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The mu1 opioid receptor gene, OPRM1, has long been a high-priority candidate for human genetic studies of addiction. Because of its potential functional significance, the non-synonymous variant rs1799971 (A118G, Asn40Asp) in OPRM1 has been extensively studied, yet its role in addiction has remained unclear, with conflicting association findings. To resolve the question of what effect, if any, rs1799971 has on substance dependence risk, we conducted collaborative meta-analyses of 25 datasets with over 28,000 European-ancestry subjects. We investigated non-specific risk for "general" substance dependence, comparing cases dependent on any substance to controls who were non-dependent on all assessed substances. We also examined five specific substance dependence diagnoses: DSM-IV alcohol, opioid, cannabis, and cocaine dependence, and nicotine dependence defined by the proxy of heavy/light smoking (cigarettes-per-day >20 vs. ≤ 10). The G allele showed a modest protective effect on general substance dependence (OR = 0.90, 95% C.I. [0.83-0.97], p value = 0.0095, N = 16,908). We observed similar effects for each individual substance, although these were not statistically significant, likely because of reduced sample sizes. We conclude that rs1799971 contributes to mechanisms of addiction liability that are shared across different addictive substances. This project highlights the benefits of examining addictive behaviors collectively and the power of collaborative data sharing and meta-analyses.

Chronic cigarette use has been consistently associated with differences in the neuroanatomy of smokers relative to nonsmokers in case-control studies. However, the etiology underlying the relationships between brain structure and cigarette use is unclear. A community-based sample of male twin pairs ages 51-59 (110 monozygotic pairs, 92 dizygotic pairs) was used to determine the extent to which there are common genetic and environmental influences between brain structure and average lifetime cigarette use. Brain structure was measured by high-resolution structural magnetic resonance imaging, from which subcortical volume and cortical volume, thickness and surface area were derived. Bivariate genetic models were fitted between these measures and average lifetime cigarette use measured as cigarette pack-years. Widespread, negative phenotypic correlations were detected between cigarette pack-years and several cortical as well as subcortical structures. Shared genetic and unique environmental factors contributed to the phenotypic correlations shared between cigarette pack-years and subcortical volume as well as cortical volume and surface area. Brain structures involved in many of the correlations were previously reported to play a role in specific aspects of networks of smoking-related behaviors. These results provide evidence for conducting future research on the etiology of smoking-related behaviors using measures of brain morphology.

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

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

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