Search

Co-twin studies are an elegant and powerful design that allows controlling for the effect of confounding variables, including genetic and a range of environmental factors. There are several approaches to carry out this design. One of the methods commonly used, when contrasting continuous variables, is to calculate difference scores between members of a twin pair on two associated variables, in order to analyse the covariation of such differences. However, information regarding whether and how the different ways of estimating within-pair difference scores may impact the results is scant. This study aimed to compare the results obtained by different methods of data transformation when performing a co-twin study and test how the magnitude of the association changes using each of those approaches. Data was simulated using a direction of causation model and by fixing the effect size of causal path to low, medium, and high values. Within-pair difference scores were calculated as relative scores for diverse within-pair ordering conditions or absolute scores. Pearson's correlations using relative difference scores vary across the established scenarios (how twins were ordered within pairs) and these discrepancies become larger as the within-twin correlation increases. Absolute difference scores tended to produce the lowest correlation in every condition. Our results show that both using absolute difference scores or ordering twins within pairs, may produce an artificial decrease in the magnitude of the studied association, obscuring the ability to detect patterns compatible with causation, which could lead to discrepancies across studies and erroneous conclusions., (© 2024. The Author(s).)

Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across the study sample. Discerning these processes requires meticulous data collection through an appropriate research design and the use of suitable software to define and fit alternative models. In psychiatry, the co-occurrence of different disorders is common and can stem from various origins. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of two types of comorbidity within the population. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. Identifying the primary disorder is crucial for developing effective treatment plans. This article explores the use of finite mixture models to depict within-sample heterogeneity. We begin with the Direction of Causation (DoC) model for twin data and extend it to a mixture distribution model. This extension allows for the calculation of the likelihood of each individual's data for the two alternate causal directions. Given twin data, there are four possible pairwise combinations of causal direction. Through simulations, we investigate the Direction of Causation Twin Mixture (mixCLPM) model's potential to detect and model heterogeneity due to varying causal directions., Competing Interests: Conflicts of interest None.

The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder, and phobias) are highly prevalent, often onset early, persist throughout life, and cause substantial global disability. Although distinct in their clinical presentations, they likely represent differential expressions of a dysregulated threat-response system. Here we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant ANX risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 of the 58 associated variants were replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism, and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism underlying ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies., Competing Interests: Per Hoffmann receives Salary from the Life & Brain GmbH, Bonn, Germany. James L. Kennedy is a member of the Scientific Advisory Board for Myriad Neuroscience Inc. Ian B. Hickie was an inaugural Commissioner on Australia’s National Mental Health Commission (2012-18). He is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. He is the Chief Scientific Advisor to, and a 5% equity shareholder in, InnoWell Pty Ltd. InnoWell was formed by the University of Sydney (45% equity) and PwC (Australia; 45% equity) to deliver the $30 M Australian Government-funded Project Synergy (2017-20; a three-year program for the transformation of mental health services) and to lead transformation of mental health services internationally through the use of innovative technologies. Andrew M. Mcintosh has received research support from Eli Lilly, Janssen, and The Sackler Trust. AMM has also received speaker fees from Illumina and Janssen. Murray B. Stein has in the past 3 years received consulting income from Acadia Pharmaceuticals, Aptinyx, atai Life Sciences, Boehringer Ingelheim, Bionomics, BioXcel Therapeutics, Clexio, Eisai, EmpowerPharm, Engrail Therapeutics, Janssen, Jazz Pharmaceuticals, and Roche/Genentech. Dr. Stein has stock options in Oxeia Biopharmaceuticals and EpiVario. He is paid for his editorial work on Depression and Anxiety (Editor-in-Chief), Biological Psychiatry (Deputy Editor), and UpToDate (Co-Editor-in-Chief for Psychiatry). He has also received research support from NIH, Department of Veterans Affairs, and the Department of Defense. He is on the scientific advisory board for the Brain and Behavior Research Foundation and the Anxiety and Depression Association of America. Joel Gelernter is named as an inventor on PCT patent application #15/878,640 entitled: “Genotype-guided dosing of opioid agonists,” filed January 24, 2018 and issued on January 26, 2021 as U.S. Patent No. 10,900,082; and is paid for editorial work for the journal “Complex Psychiatry.” Iiris Hovatta received speaker’s honoraria from Lundbeck. Ole A. Andreassen received speaker’s honorarium from Lundbeck and Sunovion, consultant for Cortechs.ai and Precision Health AS. Katharina Domschke has been a member of the Steering Committee Neurosciences, Janssen, Inc. until 2022 and is currently a member of the Board of the German National Society of Psychiatry (DGPPN) and the Neurotorium Editorial Board of the Lundbeck Foundation. Jordan W. Smoller is a member of the Scientific Advisory Board of Sensorium Therapeutics (with equity) and has received an honorarium for an internal seminar Tempus Labs. He is PI of a collaborative study of the genetics of depression and bipolar disorder sponsored by 23andMe for which 23andMe provides analysis time as in-kind support but no payments. Eduard Maron has received research support and has also received speaker fees from Lundbeck. Hans J. Grabe has received travel grants and speakers honoraria from Indorsia, Neuraxpharm, Servier and Janssen Cilag. Henrik Larsson has served as a speaker for Evolan Pharma, Medici and Shire/Takeda and has received research grants from Shire/Takeda; all outside the submitted work. Gerome Breen is an advisory board member for Compass Pathways. Jürgen Deckert is a member of the board of the German Society of Biological Psychiatry and is on the scientific advisory boards of non-profit organizations and foundations. Volker Arolt worked as an advisor for Sanofi-Adventis Germany. Zach Fuller and Xin Wang are employees of 23andMe and hold stock or stock options in 23andMe. All other authors have no competing interests to declare.

Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Epigenome-wide association studies (EWAS) aim to identify differentially methylated loci associated with complex traits and disorders. EWAS of cigarette smoking shows some of the most widespread DNA methylation (DNAm) associations in blood. However, traditional EWAS cannot differentiate between causation and confounding, leading to ambiguity in etiological interpretations. Here, we apply an integrated approach combining Mendelian Randomization and twin-based Direction-of-Causation analyses (MR-DoC) to examine causality underlying smoking-associated blood DNAm changes in the Netherlands Twin Register (N=2577). Evidence across models suggests that current smoking's causal effects on DNAm likely drive many of the previous EWAS findings, implicating functional pathways relevant to several adverse health outcomes of smoking, including hemopoiesis, cell- and neuro-development, and immune regulation. Additionally, we find evidence of potential reverse causal influences at some DNAm sites, with 17 of these sites enriched for gene regulatory functional elements in the brain. The top three sites with evidence of DNAm's effects on smoking annotate to genes involved in G protein-coupled receptor signaling ( GNG7 , RGS3 ) and innate immune response ( SLC15A4 ), elucidating potential biological risk factors for smoking. This study highlights the utility of integrating genotypic and DNAm measures in twin cohorts to clarify the causal relationships between health behaviors and blood DNAm., Competing Interests: Conflicts of Interest Nothing to declare.

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.

Common genetic variation has been associated with multiple phenotypic features in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous phenotypic spectrum is limited. Here, we developed and implemented a structural equation modelling framework to directly model genomic covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent with a case-only design. We identified three independent genetic factors most strongly linked to language performance, behaviour and developmental motor delay, respectively, studying an autism community sample (N = 5331). The three-factorial structure was largely confirmed in independent ASD-simplex families (N = 1946), although we uncovered, in addition, simplex-specific genetic overlap between behaviour and language phenotypes. Multivariate models across cohorts revealed novel associations, including links between language and early mastering of self-feeding. Thus, the common genetic architecture in ASD is multi-dimensional with overarching genetic factors contributing, in combination with ascertainment-specific patterns, to phenotypic heterogeneity., (© 2024. The Author(s).)

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)., (© 2022. The Author(s).)

Several abilities outside literacy proper are associated with reading and spelling, both phenotypically and genetically, though our knowledge of multivariate genomic covariance structures is incomplete. Here, we introduce structural models describing genetic and residual influences between traits to study multivariate links across measures of literacy, phonological awareness, oral language, and phonological working memory (PWM) in unrelated UK youth (8-13 years, N = 6453). We find that all phenotypes share a large proportion of underlying genetic variation, although especially oral language and PWM reveal substantial differences in their genetic variance composition with substantial trait-specific genetic influences. Multivariate genetic and residual trait covariance showed concordant patterns, except for marked differences between oral language and literacy/phonological awareness, where strong genetic links contrasted near-zero residual overlap. These findings suggest differences in etiological mechanisms, acting beyond a pleiotropic set of genetic variants, and implicate variation in trait modifiability even among phenotypes that have high genetic correlations., (© 2021. The Author(s).)

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.

Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

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.

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/ ).

Objectives: Little is known about the early recovery phase occurring at-home after anesthesia and surgery in ambulatory surgical patients. We studied quantitative oximetry and quality-of-life metrics in the first 48 hours after same-day orthopedic surgery examining the association between the recovery metrics and specific patient and procedural factors., Methods: We used the STOP-Bang score to quantify patient risk for obstructive sleep apnea in 50 adult patients at 2 centers using continuous portable oximetry and patient journaling. Parametric statistical procedures were used to assess relationships among patient and procedural factors and desaturation events., Results: Higher STOP-Bang scores were predictive of the number and duration of desaturation events below mild and severe thresholds for arterial oxygen saturation during their first 48 hours after discharge from ambulatory surgery. Older patients and patients with higher BMI in particular were at an increased risk of mild and severe arterial oxygen desaturation. Using a home CPAP reduced the number of desaturation events. Of interest, taking opiate analgesics decreased the number of desaturation events., Conclusions: Given the absence of systematic research of early ambulatory anesthesia/surgery recovery at home and concerns of postoperative respiratory events, our results have clear implications for patient safety. Our results imply that screening based on noninvasive STOP-Bang scores may allow for suggestions for recovery from ambulatory surgery, such as encouraging patients with high scores to use home CPAP and aggressive education regarding use of opiates., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.)

