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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. [ABSTRACT FROM AUTHOR]

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 (Minică 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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The diversity and dominant bacterial taxa in the vagina are reported to be influenced by multiple intrinsic and extrinsic factors, including but not limited to pregnancy, contraceptive use, pathogenic states, socioeconomic status, and ancestry. However, the extent to which host genetic factors influence variation in the vaginal microbiota is unclear. We used a biometrical genetic approach to determine whether host genetic factors contribute to inter-individual differences in taxa from a sample of 332 twins who self-identified as being of African (44 pairs) or European ancestry (122 pairs). Lactobacillus crispatus, a major determinant of vaginal health, was identified as heritable among European American women (narrow-sense heritability = 34.7%, P-value = 0.018). Heritability of L. crispatus is consistent with the reduced prevalence of adverse reproductive disorders, including bacterial vaginosis and preterm birth, among women of European ancestry. Wright et al. apply biometric genetic approach to identify the extent to which host genetic factors influence species-level variation in the vaginal microbiota. Their study suggests that Lactobacillus crispatus, a major determinant of vaginal health, is heritable among European American women, consistent with the reduced prevalence of adverse reproductive disorders in them. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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/). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono- and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but age-appropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA. [ABSTRACT FROM AUTHOR]

The Classical Twin Method (CTM) compares the similarity of monozygotic (MZ) twins with that of dizygotic (DZ) twins to make inferences about the relative importance of genes and environment in the etiology of individual differences. The design has been applied to thousands of traits across the biomedical, behavioral and social sciences and is arguably the most widely used natural experiment known to science. The fundamental assumption of the CTM is that trait relevant environmental covariation within MZ pairs is the same as that found within DZ pairs, so that zygosity differences in within-pair variance must be due to genetic factors uncontaminated by the environment. This equal environments assumption (EEA) has been, and still is hotly contested, and has been mentioned as a possible contributing factor to the missing heritability conundrum. In this manuscript, we introduce a new model for testing the EEA, which we call the Augmented Classical Twin Design which uses identity by descent (IBD) sharing between DZ twin pairs to estimate separate environmental variance components for MZ and DZ twin pairs, and provides a test of whether these are equal. We show through simulation that given large samples of DZ twin pairs, the model provides unbiased estimates of variance components and valid tests of the EEA under strong assumptions (e.g. no epistatic variance, IBD sharing in DZ twins estimated accurately etc.) which may not hold in reality. Sample sizes in excess of 50,000 DZ twin pairs with genome-wide genetic data are likely to be required in order to detect substantial violations of the EEA with moderate power. Consequently, we recommend that the Augmented Classical Twin Design only be applied to datasets with very large numbers of DZ twin pairs (> 50,000 DZ twin pairs), and given the strong assumptions relating to the absence of epistatic variance, appropriate caution be exercised regarding interpretation of the results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Graph The Reverend Dr. Lindon John Eaves was an eminent professor in behavior genetics, with an international reputation as an exceptional scientist. Dr. Eaves was also highly influential in his supervision of graduate and post-doctoral students at the University of Birmingham, the University of Oxford, and subsequently VCU, of whom many have very distinguished careers of their own. Professor Eaves was a distinguished professor, scholar, mentor and friend, but foremost a genuine human being. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more conservative than most twin researchers realize. This makes the numerical Type II error rates higher than theoretically expected. Furthermore, the discrepancy between the observed and expected error rates increases as more variables are included in the analysis and can have profound implications for hypothesis testing and statistical inference. In two simulation studies, we examine the Type I error rates for the Cholesky decomposition and Correlated Factors models. Both show markedly lower than nominal Type I error rates under the null hypothesis, a discrepancy that increases with the number of variables in the model. In addition, we observe slightly biased parameter estimates for the Cholesky decomposition and Correlated Factors models. By contrast, if the variance-covariance matrices for variance components are estimated directly (without constraints), the numerical Type I error rates are consistent with theoretical expectations and there is no bias in the parameter estimates regardless of the number of variables analyzed. We call this the direct symmetric approach. It appears that each model-implied boundary, whether explicit or implicit, increases the discrepancy between the numerical and theoretical Type I error rates by truncating the sampling distributions of the variance components and inducing bias in the parameters. The direct symmetric approach has several advantages over other multivariate twin models as it corrects the Type I error rate and parameter bias issues, is easy to implement in current software, and has fewer optimization problems. Implications for past and future research, and potential limitations associated with direct estimation of genetic and environmental covariance matrices are discussed. [ABSTRACT FROM AUTHOR]

