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

TitleContemporary Modeling of Gene × Environment Effects in Randomized Multivariate Longitudinal Studies.
Publication TypeJournal Article
Year of Publication2010
AuthorsMcArdle, JJ, Prescott, CA
JournalPerspect Psychol Sci
Volume5
Issue5
Pagination606-21
Date Published2010 Sep
ISSN Number1745-6916
Abstract

<p>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. </p>

DOI10.1177/1745691610383510
User Guide Notes

http://www.ncbi.nlm.nih.gov/pubmed/22472970?dopt=Abstract

Endnote Keywords

Genome-Wide Association Study/Genome-Wide Association Study/Genotyping/Environment/Methodology/genetics/genetics

Endnote ID

25290

Alternate JournalPerspect Psychol Sci
Citation Key7482
PubMed ID22472970
PubMed Central IDPMC3004154
Grant ListR01 AG007137 / AG / NIA NIH HHS / United States
R37 AG007137 / AG / NIA NIH HHS / United States
R37 AG007137-20 / AG / NIA NIH HHS / United States
U01 AG009740 / AG / NIA NIH HHS / United States