Major depression (MD) is a debilitating mental health condition with peak prevalence occurring early in life. Genome-wide examination of DNA methylation (DNAm) offers an attractive complement to studies of allelic risk given it can reflect the combined influence of genes and environment. The current study used monozygotic twins to identify differentially and variably methylated regions of the genome that distinguish twins with and without a lifetime history of early-onset MD. The sample included 150 Caucasian monozygotic twins between the ages of 15 and 20 (73% female; Mage = 17.52 SD = 1.28) who were assessed during a developmental stage characterized by relatively distinct neurophysiological changes. All twins were generally healthy and currently free of medications with psychotropic effects. DNAm was measured in peripheral blood cells using the Infinium Human BeadChip 450 K Array. MD associations with early-onset MD were detected at 760 differentially and variably methylated probes/regions that mapped to 428 genes. Genes and genomic regions involved neural circuitry formation, projection, functioning, and plasticity. Gene enrichment analyses implicated genes related to neuron structures and neurodevelopmental processes including cell-cell adhesion genes (e.g., PCDHA genes). Genes previously implicated in mood and psychiatric disorders as well as chronic stress (e.g., NRG3) also were identified. DNAm regions associated with early-onset MD were found to overlap genetic loci identified in the latest Psychiatric Genomics Consortium meta-analysis of depression. Understanding the time course of epigenetic influences during emerging adulthood may clarify developmental phases where changes in the DNA methylome may modulate individual differences in MD risk.

Anxiety disorders (ANX), namely generalized anxiety, panic disorder, and phobias, are common, etiologically complex syndromes that show increasing prevalence and comorbidity throughout adolescence and beyond. Few genome-wide association studies (GWAS) examining ANX risk have been published and almost exclusively in individuals of European ancestry. In this study, we phenotyped participants from the Army Study To Assess Risk and Resilience in Servicemembers (STARRS) to approximate DSM-based ANX diagnoses. We factor-analyzed those to create a single dimensional anxiety score for each subject. GWAS were conducted using that score within each of three ancestral groups (EUR, AFR, LAT) and then meta-analyzed across ancestries (N Total = 16,510). We sought to (a) replicate prior ANX GWAS findings in ANGST; (b) determine whether results extended to other ancestry groups; and (c) meta-analyze with ANGST for increased power to identify novel susceptibility loci. No reliable genome-wide significant SNP associations were detected in STARRS. However, SNPs within the CAMKMT gene located in region 2p21 associated with shared ANX risk in ANGST were replicated in EUR soldiers but not other ancestry groups. Combining EUR STARRS and ANGST (N = 28,950) yielded a more robust 2p21 association signal (p = 9.08x10 -11 ). Gene-based analyses supported three genes within 2p21 and LBX1 on chromosome 10. More powerful ANX genetic studies will be required to identify further loci., (© 2019 Wiley Periodicals, Inc.)

Background: Internalizing disorders (IDs), consisting of syndromes of anxiety and depression, are common, debilitating conditions often beginning early in life. Various trait-like psychological constructs are associated with IDs. Our prior analysis identified a tripartite model of Fear/Anxiety, Dysphoria, and Positive Affect among symptoms of anxiety and depression and the following constructs in youth: anxiety sensitivity, fearfulness, behavioral activation and inhibition, irritability, neuroticism, and extraversion. The current study sought to elucidate their overarching latent genetic and environmental risk structure., Methods: The sample consisted of 768 juvenile twin subjects ages 9-14 assessed for the nine, abovementioned measures. We compared two multivariate twin models of this broad array of phenotypes., Results: A hypothesis-driven, common pathway twin model reflecting the tripartite structure of the measures were fit to these data. However, an alternative independent pathway model provided both a better fit and more nuanced insights into their underlying genetic and environmental risk factors., Conclusions: Our findings suggest a complex latent genetic and environmental structure to ID phenotypes in youth. This structure, which incorporates both clinical symptoms and various psychological traits, informs future phenotypic approaches for identifying specific genetic and pathophysiological mechanisms underlying ID risk., (© 2020 Wiley Periodicals, Inc.)

Professor Nicholas (Nick) Martin spearheaded initial investigations into the genetic basis of political attitudes and behaviors, demonstrating that behaviors that are perceived as socially constructed could have a biological basis. As he showed, the typical mode of inheritance for political attitudes consists of approximately equal proportions of variance from additive genetic, shared environmental and unique environmental sources. This differs from other psychological variables, such as personality traits, which tend to be characterized by genetic and unique environmental sources of variation. By treating political attitudes as a model phenotype, Nick Martin was able to leverage the unique pattern of observed intergenerational transmission for political attitudes to reexamine the quintessential assumptions of the classical twin model. Specifically, by creatively leveraging the nuances of the genetic architecture of political attitudes, he was able to demonstrate the robustness of the equal environments assumption and suggest corrections to account for assortative mating. These advances have had a substantial impact on both the fields of political science, as well as behavioral and quantitative genetics.

