Estimation of individual genetic and environmental profiles in longitudinal designs

Parameter estimates obtained in the genetic analysis of longitudinal data can be used to construct individual genetic and environmental profiles across time. Such individual profiles enable the attribution of individual phenotypic change to changes in the underlying genetic or environmental processes and may lead to practical applications in genetic counseling and epidemiology. Simulations show that individual estimates of factor scores can be reliably obtained. Decomposition of univariate, and to a lesser extent of bivariate, phenotypic time series may yield estimates of independent individual G(t) and E(t), however, that are intercorrelated. The magnitude of these correlations depends somewhat on the autocorrelation structure of the underlying series, but to obtain completely independent estimates of genetic and environmental individual profiles, at least three measured indicators are needed at each point in time. KEY WORDS: longitudinal genetic analysis; environmental profiles; genetic profiles; factor scores; Kalman filter

LISREL analysis of twin data with structured means

Introduces a method to test the hypothesis that the phenotypic means and the phenotypic covariances can be modeled with the same common genetic and environmental factors. LISREL can be used to implement the method. An illustration with simulated twin data is provided

Application of nonlinear factor analysis to genotype-environment interaction

The intention of this paper is to show how the methods of nonlinear factor analysis as developed by McDonald (Br. J. Math. Stat. Psychol. 20:205-215, 1967) can be used to study genotype-environment interaction. The method is applied to the interaction of genotype and within-family en-vironmental influences. Simulated twin data are used to illustrate how this type of interaction may be detected and estimated. It is shown that estimates of genetic influences are not affected by G x E interaction. KEY WORDS: genotype-environment interaction; nonlinear factor analysis; twin data

Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on sbc occasions. KEY WORDS: repeated measures; genetic and environmental covariance structure; mean trend; longitudinal twin data; genetic simplex mode; LISREL

Stability of non-Boussinesq convection via the complex Ginzburg-Landau model

[Abstract]: A cubic complex Ginzburg-Landau model is derived for the flow of a general fluid near a bifurcation point. Solutions are obtained for the natural convection flow of air in a differentially heated tall closed cavity under non-Boussinesq conditions. The model is used to analyse various types of instabilities. In particular, it is found that nonlinear fluid properties variations with temperature lead to a convective instability of the flow when the temperature difference becomes sufficiently large. This is in contrast to classical results in the Boussinesq limit where the instability is found to be always absolute. The results obtained using the model for an infinitely tall cavity are in excellent agreement with those of direct numerical simulations for a cavity of aspect ratio 40

GENERALIZED RANK ANNIHILATION METHOD .1. DERIVATION OF EIGENVALUE PROBLEMS

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