Depression and parental bonding: Cause, consequence, or genetic covariance?
Article first published online: 3 JUN 2005
Copyright © 1994 Wiley-Liss, Inc., A Wiley Company
Volume 11, Issue 6, pages 503–522, 1994
How to Cite
Neale, M. C., Walters, E., Heath, A. C., Kessler, R. C., Pérusse, D., Eaves, L. J. and Kendler, K. S. (1994), Depression and parental bonding: Cause, consequence, or genetic covariance?. Genet. Epidemiol., 11: 503–522. doi: 10.1002/gepi.1370110607
- Issue published online: 3 JUN 2005
- Article first published online: 3 JUN 2005
- Manuscript Revised: 15 MAR 1992
- Manuscript Received: 31 JUL 1991
- instrumental variable;
- structural equation modeling;
It is shown how information on the direction of causation between variables may be obtained from a cross-sectional study of pairs of relatives. This method is applied to the study of the relationship between ratings of parents' rearing style and depression in their offspring. Adult female twins ascertained from a population-based registry in Virginia completed the Center for Epidemiological Studies - Depression Scale (CESD) and a 7-item short form of the Parental Bonding Instrument (PBI) about each of their parents. Two dimensions of parental behavior, overprotectiveness and coldness, were analyzed jointly with depression data in both genetic factor and directional genetic models. Models that specify ratings of parents as a cause of depression in the offspring fit the data significantly better than models that specify depression as a cause of ratings of parents. A still better fit is obtained with models that specify common genetic variance to depression and ratings, though causal models with error variance perform almost as well. In general, ratings of fathers show more genetic and less shared environmental variance than ratings of mothers, which might arise from more consistent treatment of offspring by mothers than by fathers. No effect of children eliciting parental rearing style was detected with these data. The relative merits of instrumental variable, longitudinal, and family approaches to testing causal models are discussed. © 1994 Wiley-Liss, Inc.