algorithm that integrates the listed study variables to produce power and sample size estimations.
• Detection of weak effects and rare variants: Highlights conditions under which GxE analysis improves detection of weak genetic main effects, including scenarios with rare variants, moderate exposure prevalence, and strong interactions that may reduce required sample size relative to marginal tests.

Scientific Applications:

• Study design for association studies: Guides planning of indirect 1:1 matched case–control genetic association studies by quantifying sample size and power trade-offs.
• Identification of subtle genetic influences: Enables detection strategies for genetic variants with weak marginal effects through incorporation of GxE analyses.
• Investigation of complex diseases: Supports study designs that examine the interplay between genetic predisposition and environmental exposures in complex disease etiology.

Methodology:

Computes power and sample size using an algorithm that evaluates true genetic main effects, disease and marker allele frequencies, LD between loci, prevalence of environmental exposures, interaction magnitudes, and 1:1 matching in indirect matched case–control studies.