Justin D. Tubbs

Studies on schizophrenia-associated rare copy number variants (CNVs) have predominantly focused on people of European (EUR) ancestry. Here we present a rare CNV study of schizophrenia in East Asian (EAS) populations, comprising 20,903 cases and 23,258 controls. We observed a significantly elevated genome-wide rare CNV burden in EAS cases compared with controls. Cross-population comparisons showed largely consistent rare CNV effects on schizophrenia risk. In the EAS sample, we identified nine genome-wide-significant schizophrenia-associated rare CNV loci. Meta-analysis with EUR data yielded 14 significant loci, including 8 that reached genome-wide significance for the first time. Genes within these 14 loci were significantly less tolerant to loss-of-function variants than genes in other CNV loci. The new rare CNVs associated with schizophrenia in EAS populations showed higher carrier frequencies in EAS than in EUR populations (0.38% versus 0.0017%). Overall, this study underscores the importance of increasing population diversity to fully capture the genetic underpinnings of schizophrenia.

Aims: Cross‐national research on mental health trajectories requires harmonised measures that are valid and comparable. However, measurement scales often differ between cohorts in item content and cultural context. This study aimed to develop a framework for harmonising mental health measures across international cohorts, focussing on depression and anxiety symptoms. Methods: We applied a structured, theory‐driven approach to align item content across two longitudinal ageing cohorts that used different self‐report instruments with heterogeneous item codings: ELSA‐UK and ELSA‐Brasil. Data were collected before, during, and after the COVID‐19 pandemic, providing a natural experiment for examining temporal changes in mental health. The harmonisation process involved: (1) mapping items to DSM‐5 symptom domains via expert review; (2) transforming response formats into harmonised ordinal indicators; (3) leveraging the Harmony AI tool to identify semantically equivalent items; and (4) testing measurement invariance across waves and cohorts using multi‐group confirmatory factor analysis (MGCFA). Results: Scalar invariance was achieved for depression in both longitudinal and cross‐cohort models, enabling meaningful latent mean comparisons between cohorts. For anxiety, scalar invariance was supported within but not across cohorts, likely due to the limited number of conceptually matching items. Conclusion: The proposed harmonisation framework demonstrates the feasibility of robust cross‐cultural comparisons of mental health constructs. Its success depends on the conceptual alignment and quality of available items. This approach provides a methodological template for future harmonisation efforts in global health and psychiatric epidemiology.

In about a quarter of people with schizophrenia-spectrum disorder (SSD), the illness is unresponsive to standard antipsychotic treatment, yet the biological mechanisms underlying this remain poorly understood. Although such treatment-resistant schizophrenia (TRS) shares a substantial genetic liability with treatment-responsive schizophrenia, the limited efficacy of dopamine antagonists in TRS indicates that mechanisms beyond dopamine signalling likely contribute to treatment-resistance, requiring the identification of alternative biological pathways. This is the first cross-ancestry genetic study to investigate the genetic architecture of TRS, by directly comparing patients with treatment-resistant and treatment-responsive schizophrenia in two independent Hong Kong (N=798) and STRATA-G consortium (N=1243) cohorts. Using an integrated multi-level analytic framework, we conducted a genome-wide association study (GWAS) with gene-based and gene set-based analyses, pathway polygenic-risk-scores, and transcriptome-wide association study (TWAS). We further conducted gene-set enrichment analysis focusing on expert-curated synaptic pathways and brain tissues. Genetic signals at the gene, pathway, and genetically predicted expression levels were identified within each ancestry. Whereas limited power constrained individual loci discovery and cross-ancestry concordance, enrichment analyses indicated heterogeneous signals across cohorts, including differences in effect direction, but highlighted cohort-specific, synapse-related biology, particularly pathways involved in presynaptic vesicle dynamics, neurotransmission, and synaptic organization. Collectively, these findings highlight synaptic biology as one potential pathway-level signal from common-variant genetic effects associated with treatment resistance in SSD, despite minimal SNP-level discovery. Our work suggests there is promise in pathway-level and multi-omics approaches to elucidate biologically meaningful heterogeneity within SSD and provides support for synaptic mechanisms as potential targets for understanding and stratifying treatment-resistance.

