Our goal in this study was to clarify the structure of genetic and environmental risk factors for syndromal and subsyndromal common axis I disorders and all axis II personality disorders as assessed by a criterion count. Using multivariate twin analyses, we identified, from the 22 disorders examined, four coherent genetic factors: axis I internalizing, axis II internalizing, axis I externalizing, and axis II externalizing.
Although not without important limitations (see below), these findings provide, for the first time, a view of the etiological structure of a substantial proportion of common psychiatric disorders. Furthermore, this structure, especially the genetic factors, is coherent and clinically sensible. The structure of the genetic risks for these disorders is neither extremely simple (e.g., just one dimension of underlying risk) nor bewilderingly complex. Of the many interesting results from these analyses, three are particularly noteworthy.
First, we replicated and extended the results of our earlier multivariate twin analysis, which included only seven disorders but clearly identified genetic internalizing and externalizing factors (13
). The present study provides further support for the importance and generalizability of the internalizing and externalizing genetic dimensions of risk for common psychiatric disorders. While we identified separate internalizing and externalizing factors for axis I and II disorders, they were moderately intercorrelated.
Second, our results provide—for the first time to our knowledge—some support, from a genetic perspective, for the decision in DSM-III to distinguish between axis I and axis II disorders. The genetic substrate for axis II disorders is, in our analyses, at least partially separable from those factors that predispose to axis I disorders. However, the axis I and II disorders that loaded on our genetic factors are not isomorphic with those articulated by DSM-III. Two axis I disorders—dysthymia and social phobia—were included in the internalizing axis II cluster. The concept of dysthymia evolved in part from the concept of “depressive personality” (16
). Our results suggest that from a genetic perspective, it may be better placed with the personality disorders than in the mood disorders section. A debate has long simmered about the relationship between social phobia and avoidant personality disorder (see, for example, references 44
). Our results suggest that from a genetic perspective, social phobia belongs with avoidant personality disorder on axis II.
Our results supporting a genetic distinction between axis I and axis II disorders might be seen as surprising, given previous evidence that they are highly comorbid and hard to distinguish empirically (48
). Another plausible interpretation of our findings, particularly for internalizing disorders, is that different sets of genetic risk factors predispose to psychiatric disorders that are typically transient and episodic in nature and those that are characteristically more chronic.
Third, “transitional” disorders with substantial loadings on two genetic factors provide further insights into the structure of the genetic risk for psychiatric disorders. Furthermore, the existence of these transitional disorders indicates that the psychiatric disorders in our current classification do not neatly fall into our four proposed clusters. Individuals with high criterion counts for borderline personality disorder were predicted by our results to require elevated genetic risk for both axis I and II externalizing disorders. Paranoid personality disorder stood out because it required risk genes from both the axis II internalizing and externalizing dimensions. Eating disorders had the most unusual configuration, requiring high risk on both the axis I internalizing and the axis II externalizing dimensions.
We also identified three unique environmental factors. The first resulted from environmental experiences predisposing to all personality disorders. The second reflected environmental factors altering risk solely to internalizing axis I disorders. Consistent with our Virginia study (13
), with respect to individual-specific environmental risk factors, alcohol abuse or dependence more closely resembled major depression and generalized anxiety disorder than antisocial personality disorder, conduct disorder, or drug abuse or dependence. The third environmental factor reflected environmental exposures that predisposed to the anxiety disorders while protecting against the core externalizing disorders (or vice versa). The inverse relationship between anxiety and externalizing traits is, in our analyses, largely environmental in origin.
It is illustrative to compare the location of a few sets of disorders in genetic versus environmental space. From a genetic perspective, dysthymia sorts with the personality disorders, yet its environmental risk factors place it much closer to major depression. Environmentally, alcohol abuse or dependence shares most risk factors with internalizing disorders but shares genetic risk factors with the axis I externalizing disorders. Environmentally, borderline personality disorder has links with all personality disorders and with axis I internalizing disorders; genetically, it is closely tied to axis I and II externalizing disorders.
Consistent with our earlier study (13
), the division of common psychiatric disorders into internalizing versus externalizing factors results from genetic and not from environmental risk factors. By contrast, the division into axis I versus axis II disorders arises from the effects of both genes and the environment.
Our results are also congruent with our previous analysis in this sample of the structure of genetic risk factors for personality disorders (27
). That study, which examined only the 10 personality disorders, identified three genetic factors, the first of which loaded most heavily on histrionic, narcissistic, and borderline personality disorders—clearly reflecting our axis II externalizing factor. The second factor loaded more specifically on antisocial and borderline personality disorders—approximating our broader axis I externalizing genetic factor. The third genetic factor loaded most heavily on avoidant and schizoid personality disorders, with weaker loadings on dependent and schizotypal personality disorders—reflecting our axis II internalizing genetic factor.
