Evidence for multiple genetic factors underlying the DSM-IV criteria for alcohol dependence.
- Authors
- Kendler, K S; Aggen, S H; Prescott, C A; Crabbe, J; Neale, M C
- Year
- 2012
- Journal
- Molecular psychiatry
- PMID
- 22105626
- DOI
- 10.1038/mp.2011.153
- PMCID
- PMC3371163
To determine the number of genetic factors underlying the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for alcohol dependence (AD), we conducted structural equation twin modeling for seven AD criteria, plus two summary screening questions, in 7133 personally interviewed male and female twins from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders, who reported lifetime alcohol consumption. The best-fit twin model required three genetic and two unique environmental common factors, and criterion-specific unique environmental factors. The first genetic factor was defined by high loadings for the probe question about quantity and frequency of alcohol consumption, and tolerance criterion. The second genetic factor loaded strongly on the probe question about self-recognition of alcohol-related problems and AD criteria for loss of control, desire to quit, preoccupation and activities given up. The third genetic factor had high loadings for withdrawal and continued use despite the problems criteria. Genetic factor scores derived from these three factors differentially predicted patterns of comorbidity, educational status and other historical/clinical features of AD. The DSM-IV syndrome of AD does not reflect a single dimension of genetic liability, rather, these criteria reflect three underlying dimensions that index risk for: (i) tolerance and heavy use; (ii) loss of control with alcohol associated social dysfunction and (iii) withdrawal and continued use despite problems. While tentative and in need of replication, these results, consistent with the rodent literature, were validated by examining predictions of the genetic factor scores and have implications for gene-finding efforts in AD.
Parameter estimates from the best-fit multivariate twin model applied to the DSM-IV criteria for alcohol dependence. Three genetic and two individual-specific environmental common factors were identified, as well as individual-specific environmental factors unique to each criterion. The strongest genetic loading on each criterion is highlighted in blue. E for individual-specific environmental effects. Subscript numbers refer to common factors (for example, E1 refers to the first individual-specific environmental common factor), whereas subscript numbers following the letter ‘S’ (for specific) refers to criteria-specific factors (for example, ES1 refers to the individual environmental effects specific to the first criterion). Squares are observed variables and circles/ovals are latent variables.
LLM interpretation
This is a path diagram of a multivariate twin model showing the relationships between three latent genetic factors (green ovals) and nine observed DSM-IV criteria for alcohol dependence (squares). The model includes two common individual-specific environmental factors ($E_1, E_2$) and nine criteria-specific environmental factors ($ES_1$ through $ES_9$). Numerical parameter estimates are provided for each path, with the strongest genetic loadings for each criterion highlighted in blue.
The location in three-dimensional space defined by scores on the three genetic factors (factor 1—excess drinking and tolerance; factor 2—loss of control and social dysfunction; factor 3—withdrawal and continued use) of all individuals in the study meeting DSM-IV criteria for alcohol dependence. Circles indicate females and squares males.
LLM interpretation
This is a 3D scatter plot showing the distribution of individuals with alcohol dependence based on three genetic factors. The axes represent Genetic Factor 1 (excess drinking and tolerance), Genetic Factor 2 (loss of control and social dysfunction), and Genetic Factor 3 (withdrawal and continued use). Data points are categorized by sex, with circles representing females and squares representing males, showing a clustered distribution across the three-dimensional space.
| # | Section | Preview |
|---|---|---|
| 40 | Comment — Limitations | These results should be interpreted in the context of seven major methodological limitations. First,… |
| 41 | Comment — Limitations | Third, to obtain unbiased parameter estimates, we needed to include the screening items to… |
| 42 | Comment — Limitations | Fourth, our assessment was solely by self-report and we cannot rule out the possibility that our… |
| 43 | Comment — Limitations | Fifth, as pointed out by Lessov et al,60 correlated measurement errors could bias our parameter… |
| 44 | Comment — Limitations | Sixth, the total number of items available (seven criteria and two screening items) is small to… |
| 45 | Comment — Limitations | Finally, despite much effort it was not, for technical reasons, possible to obtain 95% confidence… |
| 46 | Comment — Conclusion | These results, as tentative and in need of replication, if correct have implication for efforts to… |
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