Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling

Estimating Local Structural Equation Models

Local structural equation models (LSEM) are structural equation models that study model parameters as a function of a moderator. This article reviews and extends LSEM estimation methods and discusses the implementation in the R package sirt. In previous studies, LSEM was fitted as a sequence of models separately evaluated as each value of the moderator variables. In this article, a joint estimation approach is proposed that is a simultaneous estimation method across all moderator values and also allows some model parameters to be invariant with respect to the moderator. Moreover, sufficient details on the main estimation functions in the R package sirt are provided. The practical implementation of LSEM is demonstrated using illustrative datasets and an empirical example. Moreover, two simulation studies investigate the statistical properties of parameter estimation and significance testing in LSEM.

Evolving the blank slate

We support Uchiyama et al. in the value of genetics, sample diversification, and context measurement. Against the example of vitamins, we highlight the intransigence of many phenotypes. We caution that while culture can mask genetic differences, the dependence of behaviour on genetics is reinvented and unmasked by novel challenges across generations.

Socioeconomic origin, future expectations, and educational achievement: A longitudinal three-generation study of the persistence of family advantage

Expectations about the future direct effort in goal-oriented action and may influence a range of life course outcomes, including educational attainment. Here we investigate whether such expectations are implicated in the dynamics underlying the persistence of educational advantage across family generations, and whether such dynamics have changed in recent decades in view of historical change. Focusing on the role of domain-specific (educational) and general (optimism and control) expectations, we examine parallels across parent-child cohorts in (a) the relationships between parental socioeconomic status (SES) and children's future expectations and (b) the associations between children's future expectations and their academic achievement. We estimate structural equation models using data from the prospective multigenerational Youth Development Study (N = 422 three-generation triads [G1-G2-G3]; G1 Mage in 1988 = 41.0 years, G2 Mage in 1989 = 14.7 years, G3 Mage in 2011 = 15.8 years; G2 White in 1989 = 66.4%, G3 White in 2011 = 64.4%; G1 mean annual household income, converted to 2008 equivalents = $41,687, G2 mean annual household income in 2008 dollars = $42,962; G1 mode of educational attainment = high school, G2 mode of educational attainment = some college). We find intergenerational similarity in the relationships between parental educational attainment and children's future expectations. Children's educational expectations strongly predicted their academic achievement in the second generation, but not in the third generation. With educational expansion, the more recent cohort had higher educational expectations that were less strongly related to achievement. Overall, the findings reveal dynamics underlying the persistence of educational success across generations. The role of future expectations in this intergenerational process varies across historical time, confirming a central conclusion of life span developmental psychology and life course sociological research that individual functioning is influenced by sociocultural contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Two genetic analyses to elucidate causality between body mass index and personality

BACKGROUND/OBJECTIVES

Many personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality.

SUBJECTS/METHODS

We employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation.

RESULTS

In Mendelian randomization, the allele score for BMI predicted domain-general (β = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (β = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (β = -0.05; 95% CI: -0.08, -0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (β = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (β = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25).

CONCLUSIONS

Although both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

Correlated personality change across time and age

The goal of this study was to examine differential and correlated change in personality across the adult lifespan. Studying differential and correlated change can help understand whether intraindividual trait change trajectories deviate from the norm and how these trajectories are coupled with each other. We used data from two large longitudinal panel studies from the United States that covered a total age range of 20 to 95 years on the first measurement occasion. We used correlated factor models and bivariate latent change score models to examine the rank-order stability and correlations between change across three measurement waves covering 18 years ( N = 3250) and four measurement waves covering 12 years ( N = 4145). We examined the moderation effects of continuous age on these model parameters using local structural equation modeling. The results suggest that the test–retest correlations decrease with increasing time between measurements but are unaffected by participants’ age. We found that change processes in Extraversion, Openness, Agreeableness, and Conscientiousness were strongly related, particularly in late adulthood. Correlated change patterns were highly stable across time intervals and similar to the initial cross-sectional Big Five correlations. We discuss potential mechanisms and implications for personality development research.

Genetic and Environmental Influences on Semantic Verbal Fluency Across Midlife and Later Life

Despite the relevance of semantic fluency measures to risk for dementia and psychiatric disorders, little is known about their genetic and environmental architecture in mid-to-late life. Participants represent 21,684 middle-aged and older adult twins (M = 60.84 years, SD = 11.21; Range 40-89) from six studies from three countries participating in the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium. All completed the same measure of semantic fluency (naming animals in 60 seconds). Results revealed small-to-moderate phenotypic associations with age and education, with education more strongly and positively associated with fluency performance in females than males. Heritability and environmental influences did not vary by age. Environmental variance was smaller with higher levels of education, but this effect was observed only in males. This is the largest study to examine the genetic and environmental architecture of semantic fluency, and the first to demonstrate that environmental influences vary based on levels of education.