Although experimental studies are regarded as the method of choice for determining causal influences, these are not always practical or ethical to answer vital questions in health and social research (e.g., one cannot assign individuals to a “childhood trauma condition” in studying the causal effects of childhood trauma on depression). Key to solving such questions are observational studies. Mendelian Randomization (MR) is an influential method to establish causality in observational studies. MR uses genetic variants to test causal relationships between exposures/risk factors and outcomes such as physical or mental health. Yet, individual genetic variants have small effects, and so, when used as instrumental variables, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by horizontal pleiotropy, which violates a central assumption of MR. We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments, while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, the approach allows one to employ strong instrumental variables in the form of polygenic scores, guarding against weak instrument bias, and increasing the power to detect causal effects of exposures on potential outcomes. Beside allowing to test pleiotropy directly, incorporating in MR data collected from relatives provide additional within-family data that resolve additional assumptions like random mating, the absence of the gene-environment interaction/covariance, no dyadic effects. Our approach will enhance and extend MR’s range of applications, and increase the value of the large cohorts collected at twin/family registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy. [ABSTRACT FROM AUTHOR]

A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s10519-021-10065-9 [ABSTRACT FROM AUTHOR]

This article reviews current advances in the genetics of substance use disorders (SUDs). Both genetic and environmental sources of risk are required to develop a complete picture of SUD etiology. Genetic sources of risk for SUDs are not highly substance specific in their effects. Genetic and environmental risks for SUDs typically do not only add together but also interact with each other over development. Risk gene identification for SUDs has been difficult, with one recent success in identifying nicotinic receptor variants that affect risk for nicotine dependence. The impact of genetic variants on SUD risk will individually be small. Although genetic epidemiologic methods are giving us an increasingly accurate map of broad causal pathways to SUDs, gene discovery will be needed to identify the specific biological systems. Identifying these risk genes and understanding their action will require large clinical samples, and interaction between these studies and work in model organisms. [ABSTRACT FROM AUTHOR]

Twin and family studies have provided overwhelming evidence for the genetic basis of individual differences in tobacco initiation (TI), regular smoking (RS) and nicotine dependence (ND). However, only a few genes have been reliably associated with ND. We used a finite mixture distribution model to examine the significance and effect size of the association of previously identified and replicated specific variants in the CHRNA5 and CHRNA3 receptor genes with ND, against the background of genetic and environmental risk factors for ND. We hypothesize that additional phenotypic information in relatives who have not been genotyped can be used to increase the power of detecting the genetic variant. The nicotine measures were assessed by personal interview in female, male and opposite sex twin pairs ( N = 4,153) from the population-based Virginia Twin Registry. Three SNPs in the CHRNA5 and CHRNA3 receptor genes, previously shown to be significantly associated with ND in this sample, were replicated in the augmented analyses; they accounted for less than one percent of the genetic variance in liability to ND, which is estimated to be over 50% of the phenotypic variance. The significance of these effects was increased by adding twins with phenotype but without genotype data, but gains are limited and variable. The SNPs associated with ND did not show a significant association with either TI or RS and appear to be specific to the addictive stage of ND, characterized by current smoking and smoking a large amount of cigarettes per day. Furthermore, these SNPs did not appear to be associated with the remaining items comprising the FTND scale. This study confirmed a significant contribution of the CHRNA receptor on different forms of tobacco dependence. However, the genetic variant only accounted for little of the total genetic variance for liability to ND. Including phenotypic data on ungenotyped relatives can improve the statistical power to detect the effects of genetic variants when they contribute to individual differences in the phenotype. [ABSTRACT FROM AUTHOR]

