Genotype × Environment Interaction in Psychiatric Genetics: Deep Truth or Thin Ice?

• Published in: Twin research and human genetics Vol. 20; no. 3; pp. 187 - 196
• Main Author: Eaves, Lindon
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.06.2017
• Subjects: Behavior; Check lists; Computer Simulation; Diseases in Twins - genetics; Diseases in Twins - physiopathology; Endorsements; Environmental factors; Epidemiology; Gene-Environment Interaction; Genetic analysis; Genetic transformation; Genotype; Genotype & phenotype; Genotype-environment interactions; Genotypes; Humans; Liability; Mental disorders; Mental Disorders - genetics; Mental Disorders - physiopathology; Models, Theoretical; Random variables; Risk Factors; Twins; Twins, Dizygotic - genetics; Twins, Monozygotic - genetics; psychometrics; twins; G × E interaction; psychiatric genetics
• ISSN: 1832-4274; 1839-2628
• DOI: 10.1017/thg.2017.19

Background: There continues to be significant investment in the detection of genotype × environment interaction (G × E) in psychiatric genetics. The implications of the method of assessment for the genetic analysis of psychiatric disorders are examined for simulated twin data on symptom scores and environmental covariates. Methods: Additive and independent genetic and environmental risks were simulated for 10,000 monozygotic (MZ) and 10,000 dizygotic (DZ) twin pairs and the ‘subjects’ administered typical simulated checklists of clinical symptoms and environmental factors. A variety of standard tests for G × E were applied to the simulated additive risk scores, sum scores derived from the checklists and transformed sum scores. Results: All analyses revealed no evidence for G × E for latent risk but marked evidence for G × E and other effects of modulation in the sum scores. These effects were all removed by transformation. An integrated genetic and psychometric model, accounting for both the causes of latent liability and a theory of measurement, was fitted to a sample of the simulated sum-score data and showed that there was no significant modulation of the parameters of the genetic model by environmental covariates (i.e., no G × E). Conclusions: Claims to detect G × E based on analytical methods that ignore the theory of measurement must be subjected to greater scrutiny prior to publication.