Adolescent Big Five personality and pubertal development: Pubertal hormone concentrations and self-reported pubertal status

In early adolescence, levels of conscientiousness and agreeableness have been found to temporarily decrease, with levels of neuroticism increasing, indicating a dip in personality maturation. It is unknown whether these changes are related to the process of puberty, a major developmental milestone with numerous changes for children. Here, we first replicated the dip in personality maturity in early adolescence (N = 2640, age range 8-18, 51% girls, 65% non-Hispanic white, 21% Hispanic/Latino, 10% African American, 9% other, roughly 33% of families received means-tested public assistance) and tested associations between the Big Five personality dimensions and pubertal development and timing across late childhood and adolescence (n = 1793). Pubertal development was measured using both hormonal assays (DHEA, testosterone, and progesterone) and self-reports of secondary sex characteristics. Of hormonal measures, only higher DHEA concentrations were associated with lower conscientiousness and openness. Nonparametric moderation analyses using LOSEM indicated Complex Age × Sex interactions involving all three hormones. Self-reported pubertal development was associated with lower extraversion, conscientiousness, agreeableness, and openness. More advanced pubertal timing was also related to lower levels of extraversion, conscientiousness, and agreeableness. All associations were small. As some evidence was found for small associations between pubertal development and lower levels of conscientiousness and agreeableness, a dip in personality maturation in these personality traits may be partly due to pubertal development in early adolescence. Overall, results did not indicate that pubertal development was the primary explanation of the maturity dip in adolescent personality. Many small influences likely accumulate to explain the dip in personality maturity in early adolescence. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Kids becoming less alike: A behavioral genetic analysis of developmental increases in personality variance from childhood to adolescence

Recent work in personality development has indicated that the magnitude of individual differences in personality increases over child development. Do such patterns reflect the differentiation of individuals by genotype, an increasing influence of environmental factors, or some (interactive) combination of the two? Using a population-based sample of over 2,500 twins and multiples from the Texas Twin Project, we estimated age trends in the variances in self- and parent-reported measures of the Big Five personality traits between Ages 8 and 18 years. We then estimated age trends in the genetic and environmental components of variance in each measure. Individual differences in personality increased in magnitude from childhood through mid-adolescence. This pattern emerged using both children's self-reports and ratings provided by their parents, and was primarily attributable to increases in the magnitude of genetic influences. Most of the increasing genetic variance appeared nonadditive, pointing to the possibility that developmental processes tend to make genetically similar individuals disproportionately more alike in their personality traits over time. These findings could reflect increasing or accumulating effects of trait-by-trait interactions; person-by-environment transactions, whereby genetically similar people are disproportionally likely to experience similar environments; the activation of dominant genes across developmental transitions (e.g., puberty); or some combination of these three processes, among other factors. Theories of personality development will need to accommodate these descriptive findings, and longitudinal, genetically informed designs are needed to test some of the specific hypotheses springing from this study. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Testing Cold and Hot Cognitive Control as Moderators of a Network of Comorbid Psychopathology Symptoms in Adolescence

Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents (n = 849). We implemented a method for assessing nonparametric moderation of psychopathology networks to evaluate differences in network structure across levels of intelligence and emotional control. Symptoms generally clustered by clinical diagnoses, but specific between-cluster bridge connections emerged. Internalizing symptoms demonstrated unique connections with aggression symptoms of interpersonal irritability, whereas externalizing symptoms showed more diffuse interconnections. Aggression symptoms identified as bridge nodes in the cross-sectional network were enriched for longitudinal associations with internalizing symptoms. Cross-domain connections did not significantly vary across intelligence but were weaker at lower emotional control. Our findings highlight transdiagnostic symptom relationships that may underlie co-occurrence of clinical diagnoses or higher-order factors of psychopathology.

Genetic and Environmental Influences on Achievement Goal Orientations Shift with Age

Students engage in learning activities with different achievement goal orientations. Some students pursue learning for learning sake (i.e. mastery goal orientation), some are driven by gaining favorable judgement of their performance (i.e. performance approach goal orientation), and others focus on avoiding negative judgement (i.e. performance avoidance goal orientation). These goal orientations are linked with academic achievement, and troublingly, students report decreasing levels of goal orientations across the school years. However, little is known concerning the mechanisms that drive this decline. In a large (N = 891 twin pairs) cross-sectional genetically informative sample (age = 8 to 22 years), we found that older students reported lower goal orientations. Then, we identified shifts in the magnitude of genetic and environmental variance in each goal orientation. For example, variance in mastery goal orientation was primarily associated with environmental factors during the elementary school years. As students entered high school, genetic influences increased, replacing shared environmental influences. Finally, we situated these findings in the larger nomological network by testing associations with psychological constructs (e.g. personality and cognitive ability) and contextual variables (e.g. parents, schools, and peers). The development of academic motivation is complex with many interconnecting factors that appear to shift with age.

