The preponderance of research on the study of ethnocentrism has primarily attributed such attitudes to learned behavior. The research here advances the argument that both socialization and genetic inheritance contribute to the development of ethnocentric attitudes and behavior. This analysis employs the Minnesota Twins Political Survey data consisting of 596 complete twin pairs. Using the classical twin design, we employed structural equation modeling to model the covariance of twins in regards to additive genetic effects, shared environmental effects, and unique environmental effects (i.e., the classic ACE model). The findings reveal that genetic inheritance is significant in explaining the variance in genetic attitudes. Specifically, genetic inheritance explains 18% of the variance, with the overwhelming 82% being explained by the unique environment.

Segregation and twin disease concordance analyses have assumed a theoretical underlying liability following a multivariate normal distribution. For reasons of computation, of incorporation of measured explanatory variables, and of testing of fit and assumptions, newer analytical methods are being developed. The regressive logistic model (RLM) relies on expressing the pedigree likelihood as a product of conditional probabilities, one for each individual. In addition to logistic regression modelling of measured epidemiological variables on disease prevalence, there is modelling of vertical transmission, of transmission of unmeasured genotypes and of sibship environment. This paper discusses methods for the analysis of binary traits in twins and in pedigrees. Some extensions to the RLM for pedigrees which include twins are proposed. These enable exploration of twin concordance in the context of the twins' common parenthood, the sibship similarities within the family, and the twins' similarity in age, sex, genes and environment.

The simultaneous analysis of means and covariance structures is applied to longitudinal twin data. Body weight was measured on six occasions in a sample of young female MZ and DZ twins. When average body weight at the first measurement occasion, as well as the increments in weight at later occasions, are specified in the genetic part of the model that also adequately explains the covariance structure, a good fit is obtained. In this application the increase in body weight at each occasion is weighted by the square root of the genetic variance innovation terms that represent the new genetic variance entering into the process.

In previous papers a stochastic model of the ageing process has been proposed. Some genetic parameters (redundance, repair) have been used to explain the observed differential predisposition to the process and family heredity. Because the process is basically due to effective random mutations, any individual of the population would be predisposed differently to ageing according to the structure of his/her genome. In the present paper, the previous model is generalized to take into account an additional genetic parameter, namely, the stability against random mutations, defined as the probability that a random mutation in a codon would produce no mutation in the corresponding protein. Estimation problems connected with the model are approached on the basis of twin data in maximum likelihood estimation as well as in bayesian framework. Some comparisons between the two methods are reported.

The genetic analysis of physiological time series has to accommodate the presence of autocorrelation. This can be accomplished by means of orthogonal transformation of the series, thus enabling the use of standard genetic analysis techniques for the sequence of uncorrelated transforms. In view of the oscillatory character which typifies various physiological time series, it is customary to invoke spectral techniques for the analysis of these series. It can be shown that spectral analysis is an orthogonal transformation that asymptotically resembles principal component analysis. Consequently, standard genetic analysis methods for the uncorrelated spectral transforms may be used. This approach will be illustrated with simulated and real (heart rate) data for univariate twin time series. Furthermore, it will be indicated that the proposed analysis can be readily generalized to multivariate time series.

A BASIC computer program designed to facilitate analysis of twin data is presented. The program estimates genetic parameters and tests their statistical significance based on the genetic and environmental hypothesis.