Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction

Genomics of Attention Deficit Hyperactivity Disorder: What the Clinician Needs to Know

This report provides an update on current knowledge and applications of genomic research in attention deficit hyperactivity disorder (ADHD). The history, principles, and underlying assumptions for genetic studies on psychiatric disorders are reviewed. Recent DNA sequencing and genome-wide association studies have revealed common and rare genetic variants associated with ADHD. Communication of genetic knowledge in meetings with patients and their relatives and common misconceptions are addressed. The importance of recognizing genetic syndromes masquerading as ADHD or other common psychiatric disorders is emphasized and how genetic information can be used to improve diagnosis and therapy are discussed.

Integrating HiTOP and RDoC frameworks Part I: Genetic architecture of externalizing and internalizing psychopathology

BACKGROUND

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).

METHODS

We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in individuals genetically similar to European reference panels (EUR-like; n = 16,400 to 1,074,629). Traits included clinical (e.g. major depressive disorder, alcohol use disorder) and subclinical measures (e.g. risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.

RESULTS

A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.

CONCLUSIONS

The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

Genomic approaches to explore susceptibility and pathogenesis of alcohol use disorder and alcohol-associated liver disease

Excessive alcohol use is a major risk factor for the development of an alcohol use disorder (AUD) and contributes to a wide variety of other medical illnesses, including alcohol-associated liver disease (ALD). Both AUD and ALD are complex and causally interrelated diseases, and multiple factors other than alcohol consumption are implicated in the disease pathogenesis. While the underlying pathophysiology of AUD and ALD is complex, there is substantial evidence for a genetic susceptibility of both diseases. Current genome-wide association studies indicate that the genes associated with clinical AUD only poorly overlap with the genes identified for heavy drinking and, in turn, neither overlap with the genes identified for ALD. Uncovering the main genetic factors will enable us to identify molecular drivers underlying the pathogenesis, discover potential targets for therapy, and implement patient care early in disease progression. In this review, we described multiple genomic approaches and their implications to investigate the susceptibility and pathogenesis of both AUD and ALD. We concluded our review with a discussion of the knowledge gaps and future research on genomic studies in these 2 diseases.

Associations between air pollution and surrounding greenness with internalizing and externalizing behaviors among schoolchildren

BACKGROUND

Air pollution and greenness are emerging as modifiable risk and protective factors, respectively, in child psychopathology. However, research shows inconsistencies. Here, we examined associations between air pollution and surrounding greenness with internalizing and externalizing behaviors. In addition, the potential modifying role of the genetic susceptibility for these traits and socioeconomic status (SES) was explored.

METHODS

This population-based study included 4485 schoolchildren aged 5-18 years from Spain. Internalizing and externalizing behaviors were assessed using the Child Behavior Checklist (CBCL). Average air pollution (NO2, PM2.5, PM10, PMcoarse, and PM2.5 absorbance) and surrounding greenness (NDVI within 100-m, 300-m, and 500-m buffers) school exposure were estimated for 12 months before outcome assessment. Genetic liability was assessed by computing polygenic risk scores (PRS) and SES was calculated using the Hollingshead Four-Factor Index. Associations were analyzed using negative binomial mixed-effects models.

RESULTS

Although no associations survived multiple testing, we found that increases of 5.48 μg/m3 in PM10 and 2.93 μg/m3 in PMcoarse were associated with a 6% (Mean Ratio (MR) = 1.06; 95% CI: 1.01-1.12) and a 4% (MR = 1.04; 95% CI: 1.00-1.09) increase in internalizing behavior scores. A 0.1 increase in NDVI within a 100-m buffer was associated with a 6% decrease in externalizing behavior (MR = 0.94; 95% CI: 0.89-0.99). Neither differences by sex or age, or moderation effects by PRS or SES, were observed.

CONCLUSIONS

We found preliminary evidence of detrimental effects of air pollution on internalizing behavior and protective effects of greenness on externalizing behavior, which were not modified by sex, age, SES, or genetic liability. If confirmed, these results reinforce the need for improving air quality, especially around schools, as part of preventive strategies focused on childhood psychopathology.

Associations Between a Genetic Liability Toward Externalizing and Behavioral Outcomes Spanning Toddlerhood Through Early Adulthood in Five Developmental Cohorts

OBJECTIVE

Understanding how genetic risk unfolds across development will be important for using genetics to inform prevention and early intervention. The current study leverages information from 5 large datasets to characterize behavioral manifestations of a genetic liability toward externalizing from ages 6 months to 26 years.

METHOD

We used polygenic scores (PGS) derived from a multivariate genome-wide association study (GWAS) of externalizing that identified hundreds of significantly associated genetic variants (EXTPGS) to estimate associations of genetic liability with relevant phenotypes within and across developmental periods, ranging from toddlerhood to early adulthood. We used data from 5 population- and family-based datasets spanning 3 countries.

RESULTS

The EXTPGS was significantly associated with a breadth of externalizing phenotypes from toddlerhood to early adulthood. Higher EXTPGS was consistently associated with measures of impulsivity from early adolescence to early adulthood. Individuals with higher EXTPGS were more likely to experience conduct problems and symptoms of oppositional defiant and attention-deficit/hyperactivity disorders. Furthermore, the EXTPGS was associated with higher levels of substance use and problems beginning in early adolescence through early adulthood, including alcohol and illicit drug use. There was minimal evidence for sex interactions.

CONCLUSION

A genetic liability toward externalizing is associated a wide array of behaviors and psychiatric/substance use outcomes beginning as early as childhood and through emerging adulthood. The early emergence and breadth of behaviors associated with a genetic liability toward externalizing could inform prevention and intervention.

CONVEX APPROACHES TO ISOLATE THE SHARED AND DISTINCT GENETIC STRUCTURES OF SUBPHENOTYPES IN HETEROGENEOUS COMPLEX TRAITS

Groups of complex diseases, such as coronary heart diseases, neuropsychiatric disorders, and cancers, often display overlapping clinical symptoms and pharmacological treatments. The shared associations of genetic variants across diseases has the potential to explain their underlying biological processes, but this remains poorly understood. To address this, we model the matrix of summary statistics of trait-associated genetic variants as the sum of a low-rank component - representing shared biological processes - and a sparse component, representing unique processes and arbitrarily corrupted or contaminated components. We introduce Clorinn, an open-source Python software that uses convex optimization algorithms to recover these components by minimizing a weighted combination of the nuclear norm and of the L1 norm. Among others, Clorinn provides two significant benefits: (a) Convex optimization guarantees reproducibility of the components, and (b) The low-rank "uncorrupted" matrix allows robust singular value decomposition (SVD) and principal component analysis (PCA), which are otherwise highly sensitive to outliers and noise in the input matrix. In extensive simulations, we observe that Clorinn outperforms state-of-the-art approaches in capturing the shared latent factors across phenotypes. We apply Clorinn to estimate 200 latent factors from GWAS summary data of 2,110 phenotypes measured in European-ancestry Pan-UK BioBank individualsN = 420 , 531 and 14 psychiatric disorders.

Single-Nucleus Transcriptomics Identifies Neuroblast Migration Programs Sensitive to Reelin and Cannabis in the Adolescent Nucleus Accumbens

The interplay between cannabis exposure during adolescence and genetic predisposition has been linked to increased vulnerability to psychiatric disorders. To investigate the molecular underpinnings of this interaction, we performed single-nucleus RNA sequencing of the nucleus accumbens (NAc) in a mouse model of Reln haploinsufficiency, a genetic risk factor for psychiatric disorders, following adolescent exposure to tetrahydrocannabinol (THC), the primary psychoactive component of cannabis. We identified a gene co-expression network influenced by both Reln genotype and THC, enriched in genes associated with human psychiatric disorders and predominantly expressed in a GABAergic neuroblast subpopulation. We showed that neuroblasts actively migrated in the adolescent NAc, but declined with age. Cell-to-cell communication analysis further revealed that these neuroblasts receive migratory cues from cholecystokinin interneurons, which express high levels of cannabinoid receptors. Together, these findings provide mechanistic insights into how adolescent THC exposure and genetic risk factors may impair GABAergic circuit maturation.

