Biometric Modeling of Gene-Environment Interplay: The Intersection of Theory and Method and Applications for Social Inequality

Abstract

For more than a decade, biometric moderation models have been used to examine whether genetic and environmental influences on individual differences might vary within the population. These quantitative Gene × Environment interaction models have the potential to elucidate not only when genetic and environmental influences on a phenotype might differ, but also why, as they provide an empirical test of several theoretical paradigms that serve as useful heuristics to explain etiology-diathesis-stress, bioecological, differential susceptibility, and social control. In the current article, we review how these developmental theories align with different patterns of findings from statistical models of gene-environment interplay. We then describe the extant empirical evidence, using work by our own research group and others, to lay out genetically informative plausible accounts of how phenotypes related to social inequality-physical health and cognition-might relate to these theoretical models.

Figure 1

Bivariate moderation model. The model is shown for only one member of a twin pair. Genetic and environmental influences on the Outcome Variable vary by level of the Moderator Variable. A=additive genetics, C= shared environmental influences, and E=non-shared environmental influences. A_C, C_C, and E_C are variance components underlying the Moderator that also influence the Outcome (i.e., “common components”), and A_U, C_U, and E_U represent residual (“unique”) variance in the Outcome after accounting for the variance in common with the Moderator. β coefficients index the direction and magnitude of moderation. When all β coefficients are set to zero, this represents no moderation effects. Total phenotypic variance in the Outcome can be calculated by squaring and summing all of the paths leading to it: Var(Outcome) = (a_C+β_acM)² + (a_U+β_auM)² + (c_C+β_ccM)² + (c_U+β_cuM)² + (e_C+β_ecM)² + (e_U+β_euM)².

Figure 2

Example of patterns of quant G×E for different theoretical models. Genetic (A), shared environmental (C) and nonshared environmental (E) components of variance for an outcome are plotted as a function of an environmental moderator variable, shown on the X axis at five different levels: -2, -1, 0, 1, and 2 Standard Deviations from the Mean. Panel (A): Diathesis-stress model plotted with standardized variance components. Panel (B): Diathesis-stress model plotted with raw variance components. Panel (C). Bioecological model. Panel (D). Differential susceptibility model.