Application of nonlinear factor analysis to genotype-environment interaction

Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.

Testing systematic genotype by environment interactions using item level data

Investigating genotype by environment interactions (GxE) is generally considered challenging due to the scale dependency of the interaction effect. The present paper illustrates the problems associated with testing for GxEs on summed item scores within the well-known ACE model. That is, it is shown how genuine GxEs may be masked and how spurious interactions can arise from scaling issues in the data. A solution is proposed which explicitly distinguishes between a measurement model for the ordinal item responses and a biometric model in which the GxE effects are investigated. The new approach is studied in a simulation study using both a scenario in which the measurement instrument suffers from mild scaling problems and a scenario in which the measurement instrument suffers from severe scaling problems. Results indicate that the severity of the scale problems affects the power to detect GxE, but it rarely results in false positives. We illustrate the new approach on a real dataset concerning affect.

Genetic epidemiology of attention deficit hyperactivity disorder (ADHD index) in adults

Context

In contrast to the large number of studies in children, there is little information on the contribution of genetic factors to Attention Deficit Hyperactivity Disorder (ADHD) in adults.

Objective

To estimate the heritability of ADHD in adults as assessed by the ADHD index scored from the CAARS (Conners' Adult ADHD Rating Scales).

Design

Phenotype data from over 12,000 adults (twins, siblings and parents) registered with the Netherlands Twin Register were analyzed using genetic structural equation modeling.

Main outcome measures

Heritability estimates for ADHD from the twin-family study.

Results

Heritability of ADHD in adults is estimated around 30% in men and women. There is some evidence for assortative mating. All familial transmission is explained by genetic inheritance, there is no support for the hypothesis that cultural transmission from parents to offspring is important.

Conclusion

Heritability for ADHD features in adults is present, but is substantially lower than it is in children.

Genetic and environmental contributions to loneliness in adults: the Netherlands twin register study

Heritability estimates based on two small studies in children indicate that the genetic contribution to individual differences in loneliness is approximately 50%. Heritability estimates of complex traits such as loneliness may change across the lifespan, however, as the frequency, duration, and range of exposure to environmental influences accrues, or as the expression of genetic factors changes. We examined data on loneliness from 8,387 young adult and adult Dutch twins who had participated in longitudinal survey studies. A measure of loneliness was developed based on factor analyses of items of the YASR (The Young Adult Self Report). Variation in loneliness was analyzed with genetic structural equation models. The estimate of genetic contributions to variation in loneliness in adults was 48%, which is similar to the heritability estimates found previously in children. There was no evidence for sex or age differences in genetic architecture. Sex differences in prevalence were significant, but we did not see an association with age or birth cohort. All resemblance between twin relatives was explained by shared genes, without any suggestion of a contribution of shared environmental factors.

Identification of co-regulated transcripts affecting male body size in Drosophila

Factor analysis is applied to microarray data in order to relate gene networks to complex traits and identifies a factor associated with body size in Drosophila simulans.

Factor analysis is an analytic approach that describes the covariation among a set of genes through the estimation of 'factors', which may be, for example, transcription factors, microRNAs (miRNAs), and so on, by which the genes are co-regulated. Factor analysis gives a direct mechanism by which to relate gene networks to complex traits. Using simulated data, we found that factor analysis clearly identifies the number and structure of factors and outperforms hierarchical cluster analysis. Noise genes, genes that are not correlated with any factor, can be distinguished even when factor structure is complex. Applied to body size in Drosophila simulans, an evolutionarily important complex trait, a factor was directly associated with body size.

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

The object of this paper is to indicate that the Pearson-Lawley selection rules form a plausible general theory for the simultaneous genetic analysis of means and covariance structure. Models are presented based on phenotypic selection and latent selection. Previously presented quantitative genetic models to decompose means and covariance structure simultaneously are reconsidered as instances of latent selection. The selection rules are very useful in the context of behavior genetic modeling because they lead to testable models and a conceptual framework for explaining variation between and within groups by the same genetic and environmental factors.

Estimation of individual genetic and environmental factor scores

Implicit in the application of the common-factor model as a method for decomposing trait covariance into a genetic and environmental part is the use of factor scores. In multivariate analyses, it is possible to estimate these factor scores for the communal part of the model. Estimation of scores on latent factors in terms of individual observations within the context of a twin/family study amounts to estimation of individual genetic and environmental scores. Such estimates may be of both theoretical and practical interest and may be provided with confidence intervals around the individual estimates. The method is first illustrated with stimulated twin data and next is applied to blood pressure data obtained in a Dutch sample of 59 male adolescent twin pairs. Subjects with high blood pressure can be distinguished into groups with high genetic or high environmental scores.

Using factor scores to detect G X E interactive origin of "pure" genetic or environmental factors obtained in genetic covariance structure analysis

Moment expressions for individual factor scores can serve as simple tests for the presence of a particular class of interaction factors that are disguised as pure genetic and/or environmental factors. That is, individual genetic and environmental factor scores may be used to construct fourth-order moments of these factors in order to test whether a common genetic or environmental factor in the multivariate genetic factor model is in fact of the interactive origin concerned. Expected fourth-order moments are derived for cases with and without interaction. Application of fourth-order moments of factor scores to detect interactive origin of common factors is illustrated with simulated twin data.

Testing structural equation models for twin data using LISREL

Simple genetic models can be fitted to twin data using software packages such as LISREL (Jöreskog and Sörbom, 1986a). After discussion of data preparation and routine checks on possible violation of assumptions of the twin method, we illustrate univariate, bivariate, and multivariate genetic models which can be tested in cross-sectional twin data using LISREL. These include models for cohort or cohabitation effects, genotype x sex interaction, and certain types of genotype x environment interaction and genotype-environment correlation.