Both sustained attention (SA) and executive functioning (EF) are critical for adaptive behavior and compromised across a range of psychiatric disorders, yet their etiological relationships remain underexplored. Here, we report the first multivariate characterization of molecular genetic influences on SA (vigilance, lapse recovery cost, lapse propensity) and EF (processing speed, response selection, working memory). In collaboration with 23andMe, we report previously unpublished genome-wide association studies of these phenotypes in a single cohort of more than 20,000 individuals enriched for diagnoses of major depressive disorder and bipolar disorder. We used Genomic Structural Equation Modeling to formally model patterns of genetic covariance among these task-based measures of cognition, and their relationships with other cognitive, clinical, and imaging-derived phenotypes. We identified two distinct latent genetic factors: one influencing EF and one influencing SA. Both the EF and SA factors were genetically correlated with cognitive and clinical phenotypes, with each latent factor uniquely linked to liability for psychiatric disorders, including attention-deficit/hyperactivity disorder. However, genetic correlations with imaging-derived neuroanatomical phenotypes were modest and non-significant after correction for multiple comparisons. Collectively, these results provide an initial multivariate genetic characterization of SA and EF, though replication in larger and more diverse samples will be important. They suggest that genetic influences on sustained attention are generally distinct from those that influence executive function. The EF and SA factors show distinct patterns of genetic overlap with multiple cognitive and psychiatric outcomes, underscoring the need for more granular cognitive phenotyping to generate new insights into the genetic architecture of cognition and the etiology of psychopathology.

Importance: The impact of COVID-19 containment policies (e.g., physical distancing, school closures) on population anxiety has been debated and difficult to resolve. Objective: To estimate the joint effects of state-level COVID-19 containment policies on anxiety symptoms during the early pandemic. Design: Retrospective analysis of a prospective cohort with cross-sectional outcome assessment. Setting: All of Us Research Program, a U.S. national research cohort. Participants: 40,610 adult participants who completed the All of Us COPE survey in July 2020. Exposures: Seven state-level COVID-19 containment policies (school closures, workplace closures, cancellation of public events, restrictions on gatherings, public transport closures, stay-at-home requirements, and restrictions on internal movement) measured from March 22 to May 23, 2020, via the Oxford COVID-19 Government Response Tracker (OxCGRT). Main outcomes and measures: The primary outcome was anxiety symptoms (GAD-7) in July 2020. Using quantile g-computation, we classified policies as anxiety-increasing or anxiety-decreasing by the sign of their training-set contributions, then re-estimated joint effects in a holdout testing set. Results: Among participants (64% female; mean age: 57.8 years), 13.3% (n=5398) reported moderate-to-severe anxiety (GAD-7 score 10-21) in July 2020. The joint effect of all seven containment policies was not significant (β = 1.88, 95% CI: -0.51 to 4.28, p = 0.12). An anxiety-increasing joint effect from 4 policies (school, workplace, public events, internal movement; β = 2.98, 95% CI: 0.30 to 5.66, p = 0.03) and an anxiety-decreasing joint effect from 3 policies (gatherings, public transport, stay-at-home; β = -1.10, 95% CI: -1.75 to -0.44, p = 0.002) reached significance. Effects were largest in adults 18-44 (anxiety-increasing β = 8.93, 95% CI: 1.50 to 16.37, p = 0.02; anxiety-decreasing β = -2.81, 95% CI: -4.98 to -0.64, p = 0.01), with no significant effects in adults 45 and older. Conclusions and Relevance: Modeling seven containment policies jointly showed no net anxiety effect, a result that masked opposing-direction effects. Partitioning by effect direction revealed significant joint effects exceeding single-policy estimates, with young-adult point estimates above the 4-point GAD-7 minimal clinically important difference (MCID) though lower CI bounds fell below it. These findings may inform the use of containment policies in future pandemics, given their differing association with population anxiety.