A systematic review and meta-analyses of the temporal stability and convergent validity of risk preference measures

Understanding whether risk preference represents a stable, coherent trait is central to efforts aimed at explaining, predicting and preventing risk-related behaviours. We help characterize the nature of the construct by adopting a systematic review and individual participant data meta-analytic approach to summarize the temporal stability of 358 risk preference measures (33 panels, 57 samples, 579,114 respondents). Our findings reveal noteworthy heterogeneity across and within measure categories (propensity, frequency and behaviour), domains (for example, investment, occupational and alcohol consumption) and sample characteristics (for example, age). Specifically, while self-reported propensity and frequency measures of risk preference show a higher degree of stability than behavioural measures, these patterns are moderated by domain and age. Crucially, an analysis of convergent validity reveals a low agreement across measures, questioning the idea that they capture the same underlying phenomena. Our results raise concerns about the coherence and measurement of the risk preference construct.

Genetic Propensity for Delay Discounting and Educational Attainment in Adults Are Associated With Delay Discounting in Preadolescents: Findings From the Adolescent Brain Cognitive Development Study

Higher delay discounting (DD) (i.e., propensity to devalue larger, delayed rewards over immediate, smaller rewards) is a transdiagnostic marker underpinning multiple health behaviors. Although genetic influences account for some of the variability in DD among adults, less is known about the genetic contributors to DD among preadolescents. We examined whether polygenic scores (PGS) for DD, educational attainment, and behavioral traits (i.e., impulsivity, inhibition, and externalizing behavior) were associated with phenotypic DD among preadolescents. Participants included youth (N = 8982, 53% male) from the Adolescent Brain Cognitive Development Study who completed an Adjusting Delay Discounting Task at the 1-year follow-up and had valid genetic data. PGS for DD, educational attainment, impulsivity, inhibition, and externalizing behaviors were created based on the largest GWAS available. Separate linear mixed effects models were conducted in individuals most genetically similar to European (EUR; n = 4972), African (AFR; n = 1769), and Admixed American (AMR; n = 2241) reference panels. After adjusting for age, sex, income, and the top ten genetic ancestry principal components, greater PGS for DD and lower educational attainment (but not impulsivity, inhibition, or externalizing) were associated with higher rates of DD (i.e., preference for sooner, smaller rewards) in participants most genetically similar to EUR reference panels. Findings provide insight into the influence of genetic propensity for DD and educational attainment on the discounting tendencies of preadolescents, particularly those most genetically similar to European reference samples, thereby advancing our understanding of the etiology of choice behaviors in this population.

Polygenic Scores and Mood Disorder Onsets in the Context of Family History and Early Psychopathology

Importance

Bipolar disorder (BD) and major depressive disorder (MDD) aggregate within families, with risk often first manifesting as early psychopathology, including attention-deficit/hyperactivity disorder (ADHD) and anxiety disorders.

Objective

To determine whether polygenic scores (PGS) are associated with mood disorder onset independent of familial high risk for BD (FHR-BD) and early psychopathology.

Design, Setting, and Participants

This cohort study used data from 7 prospective cohorts enriched in FHR-BD from Australia, Canada, the Netherlands, Spain, and the US. Participants with FHR-BD, defined as having at least 1 first-degree relative with BD, were compared with participants without FHR for any mood disorder. Participants were repeatedly assessed with variable follow-up intervals from July 1992 to July 2023. Data were analyzed from August 2023 to August 2024.

Exposures

PGS indexed genetic liability for MDD, BD, anxiety, neuroticism, subjective well-being, ADHD, self-regulation, and addiction risk factor. Semistructured diagnostic interviews with relatives established FHR-BD. ADHD or anxiety disorder diagnoses before mood disorder onset constituted early psychopathology.

Main Outcomes and Measures

The outcome of interest, mood disorder onset, was defined as a consensus-confirmed new diagnosis of MDD or BD. Cox regression examined associations of PGS, FHR-BD, ADHD, and anxiety with mood disorder onset. Kaplan-Meier curves and log-rank tests evaluated the probability of onset by PGS quartile and familial risk status.

Results

A total of 1064 participants (546 [51.3%] female; mean [SD] age at last assessment, 21.7 [5.1] years), including 660 with FHR-BD and 404 without FHR for any mood disorder, were repeatedly assessed for mental disorders. A total of 399 mood disorder onsets occurred over a variable mean (SD) follow-up interval of 6.3 (5.7) years. Multiple PGS were associated with onset after correcting for FHR-BD and early psychopathology, including PGS for ADHD (hazard ratio [HR], 1.19; 95% CI, 1.06-1.34), self-regulation (HR, 1.19; 95% CI, 1.06-1.34), neuroticism (HR, 1.18; 95% CI, 1.06-1.32), MDD (HR, 1.17; 95% CI, 1.04-1.31), addiction risk factor (HR, 1.16; 95% CI, 1.04-1.30), anxiety (HR, 1.15; 95% CI, 1.02-1.28), BD (HR, 1.14; 95% CI, 1.02-1.28), and subjective well-being (HR, 0.89; 95% CI, 0.79-0.99). High PGS for addiction risk factor, anxiety, BD, and MDD were associated with increased probability of onset in the control group. High PGS for ADHD and self-regulation increased rates of onset among participants with FHR-BD. PGS for self-regulation, ADHD, and addiction risk factors showed stronger associations with onsets of BD than MDD.

Conclusions and Relevance

In this cohort study, multiple PGS were associated with mood disorder onset independent of family history of BD and premorbid diagnoses of ADHD or anxiety. The association between PGS and mood disorder risk varied depending on family history status.

Polygenic overlap of substance use behaviors and disorders with externalizing and internalizing problems independent of genetic correlations

BACKGROUND

Externalizing and internalizing pathways may lead to the development of substance use behaviors (SUBs) and substance use disorders (SUDs), which are all heritable phenotypes. Genetic correlation studies have indicated differences in the genetic susceptibility between SUBs and SUDs. We investigated whether these substance use phenotypes are differently related to externalizing and internalizing problems at a genetic level.

METHODS

We analyzed data from genome-wide association studies (GWAS) of four SUBs and SUDs, five externalizing traits, and five internalizing traits using the bivariate causal mixture model (MiXeR) to estimate genetic overlap beyond genetic correlation.

RESULTS

Two distinct patterns were found. SUBs demonstrated high genetic overlap but low genetic correlation of shared variants with internalizing traits, suggesting a pattern of mixed effect directions of shared genetic variants. Conversely, SUDs and externalizing traits exhibited considerable genetic overlap with moderate to high positive genetic correlation of shared variants, suggesting concordant effect direction of shared risk variants.

CONCLUSIONS

These results highlight the importance of the externalizing pathway in SUDs as well as the limited role of the internalizing pathway in SUBs. As MiXeR is not intended for the identification of specific genes, further studies are needed to reveal the underlying shared mechanisms of these traits.

The causal role of male pubertal timing for the development of externalizing and internalizing traits: results from Mendelian randomization studies

BACKGROUND

Preexisting epidemiological studies suggest that early pubertal development in males is associated with externalizing (e.g. conduct problems, risky behavior, and aggression) and internalizing (e.g. depression and anxiety) traits and disorders. However, due to problems inherent to observational studies, especially of residual confounding, it remains unclear whether these associations are causal. Mendelian randomization (MR) studies take advantage of the random allocation of genes at conception and can establish causal relationships.

METHODS

In this study, N = 76 independent genetic variants for male puberty timing (MPT) were derived from a large genome-wide association study (GWAS) on 205,354 participants and used as an instrumental variable in MR studies on 17 externalizing and internalizing traits and psychopathologies utilizing outcome GWAS with 16,400-1,045,957 participants.

RESULTS

In these MR studies, earlier MPT was significantly associated with higher scores for the overarching phenotype of 'Externalizing Traits' (b = -0.03, 95% CI [-0.06, -0.01]). However, this effect was likely driven by an earlier age at first sexual contact (b = -0.17, 95% CI [-0.21, -0.13]), without evidence for an effect on further externalizing phenotypes. Regarding internalizing phenotypes, earlier MPT was associated with higher levels of the 'Depressed Affect' subdomain of neuroticism (b = -0.04, 95% CI [-0.07, -0.01]). Late MPT was related to higher scores of internalizing traits in early life (b = 0.04, 95% CI [0.01, 0.08]).

CONCLUSIONS

This comprehensive MR study supports a causal effect of MPT on specific traits and behaviors. However, no evidence for an effect of MPT on long-term clinical outcomes (depression, anxiety disorders, alcohol dependency, cannabis abuse) was found.

Trajectories of genetic risk across dimensions of alcohol use behaviors

Background

Alcohol use behaviors (AUBs) manifest in a variety of normative and problematic ways across the life course, all of which are heritable. Twin studies show that genetic influences on AUBs change across development, but this is usually not considered in research identifying and investigating the genes linked to AUBs.

