Fitting Procedures for Novel Gene-by-Measured Environment Interaction Models in Behavior Genetic Designs.
For quantitative behavior genetic (e.g., twin) studies, Purcell proposed a novel model for testing gene-by-measured environment (GxM) interactions while accounting for gene-by-environment correlation. Rathouz et al. expanded this model into a broader class of non-linear biometric models for quantifying and testing such interactions. In this work, we propose a novel factorization of the likelihood for this class of models, and adopt numerical integration techniques to achieve model estimation, especially for those without close-form likelihood. The validity of our procedures is established through numerical simulation studies. The new procedures are illustrated in a twin study analysis of the moderating effect of birth weight on the genetic influences on childhood anxiety. A second example is given in an online appendix. Both the extant GxM models and the new non-linear models critically assume normality of all structural components, which implies continuous, but not normal, manifest response variables.
Department of Statistics, University of Wisconsin-Madison, Madison, USA.
Hao
H
Zheng
Paul J
PJ
Rathouz
D000465:Algorithms; D001007:Anxiety / Q000235:genetics; D001724:Birth Weight; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004777:Environment; D059647:Gene-Environment Interaction; D005824:Genetics, Behavioral / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D012107:Research Design; D018433:Twin Studies as Topic; D014427:Twins / Q000235:genetics