Background: Substance use, substance use disorders (SUDs), and psychiatric disorders commonly co-occur. Genetic risk common to these complex traits is an important explanation; however, little is known about how polygenic risk for tobacco or alcohol use overlaps the genetic risk for the comorbid SUDs and psychiatric disorders., Methods: We constructed polygenic risk scores (PRSs) using GWAS meta-analysis summary statistics from a large discovery sample, GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN), for smoking initiation (SI; N = 631,564), age of initiating regular smoking (AI; N = 258,251), cigarettes per day (CPD; N = 258,999), smoking cessation (SC; N = 312,273), and drinks per week (DPW; N = 527,402). We then estimated the fixed effect of these PRSs on the liability to 15 phenotypes related to tobacco and alcohol use, substance use disorders, and psychiatric disorders in an independent target sample of Australian adults., Results: After adjusting for multiple testing, 10 of 75 combinations of discovery and target phenotypes remained significant. PRS-SI (R 2 range: 1.98%-5.09 %) was positively associated with SI, DPW, and with DSM-IV and FTND nicotine dependence, and conduct disorder. PRS-AI (R 2 : 3.91 %) negatively associated with DPW. PRS-CPD (R 2 : 1.56 %-1.77 %) positively associated with DSM-IV nicotine dependence and conduct disorder. PRS-DPW (R 2 : 3.39 %-6.26 %) positively associated with only DPW. The variation of DPW was significantly influenced by sex*PRS-SI, sex*PRS-AI and sex*PRS-DPW. Such interaction effect was not detected in the other 14 phenotypes., Conclusions: Polygenic risks associated with tobacco use are also associated with liability to alcohol consumption, nicotine dependence, and conduct disorder., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background and Aims: The non-medical use of over-the-counter or prescribed analgesics (NMUA) is a significant public health problem. Little is known about the genetic and environmental etiology of NMUA and how these risks relate to other classes of substance use and misuse. Our aims were to estimate the heritability NMUA and sources of genetic and environmental covariance with cannabis and nicotine use, cannabis and alcohol use disorders and nicotine dependence in Australian twins., Design: Biometrical genetic analyses or twin methods using structural equation univariate and multivariate modeling., Setting: Australia., Participants: A total of 2007 young adult twins [66% female; μ age  = 25.9, standard deviation (SD) = 3.6, range = 18-38] from the Brisbane Longitudinal Twin Study retrospectively assessed between 2009 and 2016., Measurements: Self-reported NMUA (non-opioid or opioid-based), life-time nicotine, cannabis and opioid use, DSM-V cannabis and alcohol use disorders and the Fagerström Test for Nicotine Dependence., Findings: Life-time NMUA was reported by 19.4% of the sample. Univariate heritability explained 46% [95% confidence interval (CI) = 0.29-0.57] of the risks in NMUA. Multivariate analyses revealed that NMUA is moderately associated genetically with cannabis (r g  = 0.41) and nicotine (r g  = 0.45) use and nicotine dependence (r g  = 0.34). In contrast, the genetic correlations with cannabis (r g  = 0.15) and alcohol (r g  = 0.07) use disorders are weak., Conclusions: In young male and female adults in Australia, the non-medical use of over-the-counter or prescribed analgesics appears to have moderate heritability. NMUA is moderately associated with cannabis and nicotine use and nicotine dependence. Its genetic etiology is largely distinct from that of cannabis and alcohol use disorders., (© 2019 Society for the Study of Addiction.)

The modulation of the startle response (SR) by threatening stimuli (fear-potentiated startle; FPS) is a proposed endophenotype for disorders of the fearful-fearlessness spectrum. FPS has failed to show evidence of heritability, raising concerns. However, metrics used to index FPS-and, importantly, other conditional phenotypes that are dependent on a baseline-may not be suitable for the approaches used in genetic epidemiology studies. Here, we evaluated multiple metrics of FPS in a population-based sample of preadolescent twins (N = 569 from 320 twin pairs, M age = 11.4) who completed a fear-conditioning paradigm with airpuff-elicited SR on two occasions (~1 month apart). We applied univariate and multivariate biometric modeling to estimate the heritability of FPS using several proposed standardization procedures. This was extended with data simulations to evaluate biases in heritability estimates of FPS (and similar metrics) under various scenarios. Consistent with previous studies, results indicated moderate test-retest reliability (r = 0.59) and heritability of the overall SR (h 2 = 34%) but poor reliability and virtually no unique genetic influences on FPS when considering a raw or standardized differential score that removes baseline SR. Simulations demonstrated that the use of differential scores introduces bias in heritability estimates relative to jointly analyzing baseline SR and FPS in a multivariate model. However, strong dependency of FPS on baseline levels makes unique genetic influences virtually impossible to detect regardless of methodology. These findings indicate that FPS and other conditional phenotypes may not be well suited to serve as endophenotypes unless such codependency can be disentangled., (© 2019 Society for Psychophysiological Research.)

Background: Co-morbid substance use is very common. Despite a historical focus using genetic epidemiology to investigate comorbid substance use and misuse, few studies have examined substance-substance associations using polygenic risk score (PRS) methods., Methods: Using summary statistics from the largest substance use GWAS to date (258,797- 632,802 subjects), GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), we constructed PRSs for smoking initiation (PRS-SI), age of initiation of regular smoking (PRS-AI), cigarettes per day (PRS-CPD), smoking cessation (PRS-SC), and drinks per week (PRS-DPW). We then estimated the fixed effect of individual PRSs on 22 lifetime substance use and substance use disorder phenotypes collected in an independent sample of 2463 young Australian adults using genetic restricted maximal likelihood (GREML) in Genome-wide Complex Trait Analysis (GCTA), separately in females, males and both sexes together., Results: After accounting for multiple testing, PRS-SI significantly explained variation in the risk of cocaine (0.67%), amphetamine (1.54%), hallucinogens (0.72%), ecstasy (1.66%) and cannabis initiation (0.97%), as well as DSM-5 alcohol use disorder (0.72%). PRS-DPW explained 0.75%, 0.59% and 0.90% of the variation of cocaine, amphetamine and ecstasy initiation respectively. None of the 22 phenotypes including emergent classes of substance use were significantly predicted by PRS-AI, PRS-CPD, and PRS-SC., Conclusions: To our knowledge, this is the first study to report significant genetic overlap between the polygenic risks for smoking initiation and alcohol consumption and the risk of initiating major classes of illicit substances. PRSs constructed from large discovery GWASs allows the detection of novel genetic associations., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This study uses novel approaches to examine genetic and environmental influences shared between childhood behavioral inhibition (BI) and symptoms of preadolescent anxiety disorders. Three hundred and fifty-two twin pairs aged 9-13 and their mothers completed questionnaires about BI and anxiety symptoms. Biometrical twin modeling, including a direction-of-causation design, investigated genetic and environmental risk factors shared between BI and social, generalized, panic and separation anxiety. Social anxiety shared the greatest proportion of genetic (20%) and environmental (16%) variance with BI with tentative evidence for causality. Etiological factors underlying BI explained little of the risk associated with the other anxiety domains. Findings further clarify etiologic pathways between BI and anxiety disorder domains in children.