Aims

Understanding the dynamics of how genes shape AUBs could point to critical periods in which interventions may be most effective and provide insight into the mechanisms behind AUB-related genes. In this project, we investigate how genetic associations with AUBs unfold across development using longitudinal modelling of polygenic scores (PGSs).

Design

Using results from genome-wide association studies (GWASs), we created PGSs to index individual-level genetic risk for multiple AUB-related dimensions: Consumption, Problems, a variable pattern of drinking associated with a preference for beer (BeerPref), and externalizing behavior (EXT). We created latent growth curve models and tested PGSs as predictors of latent growth factors (intercept, slope, quadratic) underlying trajectories of AUBs.

Setting

PGSs were derived in six longitudinal epidemiological cohorts from the US, UK, and Finland.

Participants

Participant data were obtained from AddHealth, ALSPAC, COGA, FinnTwin12, the older Finnish Twin Cohort, and Spit for Science (total N = 19,194). These cohorts included individuals aged 14 to 67, with repeated measures collected over a span of 4 to 36 years.

Measurements

Primary measures included monthly frequency of typical alcohol consumption (CON) and heavy episodic drinking (HED).

Findings

Results indicated that higher PGSs for all AUBs are robustly associated with higher mean levels of CON and/or HED (B = 0.064-0.333, p < 3.09E-04). However, these same genetic indices were largely not associated with drinking trajectories across cohorts. In the meta-analysis, only PGSs for chronic alcohol Problems consistently predicted a steeper slope (increasing trajectory) of CON across time (B = 0.470, p = 4.20E-06).

Conclusions

The results indicate that genetic associations with AUBs not only differ between behaviors, but also across developmental time points and across cohorts. Genetic studies that take such heterogeneity into account are needed to better represent the underlying etiology of AUBs. Individual-level genetic profiles may be useful to point to personalized intervention timelines, particularly for individuals with high alcohol Problems genetic risk scores.

Interplay between polygenic risk and family processes in predicting trajectories of adolescent externalizing behaviors

Introduction

There is limited understanding on how polygenic scores derived from genome-wide association studies of adult and child psychopathology may uniquely predict childhood traits. The current study took a developmental approach to examine the interplay between adult-based and child-based polygenic scores with family processes in predicting trajectories of externalizing behaviors from late childhood to early adolescence among racially-ethnically diverse youth.

Method

Data were drawn from the non-Hispanic White (N = 5,907), non-Hispanic Black (N = 1,694), and Hispanic youth (N = 2,117) from the adolescent brain cognitive development (ABCD) study. Parents reported on youth externalizing behaviors at baseline (T1, age 9/10), 1-year (T2, age 10/11), 2-year (T3, age 11/12), and 3-year (T4, age 12/13) follow-up assessments. Youth reported on parenting and family environment at T1 and provided saliva or blood samples for genotyping.

Results

Both polygenic scores for adult externalizing and childhood aggression predicted greater likelihood of following trajectories with higher externalizing behaviors. Among non-Hispanic White youth, polygenic scores also predicted greater family conflict, which in turn predicted higher externalizing behavior trajectories.

Discussion

Our findings indicated that both adult-based and child-based polygenic scores for externalizing behaviors are useful in predicting trajectories of externalizing behaviors, highlighting developmental continuity in genetic influences. Family processes, especially family conflict, play an important role in adolescent externalizing behaviors across racial-ethnic groups, suggesting the need to target family conflict in intervention efforts. Findings also highlight the importance of conducting research in diverse populations, including improving diversity in genetically informed studies.

Bioenergetic-related gene expression in the hippocampus predicts internalizing vs. externalizing behavior in an animal model of temperament

Externalizing and internalizing behavioral tendencies underlie many psychiatric and substance use disorders. These tendencies are associated with differences in temperament that emerge early in development via the interplay of genetic and environmental factors. To better understand the neurobiology of temperament, we have selectively bred rats for generations to produce two lines with highly divergent behavior: bred Low Responders (bLRs) are highly inhibited and anxious in novel environments, whereas bred High Responders (bHRs) are highly exploratory, sensation-seeking, and prone to drug-seeking behavior. Recently, we delineated these heritable differences by intercrossing bHRs and bLRs (F0-F1-F2) to produce a heterogeneous F2 sample with well-characterized lineage and behavior (exploratory locomotion, anxiety-like behavior, Pavlovian conditioning). The identified genetic loci encompassed variants that could influence behavior via many mechanisms, including proximal effects on gene expression. Here we measured gene expression in male and female F0s (n = 12 bHRs, 12 bLRs) and in a large sample of heterogeneous F2s (n = 250) using hippocampal RNA-Seq. This enabled triangulation of behavior with both genetic and functional genomic data to implicate specific genes and biological pathways. Our results show that bHR/bLR differential gene expression is robust, surpassing sex differences in expression, and predicts expression associated with F2 behavior. In F0 and F2 samples, gene sets related to growth/proliferation are upregulated with bHR-like behavior, whereas gene sets related to mitochondrial function, oxidative stress, and microglial activation are upregulated with bLR-like behavior. Integrating our F2 RNA-Seq data with previously-collected whole genome sequencing data identified genes with hippocampal expression correlated with proximal genetic variation (cis-expression quantitative trait loci or cis-eQTLs). These cis-eQTLs successfully predict bHR/bLR differential gene expression based on F0 genotype. Sixteen of these genes are associated with cis-eQTLs colocalized within loci we previously linked to behavior and are strong candidates for mediating the influence of genetic variation on behavioral temperament. Eight of these genes are related to bioenergetics. Convergence between our study and others targeting similar behavioral traits revealed five more genes consistently related to temperament. Overall, our results implicate hippocampal bioenergetic regulation of oxidative stress, microglial activation, and growth-related processes in shaping behavioral temperament, thereby modulating vulnerability to psychiatric and addictive disorders.

SNP-Associated Differential Methylation in ARHGEF38: Insights into Genetic-Epigenetic Interactions

Objective

Associations have been seen between suicidal behavior and differential DNA methylation of certain genes, with one study showing significant hypomethylation of ARHGEF38 in postmortem brain samples from individuals with bipolar disorder who died by suicide. Our objective was to explore ARHGEF38 methylation in individuals with bipolar disorder and a history of suicide attempt.

Method

With pyrosequencing, we looked at the previously identified region of interest in ARHGEF38. We investigated the methylation levels of 3 CpG sites in 47 individuals with bipolar disorder and a history of suicide attempt, 47 individuals with bipolar disorder without a history of suicide attempt, and 47 non-bipolar disorder controls.

Results

None of the CpG sites measured had an association between groups, although there were distinct clusters of differential methylation in each group. Applying genotypes of SNPs found in the region of interest, rs2121558 and rs1447093, these clusters showed stepwise methylation at each CpG site, regardless of phenotype.

Conclusions

In this relatively small sample size study, differential methylation in ARHGEF38 was not associated with history of suicide attempt, failing to replicate findings from a related outcome, suicide death. However, we did provide evidence of SNP and DNA methylation interplay in this region. This highlights the potential relevance of considering genetics when interrogating epigenetic mechanisms.

Highlights

ARHGEF38 methylation is not associated with bipolar disorder and suicide attempt Methylation of ARHGEF38 is heavily influenced by the presence of SNPs Suicide phenotype, genetics, and sample type impact DNA methylation.

Do polygenic indices capture "direct" effects on child externalizing behavior problems? Within-family analyses in two longitudinal birth cohorts

Failures of self-control can manifest as externalizing behaviors (e.g., aggression, rule-breaking) that have far-reaching negative consequences. Researchers have long been interested in measuring children's genetic risk for externalizing behaviors to inform efforts at early identification and intervention. Drawing on data from the Environmental Risk Longitudinal Twin Study (N = 862 twins) and the Millennium Cohort Study (N = 2,824 parent-child trios), two longitudinal cohorts from the UK, we leveraged molecular genetic data and within-family designs to test for genetic associations with externalizing behavior that are not affected by common sources of environmental influence. We found that a polygenic index (PGI) calculated from genetic variants discovered in previous studies of self-controlled behavior in adults captures direct genetic effects on externalizing problems in children and adolescents when evaluated with rigorous within-family designs (β's = 0.13-0.19 across development). The externalizing behavior PGI can usefully augment psychological studies of the development of self-control.