These results need to be interpreted in the context of nine additional potentially significant limitations. First, our results are obtained in native-born young adult Norwegian twins and may not generalize to other ethnic or age groups.
Second, as many important psychiatric disorders (e.g., schizophrenia, autism, bipolar illness) were not included in these analyses, no claims can be made for our identification of the structure of risk factors for all psychiatric illness.
Third, using traditional statistical methods, we were unable to estimate results separately in male and female participants. While we controlled for prevalence differences across the sexes, we cannot rule out the possibility that we have averaged results of the two sexes that might meaningfully differ from one another. However, three findings reduce our concern that we have thereby introduced significant biases in our findings. In our earlier multivariate study in the Virginia Twin Registry (13
), once we accounted for differences in prevalence, we were able, in a much larger twin sample, to constrain to equality parameter estimates across the sexes. In all of our previous analyses of the axis I and II disorders in this Norwegian sample, we have failed to find evidence for sex-specific genetic or environmental effects (17
). Finally, we examined several models of our 22 disorders treating the criterion counts and subthreshold and threshold diagnoses as normally distributed variables. While this approach does not correctly capture the distributional properties of our variables, it nonetheless provides some useful information. Compared to the full model with separate parameter estimates for male and female participants, a model constraining all the genetic and environmental parameters to equality in the two sexes provided a much better fit using the Bayesian information criterion, a fit index particularly well suited for complex models (53
Fourth, we were unable to test, using standard twin model fitting, whether the addition of shared environmental factors would improve the fit of this large multivariate model. However, a wide range of previous analyses with most of the disorders included in our model failed to find evidence for substantial shared environmental effects (17
). Furthermore, treating the criterion counts and subthreshold and threshold diagnoses as normally distributed, we compared the full model and a model that dropped all of the shared environmental parameters. It fitted much better using the Bayesian information criterion and also was clearly superior to a model that dropped all the additive genetic parameters. While we cannot rule out a modest degree of confounding of genetic with shared environmental effects, it is unlikely that this confounding is substantial.
Fifth, we could not formally test the number of genetic and environmental factors extracted. We therefore had to rely on the more traditional methods of the scree plot and clinical interpretation. We feel confident, however, that four genetic and three specific environmental factors represent the most parsimonious structure that well accounts for the observed results.
Sixth, we lacked the ability to calculate confidence intervals for the individual parameter estimates. Given the size of our sample, we suspect that our parameter estimates are known with only moderate accuracy (55
). However, it is the broad pattern of our findings rather than the specific value of any individual parameter that is probably of greatest value in these analyses.
Seventh, substantial attrition was observed from the original birth registry through three waves of contact. However, detailed analyses of the predictors of nonresponse across waves (26
) revealed that cooperation was strongly predicted by sex, zygosity, age, and education but not psychiatric symptoms or self-report personality disorder items that have been shown empirically to predict DSM-IV personality disorder criteria in the personal interview phase. For example, among 45 predictors, including 22 mental health variables, only two—older age and monozygosity—predicted cooperation in the personal interview phase. Twin analyses of 25 mental health-related variables from earlier questionnaires reflecting psychiatric and personality disorder symptoms and substance use revealed no significant differences between those who completed a personal interview and those who did not (26
). Thus it is unlikely that attrition introduced bias in the estimates of the etiological role of genetic and environmental risk factors for this broad range of mental health indicators. Our sample is probably broadly representative of the Norwegian population with respect to psychopathology.
Eighth, could our results be sensitive to the specific method of factor extraction? In addition to the oblique geomin rotations, we examined solutions obtained by the orthogonal varimax and oblique promax methods. All four genetic factors and the first environmental factor were stable across rotational methods, with only small differences on the second and third environmental factors (e.g., higher cross-loadings for antisocial personality disorder and drug abuse or dependence). The main features of our results, especially the four genetic factors, were stable with respect to the method of factor extraction.
Finally, could method variance account for critical parts of our findings? We used two separate instruments with different formats for the assessment of axis I versus conduct and personality disorders. However, our results suggest that this concern is unwarranted. Our second genetic factor contained five axis II personality disorders and two axis I disorders. Antisocial personality disorder and conduct disorder, both assessed in our personality disorder interview, were placed in the third genetic factor and the third environmental factor each time with other axis I disorders. This pattern of results is not consistent with a method variance account.