Structural equation modeling of gene-environment interactions in coronary heart disease.

Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.