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Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect

Author

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  • Bruins, Susanne

    (Vrije Universiteit)

  • Hottenga, Jouke-Jan
  • Neale, Michael C.
  • Pool, René
  • Boomsma, Dorret
  • Dolan, Conor V.

Abstract

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.

Suggested Citation

  • Bruins, Susanne & Hottenga, Jouke-Jan & Neale, Michael C. & Pool, René & Boomsma, Dorret & Dolan, Conor V., 2023. "Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect," OSF Preprints 7a4v8_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:7a4v8_v1
    DOI: 10.31219/osf.io/7a4v8_v1
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