Changes in affect longitudinally mediate associations between emotion regulation strategy use and disordered eating.

BACKGROUND: Trait-level emotion regulation (ER) difficulties are associated with eating disorders (EDs) transdiagnostically. However, little research has examined whether within-person fluctuations in ER longitudinally predict ED behaviors in daily life or the mechanisms of ER effects. Investigating daily ER could help us better understand why people experience ED behaviors at a given time. We examined whether day-to-day changes in adaptive (e.g., cognitive reappraisal) and maladaptive (e.g., rumination) ER longitudinally predicted core ED behaviors (binge eating, purging, dieting) and whether changes in affect mediated effects. METHOD: Female participants (N = 688) ages 15-30 from the Michigan State University Twin Registry reported their adaptive and maladaptive ER use, negative affect (NA), positive affect (PA), binge eating, purging, and dieting on 49 consecutive days. Using structural equation modeling, we examined whether within-person fluctuations in ER predicted same- and next-day ED behaviors and whether changes in affect mediated longitudinal ER effects. RESULTS: Greater maladaptive ER predicted increased likelihood of same-day binge eating and next-day binge eating and purging. The association between maladaptive ER and next-day binge eating and purging was mediated by increased next-day NA. In contrast, dieting was more closely related to changes in PA. Adaptive ER did not predict reduced likelihood of any ED behavior. CONCLUSIONS: Maladaptive ER may longitudinally increase risk for binge eating and purging by amplifying NA. Interventions focused on decreasing maladaptive ER and subsequent NA might help disrupt binge eating-purging cycles. Conversely, results add to evidence that PA fluctuations may play a unique role in maintaining restrictive behaviors. PUBLIC SIGNIFICANCE: Little is known about how daily changes in emotion regulation may impact disordered eating. We found that maladaptive emotion regulation (e.g., rumination) was associated with a higher likelihood of binge eating and purging on the next day because it predicted increased next-day negative affect. In contrast, dieting was more closely tied to fluctuations in positive affect. Targeting daily emotion regulation and affective processes may help disrupt cycles of disordered eating.

Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.

The heritability of blood-based biomarkers related to risk of Alzheimer's disease in a population-based sample of early old-age men.

INTRODUCTION: Despite their increased application, the heritability of Alzheimer's disease (AD)-related blood-based biomarkers remains unexplored. METHODS: Plasma amyloid beta 40 (Aß40), Aß42, the Aß42/40 ratio, total tau (t-tau), and neurofilament light (NfL) data came from 1035 men 60 to 73 years of age (µ = 67.0, SD = 2.6). Twin models were used to calculate heritability and the genetic and environmental correlations between them. RESULTS: Additive genetics explained 44% to 52% of Aß42, Aß40, t-tau, and NfL. The Aß42/40 ratio was not heritable. Aß40 and Aß42 were genetically near identical (rg  = 0.94). Both Aß40 and Aß42 were genetically correlated with NfL (rg  = 0.35 to 0.38), but genetically unrelated to t-tau. DISCUSSION: Except for Aß42/40, plasma biomarkers are heritable. Aß40 and Aß42 share mostly the same genetic influences, whereas genetic influences on plasma t-tau and NfL are largely unique in early old-age men. The absence of genetic associations between the Aßs and t-tau is not consistent with the amyloid cascade hypothesis.

MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives.

Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non-familial sources. Our model extends the MRDoC model (Minica et al. in Behav Genet 48:337-349,  https://doi.org/10.1007/s10519-018-9904-4 , 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al. in Behav Genet 23:29-50,  https://doi.org/10.1007/BF01067552 , 1993).

Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®.

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.

Alzheimer's Disease Polygenic Scores Predict Changes in Episodic Memory and Executive Function Across 12 Years in Late Middle Age.