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.

Objective: Youth with psychiatric disorders distinguished by irritability, including depression and associated trait neuroticism, show deficits in the ability to recognize facial expressions of emotion, particularly happiness. However, the contribution of genetic and environmental factors to this ability remains unknown. The present study examined this trait in twins to assess the genetic and environmental influences on face-emotion recognition abilities and their association with irritability, neuroticism, and depression., Method: Child and adolescent twins (N = 957 from 496 families) 9 to 17 years old rated their irritability (on the Affective Reactivity Index), neuroticism (on the Junior Eysenck Personality Questionnaire), and depression (on the Short Mood and Feelings Questionnaire) and completed a face-emotion labeling task. Faces depicting anger, disgust, fear, happiness, sadness, and surprise were morphed with a neutral face, yielding 10 levels of increasing emotional expressivity. Biometrical twin analyses evaluated contributions of genetic and environmental factors to the etiology of face-emotion recognition and its association with irritability, neuroticism, and depression., Results: Recognition of each emotion was heritable; common and specific sets of genetic factors influenced all emotions and individual emotions, respectively. Irritability, neuroticism, and depression were modestly and negatively correlated with emotion recognition, particularly the recognition of happiness. For irritability and neuroticism, this correlation appeared largely due to genetic factors., Conclusion: This study maps genetic and environmental contributions to face-emotion recognition and its association with irritability, neuroticism, and depression. Findings implicate common genetic factors in deficits regarding the recognition of happiness associated with irritability and neuroticism in childhood and adolescence., (Copyright © 2018 American Academy of Child and Adolescent Psychiatry. All rights reserved.)

The goal of the present investigation was to clarify and compare the structure of genetic and environmental influences on different types (e.g., physical, verbal) of peer victimization experienced by youth in pre-/early adolescence and mid-/late adolescence. Physical, verbal, social, and property-related peer victimization experiences were assessed in two twin samples (306 pairs, ages 9-14 and 294 pairs, ages 15-20). Cholesky decompositions of individual differences in victimization were conducted, and independent pathway (IP) and common pathway (CP) twin models were tested in each sample. In the younger sample, a Cholesky decomposition best described the structure of genetic and environmental contributors to peer victimization, with no evidence that common additive genetic or environmental factors influence different types of peer victimization. In the older sample, common environmental factors influenced peer victimization types via a general latent liability for peer victimization (i.e., a CP model). Whereas the pre-/early adolescent sample demonstrated no evidence of a shared genetic and environmental structure for different types of peer victimization, the mid-/late adolescent sample demonstrates the emergence of an environmentally-driven latent liability for peer victimization across peer victimization types.

Drinking alcohol is a normal behavior in many societies, and prior studies have demonstrated it has both genetic and environmental sources of variation. Using two very large samples of twins and their first-degree relatives (Australia ≈ 20,000 individuals from 8,019 families; Virginia ≈ 23,000 from 6,042 families), we examine whether there are differences: (1) in the genetic and environmental factors that influence four interrelated drinking behaviors (quantity, frequency, age of initiation, and number of drinks in the last week), (2) between the twin-only design and the extended twin design, and (3) the Australian and Virginia samples. We find that while drinking behaviors are interrelated, there are substantial differences in the genetic and environmental architectures across phenotypes. Specifically, drinking quantity, frequency, and number of drinks in the past week have large broad genetic variance components, and smaller but significant environmental variance components, while age of onset is driven exclusively by environmental factors. Further, the twin-only design and the extended twin design come to similar conclusions regarding broad-sense heritability and environmental transmission, but the extended twin models provide a more nuanced perspective. Finally, we find a high level of similarity between the Australian and Virginian samples, especially for the genetic factors. The observed differences, when present, tend to be at the environmental level. Implications for the extended twin model and future directions are discussed.

Background: Anesthesia machines are known reservoirs of bacterial species, potentially contributing to healthcare associated infections (HAIs). An inexpensive, disposable, nonpermeable, transparent anesthesia machine wrap (AMW) may reduce microbial contamination of the anesthesia machine. This study quantified the density and diversity of bacterial species found on anesthesia machines after terminal cleaning and between cases during actual anesthesia care to assess the impact of the AMW. We hypothesized reduced bioburden with the use of the AMW., Methods: In a prospective, experimental research design, the AMW was used in 11 surgical cases (intervention group) and not used in 11 control surgical cases. Cases were consecutively assigned to general surgical operating rooms. Seven frequently touched and difficult to disinfect "hot spots" were cultured on each machine preceding and following each case. The density and diversity of cultured colony forming units (CFUs) between the covered and uncovered machines were compared using Wilcoxon signed-rank test and Student's t -tests., Results: There was a statistically significant reduction in CFU density and diversity when the AMW was employed., Conclusion: The protective effect of the AMW during regular anesthetic care provides a reliable and low-cost method to minimize the transmission of pathogens across patients and potentially reduces HAIs.

