A novel approach to simulate gene-environment interactions in complex diseases

TL;DR

This paper introduces a mathematical model and a simulation tool, GENS, to generate controlled gene-environment interaction data, aiding the evaluation of statistical methods in complex disease research.

Contribution

The paper presents a novel mathematical approach and a user-friendly simulator for modeling and generating gene-environment interactions in simulated populations.

Findings

The GENS tool can simulate diverse gene-environment interaction scenarios.

The model allows for biologically meaningful and customizable simulations.

It facilitates testing statistical methods for detecting gene-environment interactions.

Abstract

Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the most part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite of a large amount of information that have been collected about both genetic and environmental risk factors, there are relatively few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in this data sets. An improving in this direction would lead to a better understanding and description of gene-environment interaction. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, like for example simulated ones. We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form. We implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main effort has been to allow user to describe characteristics of population by using standard epidemiological measures and to implement constraints to make the simulator behavior biologically meaningful.