Twin studies indicate that both intelligence and brain structure are moderately to highly heritable. Recent bivariate studies of adult twins also suggest that intelligence and brain morphometry are influenced by shared genetic factors. The current study examines shared genetic and environmental factors between brain morphometry and intelligence in a sample of children and adolescents (twins, twin siblings, and singletons; n = 649, ages 4–19). To extend previous studies, brain morphometric data were parsed into subregions (lobar gray/white matter volumes, caudate nucleus, lateral ventricles) and intelligence into verbal and nonverbal skills (Wechsler Vocabulary and Block Design subtests). Phenotypic relationships between brain volumes and intelligence were small. Verbal skills shared unique environmental effects with gray matter volumes while nonverbal skills shared genetic effects with both global and regional gray and white matter. These results suggest that distinct mechanisms contribute to the small phenotypic relationships between brain volumes and verbal versus nonverbal intelligence. [ABSTRACT FROM AUTHOR]

Using high resolution magnetic resonance imaging data, we examined the interrelationships between eight cerebral lobar volumetric measures via both exploratory and confirmatory factor analyses in a large sample ( N = 484) of pediatric twins and singletons. These analyses suggest the presence of strong genetic correlations between cerebral structures, particularly between regions of like tissue type or in spatial proximity. Structural modeling estimated that most of the variance in all structures is associated with highly correlated lobar latent factors, with differences in genetic covariance and heritability driven by a common genetic factor that influenced gray and white matter differently. Reanalysis including total brain volume as a covariate dramatically reduced the total residual variance and disproportionately influenced the additive genetic variance in all regions of interest. [ABSTRACT FROM AUTHOR]

Following the publication of Purcell’s approach to the modeling of gene by environment interaction in 2002, the interest in G × E modeling in twin and family data increased dramatically. The analytic techniques described by Purcell were designed for use with continuous data. Here we explore the re-parameterization of these models for use with ordinal and binary outcome data. Analysis of binary and ordinal data within the context of a liability threshold model traditionally requires constraining the total variance to unity to ensure identification. Here, we demonstrate an alternative approach for use with ordinal data, in which the values of the first two thresholds are fixed, thus allowing the total variance to change as function of the moderator. We also demonstrate that when using binary data, constraining the total variance to unity for a given value of the moderator is sufficient to ensure identification. Simulation results indicate that analyses of ordinal and binary data can recover both the raw and standardized patterns of results. However, the scale of the results is dependent on the specification of (threshold or variance) constraints rather than the underlying distribution of liability. Example Mx scripts are provided. [ABSTRACT FROM AUTHOR]

Linkage analysis in multivariate or longitudinal context presents both statistical and computational challenges. The permutation test can be used to avoid some of the statistical challenges, but it substantially adds to the computational burden. Utilizing the distributional dependencies between $$ \hat{\pi}$$ (defined as the proportion of alleles at a locus that are identical by descent (IBD) for a pairs of relatives, at a given locus) and the permutation test we report a new method of efficient permutation. In summary, the distribution of $$ \hat{\pi } $$ for a sample of relatives at locus x is estimated as a weighted mixture of $$ \hat{\pi } $$ drawn from a pool of ‘representative’ $$ \hat{\pi } $$ distributions observed at other loci. This weighting scheme is then used to sample from the distribution of the permutation tests at the representative loci to obtain an empirical P-value at locus x (which is asymptotically distributed as the permutation test at loci x). This weighted mixture approach greatly reduces the number of permutation tests required for genome-wide scanning, making it suitable for use in multivariate and other computationally intensive linkage analyses. In addition, because the distribution of $$ \hat{\pi } $$ is a property of the genotypic data for a given sample and is independent of the phenotypic data, the weighting scheme can be applied to any phenotype (or combination of phenotypes) collected from that sample. We demonstrate the validity of this approach through simulation. [ABSTRACT FROM AUTHOR]