OBJECTIVE: Alzheimer's disease (AD) is highly heritable, and AD polygenic risk scores (AD-PRSs) have been derived from genome-wide association studies. However, the nature of genetic influences very early in the disease process is still not well known. Here we tested the hypothesis that an AD-PRSs would be associated with changes in episodic memory and executive function across late midlife in men who were cognitively unimpaired at their baseline midlife assessment.. METHOD: We examined 1168 men in the Vietnam Era Twin Study of Aging (VETSA) who were cognitively normal (CN) at their first of up to three assessments across 12 years (mean ages 56, 62, and 68). Latent growth models of episodic memory and executive function were based on 6-7 tests/subtests. AD-PRSs were based on Kunkle et al. (Nature Genetics, 51, 414-430, 2019), p < 5×10-8 threshold. RESULTS: AD-PRSs were correlated with linear slopes of change for both cognitive abilities. Men with higher AD-PRSs had steeper declines in both memory (r = -.19, 95% CI [-.35, -.03]) and executive functioning (r = -.27, 95% CI [-.49, -.05]). Associations appeared driven by a combination of APOE and non-APOE genetic influences. CONCLUSIONS: Memory is most characteristically impaired in AD, but executive functions are one of the first cognitive abilities to decline in midlife in normal aging. This study is among the first to demonstrate that this early decline also relates to AD genetic influences, even in men CN at baseline.

Alcohol use and cognitive aging in middle-aged men: The Vietnam Era Twin Study of Aging.

OBJECTIVE: To determine associations of alcohol use with cognitive aging among middle-aged men. METHOD: 1,608 male twins (mean 57 years at baseline) participated in up to three visits over 12 years, from 2003-2007 to 2016-2019. Participants were classified into six groups based on current and past self-reported alcohol use: lifetime abstainers, former drinkers, very light (1-4 drinks in past 14 days), light (5-14 drinks), moderate (15-28 drinks), and at-risk drinkers (>28 drinks in past 14 days). Linear mixed-effects regressions modeled cognitive trajectories by alcohol group, with time-based models evaluating rate of decline as a function of baseline alcohol use, and age-based models evaluating age-related differences in performance by current alcohol use. Analyses used standardized cognitive domain factor scores and adjusted for sociodemographic and health-related factors. RESULTS: Performance decreased over time in all domains. Relative to very light drinkers, former drinkers showed worse verbal fluency performance, by -0.21 SD (95% CI -0.35, -0.07), and at-risk drinkers showed faster working memory decline, by 0.14 SD (95% CI 0.02, -0.20) per decade. There was no evidence of protective associations of light/moderate drinking on rate of decline. In age-based models, light drinkers displayed better memory performance at advanced ages than very light drinkers (+0.14 SD; 95% CI 0.02, 0.20 per 10-years older age); likely attributable to residual confounding or reverse association. CONCLUSIONS: Alcohol consumption showed minimal associations with cognitive aging among middle-aged men. Stronger associations of alcohol with cognitive aging may become apparent at older ages, when cognitive abilities decline more rapidly.

A cotwin control study of associations between financial hardship and binge eating phenotypes during COVID-19.

BACKGROUND: COVID-19 was associated with significant financial hardship and increased binge eating (BE). However, it is largely unknown whether financial stressors contributed to BE during the pandemic. We used a longitudinal, cotwin control design that controls for genetic/environmental confounds by comparing twins in the same family to examine whether financial hardship during COVID-19 was associated with BE. METHODS: Female twins (N = 158; Mage  = 22.13) from the Michigan State University Twin Registry rated financial stressors (e.g., inability to afford necessities) daily for 49 consecutive days during COVID-19. We first examined whether financial hardship was associated with BE phenotypes across the full sample. We then examined whether cotwins who differed on financial hardship also differed in BE. RESULTS: Participants who experienced greater mean financial hardship across the study had significantly greater dimensional BE symptoms, and participants who experienced greater financial hardship on a given day reported significantly more emotional eating that day. These results were replicated in cotwin control analyses. Twins who experienced more financial hardship than their cotwin across the study reported greater dimensional BE symptoms than their cotwin, and participants who experienced more financial hardship than their cotwin on a given day reported greater emotional eating that day. Results were identical when restricting analyses to monozygotic twins, suggesting associations were not due to genetic confounds. CONCLUSIONS: Results suggest that BE-related symptoms may be elevated in women who experienced financial hardship during COVID-19 independent of potential genetic/environmental confounds. However, additional research in larger samples is needed. PUBLIC SIGNIFICANCE: Little is known regarding how financial difficulties during the COVID-19 pandemic may have contributed to increased binge eating (BE). We found preliminary evidence that financial hardship during COVID-19 may be associated with greater rates of BE-related symptoms even when comparing twins from the same family. While additional research is needed, results suggest that people who experienced financial hardship during COVID-19 may be at increased risk for BE.

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.

Associations between depression and cardiometabolic health: A 27-year longitudinal study.

