Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling.

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SUB), substance use disorder (SUD), and other (non-SUB/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD. We created single-factor measurement models-each describing SUB, SUD, or BD traits-based on mutually exclusive sets of European ancestry genome-wide association study (GWAS) statistics exploring externalizing variables. We then assessed the partitioning of genetic covariance among the three facets using correlated factors models and Cholesky decomposition. Even when the residuals for indicators relating to the same substance were correlated across the SUB and SUD factors, the two factors yielded a large correlation (rg = 0.803). BD correlated strongly with the SUD (rg = 0.774) and SUB (rg = 0.778) factors. In our initial decompositions, 33% of total BD variance remained after partialing out SUD and SUB. The majority of covariance between BD and SUB and between BD and SUD was shared across all factors, and, within these models, only a small fraction of the total variation in BD operated via an independent pathway with SUD or SUB outside of the other factor. When only nicotine/tobacco, cannabis, and alcohol were included for the SUB/SUD factors, their correlation increased to rg = 0.861; in corresponding decompositions, BD-specific variance decreased to 27%. Further research can better elucidate the properties of BD-specific variation by exploring its genetic/molecular correlates.

Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling.

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SU), substance use disorder (SUD), and other (non-SU/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD. We created single-factor measurement models-each describing SUB, SUD, or BD traits--based on mutually exclusive sets of European ancestry genome-wide association study (GWAS) statistics exploring externalizing variables. We then applied trivariate Cholesky decomposition to these factors in order to identify BD-specific genomic variation and assess the partitioning of BD's genetic covariance with each of the other facets. Even when the residuals for indicators relating to the same substance were correlated across the SUB and SUD factors, the two factors yielded a large zero-order correlation (rg=.803). BD correlated strongly with the SUD (rg=.774) and SUB factors (rg=.778). In our initial decompositions, 33% of total BD variance remained after removing variance associated with SUD and SUB. The majority of covariance between BD and SU and between BD and SUD was shared across all factors. When only nicotine/tobacco, cannabis, and alcohol were included for the SUB/SUD factors, their zero-order correlation increased to rg=.861; in corresponding decompositions, BD-specific variance decreased to 27%. In summary, BD, SU, and SUD were highly genetically correlated at the latent factor level, and a significant minority of genomic BD variation was not shared with SU and/or SUD. Further research can better elucidate the properties of BD-specific variation by exploring its genetic/molecular correlates.

Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing.

Importance: Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective: To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design, Setting, and Participants: This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Main Outcomes and Measures: Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results: In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. Conclusions and Relevance: The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders.

Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

Genetic and environmental influences on pubertal hormones in human hair across development.

Puberty is a complex biopsychosocial process that can affect an array of psychiatric and medical disorders emerging in adolescence. Although the pubertal process is driven by neuroendocrine changes, few quantitative genetic studies have directly measured puberty-relevant hormones. Hair samples can now be assayed for accumulation of hormones over several months. In contrast to more conventional salivary measures, hair measures are not confounded by diurnal variation or hormonal reactivity. In an ethnically and socioeconomically diverse sample of 1286 child and adolescent twins and multiples from 672 unique families, we estimated genetic and environmental influences on hair concentrations of testosterone, DHEA, and progesterone across the period of 8-18 years of age. On average, male DHEA and testosterone were highly heritable, whereas female DHEA, progesterone, and puberty were largely influenced by environmental components. We identified sex-specific developmental windows of maximal heritability in each hormone. Peak heritability for DHEA occurred at approximately 10 years of age for males and females. Peak heritability for testosterone occurred at age 12.5 and 15.2 years for males and females, respectively. Peak heritability for male progesterone occurred at 11.2 years, while the heritability of female progesterone remained uniformly low. The identification of specific developmental windows when genetic signals for hormones are maximized has critical implications for well-informed models of hormone-behavior associations in childhood and adolescence.

Twin models of environmental and genetic influences on pubertal development, salivary testosterone, and estradiol in adolescence.

OBJECTIVE: Research on sources of variation in adolescent's gonadal hormone levels is limited. We sought to decompose individual differences in adolescent testosterone, estradiol, and pubertal status, into genetic and environmental components. DESIGN: A sample of male and female adolescent twins from the greater Austin and Houston areas provided salivary samples, with a subset of participants providing longitudinal data at 2 waves. PARTICIPANTS: The sample included 902 adolescent twins, 49% female, aged 13-20 years (M = 15.91) from the Texas Twin Project. Thirty-seven per cent of twin pairs were monozygotic; 30% were same-sex dizygotic (DZ) pairs; and 33% were opposite-sex DZ pairs. MEASUREMENTS: Saliva samples were assayed for testosterone and estradiol using chemiluminescence immunoassays. Pubertal status was assessed using self-report. Biometric decompositions were performed using multivariate quantitative genetic models. RESULTS: Genetic factors contributed substantially to variation in testosterone in males and females in the follicular phase of their menstrual cycle (h2  = 60% and 51%, respectively). Estradiol was also genetically influenced in both sexes, but was predominately influenced by nonshared environmental factors. The correlation between testosterone and estradiol was mediated by a combination of genetic and environmental influences for males and females. Genetic and environmental influences on hormonal concentrations were only weakly correlated with self-reported pubertal status, particularly for females. CONCLUSIONS: Between-person variability in adolescent gonadal hormones and their interrelationship reflects both genetic and environmental processes, with both testosterone and estradiol containing sizeable heritable components.

Developmental differences in reward sensitivity and sensation seeking in adolescence: Testing sex-specific associations with gonadal hormones and pubertal development.

Sensation seeking has been found to increase, on average, from childhood to adolescence. Developmental scientists have hypothesized that this change could be driven by the rise of gonadal hormones at puberty, which affect reward-related processing in the brain. In a large, age-heterogeneous, population-based sample of adolescents and young adults (N = 810; ages 13-20 years), we tested for sex-specific associations between age, self-reported pubertal development, gonadal hormones (estradiol and testosterone) as measured in saliva, reward sensitivity as measured by a multivariate battery of in-laboratory tasks (including the Iowa gambling task, balloon analogue risk task, and stoplight task), and self-reported sensation seeking. Reward sensitivity was more strongly associated with sensation seeking in males than females. For both males and females, reward sensitivity was unrelated to age but was higher among those who reported more advanced pubertal development. There were significant sex differences in the effects of self-reported pubertal development on sensation seeking, with a positive association evident in males but a negative association in females. Moreover, gonadal hormones also showed diverging associations with sensation seeking-positive with testosterone but negative with estradiol. Overall, the results indicate that sensation seeking among adolescents and young adults depends on a complex constellation of developmental influences that operate via sex-specific mechanisms. (PsycINFO Database Record