A twin study of generalized anxiety disorder and major depression
- PMID: 8588001
- DOI: 10.1017/s0033291700037533
A twin study of generalized anxiety disorder and major depression
Abstract
Previous analyses with a sample of female twins sampled from the general population in Virginia have suggested that generalized anxiety disorder (GAD) and major depression (MD) share their genetic determinants but have partly different environmental determinants. The goal of this report is to examine whether these findings apply to samples that include male as well as female twins and contain high proportions of subjects who had been hospitalized for MD. The subjects were ascertained through two different sources: (i) index probands were ascertained through the Swedish Psychiatric Twin Registry for a diagnosis of unipolar or bipolar affective illness; (ii) control twin probands were ascertained through the Swedish Twin Registry. Subjects were sent questionnaires for the assessment of lifetime history of GAD and MD. Positing multinormal distribution of the liability for GAD and MD, we fitted bivariate models to examine the sources of comorbidity. The full model included additive genetic effects, shared environment and individual-specific environment, as well as scalar and non-scalar sex limitations and different thresholds across genders. The best-fitting model included: (i) a genetic correlation of unity; (ii) no common environment; (iii) an individual-specific environmental correlation of 0.28; (iv) different thresholds across genders, but neither scalar nor non-scalar sex-limitations. A model that included additive and dominant genetic effects and individual-specific environment, with correlation of unity for both additive and dominant genetic effects, provided an equivalent fit. These analyses confirm that GAD and MD share the same genetic factors but that their environmental determinants are mostly distinct. Moreover, the present report supports the feasibility of combining clinical ascertained and general-population samples into a single bivariate analysis.
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