GEInter

GEInter performs robust gene-environment (G-E) interaction analysis to identify, estimate, and predict G-E interactions in studies with data contamination and long-tailed distributions.

Key Features:

• Robust analysis: Supports both marginal and joint G-E analyses using statistical methods tailored for contaminated and long-tailed data.
• Response types: Accommodates continuous and censored survival responses.
• Missing data support: Handles datasets with or without missing values.
• Inference and output: Provides functions for identification, estimation, visualization, and prediction of G-E interactions.
• Implementation: Implemented as an R package for use within the R statistical environment.

Scientific Applications:

• Complex disease research: Identification and estimation of G-E interactions in studies of complex disease etiology.
• Large-scale genomic studies: Applicable to analyses of large genomic datasets, including The Cancer Genome Atlas (TCGA).
• Epidemiological and clinical studies: Suited for studies with continuous outcomes and censored survival data to inform multifactorial disease mechanisms and potential targeted interventions.

Methodology:

Implements robust statistical techniques for marginal and joint G-E interaction analysis, provides functions for identification, estimation, visualization, and prediction, and explicitly handles missing data, data contamination, long-tailed distributions, and continuous and censored survival outcomes within an R package.