GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits

Area of Science:

• Genetics
• Biostatistics
• Computational Biology

Background:

• Identifying gene-gene (G×G) and gene-environment (G×E) interactions is crucial for understanding complex traits.
• Existing methods face challenges with diverse data types and study designs.

Purpose of the Study:

• To develop a versatile software package implementing generalized multifactor dimensionality reduction (GMDR) analyses.
• To accommodate a wide range of phenotypes, study designs, and covariate adjustments.

Main Methods:

• Development of a comprehensive GMDR software package.
• Implementation of analyses for continuous, count, dichotomous, polytomous, ordinal, survival, and multivariate phenotypes.
• Support for unrelated case-control, family-based, and pooled sample designs, with covariate adjustment.

Main Results:

• The developed GMDR package enables unified analysis of diverse genetic data, including large-scale and genome-wide datasets.
• The software incorporates data management, preprocessing, and permutation testing for robust interaction detection.
• Scalable performance allows efficient analysis of substantial datasets.

Conclusions:

• The versatile GMDR software package provides a practical solution for detecting complex G×G and G×E interactions.
• This tool facilitates advanced genetic studies across various data types and research designs.
• The software is publicly available to support the research community.