Although borderline personality disorder (BPD) traits decline from adolescence to adulthood, comorbid psychopathology such as symptoms of major depressive disorder (MDD), alcohol use disorder (AUD), and drug use disorders (DUDs) likely disrupt this normative decline. Using a longitudinal sample of female twins (N = 1,763), we examined if levels of BPD traits were correlated with changes in MDD, AUD, and DUD symptoms from ages 14 to 24. A parallel process biometric latent growth model examined the contributions of genetic and environmental factors to the relationships between developmental components of these phenotypes. Higher BPD trait levels predicted a greater rate of increase in AUD and DUD symptoms, and higher AUD and DUD symptoms predicted a slower rate of decline of BPD traits from ages 14 to 24. Common genetic influences accounted for the associations between BPD traits and each disorder, as well as the interrelationships of AUD and DUD symptoms. Both genetic and nonshared environmental influences accounted for the correlated levels between BPD traits and MDD symptoms, but solely environmental influences accounted for the correlated changes between the two over time. Results indicate that higher levels of BPD traits may contribute to an earlier onset and faster escalation of AUD and DUD symptoms, and substance use problems slow the normative decline in BPD traits. Overall, our data suggests that primarily genetic influences contribute to the comorbidity between BPD features and substance use disorder symptoms. We discuss our data in the context of two major theories of developmental psychopathology and comorbidity.

Understanding the factors that contribute to behavioral traits is a complex task, and partitioning variance into latent genetic and environmental components is a useful beginning, but it should not also be the end. Many constructs are influenced by their contextual milieu, and accounting for background effects (such as gene-environment correlation) is necessary to avoid bias. This study introduces a method for examining the interplay between traits, in a longitudinal design using differential items in sibling pairs. The model is validated via simulation and power analysis, and we conclude with an application to paternal praise and ADHD symptoms in a twin sample. The model can help identify what type of genetic and environmental interplay may contribute to the dynamic relationship between traits using a cross-lagged panel framework. Overall, it presents a way to estimate and explicate the developmental interplay between a set of traits, free from many common sources of bias.

Recognizing others' emotional expressions is vital for socioemotional development; impairments in this ability occur in several psychiatric disorders. Further study is needed to map the development of this ability and to evaluate its components as potential transdiagnostic endophenotypes. Before doing so, however, research is required to substantiate the test-retest reliability of scores of the face emotion identification tasks linked to developmental psychopathology. The current study estimated test-retest reliability of scores of one such task, the facial expression labeling task (FELT) among a sample of twin children (N = 157; ages 9-14). Participants completed the FELT at two visits two to five weeks apart. Participants discerned the emotion presented of faces depicting six emotions (i.e., happiness, anger, sadness, fear, surprise, and disgust) morphed with a neutral face to provide 10 levels of increasing emotional expressivity. The present study found strong test-retest reliability (Pearson r) of the FELT scores across all emotions. Results suggested that data from this task may be effectively analyzed using a latent growth curve model to estimate overall ability (i.e., intercept; r's = 0.76-0.85) and improvement as emotions become clearer (i.e., linear slope; r's = 0.69-0.83). Evidence of high test-retest reliability of this task's scores informs future developmental research and the potential identification of transdiagnostic endophenotypes for child psychopathology. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because fitting a SEM at each single nucleotide polymorphism (SNP) along the genome was hitherto considered to be too computationally demanding. By developing a method that can efficiently fit SEMs, it is possible to expand the set of models that can be tested. This is particularly necessary in psychiatric and behavioral genetics, where the statistical methods are often handicapped by phenotypes with large components of stochastic variance. Due to the enormous amount of data that genome-wide scans produce, the statistical methods used to analyze the data are relatively elementary and do not directly correspond with the rich theoretical development, and lack the potential to test more complex hypotheses about the measurement of, and interaction between, comorbid traits. In this paper, we present a method to test the association of a SNP with multiple phenotypes or a latent construct on a genome-wide basis using a diagonally weighted least squares (DWLS) estimator for four common SEMs: a one-factor model, a one-factor residuals model, a two-factor model, and a latent growth model. We demonstrate that the DWLS parameters and p-values strongly correspond with the more traditional full information maximum likelihood parameters and p-values. We also present the timing of simulations and power analyses and a comparison with and existing multivariate GWAS software package.

Power is a ubiquitous, though often overlooked, component of any statistical analyses. Almost every funding agency and institutional review board requires that some sort of power analysis is conducted prior to data collection. While there are several excellent on line power calculators for independent observations, twin studies pose unique challenges that are not incorporated into these algorithms. The goal of the current manuscript is to outline a general method for calculating power in twin studies, and to provide functions to allow researchers to easily conduct power analyses for a range of common twin models. Several scenarios are discussed to demonstrate the importance of various factors that influence the power within the classical twin design and to serve as examples for the provided functions.