Despite the importance of extrapair copulation (EPC) in human evolution, almost nothing is known about the design features of EPC detection mechanisms. We tested for sex differences in EPC inference-making mechanisms in a sample of 203 young couples. Men made more accurate inferences (φmen = 0.66, φwomen = 0.46), and the ratio of positive errors to negative errors was higher for men than for women (1.22 vs. 0.18). Since some may have been reluctant to admit EPC behavior, we modeled how underreporting could have influenced these results. These analyses indicated that it would take highly sex-differentiated levels of underreporting by subjects with trusting partners for there to be no real sex difference. Further analyses indicated that men may be less willing to harbor unresolved suspicions about their partners’ EPC behavior, which may explain the sex difference in accuracy. Finally, we estimated that women underreported their own EPC behavior (10%) more than men (0%). [ABSTRACT FROM AUTHOR]

Several association studies support the hypothesis that genetic variants can modify the influence of environmental factors on behavioral outcomes, i.e., G × E interaction. The case-control design used in these studies is powerful, but population stratification with respect to allele frequencies can give rise to false positive or false negative associations. Stratification with respect to the environmental factors can lead to false positives or false negatives with respect to environmental main effects and G × E interaction effects as well. Here we present a model based on Fulker et al. () and Purcell () for the study of G × E interaction in family-based association designs, in which the effects of stratification can be controlled. Simulations illustrate the power to detect genetic and environmental main effects, and G × E interaction effects for the sib pair design. The power to detect interaction was studied in eight different situations, both with and without the presence of population stratification, and for categorical and continuous environmental factors. Results show that the power to detect genetic and environmental main effects, and G × E interaction effects, depends on the allele frequencies and the distribution of the environmental moderator. Admixture effects of realistic effect size lead only to very small stratification effects in the G × E component, so impractically large numbers of sib pairs are required to detect such stratification. [ABSTRACT FROM AUTHOR]

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes. [ABSTRACT FROM AUTHOR]

Neale and Kendler (, Am J Hum Genet 57:935–953) described 13 comorbidity models, providing the most comprehensive set of alternative hypotheses regarding the possible causes of comorbidity to date. This research note describes an extension of the Neale and Kendler model fitting approach that permits the inclusion of measured covariates. An example of 13 models examining the comorbidity between alcohol dependence and illicit drug dependence is presented. [ABSTRACT FROM AUTHOR]

Considerable evidence from twin and adoption studies indicates that genetic and shared environmental factors play a significant role in the initiation of smoking behavior. Although twin and adoption designs are powerful to detect genetic and environmental influences, they do not provide information on the processes of assortative mating and parent–offspring transmission and their contribution to the variability explained by genetic and/or environmental factors. We examined the role of genetic and environmental factors for smoking initiation using an extended kinship design. This design allows the simultaneous testing of additive and non-additive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission. A dichotomous lifetime smoking measure was obtained from twins and relatives in the Virginia 30,000 sample. Results demonstrate that both genetic and environmental factors play a significant role in the liability to smoking initiation. Major influences on individual differences appeared to be additive genetic and unique environmental effects, with smaller contributions from assortative mating, shared sibling environment, twin environment, cultural transmission and resulting genotype–environment covariance. The finding of negative cultural transmission without dominance led us to investigate more closely two possible mechanisms for the lower parent–offspring correlations compared to the sibling and DZ twin correlations in subsets of the data: (i) age × gene interaction, and (ii) social homogamy. Neither mechanism provided a significantly better explanation of the data, although age regression was significant. This study showed significant heritability, partly due to assortment, and significant effects of primarily non-parental shared environment on smoking initiation. [ABSTRACT FROM AUTHOR]

Twin data can provide valuable insight into the relationship between the stages of phenomena such as disease or substance abuse. Initiation of substance use may be caused by factors that are the same as, partially shared with, or completely independent of those that cause progression from use to abuse. Comparison of rates of progression among the cotwins of twins who do vs. do not initiate provides indirect information about the relationship between initiation and progression. Existing models for this relationship have been difficult to extend because they are usually expressed in terms of explicit integrals. In this paper, the problem is overcome by regarding the analysis of twin data on initiation and progression as a special case of missing data, in which individuals who do not initiate are regarded as having missing data on progression measures. Using the general framework for the analysis of ordinal data with missing values available in Mx makes extensions that include other variables much easier. The effects of continuous covariates such as age on initiation and progression becomes simple. Also facilitated are the examination of initiation and progression in two or more substances, and transition models with two or more steps. The methods are illustrated with data on the effects of cohort on liability to cannabis use and abuse, bivariate analysis of tobacco use and dependence and cannabis use and abuse, and the relationships between initiation of smoking, regular smoking and nicotine dependence. Other suitable applications include the relationship between symptoms and diagnosis, such as fears and the progression to phobia. [ABSTRACT FROM AUTHOR]