umx: Twin and Path-Based Structural Equation Modeling in R

Structural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

Nonlinear Indicator-Level Moderation in Latent Variable Models

Linear, nonlinear, and nonparametric moderated latent variable models have been developed to investigate possible interaction effects between a latent variable and an external continuous moderator on the observed indicators in the latent variable model. Most moderation models have focused on moderators that vary across persons but not across the indicators (e.g., moderators like age and socioeconomic status). However, in many applications, the values of the moderator may vary both across persons and across indicators (e.g., moderators like response times and confidence ratings). Indicator-level moderation models are available for categorical moderators and linear interaction effects. However, these approaches require respectively categorization of the continuous moderator and the assumption of linearity of the interaction effect. In this article, parametric nonlinear and nonparametric indicator-level moderation methods are developed. In a simulation study, we demonstrate the viability of these methods. In addition, the methods are applied to a real data set pertaining to arithmetic ability.

Does the heritability of cognitive abilities vary as a function of parental education? Evidence from a German twin sample

A well-known hypothesis in the behavioral genetic literature predicts that the heritability of cognitive abilities is higher in the presence of higher socioeconomic contexts. However, studies suggest that the effect of socioeconomic status (SES) on the heritability of cognitive ability may not be universal, as it has mostly been demonstrated in the United States, but not in other Western nations. In the present study we tested whether the importance of genetic and environmental effects on cognitive abilities varies as a function of parental education in a German twin sample. Cognitive ability scores (general, verbal, and nonverbal) were obtained on 531 German twin pairs (192 monozygotic, 339 dizygotic, ranging from 7 to 14 years of age; M_age = 10.25, SD = 1.83). Data on parental education were available from mothers and fathers. Results for general cognitive ability and nonverbal ability indicated no significant gene x parental education interaction effect. For verbal ability, a significant nonshared environment (E) x parental education interaction was found in the direction of greater nonshared environmental influences on verbal abilities among children raised by more educated parents.

Genetic and environmental influences on pubertal hormones in human hair across development

Puberty is a complex biopsychosocial process that can affect an array of psychiatric and medical disorders emerging in adolescence. Although the pubertal process is driven by neuroendocrine changes, few quantitative genetic studies have directly measured puberty-relevant hormones. Hair samples can now be assayed for accumulation of hormones over several months. In contrast to more conventional salivary measures, hair measures are not confounded by diurnal variation or hormonal reactivity. In an ethnically and socioeconomically diverse sample of 1286 child and adolescent twins and multiples from 672 unique families, we estimated genetic and environmental influences on hair concentrations of testosterone, DHEA, and progesterone across the period of 8-18 years of age. On average, male DHEA and testosterone were highly heritable, whereas female DHEA, progesterone, and puberty were largely influenced by environmental components. We identified sex-specific developmental windows of maximal heritability in each hormone. Peak heritability for DHEA occurred at approximately 10 years of age for males and females. Peak heritability for testosterone occurred at age 12.5 and 15.2 years for males and females, respectively. Peak heritability for male progesterone occurred at 11.2 years, while the heritability of female progesterone remained uniformly low. The identification of specific developmental windows when genetic signals for hormones are maximized has critical implications for well-informed models of hormone-behavior associations in childhood and adolescence.

Genetic and environmental influences on household financial distress

Heterogeneity of household financial outcomes emerges from various individual and environmental factors, including personality, cognitive ability, and socioeconomic status (SES), among others. Using a genetically informative data set, we decompose the variation in financial management behavior into genetic, shared environmental and non-shared environmental factors. We find that about half of the variation in financial distress is genetically influenced, and personality and cognitive ability are associated with financial distress through genetic and within-family pathways. Moreover, genetic influences of financial distress are highest at the extremes of SES, which in part can be explained by neuroticism and cognitive ability being more important predictors of financial distress at low and high levels of SES, respectively.

