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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.

COVID-19 was associated with significant financial hardship and increased binge eating (BE). However, it is largely unknown whether financial stressors contributed to BE during the pandemic. We used a longitudinal, cotwin control design that controls for genetic/environmental confounds by comparing twins in the same family to examine whether financial hardship during COVID-19 was associated with BE.Female twins (N = 158; MParticipants who experienced greater mean financial hardship across the study had significantly greater dimensional BE symptoms, and participants who experienced greater financial hardship on a given day reported significantly more emotional eating that day. These results were replicated in cotwin control analyses. Twins who experienced more financial hardship than their cotwin across the study reported greater dimensional BE symptoms than their cotwin, and participants who experienced more financial hardship than their cotwin on a given day reported greater emotional eating that day. Results were identical when restricting analyses to monozygotic twins, suggesting associations were not due to genetic confounds.Results suggest that BE-related symptoms may be elevated in women who experienced financial hardship during COVID-19 independent of potential genetic/environmental confounds. However, additional research in larger samples is needed.Little is known regarding how financial difficulties during the COVID-19 pandemic may have contributed to increased binge eating (BE). We found preliminary evidence that financial hardship during COVID-19 may be associated with greater rates of BE-related symptoms even when comparing twins from the same family. While additional research is needed, results suggest that people who experienced financial hardship during COVID-19 may be at increased risk for BE.

ImportanceSubjective memory concern has long been considered a state-related indicator of impending cognitive decline or dementia. The possibility that subjective memory concern may itself be a heritable trait is largely ignored, yet such an association would substantially confound its use in clinical or research settings.ObjectiveTo assess the heritability and traitlike dimensions of subjective memory concern and its clinical correlates.Design, Setting, and ParticipantsThis longitudinal twin cohort study was conducted from 1967 to 2019 among male adults with a mean (SD) age of 37.75 (2.52) years to follow-up at mean ages of 56.15 (2.72), 61.50 (2.43), and 67.35 (2.57) years (hereafter, 38, 56, 62, and 67 years, respectively) in the Vietnam Era Twin Study of Aging. The study included a national community-dwelling sample with health, education, and lifestyle characteristics comparable to a general sample of US men in this age cohort. Participants were monozygotic and dizygotic twins randomly recruited from the Vietnam Era Twin Registry. Data were analyzed from May 2021 to December 2022.Main Outcomes and MeasuresMeasures included subjective memory concern at 4 time points; objective memory, depressive symptoms, and anxiety at the last 3 time points; negative emotionality (trait neuroticism) at age 56 years; polygenic risk scores (PRSs) for neuroticism, depression, and Alzheimer disease; APOE genotype; and parental history of dementia. Primary outcomes were heritability and correlations between subjective memory concern and other measures.ResultsThe sample included 1555 male adults examined at age 38 years, 520 at age 56 years (due to late introduction of subjective memory concern questions), 1199 at age 62 years, and 1192 at age 67 years. Phenotypically, subjective memory concerns were relatively stable over time. At age 56 years, subjective memory concern had larger correlations with depressive symptoms (r, 0.32; 95% CI, 0.21 to 0.42), anxiety (r, 0.36; 95% CI, 0.18 to 0.51), and neuroticism (r, 0.34; 95% CI, 0.26 to 0.41) than with objective memory (r, −0.24; 95% CI, −0.33 to −0.13). Phenotypic results were similar at ages 62 and 67 years. A best-fitting autoregressive twin model indicated that genetic influences on subjective memory concern accumulated and persisted over time (h2 = 0.26-0.34 from age 38-67 years). At age 56 years, genetic influences for subjective memory concern were moderately correlated with genetic influences for anxiety (r, 0.36; 95% CI, 0.18 to 0.51), negative emotionality (r, 0.51; 95% CI, 0.44-0.57), and depressive symptoms (r, 0.20; 95% CI, 0.10 to 0.29) as well as objective memory (r, −0.22; 95% CI, −0.30 to −0.14). Similar genetic correlations were seen at ages 62 and 67 years. The neuroticism PRS was associated with subjective memory concern at age 38 years (r, 0.10; 95% CI, 0.03. to 0.18) and age 67 years (r, 0.09; 95% CI, 0.01 to 0.16). Subjective memory concern was not associated with any Alzheimer disease risk measures.Conclusions and RelevanceThis cohort study found stable genetic influences underlying subjective memory concern dating back to age 38 years. Subjective memory concern had larger correlations with affect-related measures than with memory-related measures. Improving the utility of subjective memory concern as an indicator of impending cognitive decline and dementia may depend on isolating its statelike component from its traitlike component.

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

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

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

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

Weighted least squares (WLS) estimation has tremendous utility for structural equation models (SEMs) that include ordered categorical data. However, some common statistical programs for fitting these models lack full flexibility in specification. Moreover, the popular approach toward model identification of ordinal variables arbitrarily constrains researchers and limits their ability to construct theories involving ordinal data. We develop a novel approach for identifying SEMs with ordered categorical variables that allows a wider variety of model specifications and theories, furthermore implementing this approach in OpenMx. We review WLS and ordinal variables both in general and as implemented in common software programs. Then we discuss the novel approach taken by the OpenMx implementation and derive analytic criteria for ordinal data identification. We conclude by posing new research questions that may now be addressed.