Sleep and circadian traits have been widely studied using common variants, but the contribution of rare coding variation remains unclear. We analyzed rare coding variants in 397,065 whole-exome sequenced UK Biobank participants across 36 sleep phenotypes from self-report, diagnoses, sleep medication use and accelerometry, and meta-analyzed results with 171,536 whole-genome sequenced All of Us participants of diverse ancestries, with replication in the Mass General Brigham Biobank (N = 31,275). We identified 260 genes associated with sleep phenotypes, including novel associations with sleep medication use in 29 genes and 24 out of 29 have not previously been reported with any sleep phenotypes. We observed modest but significant rare variant heritability and strong genetic correlations between sleep medication use, insomnia and fatigue. Temporal gene expression trajectory analyses indicate that genes associated with self-reported sleep traits show constant high prenatal expression, whereas genes linked to sleep medication phenotypes exhibit peak expression in the late prenatal period. These findings highlight distinct biological mechanisms captured by different measurement sources of sleep phenotypes and reveal rare-variant-informed targets for therapeutic discovery.

Importance: Individuals with psychiatric disorders face elevated cardiometabolic risk which is linked to increased mortality. The extent to which this reflects shared pathogenesis or the downstream effects of illness and treatment remains poorly understood. Objective: To characterize the direct pleiotropic effects of psychiatric genetic liability on circulating metabolites and aggregate cardiometabolic risk, independent of psychiatric diagnosis and psychotropic medication use. Design, Setting and Participants: Cross-sectional analysis of Mass General Brigham Biobank participants with metabolomic profiling, genomic data, and linked electronic health records. Exposures: Genetic liability to nine psychiatric disorders quantified using polygenic risk scores (PRS): attention deficit/hyperactivity disorder (ADHD), anorexia nervosa (ANO), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), PTSD, schizophrenia (SCZ), and substance use disorder (SUD). Main Outcomes and Measures: 249 circulating metabolites and four metabolomic risk scores (MRS) for type 2 diabetes, myocardial infarction, ischemic stroke, and vascular dementia. PRS-metabolite associations were estimated using nested models adjusting for lifetime psychiatric diagnosis and psychotropic medication use. Results: Across 25,290 participants, we identified 604 significant PRS-metabolite associations (Bonferroni p< 1.36 × 10 ^-4 ), of which 89% persisted after adjustment for lifetime diagnosis and medication use, suggesting that the direct genetic effects on metabolism are largely independent of illness or treatment. PRS for MDD, PTSD, and ADHD showed the most extensive dysregulation, with a transdiagnostic pattern of elevated lipids and systemic inflammation, specifically triglycerides (β = 0.04 to 0.05, all p< 4.4 ×10 ^-13 ) and glycoprotein acetyls (β = 0.05, all p< 2.2 ×10 ^-16 ). Notably, PRS for SCZ and BD showed minimal metabolite dysregulation despite having the strongest association with their target diagnoses. PRS for MDD, PTSD, ADHD, and SUD were associated with increased MRS across cardiometabolic conditions (β = 0.03 to 0.08, all p< 2.1 ×10 ^-4 ). Sensitivity analyses controlling for BMI or excluding participants without any psychiatric history (N: 21,305 and 11,150, respectively) showed a similar pattern. Conclusions and Relevance: Psychiatric genetic liability is associated with systemic metabolic dysregulation independent of illness onset or treatment, supporting a partially pleiotropic basis for psychiatric-cardiometabolic comorbidity. Key findings: Question: Does genetic liability to psychiatric disorders influence peripheral metabolism independent of psychiatric diagnosis and treatment? Findings: In 25,290 participants, polygenic scores for major depressive disorder, posttraumatic stress disorder, and attention deficit hyperactivity disorder showed widespread associations with metabolic dysregulation, such as elevated triglycerides and systemic inflammation, which persisted after adjusting for diagnosis and medication use. Polygenic scores for schizophrenia and bipolar disorder showed minimal metabolite associations despite their well-established cardiometabolic burden. Meaning: Shared genetic pathways partially underlie psychiatric and cardiometabolic comorbidity, implicating transdiagnostic biomarker profiles.