To investigate the utility of longitudinal data in genetic analyses of symptoms of anxiety and depression, we assessed individual differences between age 7 and 18 using growth mixture models, and investigated the genetic and non-genetic factors contributing to the trajectories. Mothers of 7,706 girl and 7,418 boy twins from the Netherlands Twin Register rated the anxious depression scale (SxAnxDep) of the Child Behavior Check List (CBCL) at age 7, 10, and 12 years. Two thousand seven hundred and six girl and 1,856 boy twins completed the Youth Self Report (YSR) at age 14, 16, and 18. While individual trajectories varied considerably, these differences were largely idiosyncratic and could not be grouped into separate latent classes with class-specific average growth curves. The intercept, which reflects the individuals' baseline level of SxAnxDep across time, explained 55-58% of the total phenotypic variance. The slope factor, which captures a common average trend over time, did not explain variance in the phenotype. This finding also underlines the high level of idiosyncrasy of trajectories that lack a common longitudinal structure. The analyses of twin data showed that the random intercept factor of SxAnxDep during childhood and during adolescence is considerably more heritable than the observations at any single age, namely between 60% and 84%. One explanation is that different factors contribute to the level of symptoms of anxiety and depression at any given time point, including temporary events and emotions. When considering baseline stability, these temporary influences average out, with the result of a more reliable and more heritable phenotype. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

P values have become the scapegoat for a wide variety of problems in science. P values are generally over-emphasized, often incorrectly applied, and in some cases even abused. However, alternative methods of hypothesis testing will likely fall victim to the same criticisms currently leveled at P values if more fundamental changes are not made in the research process. Increasing the general level of statistical literacy and enhancing training in statistical methods provide a potential avenue for identifying, correcting, and preventing erroneous conclusions from entering the academic literature and for improving the general quality of patient care.

In the classical twin study, phenotypic variation is often partitioned into additive genetic (A), common (C) and specific environment (E) components. From genetical theory, the outcome of genotype by environment interaction is expected to inflate A when the interacting factor is shared (i.e., C) between the members of a twin pair. We show that estimates of both A and C can be inflated. When the shared interacting factor changes the size of the difference between homozygotes' means, the expected sibling or DZ twin correlation is .5 if and only if the minor allele frequency (MAF) is .5; otherwise the expected DZ correlation is greater than this value, consistent (and confounded) with some additional effect of C. This result is considered in the light of the distribution of minor allele frequencies for polygenic traits. Also discussed is whether such interactions take place at the locus level or affect an aggregated biological structure or system. Interactions with structures or endophenotypes that result from the aggregated effects of many loci will generally emerge as part of the A estimate.

Background: The role of anesthesia providers in dispersing potentially pathogenic material from one patient to another during intraoperative care needs further study. In this study we aimed (1) to quantify the dispersion of a surrogate pathogen from a simulated patient's mouth to the anesthesia workstation during routine anesthetic induction, (2) to test the hypothesis that there would be fewer contamination sites by providers who used a double-gloving technique, and (3) to examine the effectiveness of between-case anesthesia apparatus disinfection., Methods: Twenty subjects were randomized to a single pair of gloves group (group 1) or a double-gloved group (group 2) and completed a simulated general anesthesia induction, completing a standardized set of interventions. Dispersion of a surrogate pathogen dye placed in the oral cavity of the simulated patient was tracked by a blinded observer and photography. Standard cleaning of the workstation was performed, and residual dye was quantified. Group performance was plotted using regression analysis and rate of contamination compared using parametric statistics., Results: Group 1 contaminated an average of 16.0 (SEM = 0.89) sites compared with group 2, who contaminated an average of 7.6 (SEM = 0.85). The cart drawers, gas flow dials, medication vials, and ventilator controls were significantly contaminated by group 1, but not by group 2 (P < .05 in all cases). There were similar rates of contamination in both groups for the airway equipment, breathing system, intravenous access ports, and the roll of tape used to secure the endotracheal tube. Once the airway management phase of the induction ended, new site contamination continued at a high rate in group 1 but not group 2., Conclusions: A double-gloving technique was associated with less spread of an oral inoculum to the workstation but was not uniformly protective. Between-case cleaning was ineffective in removing the contaminant, indicating that biologic material from one patient may be present when subsequent patients are cared for. This suggests risks for the current patient (eg, skin or oral site transfer to an intravenous site) and also may place future patients at risk. Importantly, using models that simulate actual clinical events can inform clinical practice and decipher challenging areas of ergonomics., (Copyright © 2016 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Conflicting reports exist on the direction of the relationship between social anxiety (SA) and alcohol/cigarette use (AU/CU) and alcohol/nicotine dependence (AD/ND), with both positive and negative associations reported. A prospective, longitudinal sample of Finnish twins (n = 1,906) was used to test potential explanations for these discrepancies. Specifically, this study used peer, parent, and teacher ratings of SA, and a clinical interview screening item for social anxiety disorder (SAD-Sc) to examine associations between SA and AU/CU and AD/ND from early adolescence into young adulthood. Peer-rated SA was negatively associated with AU, CU, and AD from age 14 through age 22, implying a protective effect (β = -0.01 to -.03). Teacher- and parent-rated SA associations were in the same directions but weaker or nonsignificant, indicating that aspects of SA that are recognizable by peers may be most relevant to AU/CU. Self-reported SAD-Sc was also negatively associated with AU, but positively associated with AD symptoms in young adulthood (β = 0.38). Our findings partially support the existence of different associations between SA and AU versus AD, but only in the context of SAD-Sc rather than trait SA. Neither trait SA nor SAD-Sc significantly predicted ND symptoms, although SAD-Sc was associated with both cigarette abstinence and daily smoking. These findings suggest that adolescent SA is modestly associated with lower AU/CU, although there may be some individuals with more severe SA who develop alcohol problems later in life. There was little evidence of a common underlying liability contributing to both SA and alcohol/cigarette use. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)