The heritability of age at first cigarette was estimated in 5883 Dutch twins and siblings registered with the Netherlands Twin Register. Heritability was 60% for males and 39% for females. Shared environmental influences were found in females only (30%). Linkage analyses were performed on data of 422 DZ twins and siblings from 175 families, forming 368 sibling pairs. Genomic regions that may harbor susceptibility loci for age at first cigarette with LOD score greater than 2 were detected on chromosomes 5, 14 and 22. A simultaneous analysis of these three genomic regions showed that most of the variance was explained by the linkage effect on chromosome 5 (205 cM). This peak encloses the D1A dopamine receptor gene which is a functional candidate gene for smoking behavior. [ABSTRACT FROM AUTHOR]

Although it is of lifelong importance, reading ability is studied primarily in children and adolescents. We examined variation in word recognition in 347 middle-aged male twin pairs. Overall heritability ( a2) was 0.45, and shared environmental influences ( c2) were 0.28. However, parental education moderated heritability such that a2 was 0.21 at the lowest parental education level and 0.69 at the highest level; c2 was 0.52 and 0.00, respectively. This constitutes a parental education × environment interaction. The higher heritability was due to a decrease in the magnitude of shared environmental factors, rather than an increase in the magnitude of genetic factors. Other cognitive studies have reported gene × environment interactions, but patterns may differ as a function of age or specific cognitive abilities. Our results suggest that shared environmental factors in families with low parental education have long-lasting effects on word recognition ability, well beyond any critical period for developing reading proficiency. [ABSTRACT FROM AUTHOR]

Background: Perfectionism may be a premorbid risk factor for eating disorders. Evidence of familial transmission suggests features of perfectionism may be genetically determined. This study examines the structure of perfectionism using classical twin design models. Methods: Independent (IP) and common (CP) pathway models are used to investigate the extent to which genetic and environmental factors can help to identify and differentiate three behavioral domains of Perfectionism as measured by a shortened version of the Multidimensional Perfectionism Scale (MPS) [Frost et al. (1990). Cognit. Ther. Res. 14: 449–468]. Three of the original subscales were included: Personal standards (PS), Doubts about actions (DA), Concern over mistakes (CM). We studied a sample of 1022 paired and unpaired female twins from the Mid-Atlantic Twin Registry. Results: MZ correlations were consistently higher than DZ twin correlations for all three composite subscales. The multivariate independent pathway model provided a better fit to the twin correlations then did the more parsimonious common pathway model suggesting the pattern of familial resemblance for the three subscales is not well characterized by a unidimensional perfectionism factor. CM phenotypic variance was completely accounted for by common heritability influences in both the IP and CP models. Based on the IP model results, there was evidence that PS and CM but not DA shared some common genetic effects, with DA and CM sharing some common environmental factors. Conclusions: These multivariate twin modeling results support conceptualizations of perfectionism as a multidimensional construct. The biometric structural results for the three subscales examined here suggest CM is the core feature of Perfectionism with DA and PS serving as indicators of CM. Although not the best fitting model, the common pathway model estimated this behavioral domain to be isomorphic with the construct of perfectionism. The better fitting independent pathway model provided evidence of non-trivial differences in the pattern of heritability for CM, DA, and PS. [ABSTRACT FROM AUTHOR]

