title: Examining gene-environment interactions in comorbid depressive and disruptive behavior disorders using a Bayesian approach.
abstract: OBJECTIVE
  The objective of this study was to apply a Bayesian statistical analytic approach that minimizes multiple testing problems to explore the combined effects of chronic low familial support and variants in 12 candidate genes on risk for a common and debilitating childhood mental health condition.
  
  
  METHODS
  Bayesian mixture modeling was used to examine gene by environment interactions among genetic variants and environmental factors (family support) associated in previous studies with the occurrence of comorbid depression and disruptive behavior disorders youth, using a sample of 255 children.
  
  
  RESULTS
  One main effect, variants in the oxytocin receptor (OXTR, rs53576) was associated with increased risk for comorbid disorders. Two significant gene × environment and one signification gene × gene interactions emerged. Variants in the nicotinic acetylcholine receptor α5 subunit (CHRNA5, rs16969968) and in the glucocorticoid receptor chaperone protein FK506 binding protein 5 (FKBP5, rs4713902) interacted with chronic low family support in association with child mental health status. One gene × gene interaction, 5-HTTLPR variant of the serotonin transporter (SERT/SLC6A4) in combination with μ opioid receptor (OPRM1, rs1799971) was associated with comorbid depression and conduct problems.
  
  
  CONCLUSIONS
  Results indicate that Bayesian modeling is a feasible strategy for conducting behavioral genetics research. This approach, combined with an optimized genetic selection strategy (Vrieze et al., 2012), revealed genetic variants involved in stress regulation (FKBP5, SERT × OPMR), social bonding (OXTR), and nicotine responsivity (CHRNA5) in predicting comorbid status.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington, USA; Seattle Children's Research Institute, Center for Child Health, Behavior, and Development, USA. Electronic address: adriam@uw.edu.
authors.forename: Molly
authors.initials: M
authors.lastname: Adrian
authors.affiliation: University of California Los Angeles, Semel Institute, USA.
authors.forename: Cara
authors.initials: C
authors.lastname: Kiff
authors.affiliation: Department of Statistics, University of Washington, USA.
authors.forename: Chris
authors.initials: C
authors.lastname: Glazner
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington, USA.
authors.forename: Ruth
authors.initials: R
authors.lastname: Kohen
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington, USA.
authors.forename: Julia Helen
authors.initials: JH
authors.lastname: Tracy
authors.affiliation: Seattle Children's Research Institute, Center for Child Health, Behavior, and Development, USA.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington, USA; Seattle Children's Research Institute, Center for Child Health, Behavior, and Development, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: McCauley
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington, USA; Seattle Children's Research Institute, Center for Child Health, Behavior, and Development, USA.
authors.forename: Ann
authors.initials: A
authors.lastname: Vander Stoep
mesh_terms: D000293:Adolescent; D001499:Bayes Theorem; D002648:Child; D015897:Comorbidity; D003863:Depression / Q000453:epidemiology* / Q000235:genetics*; D004843:Epistasis, Genetic; D005190:Family / Q000523:psychology; D005260:Female; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies; D060005:Genotyping Techniques; D006801:Humans; D016015:Logistic Models; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology* / Q000235:genetics*; D012189:Retrospective Studies
keywords: Bayesian mixture modeling; Comorbidity; Depression; Disruptive behavior; Markov chain Monte Carlo