Genome-wide association study reveals multiple loci for nociception and opioid consumption behaviors associated with heroin vulnerability in outbred rats

The increased prevalence of opioid use disorder (OUD) makes it imperative to disentangle the biological mechanisms contributing to individual differences in OUD vulnerability. OUD shows strong heritability, however genetic variants contributing to vulnerability remain poorly defined. We performed a genome-wide association study using over 850 male and female heterogeneous stock (HS) rats to identify genes underlying behaviors associated with OUD such as nociception, as well as heroin-taking, extinction and seeking behaviors. By using an animal model of OUD, we were able to identify genetic variants associated with distinct OUD behaviors while maintaining a uniform environment, an experimental design not easily achieved in humans. Furthermore, we used a novel non-linear network-based clustering approach to characterize rats based on OUD vulnerability to assess genetic variants associated with OUD susceptibility. Our findings confirm the heritability of several OUD-like behaviors, including OUD susceptibility. Additionally, several genetic variants associated with nociceptive threshold prior to heroin experience, heroin consumption, escalation of intake, and motivation to obtain heroin were identified. Tom1, a microglial component, was implicated for nociception. Several genes involved in dopaminergic signaling, neuroplasticity and substance use disorders, including Brwd1, Pcp4, Phb1l2 and Mmp15 were implicated for the heroin traits. Additionally, an OUD vulnerable phenotype was associated with genetic variants for consumption and break point, suggesting a specific genetic contribution for OUD-like traits contributing to vulnerability. Together, these findings identify novel genetic markers related to the susceptibility to OUD-relevant behaviors in HS rats.

Trans-ancestry Genome-Wide Analyses in UK Biobank Yield Novel Risk Loci for Major Depression

Most genome-wide association studies (GWASs) of depression focus on broad, heterogeneous outcomes, limiting the discovery of genomic risk loci specific to major depressive disorder (MDD). Previous UK Biobank (UKB) studies had limited ability to pinpoint MDD-associated loci due to a smaller sample with strictly defined MDD outcomes and further exclusion of many participants based on ancestry or relatedness, significantly underutilizing this resource's potential for elucidating the genetic architecture of MDD. Here, we present novel genomic insights into MDD by fully utilizing existing UKB data through (1) a trans-ancestry GWAS pipeline using two complementary approaches controlling for population structure and relatedness and (2) an increased sample with MDD symptom-level data across two mental health assessments. We identified strict MDD outcomes among 211,535 participants, representing a 38% increase in eligible participants from prior studies with only one assessment. Ancestrally inclusive analyses yielded 61 genomic risk loci across depression phenotypes, compared to 47 in the analyses restricted to participants genetically similar to European ancestry. Fourteen of these loci, including five novel, were associated with strict MDD phenotypes, whereas only one locus has been previously reported in UKB. MDD-associated genomic loci and predicted gene expression levels showed little overlap with broad depression, indicating higher specificity. Notably, polygenic scores based on these results were significantly associated with depression diagnoses across ancestry groups in the All of Us Research Program, highlighting the shared genetic architecture across populations. While the trans-ancestry analyses, which included non-European participants, increased the number of associated loci, the discovery of non-European ancestry-specific loci was limited, underscoring the need for larger, globally representative studies of MDD. Importantly, beyond these results, our GWAS pipeline will facilitate inclusive analyses of other traits and disorders, helping improve statistical power, representation, and generalizability in genomic studies.

Using twin-pairs to assess potential bias in polygenic prediction of externalising behaviours across development

Prediction from polygenic scores may be confounded by sources of passive gene-environment correlation (rGE; e.g. population stratification, assortative mating, and environmentally mediated effects of parental genotype on child phenotype). Using genomic data from 10 000 twin pairs, we asked whether polygenic scores from the most recent externalising genome-wide association study predict conduct problems, ADHD symptomology and callous-unemotional traits, and whether these predictions are biased by rGE. We ran regression models including within-family and between-family polygenic scores, to separate the direct genetic influence on a trait from environmental influences that correlate with genes (indirect genetic effects). Findings suggested that this externalising polygenic score is a good index of direct genetic influence on conduct and ADHD-related symptoms across development, with minimal bias from rGE, although the polygenic score predicted less variance in CU traits. Post-hoc analyses showed some indirect genetic effects acting on a common factor indexing stability of conduct problems across time and contexts.

Impulsivity facets and substance use involvement: insights from genomic structural equation modeling

BACKGROUND

Impulsivity is a multidimensional trait associated with substance use disorders (SUDs), but the relationship between distinct impulsivity facets and stages of substance use involvement remains unclear.

METHODS

We used genomic structural equation modeling and genome-wide association studies (N = 79,729-903,147) to examine the latent genetic architecture of nine impulsivity traits and seven substance use (SU) and SUD traits.

RESULTS

We found that the SU and SUD factors were strongly genetically inter-correlated (rG=0.77) but their associations with impulsivity facets differed. Lack of premeditation, negative and positive urgency were equally positively genetically correlated with both the SU (rG=.0.30-0.50) and SUD (rG=0.38-0.46) factors; sensation seeking was more strongly genetically correlated with the SU factor (rG=0.27 versus rG=0.10); delay discounting was more strongly genetically correlated with the SUD factor (rG=0.31 versus rG=0.21); and lack of perseverance was only weakly genetically correlated with the SU factor (rG=0.10). After controlling for the genetic correlation between SU/SUD, we found that lack of premeditation was independently genetically associated with both the SU (β=0.42) and SUD factors (β=0.21); sensation seeking and positive urgency were independently genetically associated with the SU factor (β=0.48, β=0.33, respectively); and negative urgency and delay discounting were independently genetically associated with the SUD factor (β=0.33, β=0.36, respectively).

CONCLUSIONS

Our findings show that specific impulsivity facets confer risk for distinct stages of substance use involvement, with potential implications for SUDs prevention and treatment.

Impact of life adversity and gene expression on psychiatric symptoms in children and adolescents: findings from the Brazilian high risk cohort study

Introduction

While the influence of both genetic and environmental factors on the development of psychiatric symptoms is well-recognized, the precise nature of their interaction throughout development remains a subject of ongoing debate. This study investigated the association between the expression of 78 candidate genes, previously associated with psychiatric phenotypes, in peripheral blood and both adversity and psychopathology in a sample of 298 young individuals assessed at two time points from the Brazilian High Risk Cohort Study for Mental Conditions (BHRCS).

Methods

Psychopathology was assessed using the Child Behavior Checklist (CBCL), considering the total CBCL, p-factor (i.e. general factor of psychopathology), and internalizing and externalizing symptoms as clinical variables. The life adversities considered in this study includes four composite variables: child maltreatment, stressful life events, threat and deprivation. Gene expression was measured using next-generation sequencing for target genes and differential gene expression was analyzed with the DESeq2 package.

Results

Mixed models revealed six genes associated with internalizing symptoms: NR3C1, HSPBP1, SIN3A, SMAD4, and CRLF3 genes exhibited a negative correlation with these symptoms, while FAR1 gene showed a positive correlation. Additionally, we also found a negative association between USP38 gene expression and externalizing symptoms. Finally, DENND11 and PRRC1 genes were negatively associated with deprivation, a latent factor characterized by neglect, parental absence, and measures of material forms of deprivation. No mediation or moderation effect was observed of gene expression on the association between life adversities and psychiatric symptoms, meaning that they might influence distinct pathways.

Discussion

Among these nine genes, NR3C1, which encodes a glucocorticoid receptor, is by far the most investigated, being associated with depressive symptoms, early life adversity, and stress. While further research is needed to fully understand the complex relationship between gene expression, life adversities, and psychopathology, our findings provide valuable insights into the molecular mechanisms underlying mental disorders.

Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder

Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictors of treatment response poses great challenges for personalized treatment. The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. Based on genotype data of medication-naïve patients with ADHD who received pharmacological treatments for 12 weeks, the current study performed GWAS using the percentage changes in ADHD-RS score as phenotype. Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-wide P thresholds (E-02, E-03, E-04, E-05) as inputs. The current GWAS results identified two significant loci (rs10880574, P = 2.39E-09; rs2000900, P = 3.31E-09) which implicated two genes, TMEM117 and MYO5B, that were primarily associated with both brain- and gut-related disorders. The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). Notably, the variant that contributed most to the CNN model was NKAIN2, an ADHD-related gene, which is also associated with metabolic processes. To conclude, the integration of GWAS and DL methods revealed new genes contribute to ADHD pharmacological treatment responses, and underscored the interplay between neural systems and metabolic processes, potentially providing critical insights into precision treatment. Furthermore, our CNN model exhibited good performance in an independent dataset, encouraged future studies and implied potential clinical applications.