Given that knowledge regarding the etiology of comorbidity between disorders can have a significant impact on research regarding the classification, treatment, and etiology of the disorders, the ability to reject incorrect hypotheses regarding the causes of comorbidity is very important. A simulation study was conducted to assess the validity of the Neale and Kendler (1995) model-fitting approach in examining the etiology of comorbidity between two disorders. First, data were simulated under the assumptions of the 13 alternative comorbidity models described by Neale and Kendler. Second, model-fitting analyses testing the comorbidity models were conducted on the simulated datasets. Thirteen sets of data with varying model parameters were simulated to test Neale and Kendler's assertion that their model-fitting approach is appropriate across a range of potential prevalences and degrees of familiality. The validity of the model-fitting approach in examining unselected twin data and a combination of selected family data and unselected family data was explored. The model-fitting approach successfully discriminated several classes of comorbidity models, although discrimination between models within classes of related models was less accurate. Results suggest that the model-fitting approach can be a useful tool in examining the etiology of the comorbidity between disorders if the caveats of the present study's results are considered carefully. As predicted by Neale and Kendler, variations in the disorder prevalences and familial correlations did not affect the validity of their model-fitting approach, but affected the power to discriminate the correct model. As suggested by Neale and Kendler, the model-fitting approach can be applied to both unselected and selected data and to both twin and family data. [ABSTRACT FROM AUTHOR]

Under certain conditions, cross-sectional analysis of cross-twin intertrait correlations can provide important information about the direction of causation (DOC) between two variables. A community-based sample of Australian female twins aged 18 to 45 years was mailed an extensive Health and Lifestyle Questionnaire (HLQ) that covered a wide range of personality and behavioral measures. Included were self-report measures of recent psychological distress and perceived childhood environment (PBI). Factor analysis of the PBI yielded three interpretable dimensions: Coldness, Overprotection, and Autonomy. Univariate analysis revealed that parental Overprotection and Autonomy were best explained by additive genetic, shared, and nonshared environmental effects (ACE), whereas the best-fitting model for PBI Coldness and the three measures of psychological distress (Depression, Phobic Anxiety, and Somatic Distress) included only additive genetic and nonshared environmental effects (AE). A common pathway model best explained the covariation between (1) the three PBI dimensions and (2) the three measures of psychological distress. DOC modeling between latent constructs of parenting and psychological distress revealed that a model which specified recollected parental behavior as the cause of psychological distress provided a better fit than a model which specified psychological distress as the cause of recollected parental behavior. Power analyses and limitations of the findings are discussed. [ABSTRACT FROM AUTHOR]

Little is known about the etiology of bulimia nervosa and the attitudes associated with it. We have undertaken a study of selected (45 pairs) and unselected (106 pairs) female twins to elucidate the broad causes of individual differences in these behaviours and attitudes. The selected sample was chosen on the basis of at least one of the twin pair having a lifetime incidence of bulimia nervosa. Biometrical model fitting, which corrected for the biased twin correlations of the ascertained group, was used to investigate the genetic and environmental risk factors contributing to the development of bulimia nervosa. The best-fitting model showed that individual variation was best explained by additive genetic influences (62%) and nonshared environmental influences (38%). The proportion of genetic variance affecting individual variation in the ascertained group and the random group was not found to be significantly different. In summary, it is suggested that it may not be necessary to supplement a randomly selected sample with an ascertained sample when investigating the liability to a low-prevalence psychiatric disorder if a continuous measure of that disorder is available. [ABSTRACT FROM AUTHOR]

Not much is known about the genetic and environmental determinants of various aspects of substance use in adolescents. This study examined whether the inheritance of initiation of tobacco use in adolescents is independent of the inheritance of the number of cigarettes smoked. Alternative multifactorial threshold models were applied to data on tobacco use in 1676 Dutch adolescent twin pairs. The three models that were considered are (i) the single liability dimension model, (ii) the independent liability dimension model, and (iii) the combined model (CM). The results showed that there is not one underlying continuum of liability to smoking. The CM was the best-fitting model. This model postulates that there are separate initiation and quantity dimensions but allows for the possibility that there are some individuals who are so low on the liability to level of consumption that they are not using tobacco. There were no differences between males and females in the magnitude of the genetic and environmental influences on individual differences in smoking initiation and quantity smoked. Smoking initiation was influenced by genetic factors (39%) and shared environmental influences (54%). Once smoking is initiated genetic factors determine to a large extent (86%) the quantity that is smoked. [ABSTRACT FROM AUTHOR]

