A Brief History of the Collaboration between Dr Nathan Gillespie and Professor Nick Martin Including Personal Reflections

This article describes Dr Nathan Gillespie’s PhD training and supervision under Professor Nick Martin and their ongoing collaborations. Drs Gillespie and Martin have collaborated on numerous biometrical genetic analyses applied to cross-sectional and longitudinal twin data, combined molecular and phenotypic modeling, as well as genomewide meta-analyses of psychoactive substance use and misuse. Dr Gillespie remains an active collaborator with Professor Martin, including ongoing data collection, analysis and publications related to the Brisbane Longitudinal Twin Study.

Genetic and Environmental Stability of Intelligence in Childhood and Adolescence

The present study examined the genetic and environmental contributions to the temporal stability of verbal, non-verbal and general intelligence across a developmental period spanning childhood and adolescence (5–18 years). Longitudinal twin data collected in four different studies on a total of 1,748 twins, comprising 4,641 measurement points in total, were analyzed using genetic adaptations of the simplex model. The heterogeneity in the type of instrument used to assess psychometric intelligence across the different subsamples and ages allowed us to address the auxiliary question of how to optimally utilize the existing longitudinal data in the context of gene-finding studies. The results were consistent across domains (verbal, non-verbal and general intelligence), and indicated that phenotypic stability was driven primarily by the high stability of additive genetic factors, that the stability of common environment was moderate, and that the unique environment contributed primarily to change. The cross-subscale stability was consistently low, indicating a small overlap between different domains of intelligence over time. The high stability of additive genetic factors justifies the use of a linear combination of scores across the different ages in the context of gene-finding studies.

Latent Curve Analyses of Longitudinal Twin Data Using a Mixed-Effects Biometric Approach

In a recent article McArdle and Prescott (2005) showed how simultaneous estimation of the bio-metric parameters can be easily programmed using current mixed-effects modeling programs (e.g., SAS PROC MIXED). This article extends these concepts to deal with mixed-effect modeling of longitudinal twin data. The biometric basis of a polynomial growth curve model was used by Vandenberg and Falkner (1965) and this general class of longitudinal models was represented in structural equation form as a latent curve model by McArdle (1986). The new mixed-effects modeling approach presented here makes it easy to analyze longitudinal growth-decline models with biometric components based on standard maximum likelihood estimation and standard indices of goodness-of-fit (i.e., χ2, df, εa). The validity of this approach is first checked by the creation of simulated longitudinal twin data followed by numerical analysis using different computer programs (i.e., Mplus, Mx, MIXED, NLMIXED). The practical utility of this approach is examined through the application of these techniques to real longitudinal data from the Swedish Adoption/Twin Study of Aging (Pedersen et al., 2002). This approach generally allows researchers to explore the genetic and nongenetic basis of the latent status and latent changes in longitudinal scores in the absence of measurement error. These results show the mixed-effects approach easily accounts for complex patterns of incomplete longitudinal or twin pair data. The results also show this approach easily allows a variety of complex latent basis curves, such as the use of age-at-testing instead of wave-of-testing. Natural extensions of this mixed-effects longitudinal approach include more intensive studies of the available data, the analysis of categorical longitudinal data, and mixtures of latent growth-survival/ frailty models.

Simultaneous Genetic Analysis of Longitudinal Means and Covariance Structure Using the Simplex Model: Application to Repeatedly Measured Weight in a Sample of 164 Female Twins

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.

Analysis of Longitudinal Twin Data. Basic Model and Applications to Physical Growth Measures

A formal model is presented for the analysis of longitudinal twin data, based on the underlying analysis-of-variance model for repeated measures. The model is developed in terms of the expected values for the variance components representing twin concordance, and the derivation is provided for computing within-pair (intraclass) correlations, and for estimating the percent of variance explained by each component. The procedures are illustrated with physical growth data extending from birth to six years, and concordance estimates are obtained for average size and for the pattern of spurts and lags in growth. A test of significance is also described for comparing monozygotic twins with dizygotic twins. The procedures are particularly useful for assessing chronogenetic influences on development, especially whether the episodes of acceleration and lag occur in parallel for genetically matched twins. The model may be employed with psychological data also.