Depression is a highly heterogeneous condition, and identifying how symptoms present in various groups may greatly increase our understanding of its etiology. Importantly, Major Depressive Disorder is strongly linked with Substance Use Disorders, which may ameliorate or exacerbate specific depression symptoms. It is therefore quite plausible that depression may present with different symptom profiles depending on an individual's substance use status. Given these observations, it is important to examine the underlying construct of depression in groups of substance users compared to non-users. In this study we use a non-clinical sample to examine the measurement structure of the Beck Depression Inventory (BDI-II) in non-users and frequent-users of various substances. Specifically, measurement invariance was examined across those who do vs. do not use alcohol, nicotine, and cannabis. Results indicate strict factorial invariance across non-users and frequent-users of alcohol and cannabis, and metric invariance across non-users and frequent-users of nicotine. This implies that the factor structure of the BDI-II is similar across all substance use groups.

Background: Obsessive-compulsive disorder (OCD) is characterized by five major dimensions, including contamination/washing, harm/checking, symmetry/ordering, hoarding, and forbidden thoughts. How these dimensions may relate etiologically to the symptoms of other obsessive-compulsive related disorders (OCRDs) and anxiety disorders (ADs) is not well known. The aim of this study was to examine the genetic and environmental overlap between each major obsessive-compulsive dimension with the symptoms of other OCRDs and ADs., Methods: Two thousand four hundred ninety-five twins of both sexes, aged between 18 and 45 years, were recruited from the Australian Twin Registry. Measures used scores on four dimensions (obsessing (forbidden thoughts), washing, checking, and ordering) of the Obsessive-Compulsive Inventory-Revised, Dysmorphic Concerns Questionnaire, Hoarding Rating Scale, Anxiety Sensitivity Index, Social Phobia Inventory, and Stress subscale of the Depression, Anxiety, and Stress Scale. Multivariate twin modeling methods using continuous and categorized variables were performed, also controlling for age and gender., Results: Our findings suggested that forbidden thoughts and washing demonstrated the strongest genetic overlap with other AD symptoms, while ordering was genetically related to OCRD symptoms. Common genetic influences on checking symptoms were best estimated when modeling OCRDs together with AD symptoms. Common environmental factors of ordering and checking were shared with AD symptoms., Conclusions: Important shared genetic and environmental risk factors exist between OCD, OCRDs, and ADs, but which vary alongside the expression of its major dimensions., (© 2015 Wiley Periodicals, Inc.)

Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic and environmental risks underpinning internet use and their relation to psychopathology is important. This study aims to explore the genetic and environmental etiology of internet use measures and their associations with internalizing disorders and substance use disorders. The sample included 2,059 monozygotic (MZ) and dizygotic (DZ) young adult twins from the Brisbane Longitudinal Twin Study (BLTS). Younger participants reported more frequent internet use, while women were more likely to use the internet for interpersonal communication. Familial aggregation in 'frequency of internet use' was entirely explained by additive genetic factors accounting for 41% of the variance. Familial aggregation in 'frequency of use after 11 pm', 'using the internet to contact peers', and 'using the internet primarily to access social networking sites' was attributable to varying combinations of additive genetic and shared environmental factors. In terms of psychopathology, there were no significant associations between internet use measures and major depression (MD), but there were positive significant associations between 'frequency of internet use' and 'frequency of use after 11 pm' with social phobia (SP). 'Using the internet to contact peers' was positively associated with alcohol abuse, whereas 'using the internet to contact peers' and 'using the internet primarily to access social networking sites' were negatively associated with cannabis use disorders and nicotine symptoms. Individual differences in internet use can be attributable to varying degrees of genetic and environmental risks. Despite some significant associations of small effect, variation in internet use appears mostly unrelated to psychopathology.

We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques-biometrical genetic modeling, cluster analysis, and graph theory-to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function.

Objective: Little is known about the longitudinal genetic and environmental association between juvenile irritability and symptoms of anxiety and depression. This study's goal was to assess the relationship between these constructs across a critical developmental period spanning childhood to young adulthood., Method: Parents (n = 1,348 twin pairs) from the Swedish Twin Study of Child and Adolescent Development completed the Child/Adult Behavior Checklist (CBCL/ABCL) about their twin children. Data were collected during a prospective, 4-wave study starting in childhood (ages 8-9 years) and ending in young adulthood (ages 19-20 years). An irritability score and an anxious/depressed score were computed from CBCL/ABCL item endorsements. Genetically informative cross-lagged models were used to estimate the genetic and environmental relationship between these 2 constructs across time., Results: Our models suggested that irritability more strongly predicted anxious/depressed symptoms than vice versa, consistent with a causal role of irritability on anxiety/depression at older ages. This relationship was significant only in late childhood/early adolescence. Additive genetic and unique environmental factors were significant contributors to both irritability and anxious/depressed symptoms and were both specific to and shared between these 2 constructs. The same common environmental factors influenced both constructs, although these factors accounted for a smaller amount of variance than genetic or unique environmental factors., Conclusion: This study adds to our understanding of the developmental relationship between irritability and anxious/depressed symptoms and the contribution of genes and environmental factors to their association across development. Findings suggest the need to monitor for emergence of internalizing symptoms in irritable children and their potential need for therapeutic intervention., (Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)