Human obesity is associated with greater-than-average energy intake, although relatively few studies have tested the heritability of food intake. The present study examined the genetic architecture of measured caloric intake during laboratory test meals in 36 monozygotic and 18 dizygotic twin pairs. A series of analyses tested the hypotheses that (1) there would be a genetic influence on total caloric intake, (2) there would be genes influencing total caloric intake above and beyond those influencing body composition, (3) there would be a phenotypic association between total caloric intake and fat mass above and beyond any genetic influences, and (4) there would be genetic influences on macronutrient intake (i.e., fat, carbohydrate, and protein intake) above and beyond total caloric intake. Results suggested genetic influences on age- and sex-adjusted total caloric intake (24–33% of the variance), although 95% confidence intervals were wide and suggested that “true” heritability estimates might be considerably lower or higher. Caloric intake was influenced by both common and unique environmental factors. Greater-than-average caloric intake was associated with increased adiposity, despite probable genetic influences on both phenotypes. Finally, there was evidence for macronutrient-specific familial influences, although the extent to which they were genetic or environmental in origin could not be teased apart. Results suggest that human obesity may be influenced by behaviors that are themselves genetically regulated. However, further studies are needed to obtain more precise heritability estimates and a better understanding of the conditions under which genetic influences on energy intake emerge. [ABSTRACT FROM AUTHOR]

The contribution of size 3 and size 4 sibships to power in covariance structure modeling of a codominant QTL is investigated. Power calculations are based on the noncentral chi-square distribution. Sixteen sets of parameter values are considered. Results indicate that size 3 and size 4 sibships provided large increases in power over size 2 sibships. On average a size 3 (4) sibship is 3 (6 to 7) times as informative as a size 2 sibship. The increase in power does not depend on the specific effects sizes of the independent variables in the model. These findings extend results presented by Fulker and Cherny (1996) and Schork (1993). We consider the informativeness of the size 2, 3, and 4 sibships, which differ in the unique configuration of IBD sharing. Three of the 10 size 3 and 7 of the 36 size 4 sibships are particularly informative. The results presented concern random (unselective) sampling but do have implications for selective sampling. [ABSTRACT FROM AUTHOR]

We review the literature on the familial resemblance of body mass index (BMI) and other adiposity measures and find strikingly convergent results for a variety of relationships. Results from twin studies suggest that genetic factors explain 50 to 90% of the variance in BMI. Family studies generally report estimates of parent–offspring and sibling correlations in agreement with heritabilities of 20 to 80%. Data from adoption studies are consistent with genetic factors accounting for 20 to 60% of the variation in BMI. Based on data from more than 25,000 twin pairs and 50,000 biological and adoptive family members, the weighted mean correlations are .74 for MZ twins, .32 for DZ twins, .25 for siblings, .19 for parent–offspring pairs, .06 for adoptive relatives, and .12 for spouses. Advantages and disadvantages of twin, family, and adoption studies are reviewed. Data from the Virginia 30,000, including twins and their parents, siblings, spouses, and children, were analyzed using a structural equation model (Stealth) which estimates additive and dominance genetic variance, cultural transmission, assortative mating, nonparental shared environment, and special twin and MZ twin environmental variance. Genetic factors explained 67% of the variance in males and females, of which half is due to dominance. A small proportion of the genetic variance was attributed to the consequences of assortative mating. The remainder of the variance is accounted for by unique environmental factors, of which 7% is correlated across twins. No evidence was found for a special MZ twin environment, thereby supporting the equal environment assumption. These results are consistent with other studies in suggesting that genetic factors play a significant role in the causes of individual differences in relative body weight and human adiposity. [ABSTRACT FROM AUTHOR]

This article describes the computation and relative merits of likelihood-based confidence intervals, compared to other measures of error in parameter estimates. Likelihood-based confidence intervals have the advantage of being asymmetric, which is often the case with structural equation models for genetically informative studies. We show how the package Mx provides confidence intervals for parameters and functions of parameters in the context of a simple additive genetic, common, and specific environment threshold model for binary data. Previously published contingency tables for major depression in adult female twins are used for illustration. The support for the model shows a marked skew as the additive genetic parameter is systematically varied from zero to one. The impact of allowing different prevalence rates in MZ vs. DZ twins is explored by fitting a model with separate threshold parameters and comparing the confidence intervals. Despite the improvement in fit of the different prevalences model, the confidence intervals on all parameters broaden, owing to their covariance. [ABSTRACT FROM AUTHOR]