BACKGROUND: Clarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems. METHODS: The study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse ('baseline') and the longitudinal Vietnam Era Twin Study of Aging ('follow-up'). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)]. RESULTS: Total depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07-1.57), erectile dysfunction (OR 1.32, 95% CI 1.10-1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04-1.53), and sleep apnea (OR 1.40, 95% CI 1.13-1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09-1.60). CONCLUSIONS: A history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.

Associations Between Traumatic Stress, Brain Volumes and Post-traumatic Stress Disorder Symptoms in Children: Data from the ABCD Study.

Reduced volumes in brain regions of interest (ROIs), primarily from adult samples, are associated with posttraumatic stress disorder (PTSD). We extended this work to children using data from the Adolescent Brain Cognitive Development (ABCD) Study® (N = 11,848; Mage = 9.92). Structural equation modeling and an elastic-net (EN) machine-learning approach were used to identify potential effects of traumatic events (TEs) on PTSD symptoms (PTSDsx) directly, and indirectly via the volumes 300 subcortical and cortical ROIs. We then estimated the genetic and environmental variation in the phenotypes. TEs were directly associated with PTSDsx (r = 0.92) in children, but their indirect effects (r < 0.0004)-via the volumes of EN-identified subcortical and cortical ROIs-were negligible at this age. Additive genetic factors explained a modest proportion of the variance in TEs (23.4%) and PTSDsx (21.3%), and accounted for most of the variance of EN-identified volumes of four of the five subcortical (52.4-61.8%) three of the nine cortical ROIs (46.4-53.3%) and cerebral white matter in the left hemisphere (57.4%). Environmental factors explained most of the variance in TEs (C = 61.6%, E = 15.1%), PTSDsx (residual-C = 18.4%, residual-E = 21.8%), right lateral ventricle (C = 15.2%, E = 43.1%) and six of the nine EN-identified cortical ROIs (C = 4.0-13.6%, E = 56.7-74.8%). There is negligible evidence that the volumes of brain ROIs are associated with the indirect effects of TEs on PTSDsx at this age. Overall, environmental factors accounted for more of the variation in TEs and PTSDsx. Whereas additive genetic factors accounted for most of the variability in the volumes of a minority of cortical and in most of subcortical ROIs.

The Heritability of Cortical Folding: Evidence from the Human Connectome Project.

The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.

A daily diary study of emotion regulation as a moderator of negative affect-binge eating associations.

BACKGROUND: While negative affect (NA) typically increases risk for binge eating, the ultimate impact of NA may depend on a person's ability to regulate their emotions. In this daily, longitudinal study, we examined whether emotion regulation (ER) modified the strength of NA-dysregulated eating associations. METHODS: Women (N = 311) from the Michigan State University Twin Registry first reported dimensional binge eating symptoms and broad ER difficulties (e.g., limited emotional awareness, difficulty controlling emotional impulses). Participants then rated use of adaptive (cognitive reappraisal, social sharing, situation modification, and acceptance) and maladaptive (rumination, expressive suppression, and self-criticism) ER strategies, emotional eating (EE), objective binge eating (OBE), and NA once daily for 49 consecutive days. RESULTS: There were several main effects of ER on binge-eating pathology in both between-person (i.e., comparing women who differed on average) and within-person (i.e., examining fluctuations in variables day-to-day) analyses. Between-person, greater broad ER difficulties, greater maladaptive strategy use, and lower adaptive strategy use were all associated with greater binge-eating pathology. Within-person, greater maladaptive strategy use was associated with greater odds of OBE on that day and on the following day. However, neither broad ER difficulties nor use of specific strategies moderated associations between NA and dysregulated eating in between- or within-person analyses. CONCLUSIONS: While ER is independently associated with risk for dysregulated eating, it may not fully mitigate the impact of NA. Additional strategies (e.g., decreasing environmental stressors and increasing social support) may be needed to minimize NA and its impact on dysregulated eating. PUBLIC SIGNIFICANCE: Negative affect (NA; e.g., sadness, guilt) increases dysregulated eating risk. Because NA is sometimes unavoidable, we examined whether emotion regulation (ER; i.e., how a person responds to their emotions) might impact whether NA leads to dysregulated eating. Although more effective ER was associated with less dysregulated eating overall, ER did not impact the association between NA and dysregulated eating. Other approaches may therefore be needed to mitigate NA-dysregulated eating associations.

How nonshared environmental factors come to correlate with heredity.

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype-environment effects) can explain the emergence of observed differences over time. Phenotype-environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype-environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype-environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene-environment correlation over time, the advantages and challenges of including gene-environment correlation in longitudinal twin models, and recommendations for future research.

