Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures. (29th December 2014)

• Record Type: Journal Article
• Authors: Tao, Yebin; Sánchez, Brisa N.; Mukherjee, Bhramar
• Is Part Of: Statistics in medicine. Volume 34:Number 7(2015)
• Page Start: 1227
• Page End: 1241
• Publication Date: 2014-12-29
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6401
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6401-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6401-para-0001">Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene–environment interaction (<italic>G</italic> × <italic>E</italic>). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for <italic>G</italic> × <italic>E</italic> effects within the LV framework to examine effect modification of outcome–exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data‐adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data‐adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time‐varying gene–environment association. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>