The genetic and environmental influences on problem behaviors at two assessment points, three years apart, and their stability were studied in a sample of international adoptees, initially aged 10 to 15 years. Parents of 111 pairs of adopted biological siblings, 221 pairs of adopted nonbiological siblings and 1484 adopted singletons completed the Child Behavior Checklist (75 pairs, 154 pairs and 1080 singletons respectively at second assessment). At first assessment, genetic factors accounted for more than 50% of the variance in the Externalizing, Aggressive Behavior, Attention Problems and Social Problems scales. Shared environmental influences explained 40% of the variance in the Total Problem scale and less for all other scales. Nonshared environmental influences were most important for the Internalizing scale and its subscales, and for the Thought Problems and Delinquent Behavior scales. At the second assessment, genetic factors explained most of the variance in the Total Problem, Externalizing and Aggressive Behavior scales, while nonshared environmental influences explained most of the variance in all other scales. Shared environmental influences explained 33% of the variance in the Internalizing scale and less for the other scales. The stability of the Externalizing scale over time was caused mostly by genetic factors, while nonshared environmental factors mostly caused the stability of the Internalizing scale. [ABSTRACT FROM AUTHOR]

There is a robust observational relationship between lower birthweight and higher risk of cardiometabolic disease in later life. The Developmental Origins of Health and Disease (DOHaD) hypothesis posits that adverse environmental factors in utero increase future risk of cardiometabolic disease. Here, we explore if a genetic risk score (GRS) of maternal SNPs associated with offspring birthweight is also associated with offspring cardiometabolic risk factors, after controlling for offspring GRS, in up to 26,057 mother–offspring pairs (and 19,792 father–offspring pairs) from the Nord-Trøndelag Health (HUNT) Study. We find little evidence for a maternal (or paternal) genetic effect of birthweight associated variants on offspring cardiometabolic risk factors after adjusting for offspring GRS. In contrast, offspring GRS is strongly related to many cardiometabolic risk factors, even after conditioning on maternal GRS. Our results suggest that the maternal intrauterine environment, as proxied by maternal SNPs that influence offspring birthweight, is unlikely to be a major determinant of adverse cardiometabolic outcomes in population based samples of individuals. Observationally, lower birthweight is a risk factor for cardiometabolic disease. Using Mendelian Randomization, the authors investigate whether maternal genetic factors that lower offspring birthweight also increase offspring cardiometabolic risk and show that the observational correlation is unlikely to be due to the intrauterine environment. [ABSTRACT FROM AUTHOR]

Parental socioeconomic status (SES) is a strong predictor of children's educational achievement (EA), with an increasing effect throughout development. Inequality in educational outcomes between children from different SES backgrounds exists in all Western countries. It has been proposed that a cause of this inequality lies in the interplay between genetic effects and SES on EA, which might depend on society and the equality of the education system. This study adopted two approaches, a classical twin design and polygenic score (PGS) approach, to address the effect of parental SES on EA in a large sample of 12-year-old Dutch twin pairs (2479 MZ and 4450 DZ twin pairs with PGSs for educational attainment available in 2335 children) from the Netherlands Twin Register (NTR). The findings of this study indicated that average EA increased with increasing parental SES. The difference in EA between boys and girls became smaller in the higher SES groups. The classical twin design analyses based on genetic covariance structure modeling pointed to lower genetic, environmental, and thus phenotypic variation in EA at higher SES. Independent from a child's PGS, parental SES predicted EA. However, the strength of the association between PGS and EA did not depend on parental SES. In a within-family design, the twin with a higher PGS scored higher on EA than the co-twin, demonstrating that the effect of the PGS on EA was at least partly independent from parental SES. To conclude, EA depended on SES both directly and indirectly, and SES moderated the additive genetic and environmental components of EA. Adding information from PGS, in addition to parental SES, improved the prediction of children's EA. [ABSTRACT FROM AUTHOR]