One of the aims of pathway-based modeling is to understand how genetic and environmental effects are mediated through intermediate events such as changes in gene expression, epigenetic events like DNA methylation137, somatic mutations138, and small-interfering RNAs139. These phenomena have been studied in relation to disease and to a lesser extent exposure140,141, but the full pathways from genes and exposures through epigenetics to disease remain to be studied137. For example, the seminal observation142 that MZ twins start life with identical methylation patterns but subsequently diverge suggests the effect of environmental factors and may provide a mechanism for their subsequent discordance in disease. Latent variable models could be used to treat biomarker measurements as surrogate observations of a long-term unobserved process leading to disease. Various –omics technologies could provide high-dimensional measurements of intermediate processes on targeted subsamples of epidemiologic study subjects, although the multiple comparisons challenges of relating high-dimensional phenotypes to high-dimensional genotypes and interactions are even more daunting than for regular GWA studies. Alternatively, stand-alone studies or external databases can be used to construct prior covariates to inform G×E analyses of
