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

Abstract

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are 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 these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. 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, for example simulated ones.

Results: 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, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, 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 aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful.

Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

Figure 1

Multi-logistic model applied to a two genetic-one environmental factors condition. On the y-axis is reported the disease risk (R) and on the x-axis is reported the level of exposure of the environmental factor. The relationship is modelled by the Eq. 2. For each combination of genetic factors there are different α_i and β_i that models the relationship between environmental exposure and disease risk.

Figure 2

Type of GxE interactions modeled by KAPS. On the y-axis is reported the disease risk (R) and on the x-axis is reported the level of exposure of the environmental factor. The relationship is modelled by the Eq. 3. For each combination of genetic factors there are different α_i and β_i that follows the specific constraints (Table 1). In the Environmental Model (EM), the disease risk is dependent only by the environmental exposure level, thus the environment-risk relationship is the same across genotype (same slope and no shift). In the Genetic Model (GM), the disease risk depends on genetic factor only, thus the environment has no role on the disease risk (the curve is flat) while the risk is different across genotypes (height of the curve). In the third model (AM), the disease risk depends on both genetic and environmental factors; the relationship between environmental exposure and disease risk is the same in each genotype (same slope), but in each genotype there is a different basal risk (shift). In the fourth model (GEM), the genetic factor influences the relationship between environmental exposure and disease risk (slope). However, there is no different basal genetic risk (no shift).

Figure 3

Flowchart of GENS. Starting from desired population characteristics, GENS assigns to each individual the genotypes of genetic factors and the exposure levels of environmental factors. Beside, the module KAPS uses population characteristics to compute coefficients of the Multi-Logistic Model. Thus, on the basis of individual characteristics and Multi-Logistic Model, the individual disease risk is computed. The last step is the assignment of disease status to individuals (affected/not affected) according to their disease risks.