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).

Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging.

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.

Background and aims Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later. Design Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated. Setting Population-based United States sample. Participants/cases Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves. Measurements Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19). Findings In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD. Conclusions Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

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.

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated measurements. In a CLPM, the magnitude and significance of the lagged effects depend on the time interval between measurement occasions, and these effects usually become undetectable at longer intervals. To address this limitation, we integrated instrumental variables and the CLPM, allowing us to estimate simultaneously the lagged (i.e., “distal”) effects and the bidirectional cross-sectional (i.e., “proximal”) effects at each wave. The distal effects reflect Granger causal effects across time, which decay with increasing time intervals. The proximal effects at each wave capture causal influences that have accrued over time and can help infer causality when the distal effects become undetectable at longer intervals. The distal and proximal causal estimates can be tested separately or jointly through likelihood-ratio tests, unraveling distinct temporal aspects of the causal process. Significant proximal effects, with negligible distal effects, imply that the time interval is too long for studying Granger causality in a pair of variables. We demonstrate that the time interval between measurement occasions profoundly impacts the ability to identify causal effects in longitudinal data and propose strategies to detect causation regardless of the time interval in a study.

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

Blood-based neurofilament light chain (NfL) is a promising biomarker of neurodegeneration across multiple neurodegenerative diseases. However, blood-based NfL is highly associated with renal function in older adults, which leads to the concern that blood-based NfL levels may be influenced by renal function, rather than neurodegeneration alone. Despite growing interest in using blood-based NfL as a biomarker of neurodegeneration in research and clinical practices, whether renal function should always be accounted for in these settings remains unclear. Moreover, the mechanisms underlying this association between blood-based measures of NfL and renal function remain elusive. In this study, we first evaluated the effect of renal function on the associations of plasma NfL with other measures of neurodegeneration. We then examined the extent of genetic and environmental contributions to the association between plasma NfL and renal function.In a sample of 393 adults (mean age=75.22 years, range=54-90), we examined the associations of plasma NfL with cerebrospinal fluid (CSF) NfL and brain volumetric measures before and after adjusting for levels of serum creatinine (an index of renal function). In an independent sample of 969 men (mean age=67.57 years, range=61-73) that include monozygotic and dizygotic twin pairs, we replicated the same analyses and leveraged biometrical twin modeling to examine the genetic and environmental influences on the plasma NfL and creatinine association.Plasma NfL's associations with cerebrospinal fluid NfL and brain volumetric measures did not meaningfully change after adjusting for creatinine levels. Both plasma NfL and creatinine were significantly heritable (hAdjusting for renal function is unnecessary when assessing associations between plasma NfL and other measures of neurodegeneration but is necessary if plasma NfL is compared to a cutoff for classifying neurodegeneration-positive versus neurodegeneration-negative individuals. Blood-based measures of NfL and renal function are heritable and share common genetic influences.

The amyloid-tau-neurodegeneration (ATN) framework has led to an increased focus on Alzheimer’s disease (AD) biomarkers. The cost and invasiveness of obtaining biomarkers via cerebrospinal fluid has motivated efforts to develop sensitive blood-based biomarkers. Although AD is highly heritable, the biometric genetic and environmental etiology of blood-based biomarkers has never been explored. We therefore, analyzed plasma beta-amyloid (Aβ40, Aβ42, Aβ42/40), total tautau (t-tautau), and neurofilament light (NFL) biomarkers in a sample of 1,050 men aged 60 to 73 years (m=68.2, SD=2.5) from the Vietnam Era Twin Study of Aging (VETSA). Unlike Aβ and tautau, NFL does not define AD; however, as a biomarker of neurodegeneration it serves as the N component in the ATN framework. Univariate estimates suggest that familial aggregation in Aβ42, Aβ42/40, t-tau, and NFL is entirely explained by additive genetic influences accounting for 40%-58% of the total variance. All remaining variance is associated with unshared or unique environmental influences. For Aβ40, a additive genetic (31%), shared environmental (44%), and unshared environmental (25%) influences contribute to the total variance. In the more powerful multivariate analysis of Aβ42, Aβ40, t-tau, and NFL, heritability estimates range from 32% to 58%. Aβ40 and Aβ42 are statistically genetically identical (rg = 1.00, 95%CI = 0.92,1.00) and are also moderately environmentally correlated (re = 0.66, 95%CI = 0.59, 0.73). All other genetic and environmental associations were non-significant or small. Our results suggest that plasma biomarkers are heritable and that Aβ40 and Aβ42 share the same genetic influences, whereas the genetic influences on plasma t-tau and NFL are mostly unique and uncorrelated with plasma Aβ in early old-age men.

The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development. This paper reports activation patterns for fMRI tasks assessing response inhibition, working memory and reward processing obtained at baseline in the longitudinal ABCD Study, providing a reference for research into adolescent brain development.

The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region did no longer explain variation in height. Our results suggest that the phenotypic variance explained by region actually represent ancestry effects on height.

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/ ).