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Neighborhood × Serotonin Transporter Linked Polymorphic Region (5-HTTLPR) interactions for substance use from ages 10 to 24 years using a harmonized data set of African American children

Published online by Cambridge University Press:  15 June 2015

Michael Windle*
Emory University
Steven M. Kogan
University of Georgia
Sunbok Lee
University of Georgia
Yi-Fu Chen
University of Georgia
Karlo Mankit Lei
University of Georgia
Gene H. Brody
University of Georgia
Steven R. H. Beach
University of Georgia
Tianyi Yu
University of Georgia
Address correspondence and reprint requests to: Michael Windle, Department of Behavioral Sciences and Health Education, Emory University, 1518 Clifton Road NE, Room 514, Atlanta, GA 30322; E-mail:


This study investigated the influences of neighborhood factors (residential stability and neighborhood disadvantage) and variants of the serotonin transporter linked polymorphic region (5-HTTLPR) genotype on the development of substance use among African American children aged 10–24 years. To accomplish this, a harmonized data set of five longitudinal studies was created via pooling overlapping age cohorts to establish a database with 2,689 children and 12,474 data points to span ages 10–24 years. A description of steps used in the development of the harmonized data set is provided, including how issues such as the measurement equivalence of constructs were addressed. A sequence of multilevel models was specified to evaluate Gene × Environment effects on growth of substance use across time. Findings indicated that residential instability was associated with higher levels and a steeper gradient of growth in substance use across time. The inclusion of the 5-HTTLPR genotype provided greater precision to the relationships in that higher residential instability, in conjunction with the risk variant of 5-HTTLPR (i.e., the short allele), was associated with the highest level and steepest gradient of growth in substance use across ages 10–24 years. The findings demonstrated how the creation of a harmonized data set increased statistical power to test Gene × Environment interactions for an under studied sample.

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