powerGWASinteraction

powerGWASinteraction applies a two-stage analytical framework to estimate power and detect gene x gene (GxG) interactions in genome-wide association studies (GWAS).

Key Features:

• Two-Stage Analysis Framework: Performs initial tests only on single nucleotide polymorphisms (SNPs) exhibiting marginal effects to reduce the number of interactions tested and increase statistical power.
• Significance Level Computation Algorithms: Implements a Bonferroni correction that adjusts for multiple testing by considering only the interactions actually tested and a resampling procedure analogous to Lin (2006) for significance estimation.
• Approximate Power Calculations: Provides approximate power calculations to estimate the likelihood of detecting interactions under specified genetic models and study designs for planning two-stage analyses.

Scientific Applications:

• GxG interaction discovery: Facilitates identification of gene x gene (GxG) interactions in GWAS datasets.
• Study design and power estimation: Supports planning of GWAS interaction studies by estimating power for two-stage analysis designs.
• Multifactorial disease genetics: Aids investigation of genetic architectures underlying multifactorial diseases.
• Gene-environment interaction exploration: Informs analyses that explore gene-environment (GxE) interactions by improving detection of genetic interaction effects.

Methodology:

Uses a two-stage analysis that tests SNPs with marginal effects, applies a Bonferroni correction restricted to tested interactions and a resampling procedure analogous to Lin (2006) for significance assessment, performs approximate power calculations, and uses simulation studies with known genetic models and data from existing GWAS to evaluate power compared to single-stage analyses.