Integrating HiTOP and RDoC Frameworks Part I: Genetic Architecture of Externalizing and Internalizing Psychopathology

Background

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).

Methods

We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in European-ancestry individuals (n = 16,400 to 1,074,629). Traits included clinical (e.g., major depressive disorder, alcohol use disorder) and subclinical measures (e.g., risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.

Results

A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.

Conclusions

The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

Sex-stratified Genomic Structural Equation Models of Posttraumatic Stress Inform PTSD Etiology: L'utilisation de la modélisation génomique par équations structurelles stratifiée par sexe du stress post-traumatique pour expliquer l'étiologie du TSPT

OBJECTIVE

Posttraumatic stress disorder (PTSD) affects 3.9%-5.6% of the worldwide population, with well-documented sex-related differences. While psychosocial and hormonal factors affecting sex differences in PTSD and posttraumatic stress (PTS) symptom etiology have been explored, there has been limited focus on the genetic bases of these differences. Many symptom combinations may confer a PTSD diagnosis. We hypothesized that these symptom combinations have sex-specific patterns, the examination of which could inform etiological differences in PTSD genetics between males and females.

METHODS

To investigate this, we performed a sex-stratified multivariate genome-wide association study (GWAS) in unrelated UK Biobank (UKB) individuals of European ancestry. Using GWAS summary association data, genomic structural equation modelling was performed to generate sex-specific factor models using 6 indicator variables: trouble concentrating, feeling distant from others, irritability, disturbing thoughts, upset feelings, and avoidance of places/activities which remind the individual of a traumatic event.

RESULTS

Models of male and female PTSD symptoms differed substantially (local standardized root mean square difference = 3.12) and significantly (χ2(5) = 28.03, P = 3.6 × 10-5). Independent 2-factor models best fit the data in both males and females; these factors were subjected to GWAS in each sex, revealing 3 genome-wide significant loci in females, mapping to SCAND3, WDPCP, and FAM120A. No genome-wide significant loci were identified in males. All 4 PTS factors (2 in males and 2 in females) were heritable.

CONCLUSIONS

By assessing the relationship between sex and PTSD symptoms, this study informs correlative and putatively causal etiological differences between males and females which support further investigation of sex differences in PTSD genetics.

Child maltreatment as a transdiagnostic risk factor for the externalizing dimension: a Mendelian randomization study

Observational studies suggest that child maltreatment increases the risk of externalizing spectrum disorders such as attention deficit hyperactivity disorder (ADHD), conduct disorder (CD), antisocial personality disorder (ASPD), and substance use disorder (SUD). Yet, only few of such associations have been investigated by approaches that provide strong evidence for causation, such as Mendelian Randomization (MR). Establishing causal inference is essential given the growing recognition of gene-environment correlations, which can confound observational research in the context of childhood maltreatment. Evaluating causality between child maltreatment and the externalizing phenotypes, we used genome-wide association study (GWAS) summary data for child maltreatment (143,473 participants), ADHD (20,183 cases; 35,191 controls), CD (451 cases; 256,859 controls), ASPD (381 cases; 252,877 controls), alcohol use disorder (AUD; 13,422 cases; 244,533 controls), opioid use disorder (OUD; 775 cases; 255,921 controls), and cannabinoid use disorder (CUD; 14,080 cases; 343,726 controls). We also generated a latent variable 'common externalizing factor' (EXT) using genomic structural equation modeling. Genetically predicted childhood maltreatment was consistently associated with ADHD (odds ratio [OR], 10.09; 95%-CI, 4.76-21.40; P = 1.63 × 10-09), AUD (OR, 3.72; 95%-CI, 1.85-7.52; P = 2.42 × 10-04), and the EXT (OR, 2.64; 95%-CI, 1.52-4.60; P = 5.80 × 10-04) across the different analyses and pleiotropy-robust methods. A subsequent GWAS on childhood maltreatment and the externalizing dimension from Externalizing Consortium (EXT-CON) confirmed these results. Two of the top five genes with the strongest associations in EXT GWAS, CADM2 and SEMA6D, are also ranked among the top 10 in the EXT-CON. The present results confirm the existence of a common externalizing factor and an increasing vulnerability caused by child maltreatment, with crucial implications for prevention. However, the partly diverging results also indicate that specific influences impact individual phenotypes separately.

Genomic structural equation modeling of reward-related traits: exploring the genetic factor structure and its relationship with psychopathology

Reward sensitivity has a partial genetic background, and extreme levels may increase vulnerability to psychopathology. This study explores the genetic factor structure underlying reward-related traits and examines how genetic variance links to psychopathology. We modeled GWAS data from ten reward-related traits: risk tolerance (N = 975,353), extraversion (N = 122,886), sensation seeking (N = 132,395), (lack of) premeditation (N = 132,667), (lack of) perseverance (N = 133,517), positive urgency (N = 132,132), negative urgency (N = 132,559), attentional impulsivity (N = 124,739), motor impulsivity (N = 124,104), and nonplanning impulsivity (N = 123,509) to derive their genetic factor structure. A GWAS on this structure was performed, and polygenic scores (PGS) were generated to test associations with problems related to attention, hyperactivity, autism, aggression, mood, anxiety, alcohol use, smoking, and drug use problems in up to 78,000 individuals from the Dutch Lifelines Study. A two-factor model fit best - "reward interest" (openness to rewards) and "impulsivity" (pursuit of rewards with little consideration of consequences). The reward interest PGS was positively associated with hyperactivity, alcohol, smoking, and drug use, and negatively with autism spectrum problems. The impulsivity PGS was positively associated with all studied psychopathology. These findings demonstrate the feasibility of using related traits to investigate the dimensionality of reward sensitivity and how distinct aspects may be linked to different psychopathology domains.

Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability

Genomic correlates of impulsivity have been identified in several genome-wide association studies (GWAS) using cross-sectional designs, but no studies have investigated the molecular genetic correlates of impulsivity phenotypes using longitudinally constructed traits. In 3860 unrelated European participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), we constructed longitudinal phenotypes for delay discounting and impulsive personality traits (as measured by the UPPS-P impulsive behavior scales) via assessment at ages 24, 26, and 28. We conducted GWASs of impulsivity using both cross-sectional and longitudinal phenotypes, estimated heritability and their phenotypic and genetic correlations, and evaluated their association with recently-developed polygenic risk scores (PRSs) for the impulsivity indicators themselves and also related psychiatric conditions. Latent growth curve modeling revealed a stable intercept over time for all impulsivity phenotypes. High genetic correlation of cross-sectional measures over time suggested a stable genetic component for delay discounting (rg = 0.53-0.99) and sensation seeking (rg = 0.99). Heritability estimates of the stable longitudinal phenotypes substantively improved as compared to their cross-sectional counterparts, revealing a significant SNP-heritability for delay discounting (0.22; p = 0.03) and sensation seeking (0.35; p = 0.0007). Consistent with previous reports, GWAS and gene-based analyses revealed associations between specific longitudinal impulsivity indicators and CADM2 and NCAM1 genes. The PRSs for the impulsivity indicators and disorders related to self-regulation were also significantly associated with longitudinal impulsivity traits. Finally, we validated the associations between longitudinal impulsivity phenotypes and their PRSs in an independent 13-wave longitudinal study (n = 1019) and the benefit of longitudinal phenotypes in simulation studies. In this first longitudinal genetic study of impulsivity traits, the results revealed stable genomic correlates of delay discounting and sensation seeking over time and further validated the utility of recently-developed PRSs, both in relation to the observed traits and in connecting them to psychiatric disorders. More generally, these findings support using latent intercepts as novel longitudinal phenotypes to boost signal for heritability and genomic correlates of mechanisms contributing to psychiatric disease liability.