Best Practices for Binary and Ordinal Data Analyses.

The measurement of many human traits, states, and disorders begins with a set of items on a questionnaire. The response format for these questions is often simply binary (e.g., yes/no) or ordered (e.g., high, medium or low). During data analysis, these items are frequently summed or used to estimate factor scores. In clinical applications, such assessments are often non-normally distributed in the general population because many respondents are unaffected, and therefore asymptomatic. As a result, in many cases these measures violate the statistical assumptions required for subsequent analyses. To reduce the influence of the non-normality and quasi-continuous assessment, variables are frequently recoded into binary (affected-unaffected) or ordinal (mild-moderate-severe) diagnoses. Ordinal data therefore present challenges at multiple levels of analysis. Categorizing continuous variables into ordered categories typically results in a loss of statistical power, which represents an incentive to the data analyst to assume that the data are normally distributed, even when they are not. Despite prior zeitgeists suggesting that, e.g., variables with more than 10 ordered categories may be regarded as continuous and analyzed as if they were, we show via simulation studies that this is not generally the case. In particular, using Pearson product-moment correlations instead of maximum likelihood estimates of polychoric correlations biases the estimated correlations towards zero. This bias is especially severe when a plurality of the observations fall into a single observed category, such as a score of zero. By contrast, estimating the ordinal correlation by maximum likelihood yields no estimation bias, although standard errors are (appropriately) larger. We also illustrate how odds ratios depend critically on the proportion or prevalence of affected individuals in the population, and therefore are sub-optimal for studies where comparisons of association metrics are needed. Finally, we extend these analyses to the classical twin model and demonstrate that treating binary data as continuous will underestimate genetic and common environmental variance components, and overestimate unique environment (residual) variance. These biases increase as prevalence declines. While modeling ordinal data appropriately may be more computationally intensive and time consuming, failing to do so will likely yield biased correlations and biased parameter estimates from modeling them.

Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx.

There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance-covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available-genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, "mxGREML", designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature's current limitations, and our plans for its further development.

Using Multimodel Inference/Model Averaging to Model Causes of Covariation Between Variables in Twins.

OBJECTIVE: To explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters. METHODS: Behavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed. RESULTS: We illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance. CONCLUSIONS: We introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations.

GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS.

Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure's specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM's utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki ( https://jpritikin.github.io/gwsem/ ).

Clarifying the Genetic Influences on Nicotine Dependence and Quantity of Use in Cigarette Smokers.

Nicotine dependence and smoking quantity are both robustly associated with the CHRNA5-A3-B4 gene cluster in the 15q25 region, and SNP rs16969968 in particular. The purpose of this paper is to use structural equation modeling techniques (SEM) to disentangle the complex pattern of relationships between rs16969968, nicotine quantity (as measured by the number of cigarettes an individual smokes per day; CPD) and nicotine dependence (as measured by the Fagerström Test for Nicotine Dependence; FTND). CPD is an indicator, but also a potential cause, of FTND, complicating the interpretation of associations between these constructs and requires a more detailed investigation than standard GWAS or general linear regression models can provide. FTND items and genotypes were collected in four samples, with a combined sample size of 5,373 respondents. A mega-analysis was conducted using a multiple group SEM approach to test competing hypotheses regarding the relationships between the SNP rs16969968, FTND and CPD. In the best fitting model, the FTND items loaded onto two correlated factors. The first, labeled "maintenance," assesses the motivation to maintain constant levels of nicotine through out the day. The second was labeled "urgency" as its items concern the urgency to restore nicotine levels after abstinence. We focus our attention on the "maintenance" factor, of which CPD was an indicator. The best fitting model included a negative feedback loop between the Maintenance factor and CPD. Accordingly, the motivation to maintain higher levels of nicotine increased the quantity of nicotine consumed, which subsequently decreases the maintenance motivation. The fact that the Maintenance-CPD feedback model fits the data best implies that there are at least two biological pathways that lead from rs16969968 to smoking behaviors. The model is consistent with a supply and demand system, which allows individuals to achieve a homeostatic equilibrium for their nicotine concentration.

Incorporating Polygenic Risk Scores in the ACE Twin Model to Estimate A-C Covariance.

The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)-common environmental (C) covariance (σAC) identified. We study the statistical power to reject σAC = 0 in the ACE model and present the results of simulations.