We propose to study the genetic and environmental factor structure of the personality dimensions of novelty seeking, harm avoidance and reward dependence. These dimensions are of clinical importance because of their relationship to categories of personality disorder, to the differentiation of somatic and cognitive anxiety, and to the etiology of subtypes of alcohol abuse. The personality theory described by Cloninger leads to a number of strong predictions. For example, it is predicted that genetic correlations between the observed scores on these personality dimension will be zero, while there will be predictable environmental correlations between the personality dimensions. We will collect data from 2000 pairs of twins from a population based register using Cloninger's Tridimensional Personality Questionnaire (TPQ) together with measure of somatic and cognitive anxiety and of alcohol use and abuse. This will provide the first test of the genetic and environmental predictions. We will carry out a full multivariate genetic analysis of the items of the TPQ to establish whether the sales are measuring unitary genetic factors. We will determine the optimum scale composition for measuring an individual's genetic disposition to novelty seeking, harm avoidance and reward dependence. We will collect data on an additional 1700 adult males who will have been previously interviewed at home for a wide ranging psychiatric assessment including alcohol abuse. These data will provide validation for the questionnaire assessment of alcohol abuse, and independent tests of the phenotype relationships between the personality variables and subtypes of alcohol abuse. The techniques we are proposing are relatively new to clinical biology. Their wider use is restricted largely by the computational demands of multivariate genetic modelling when large numbers of variables are involved, for example when items from a questionnaire like the TPQ are to be analyzed. We propose to use this project as an opportunity to develop and refine the numerical optimization procedures necessary for multivariate genetic analysis.
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