Genetic associations between orexin genes and phenotypes related to behavioral regulation in humans, including substance use

The hypothalamic neuropeptide system of orexin (hypocretin) neurons provides projections throughout the neuraxis and has been linked to sleep regulation, feeding and motivation for salient rewards including drugs of abuse. However, relatively little has been done to examine genes associated with orexin signaling and specific behavioral phenotypes in humans. Here, we tested for association of twenty-seven genes involved in orexin signaling with behavioral phenotypes in humans. We tested the full gene set, functional subsets, and individual genes involved in orexin signaling. Our primary phenotype of interest was Externalizing, a composite factor comprised of behaviors and disorders associated with reward-seeking, motivation, and behavioral regulation. We also tested for association with additional phenotypes that have been related to orexin regulation in model organism studies, including alcohol consumption, problematic alcohol use, daytime sleepiness, insomnia, cigarettes per day, smoking initiation, and body mass index. The composite set of 27 genes corresponding to orexin function was highly associated with Externalizing, as well as with alcohol consumption, insomnia, cigarettes per day, smoking initiation and BMI. In addition, all gene subsets (except the OXR2/HCRTR2 subset) were associated with Externalizing. BMI was significantly associated with all gene subsets. The "validated factors for PPOX/HCRT" and "PPOX/HCRT upregulation" gene subsets also were associated with alcohol consumption. Individually, 8 genes showed a strong association with Externalizing, 12 with BMI, 7 with smoking initiation, 3 with alcohol consumption, and 2 with problematic alcohol use, after correction for multiple testing. This study indicates that orexin genes are associated with multiple behaviors and disorders related to self-regulation in humans. This is consistent with prior work in animals that implicated orexin signaling in motivational activation induced by salient stimuli, and supports the hypothesis that orexin signaling is an important potential therapeutic target for numerous behavioral disorders.

Systematic dissection of pleiotropic loci and critical regulons in excitatory neurons and microglia relevant to neuropsychiatric and ocular diseases

Advancements in single-cell multimodal techniques have greatly enhanced our understanding of disease-relevant loci identified through genome-wide association studies (GWASs). To investigate the biological connections between the eye and brain, we integrated bulk and single-cell multiomic profiles with GWAS summary statistics for eight neuropsychiatric and five ocular diseases. Our analysis uncovered five latent factors explaining 61.7% of the genetic variance across these 13 diseases, revealing diverse correlational patterns among them. We identified 45 pleiotropic loci with 91 candidate genes that contribute to disease risk. By integrating GWAS and single-cell profiles, we implicated excitatory neurons and microglia as key contributors in the eye-brain connections. Polygenic enrichment analysis further identified 15 pleiotropic regulons in excitatory neurons and 16 in microglia that were linked to comorbid conditions. Functionally, excitatory neuron-specific regulons were involved in axon guidance and synaptic activity, while microglia-specific regulons were associated with immune response and cell activation. In sum, these findings underscore the genetic link between psychiatric disorders and ocular diseases.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion. Here, we analyze wearable and genetic data from the Adolescent Brain Cognitive Development (ABCD) study. Leveraging >250 wearable-derived features as digital phenotypes, we show that an interpretable AI framework can objectively classify adolescents with psychiatric disorders more accurately than previously possible. To relate digital phenotypes to the underlying genetics, we show how they can be employed in univariate and multivariate genome-wide association studies (GWASs). Doing so, we identify 16 significant genetic loci and 37 psychiatric-associated genes, including ELFN1 and ADORA3, demonstrating that continuous, wearable-derived features give greater detection power than traditional case-control GWASs. Overall, we show how wearable technology can help uncover new linkages between behavior and genetics.

Cross-trait multivariate GWAS confirms health implications of pubertal timing

Pubertal timing is highly variable and is associated with long-term health outcomes. Phenotypes associated with pubertal timing include age at menarche, age at voice break, age at first facial hair and growth spurt, and pubertal timing seems to have a shared genetic architecture between the sexes. However, puberty phenotypes have primarily been assessed separately, failing to account for shared genetics, which limits the reliability of the purported health implications. Here, we model the common genetic architecture for puberty timing using a multivariate GWAS, with an effective population of 514,750 European participants. We find 266 independent variants in 197 loci, including 18 novel variants. Transcriptomic, proteome imputation and fine-mapping analyses reveal genes causal for pubertal timing, including KDM4C, LEPR, CCNC, ACP1, and PCSK1. Linkage disequilibrium score regression and Mendelian randomisation analysis establish causal associations between earlier puberty and both accelerated ageing and the risk of developing cardiovascular disease and osteoporosis. We find that alanine aminotransferase, glycated haemoglobin, high-density lipoprotein cholesterol and Parabacteroides levels are mediators of these relationships, and establish that controlling oily fish and retinol intake may be beneficial for promoting healthy pubertal development.

Understanding the Etiology of Externalizing Problems in Young Children: The Roles of Callous-Unemotional Traits and Irritability

OBJECTIVE

Callous-unemotional traits (CU), characterized as a lack of guilt and empathy, and irritability, a tendency to show anger and frustration, are 2 risk factors for externalizing behavioral problems. Externalizing problems, CU, and irritability are all heritable. However, there is a dearth of studies examining the genetic and environmental associations between the 3 domains. The present study partitioned joint and independent etiological pathways from CU and irritability to externalizing problems.

METHOD

The sample consisted of 614 pairs of 3-year-old twins from the Boston University Twin Project. Primary caregivers reported twins' externalizing problems, CU, and irritability using the Child Behavior Checklist. Biometric Cholesky models were used to estimate common and unique genetic and environmental variances among the 3 domains.

RESULTS

There were common genetic, shared environmental and nonshared environmental factors operating across all 3 domains. In addition, there were unique genetic and nonshared environmental factors, independent of the common effects, linking externalizing problems and CU, and externalizing problems and irritability, respectively. There were also genetic and nonshared environmental influences unique to externalizing problems, independent of CU and irritability.

CONCLUSION

Common genetic as well as shared and nonshared environmental associations among externalizing problems, CU, and irritability suggest, to some extent, that etiological influences are common to all 3 constructs. However, distinct genetic and child-specific nonshared environmental links separately from CU and irritability to externalizing problems, reveals the heterogeneity of externalizing problems, and suggests that they should not be considered a unitary outcome.

STUDY PREREGISTRATION INFORMATION

Study Preregistration: Understanding the Etiology of Externalizing Problems in Young Children: The Roles of Callous-Unemotional Traits and Irritability; https://doi.org/10.1016/j.jaac.2023.09.549.

Clinical, Genomic, and Neurophysiological Correlates of Lifetime Suicide Attempts among Individuals with an Alcohol Use Disorder

Introduction

Research has identified multiple risk factors associated with suicide attempt (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA.

Methods

We examined lifetime SA in 4,068 individuals with an AUD from the Collaborative Study on the Genetics of Alcoholism (23% lifetime SA; 53% female; mean age: 38). We explored risk for lifetime SA across other clinical conditions ascertained from a clinical interview, polygenic scores for comorbid psychiatric problems, and neurocognitive functioning.

Results

Participants with an AUD who attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, other substance use disorders (SUDs), and suicidal ideation. Polygenic scores for SA, depression, and PTSD were associated with increased odds of reporting an SA (ORs = 1.22-1.44). Participants who reported an SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small.

Conclusions

Overall, individuals with an AUD who report lifetime SA experience greater levels of trauma, have more severe comorbidities, and carry increased polygenic risk for other psychiatric problems. Our results demonstrate the need to further investigate SAs in the presence of SUDs.

Genetic neurodevelopmental clustering and dyslexia

Dyslexia is a learning difficulty with neurodevelopmental origins, manifesting as reduced accuracy and speed in reading and spelling. It is substantially heritable and frequently co-occurs with other neurodevelopmental conditions, particularly attention deficit-hyperactivity disorder (ADHD). Here, we investigate the genetic structure underlying dyslexia and a range of psychiatric traits using results from genome-wide association studies of dyslexia, ADHD, autism, anorexia nervosa, anxiety, bipolar disorder, major depressive disorder, obsessive compulsive disorder, schizophrenia, and Tourette syndrome. Genomic Structural Equation Modelling (GenomicSEM) showed heightened support for a model consisting of five correlated latent genomic factors described as: F1) compulsive disorders (including obsessive-compulsive disorder, anorexia nervosa, Tourette syndrome), F2) psychotic disorders (including bipolar disorder, schizophrenia), F3) internalising disorders (including anxiety disorder, major depressive disorder), F4) neurodevelopmental traits (including autism, ADHD), and F5) attention and learning difficulties (including ADHD, dyslexia). ADHD loaded more strongly on the attention and learning difficulties latent factor (F5) than on the neurodevelopmental traits latent factor (F4). The attention and learning difficulties latent factor (F5) was positively correlated with internalising disorders (.40), neurodevelopmental traits (.25) and psychotic disorders (.17) latent factors, and negatively correlated with the compulsive disorders (-.16) latent factor. These factor correlations are mirrored in genetic correlations observed between the attention and learning difficulties latent factor and other cognitive, psychological and wellbeing traits. We further investigated genetic variants underlying both dyslexia and ADHD, which implicated 49 loci (40 not previously found in GWAS of the individual traits) mapping to 174 genes (121 not found in GWAS of individual traits) as potential pleiotropic variants. Our study confirms the increased genetic relation between dyslexia and ADHD versus other psychiatric traits and uncovers novel pleiotropic variants affecting both traits. In future, analyses including additional co-occurring traits such as dyscalculia and dyspraxia will allow a clearer definition of the attention and learning difficulties latent factor, yielding further insights into factor structure and pleiotropic effects.