ACEt: An R Package for Estimating Dynamic Heritability and Comparing Twin Models

Estimating dynamic effects of age on the genetic and environmental variance components in twin studies may contribute to the investigation of gene-environment interactions, and may provide more insights into more accurate and powerful estimation of heritability. Existing parametric models for estimating dynamic variance components suffer from various drawbacks such as limitation of predefined functions. We present ACEt, an R package for fast estimating dynamic variance components and heritability that may change with respect to age or other moderators. Building on the twin models using penalized splines, ACEt provides a unified framework to incorporate a class of ACE models, in which each component can be modeled independently and is not limited by a linear or quadratic function. We demonstrate that ACEt is robust against misspecification of the number of spline knots, and offers a refined resolution of dynamic behavior of the genetic and environmental components and thus a detailed estimation of age-specific heritability. Moreover, we develop resampling methods for testing twin models with different variance functions including splines, log-linearity and constancy, which can be easily employed to verify various model assumptions. We evaluated the type I error rate and statistical power of the proposed hypothesis testing procedures under various scenarios using simulated datasets. Potential numerical issues and computational cost were also assessed through simulations. We applied the ACEt package to a Finnish twin cohort to investigate age-specific heritability of body mass index and height. Our results show that the age-specific variance components of these two traits exhibited substantially different patterns despite of comparable estimates of heritability. In summary, the ACEt R package offers a useful tool for the exploration of age-dependent heritability and model comparison in twin studies.

Genetic influences on hormonal markers of chronic hypothalamic-pituitary-adrenal function in human hair

BACKGROUND

Cortisol is the primary output of the hypothalamic-pituitary-adrenal (HPA) axis and is central to the biological stress response, with wide-ranging effects on psychiatric health. Despite well-studied biological pathways of glucocorticoid function, little attention has been paid to the role of genetic variation. Conventional salivary, urinary and serum measures are strongly influenced by diurnal variation and transient reactivity. Recently developed technology can be used to measure cortisol accumulation over several months in hair, thus indexing chronic HPA function.

METHOD

In a socio-economically diverse sample of 1070 twins/multiples (ages 7.80-19.47 years) from the Texas Twin Project, we estimated effects of sex, age and socio-economic status (SES) on hair concentrations of cortisol and its inactive metabolite, cortisone, along with their interactions with genetic and environmental factors. This is the first genetic study of hair neuroendocrine concentrations and the largest twin study of neuroendocrine concentrations in any tissue type.

RESULTS

Glucocorticoid concentrations increased with age for females, but not males. Genetic factors accounted for approximately half of the variation in cortisol and cortisone. Shared environmental effects dissipated over adolescence. Higher SES was related to shallower increases in cortisol with age. SES was unrelated to cortisone, and did not significantly moderate genetic effects on either cortisol or cortisone.

CONCLUSIONS

Genetic factors account for sizable proportions of glucocorticoid variation across the entire age range examined, whereas shared environmental influences are modest, and only apparent at earlier ages. Chronic glucocorticoid output appears to be more consistently related to biological sex, age and genotype than to experiential factors that cluster within nuclear families.

Ageing and brain white matter structure in 3,513 UK Biobank participants

Quantifying the microstructural properties of the human brain's connections is necessary for understanding normal ageing and disease. Here we examine brain white matter magnetic resonance imaging (MRI) data in 3,513 generally healthy people aged 44.64–77.12 years from the UK Biobank. Using conventional water diffusion measures and newer, rarely studied indices from neurite orientation dispersion and density imaging, we document large age associations with white matter microstructure. Mean diffusivity is the most age-sensitive measure, with negative age associations strongest in the thalamic radiation and association fibres. White matter microstructure across brain tracts becomes increasingly correlated in older age. This may reflect an age-related aggregation of systemic detrimental effects. We report several other novel results, including age associations with hemisphere and sex, and comparative volumetric MRI analyses. Results from this unusually large, single-scanner sample provide one of the most extensive characterizations of age associations with major white matter tracts in the human brain.

Part of understanding ageing involves knowing how the brain's connecting pathways change in healthy aging. Here, authors provide a detailed characterisation of data from 3513 UK Biobank participants, and show that the microstructure of these pathways becomes more similar with age.

Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models

Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.

Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models

Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene-environment (G×E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametricG×Einteraction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to ∼50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence ofG×Einteractions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.

Biometric Modeling of Gene-Environment Interplay: The Intersection of Theory and Method and Applications for Social Inequality

For more than a decade, biometric moderation models have been used to examine whether genetic and environmental influences on individual differences might vary within the population. These quantitative Gene × Environment interaction models have the potential to elucidate not only when genetic and environmental influences on a phenotype might differ, but also why, as they provide an empirical test of several theoretical paradigms that serve as useful heuristics to explain etiology-diathesis-stress, bioecological, differential susceptibility, and social control. In the current article, we review how these developmental theories align with different patterns of findings from statistical models of gene-environment interplay. We then describe the extant empirical evidence, using work by our own research group and others, to lay out genetically informative plausible accounts of how phenotypes related to social inequality-physical health and cognition-might relate to these theoretical models.