This study used a structural equation mixture model to test for an interaction between genetic variation and child maltreatment experiences predicting profiles of multi-domain adaptive functioning. Children aged 6- to 13- years (N = 1004) were recruited to attend a research day camp. Half of the children were recruited based on substantiated maltreatment histories, the other half were non-maltreated but matched on socio-economic status. During the camp, saliva was collected and 12 genetic-variants known to confer environmental sensitivity (ES) were genotyped. Measures were of prosocial behavior, antisocial behavior, withdrawn behavior, and depression were also collected. These four indicators of adaptive functioning were used in a latent class analysis (LCA). A 4-class solution was selected as a best-fitting model. The four classes characterized ‘well-adjusted’, ‘externalizing’, ‘internalizing’, and ‘socially-dominant’ groups. The number of maltreatment subtypes experienced significantly predicted this latent class variable controlling for sex and age (Wald=35.3, df=3, p<0.000). The 12 genetic-variants were formed into one formative factor. The interaction of this polygenic formative factor and the maltreatment variable (GxE) also significantly predicted the latent class variable controlling for sex, age, an age-by-maltreatment interaction term, and a sex-by-polygenic factor interaction term (Wald=13.5, df=3, p=0.004). Specifically, significant GxE odds ratios were present in the pairwise comparisons of membership in the externalizing class versus the well-adjusted class as well as the externalizing class versus the socially-dominant class. High genetic factor scores appeared to buffer the effects of maltreatment, thereby contributing to more resilient profiles of adaptive functioning.
University of Minnesota Ph.D. dissertation. August 2016. Major: Child Psychology. Advisor: Dante Cicchetti. 1 computer file (PDF); vi, 112 pages.
Gene by Environment Interaction and Adaptive Functioning in Maltreated and Nonmaltreated African American Children: A Structural Equation Mixture Model.
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