Shedding Light on Antisocial Behavior Through Genetically Informed Research

Antisocial behavior (ASB) refers to a set of behaviors that violate social norms and disregard the well-being and rights of others. In this review, we synthesize evidence from studies using genetically informed designs to investigate the genetic and environmental contributions to individual differences in ASB. We review evidence from studies using family data (twin and adoption studies) and measured DNA (candidate gene and genome-wide association studies) that have informed our understanding of ASB. We describe how genetically informative designs are especially suited to investigate the nature of environmental risk and the forms of gene-environment interplay. We also highlight clinical and legal implications, including how insights from genetically informed research can help inform prevention and intervention, and we discuss some challenges and opportunities within this field of research.

Social and Polygenic Risk Factors for Time to Comorbid Diagnoses in Individuals with Substance Use Disorders: A Phenome-Wide Survival Analysis

Importance

Persons with substance use disorders (SUD) often suffer from additional comorbidities, including psychiatric conditions and physical health problems. Researchers have explored this overlap in electronic health records (EHR) using phenome wide association studies (PheWAS) to characterize how different indicators are related to all conditions in an individual's EHR. However, analyses have been largely cross-sectional in nature.

Objective

To characterize whether various social and genetic risk factors are associated with time to comorbid diagnoses in electronic health records (EHR) after the first diagnosis of SUD.

Design

Leveraging those with EHR and whole-genome sequencing data in All of Us (N = 287,012), we explored whether social determinants of health are associated with lifetime risk of SUD. Next, within those with a diagnosed SUD (N = 17,460), we examined whether polygenic scores (PGS) were associated with time to comorbid diagnoses performing a phenome-wide survival analysis.

Setting

Participating health care organizations across the United States.

Participants

Participants in the All of Us Research Program with available EHR and genomic data.

Exposures

Social determinants of health and polygenic scores (PGS) for psychiatric and substance use disorders.

Main Outcomes and Measures

Phecodes for diagnoses derived from International Statistical Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification, codes from EHR.

Results

Multiple social and demographic risk factors were associated with lifetime SUD diagnosis. Most strikingly, those reporting an annual income <$10K had 4.5 times the odds of having an SUD diagnosis compared to those reporting $100-$150K annually (OR = 4.48, 95% CI = 4.01, 5.01). PGSs for alcohol use disorders, schizophrenia, and post-traumatic stress disorder were associated with time to their respective diagnoses (HRAUD = 1.10, 95% CI = 1.06, 1.14; HRSCZ = 1.13, 95% CI = 1.06, 1.20; HRPTSD = 1.15, 95% CI = 1.08, 1.22). A PGS for ever-smoking was associated with time to subsequent smoking related comorbidities and additional SUD diagnoses HRSMOK = 1.6 to 1.16).

Conclusions and Relevance

Social determinants, especially those related to income have profound associations with lifetime SUD risk. Additionally, PGS may include information related to outcomes above and beyond lifetime risk, including timing and severity.

Sleep debt-induced anxiety and addiction to substances of abuse: A narrative review

Substance Use Disorder (SUD) has been conceptualized as an outcome of a dysregulated reward system. However, individuals with SUD suffer from anxiety with an intensity depending on the abstinence period length. This review discusses the role of anxiety as a major contributor to the initiation and perpetuation of SUD, and its dependence on an up-regulated defense-antireward system. In addition, it is discussed that sleep debt, and its psychosocial consequences, promote anxiety, contributing to SUD generation and maintenance. Healthy sleep patterns can be disrupted by diverse medical conditions and negative psychosocial interactions, resulting in accumulated sleep debt and anxiety. Within this narrative review, we discuss the interplay between the motivation-reward and defense-antireward systems, framing the progression from recreational drug use to addiction. This interplay is nuanced by sleep debt-induced anxiety and its psychosocial consequences as contributory vulnerability factors in the genesis of addiction.

Individual differences in adolescent self-control: The role of gene-environment interplay

Self-control - the ability to alter unwanted impulses and behavior to bring them into agreement with goal-driven responses - is key during adolescence. It helps young people navigate through the myriad challenges they encounter while transitioning into adulthood. We review empirical milestones in our understanding of how individual differences in adolescent self-control exist and develop. We show how the use of molecular genetic measures allows us to move beyond nature versus nurture, and actually investigate how both nature and nurture explain individual differences in self-control. By highlighting the role of gene-environment correlations and gene-environment interactions, this paper aims to enthuse fellow researchers, with or without a background in genetics, to apply genetically sensitive designs too.

The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research

Beginning with the successful sequencing of the human genome two decades ago, the possibility of developing personalized health interventions based on one's biology has captured the imagination of researchers, medical providers, and individuals seeking health care services. However, the application of a personalized medicine approach to emotional and behavioral health has lagged behind the development of personalized approaches for physical health conditions. There is potential value in developing improved methods for integrating biological science with prevention science to identify risk and protective mechanisms that have biological underpinnings, and then applying that knowledge to inform prevention and intervention services for emotional and behavioral health. This report represents the work of a task force appointed by the Board of the Society for Prevention Research to explore challenges and recommendations for the integration of biological and prevention sciences. We present the state of the science and barriers to progress in integrating the two approaches, followed by recommended strategies that would promote the responsible integration of biological and prevention sciences. Recommendations are grounded in Community-Based Participatory Research approaches, with the goal of centering equity in future research aimed at integrating the two disciplines to ultimately improve the well-being of those who have disproportionately experienced or are at risk for experiencing emotional and behavioral problems.

Advancing Gene Discovery for Substance Use Disorders Using Additional Traits Related to Behavioral Disinhibition

Importance

Substance use disorders (SUDs) frequently co-occur with each other and with other traits related to behavioral disinhibition, a spectrum of outcomes referred to as externalizing. Nevertheless, genome-wide association studies (GWAS) typically study individual SUDs separately. This single-disorder approach ignores genetic covariance between SUDs and other traits and may contribute to the relatively limited genetic discoveries to date.

Objective

To identify the most effective model for capturing genetic relationships between SUDs and externalizing phenotypes, optimizing the detection of genetic influences on SUDs while maintaining specificity.

Design

We used Genomic SEM to estimate SNP effects on a broad factor representing liability to externalizing and SUDs, on factors representing liability to behavioral disinhibition and SUDs separately, and on residualized SUDs. Subsequent gene-based, tissue expression, and polygenic score (PGS) analyses were used to compare the ability of these alternative approaches to identify genetic influences on SUDs.

Setting

This study was carried out from May 2023 - September 2024.

Participants

We used GWAS summary statistics based on samples of European ancestry from previous studies of externalizing and SUD phenotypes in the main multivariate GWAS (N > 2.2 million). We used two independent samples to estimate polygenic associations, a family-based sample enriched for substance use problems (COGA; N = 7,530) and a population-based sample representative of the United States, (All of Us; N = 77,442).

Exposures

N/A.

Main Outcomes and Measures

Across the three factors (Externalizing; SUDs; Behavioral Disinhibition) and four residualized SUDs (alcohol, tobacco, opioid, and cannabis), we compared the number, putative function, previous associations of significant genomic risk loci and genes, and variance explained by polygenic scores in substance use outcomes.

Results

We identified genomic risk loci and genes uniquely associated with Externalizing that are relevant to the neurobiology of substance use. Genes identified for residual SUDs were involved in substance-specific processes (e.g., metabolism). The Externalizing PGS accounted for the most variance in substance outcomes relative to the PGS for the other factors and residual PGS appeared to capture substance specific signals.

Conclusions and Relevance

Our findings suggest that modeling both a broad genetic liability to externalizing behaviors and substance-specific liabilities enhances the detection of genetic effects related to SUDs and explains more variance in substance use outcomes.

Polygenic and Socioeconomic Contributions to Nicotine Use and Cardiometabolic Health in Early Mid-Life

INTRODUCTION

Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors.

AIMS AND METHODS

Drawing on data from 2337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes-use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c)-explained by PGI, SES, and lifestyle.