Stimulant use disorder (StimUD) is a significant public health problem, but genetic studies have been limited by small sample sizes. We conducted genome-wide association studies (GWAS) of StimUD in the Million Veteran Program (MVP) and All of Us (AOU), followed by meta-analysis with FinnGen and 10 additional datasets, for a total of 709,369 individuals (N _cases =33,977, N _controls =675,392) in four broad ancestry groups: European (EUR) (N _cases =22,564, N _controls =624,672), African (AFR) (N _cases =7,574, N _controls =34,189), Admixed American (AMR) (N _cases =3,657, N _controls =15,698), and East Asian (EAS) (N _cases =182, N _controls =833). Population-specific SNP heritability was 6.1% in EUR and 2.4% in AFR. We discovered a total of 19 genome-wide-significant loci, six in EUR, including DRD2 *rs5794864, P =7.32×10 ⁻¹⁰ , one in AFR, five in a multi-ancestry meta-analysis, including CHRNA5 *rs55781567, P =3.27×10 ⁻⁹ , two in a male-only meta-analysis, including FTO *rs8057044, P =9.50×10 ⁻⁹ , and five in a meta-analysis of sex-stratified results. In a hold-out AOU subsample (N _EUR =18,841, N _AFR =12,263, N _AMR =9,739), ancestry-specific polygenic risk scores were significantly associated with StimUD in EUR (OR=3.28, 95% confidence interval (CI)=2.89-3.71) and AMR (OR=2.01, 95% CI=1.71-2.37). Transcriptome-wide association studies, fine-mapping, and colocalization analyses prioritized additional genes (e.g., GPX1 , BSN ). Genetic correlation, Mendelian randomization, and causal mixture analyses revealed relationships with other substance use and use disorder phenotypes, including cannabis use disorder (r _g =0.94, P =5.43×10 ⁻²³⁷ ) and opioid use disorder (r _g =1.01, P =4.40×10 ⁻¹⁰⁷ ), and other psychiatric traits, including anxiety, depression, neuroticism, and attention-deficit/hyperactivity disorder. This is the first well-powered GWAS of StimUD, and it offers significant insights into disease biology.

Biomarkers can potentially improve the diagnosis, monitoring, and treatment of posttraumatic stress disorder (PTSD). However, PTSD biomarkers that are scalable and easily integrated into real-world clinical settings have not been identified. The analysis was conducted between June to November 2024 using genomic samples and laboratory test results recorded in the Mass General Brigham (MGB) Health System. The analysis included 23,743 European ancestry participants from the nested MGB Biobank study. The first exposure was polygenic risk score (PRS) for PTSD, calculated using the largest available European ancestry genome-wide association study (GWAS), employing a Bayesian polygenic scoring method. The second exposure was a clinical diagnosis of PTSD, determined by the presence of two or more instances of PTSD-related diagnostic codes in the longitudinal electronic health records (EHR). The primary outcomes were the inverse normal quantile transformed, median lab values of 241 laboratory traits with non-zero h²_SNP estimates. Sixteen unique laboratory traits across the cardiometabolic, hematologic, hepatic, and immune systems were implicated in both genomic and phenotypic lab-wide association scans (LabWAS). Two-sample Mendelian randomization analyses provided evidence of potential unidirectional causal effects of PTSD liability on hepatic (decreased albumin and total bilirubin), cardiometabolic (decreased HDL cholesterol and increased VLDL cholesterol), and hematologic (decreased mean platelet volume) markers. These findings demonstrate the potential of a triangulation approach to uncover scalable and clinically relevant biomarkers for PTSD.