Contemporary Modeling of Gene × Environment Effects in Randomized Multivariate Longitudinal Studies

Abstract

There is a great deal of interest in the analysis of Genotype × Environment interactions (G×E). There are some limitations in the typical models for the analysis of G×E, including well-known statistical problems in identifying interactions and unobserved heterogeneity of persons across groups. The impact of a treatment may depend on the level of an unobserved variable, and this variation may dampen the estimated impact of treatment. Some researchers have noted that genetic variation may sometimes account for unobserved, and hence unaccounted for, heterogeneity. The statistical power associated with the G×E design has been studied in many different ways, and most results show that the small effects expected require relatively large or nonrepresentative samples (i.e., extreme groups). In this article, we describe some alternative approaches, such as randomized designs with multiple measures, multiple groups, multiple occasions, and analyses, to identify latent (unobserved) classes of people. These approaches are illustrated with data from the Aging, Demographics, and Memory Study (part of the Health and Retirement Study) examining the relations among episodic memory (based on word recall), APOE4 genotype, and educational attainment (as a proxy for an environmental exposure). Randomized clinical trials (RCTs) and randomized field trials (RFTs) have multiple strengths in the estimation of causal influences, and we discuss how measured genotypes can be incorporated into these designs. Use of these contemporary modeling techniques often requires different kinds of data be collected and encourages the formation of parsimonious models with fewer overall parameters, allowing specific G×E hypotheses to be investigated with a reasonable statistical foundation.

Keywords: Gene × Environment interactions; G×E; aging; cognition; memory.

Figure 1

Model-Expected Episodic Memory over Age for Three Diagnostic Groups (from McArdle, et al., 2009)

Figure 2

Path models of alternative linear regression models with and without GxE interaction (after McArdle & Prescott, 1992)

Figure 3

Path model of measured gene longitudinal model allowing linear latent curve for Episodic Memory over all ages and data waves (after McArdle & Prescott, 1992)

Figure 4

Expected Means of Episodic Memory over Age by APOE4 Genotype and Educational Level in ADAMS/HRS Sample