RESULTS

Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately.

CONCLUSIONS

PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules.

IMPLICATIONS

Study findings point to the complementary relationship of PGI and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed.

Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome

Metabolic syndrome (MetS) is a complex hereditary condition comprising various metabolic traits as risk factors. Although the genetics of individual MetS components have been investigated actively through large-scale genome-wide association studies, the conjoint genetic architecture has not been fully elucidated. Here, we performed the largest multivariate genome-wide association study of MetS in Europe (nobserved = 4,947,860) by leveraging genetic correlation between MetS components. We identified 1,307 genetic loci associated with MetS that were enriched primarily in brain tissues. Using transcriptomic data, we identified 11 genes associated strongly with MetS. Our phenome-wide association and Mendelian randomization analyses highlighted associations of MetS with diverse diseases beyond cardiometabolic diseases. Polygenic risk score analysis demonstrated better discrimination of MetS and predictive power in European and East Asian populations. Altogether, our findings will guide future studies aimed at elucidating the genetic architecture of MetS.

Investigating Trivariate Associations Between Risky Sexual Behavior, Internalizing Problems, and Externalizing Problems: A Twin Study

Risky sexual behavior (RSB) has been linked to externalizing problems, substance use, and, in a recent study by our lab, internalizing problems. The current study builds upon previous work investigating the relationship between RSB and internalizing problems (INT) by controlling for externalizing problems (EXT) to account for the correlation between INT and EXT. We used a twin sample from Colorado (N = 2,544) to investigate phenotypic and genetic relationships between the three latent constructs, as well as potential sex differences in those relationships. We hypothesized that the relationship between RSB and INT would be stronger for females than for males, whereas the relationship between RSB and EXT would be stronger for males than for females. We used phenotypic confirmatory factor analysis and multivariate twin analyses to address research questions. Our results show significant phenotypic relationships among RSB, INT, and EXT and provide modest evidence in males for a significant association between RSB and INT that persists when controlling for EXT, a finding which we interpret with caution. Our sex differences hypothesis was not fully supported, although the direction of effects was in the direction hypothesized for the association between RSB and INT. We discuss the complexity of RSB as a phenotype and the potential implications for public health.

Alcohol Use Disorder-Associated DNA Methylation in the Nucleus Accumbens and Dorsolateral Prefrontal Cortex

Background

Alcohol use disorder (AUD) has a profound public health impact. However, understanding of the molecular mechanisms that underlie the development and progression of AUD remains limited. Here, we investigated AUD-associated DNA methylation changes within and across 2 addiction-relevant brain regions, the nucleus accumbens and dorsolateral prefrontal cortex.

Methods

Illumina HumanMethylation EPIC array data from 119 decedents (61 cases, 58 controls) were analyzed using robust linear regression with adjustment for technical and biological variables. Associations were characterized using integrative analyses of public annotation data and published genetic and epigenetic studies. We also tested for brain region-shared and brain region-specific associations using mixed-effects modeling and assessed implications of these results using public gene expression data from human brain.

Results

At a false discovery rate of ≤.05, we identified 105 unique AUD-associated CpGs (annotated to 120 genes) within and across brain regions. AUD-associated CpGs were enriched in histone marks that tag active promoters, and our strongest signals were specific to a single brain region. Some concordance was found between our results and those of earlier published alcohol use or dependence methylation studies. Of the 120 genes, 23 overlapped with previous genetic associations for substance use behaviors, some of which also overlapped with previous addiction-related methylation studies.

Conclusions

Our findings identify AUD-associated methylation signals and provide evidence of overlap with previous genetic and methylation studies. These signals may constitute predisposing genetic differences or robust methylation changes associated with AUD, although more work is needed to further disentangle the mechanisms that underlie these associations and their implications for AUD.

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

Psychiatric disorders are complex and influenced by both genetic and environmental factors. However, studying the full spectrum of these disorders is hindered by practical limitations on measuring human behavior. This highlights the need for novel technologies that can measure behavioral changes at an intermediate level between diagnosis and genotype. Wearable devices are a promising tool in precision medicine, since they can record physiological measurements over time in response to environmental stimuli and do so at low cost and minimal invasiveness. Here we analyzed wearable and genetic data from a cohort of the Adolescent Brain Cognitive Development study. We generated >250 wearable-derived features and used them as intermediate phenotypes in an interpretable AI modeling framework to assign risk scores and classify adolescents with psychiatric disorders. Our model identifies key physiological processes and leverages their temporal patterns to achieve a higher performance than has been previously possible. To investigate how these physiological processes relate to the underlying genetic architecture of psychiatric disorders, we also utilized these intermediate phenotypes in univariate and multivariate GWAS. We identified a total of 29 significant genetic loci and 52 psychiatric-associated genes, including ELFN1 and ADORA3. These results show that wearable-derived continuous features enable a more precise representation of psychiatric disorders and exhibit greater detection power compared to categorical diagnostic labels. In summary, we demonstrate how consumer wearable technology can facilitate dimensional approaches in precision psychiatry and uncover etiological linkages between behavior and genetics.

Single-nucleus RNA-sequencing of orbitofrontal cortex in rat model of methamphetamine-induced sensitization

The behavioral sensitization, characterized by escalated behavioral responses triggered by recurrent exposure to psychostimulants, involves neurobiological mechanisms that are brain-region and cell-type specific. Enduring neuroadaptive changes have been observed in response to methamphetamine (METH) within the orbitofrontal cortex (OFC), the cell-type specific transcriptional alterations in response to METH sensitization remain understudied. In this study, we utilized Single-nucleus RNA-sequencing (snRNA-seq) to profile the gene expression changes in the OFC of a rat METH sensitization model. The analyses of differentially expressed genes (DEGs) unveiled cell-type specific transcriptional reactions associated with METH sensitization, with the most significant alterations documented in microglial cells. Bioinformatic investigations revealed that distinct functional and signaling pathways enriched in microglia-specific DEGs majorly involved in macroautophagy processes and the activation of N-methyl-D-aspartate ionotropic glutamate receptors (NMDAR). To validate the translational relevance of our findings, we analyzed our snRNA-seq data in conjunction with a transcriptomic study of individuals with opioid use disorder (OUD) and a large-scale Genome-Wide Association Studies (GWAS) from multiple externalizing phenotypes related to drug addiction. The validation analysis confirmed the consistent expression changes of key microglial DEGs in human METH addiction. Moreover, the integration with GWAS data revealed associations between addiction risk genes and the DEGs observed in specific cell types, particularly microglia and excitatory neurons. Our study highlights the importance of cell-type specific transcriptional alterations in the OFC in the context of METH sensitization and their potential translational relevance to human drug addiction.

Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders

Substance use disorders (SUDs) are highly prevalent and associated with excess morbidity, mortality, and economic costs. Thus, there is considerable interest in the early identification of individuals who may be more susceptible to developing SUDs and in improving personalized treatment decisions for those who have SUDs. SUDs are known to be influenced by both genetic and environmental factors. Polygenic scores (PGSs) provide a single measure of genetic liability that could be used as a biomarker in predicting disease development, progression, and treatment response. Although PGSs are rapidly being integrated into clinical practice, there is little information to guide clinicians in their responsible use and interpretation. In this Review, we discuss the potential benefits and pitfalls of the use of PGSs in the clinical care of SUDs, highlighting current research. We also provide suggestions for important considerations prior to implementing the clinical use of PGSs and recommend future directions for research.

Gene discovery and biological insights into anxiety disorders from a large-scale multi-ancestry genome-wide association study

We leveraged information from more than 1.2 million participants, including 97,383 cases, to investigate the genetics of anxiety disorders across five continental groups. Through ancestry-specific and cross-ancestry genome-wide association studies, we identified 51 anxiety-associated loci, 39 of which were novel. In addition, polygenic risk scores derived from individuals of European descent were associated with anxiety in African, admixed American and East Asian groups. The heritability of anxiety was enriched for genes expressed in the limbic system, cerebral cortex, cerebellum, metencephalon, entorhinal cortex and brain stem. Transcriptome-wide and proteome-wide analyses highlighted 115 genes associated with anxiety through brain-specific and cross-tissue regulation. Anxiety also showed global and local genetic correlations with depression, schizophrenia and bipolar disorder and widespread pleiotropy with several physical health domains. Overall, this study expands our knowledge regarding the genetic risk and pathogenesis of anxiety disorders, highlighting the importance of investigating diverse populations and integrating multi-omics information.