Electroencephalography (EEG) has emerged as a key method for investigating the neural mechanisms through which oxytocin influences cognition and behavior. EEG is cost-effective, has excellent temporal precision, and may elucidate neural correlates of emotional and cognitive processes. EEG studies evaluating oxytocin's electrophysiological effects have, however, yielded mixed results, which is likely driven by heterogeneity in EEG measures, study designs, dosages, and samples. To investigate the effect of oxytocin administration on EEG measures, we performed two multilevel random effects meta-analyses: The first meta-analysis synthesized studies investigating the effects of oxytocin administration on different neural correlates of social and cognitive processing; the second meta-analysis synthesized studies evaluating effects of oxytocin administration on exploratory, less task-specific neural activity measures, such as the modulation of microstates. Across both meta-analyses, we synthesized 161 effect sizes from 28 randomized controlled trials with a total of 1361 participants from different population groups. These multilevel meta-analyses yielded statistically significant, small effect sizes of oxytocin administration across different EEG measures reflecting social and cognitive processes (Hedges' g = 0.14), and exploratory neural activity (Hedges' g = 0.28) with significant heterogeneity estimates (p < 0.01 and p < 0.001, respectively). Moderator analyses revealed that the different EEG measurements of interest (e.g., event-related potentials) and the proportion of female participants were found to significantly moderate the effect of oxytocin on neural EEG activity. Altogether, these meta-analyses present tentative evidence for oxytocin administration modulating a wide range of EEG-based markers of neural activity. We observed substantial heterogeneity across studies-in terms of study designs, experimental paradigms and EEG measurements, and participant characteristics. More research is warranted to map out the context-specific effects of oxytocin administration on different neural markers, to better understand the neurobiological mechanisms of oxytocin.

The prevalence, timing and disease course of mental and neurological disorders vary according to sex, yet the neurogenetic mechanisms underlying sex differences in brain disorders remain poorly understood. While sex chromosomes and hormones contribute to sex differences in brain biology, previous studies suggest a role for autosomal genetic variation as well. We investigated autosomal genetic associations with brain volumes in 15,740 females and 15,740 males from the UK Biobank, matched for age and scan site, using sex-stratified genome-wide association analyses. We applied a multivariate genome-wide approach (MOSTest) across 257 brain regions and complemented these analyses with region-specific univariate genome-wide association studies. Heritability estimates and genetic correlations were highly similar between females and males in late adulthood, indicating largely shared genetic influences on brain volumes. Many loci reaching genome-wide significance in one sex also showed signal in the other. Gene mapping in these loci yielded a greater total number of brain-volumes associated genes in females than in males. Variability in the number of mapped genes was particularly pronounced in limbic regions such as the insula, cingulate cortex, hippocampus and amygdala. Overall, our findings contribute to a better understanding of autosomal genetic influences on brain volumes in males and females and may inform future studies examining sex variability in neurobiological mechanisms relevant to brain disorders. Mental and neurobiological disorders often differ between females and males. Some disorders are more common in one sex than the other, and symptoms can present or evolve differently. Variation in brain biology, partly shaped by genetic factors, may contribute to these patterns. In this study, we investigated genetic factors linked to brain structure in females and males separately. We focused on the volumes of 257 brain regions and analyzed genetic data from more than 30,000 adults from the UK Biobank. Overall, we found that the genetic influences on brain volumes are largely similar between females and males in late adulthood. At the same time, we observed that the number of genes associated with brain volumes was higher in females than in males. These variations in number of genes were marked in brain regions involved in the limbic system. Together, our findings improve our understanding of how genetics contribute to brain structure in females and males and may inform future studies examining sex-related variation in brain disorders. Genome-wide influences on brain volumes are highly correlated between females and males in late adulthood. Gene-level variability was observed, with a higher number of genes mapped from genome-wide significant loci in females than in males in both multivariate and univariate analyses. This gene-level variability was most pronounced in limbic regions, such as the insular and cingulate cortices.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Background:Post-COVID syndrome (PCS) remains a substantial public health concern, yet its genetic determinants are poorly understood. Psychiatric disorders and related traits influence infection risk and acute COVID-19 outcomes, raising the possibility that shared genetic liability may also shape long-term symptom persistence. We examined whether polygenic scores (PGS) for schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), and neuroticism are associated with PCS, and explored potential pathways underlying these associations. Methods:We analysed three population-based cohorts from Denmark, Norway and Iceland (total n = 80,726; PCS cases = 6103) between March 2020 and June 2022. PCS was defined as COVID-19-related symptoms lasting ≥3 months. Logistic regression models estimated associations between standardised PGS and PCS among COVID-19 positive individuals, adjusting for ancestry principal components. Additional analyses assessed associations with COVID-19 infection. In a subset with available personality data, models were additionally adjusted for measured Neuroticism (NEO-FFI). Supplementary analyses examined associations between PGS and COVID-19 infection risk, and we conducted LD score regression (LDSC) and proteomic analyses as contextual genetic and biological characterisations of the PGS traits. Findings:Higher PGS for neuroticism, MDD and ADHD were consistently associated with increased odds of PCS across all cohorts (ORs per SD: ∼1.07-1.16). Quintile analyses showed a graded pattern, with the highest PGS quintile displaying 30-45% higher odds of PCS than the lowest. PGS for SCZ and BPD showed no evidence of association with PCS. PGS associations with COVID-19 infection were weaker and inconsistent. In the subset with available personality data, these associations remained essentially unchanged after adjusting for measured Neuroticism, indicating that they are not solely attributable to observed personality differences. LDSC and proteomic analyses did not alter the primary interpretation of the PGS-PCS associations. Interpretation:Polygenic liability for neuroticism, MDD, and ADHD is associated with increased risk of PCS across three national cohorts. This pattern is consistent with shared symptom-related liability contributing to these associations, although the data do not allow differentiation between post-viral sequelae and pre-existing symptom liability. While PGS explain only a modest proportion of PCS variance, they provide useful insight into underlying psychiatric and personality-related factors associated with persistent symptom reporting following COVID-19 infection. Funding:The study was funded by EU Horizon REACT study (101057129), environMENTAL study (101057429), Nordforsk (project numbers 105668 and 138929), and the Independent Research Fund Denmark (0214-00127B).

BACKGROUND:The extensive genetic overlap between anxiety disorders (ANX) and major depression (MD) may partly reflect the inclusion of comorbid cases in genome-wide association studies (GWASs). We investigated this genetic relationship between ANX and MD, with and without mutual comorbidity. METHODS:Using the UK Biobank, we performed disorder-specific GWASs for ANX-only (cases/controls = 9980/179,442) and MD-only (cases/controls = 15,301/179,038) and derived polygenic risk scores (PRSs). In the Norwegian Mother, Father, and Child Cohort Study (MoBa), we tested associations between PRS and MD-only (n = 7486), ANX-only (n = 1992), and comorbid ANX and MD (ANX-MD) (n = 3468) cases and controls (n = 85,851). PRS associations with anxiety and depression symptoms were tested in MoBa (n = 54,862). GWASs including comorbid cases (MD-comorbid [MD with comorbid ANX] or ANX-comorbid [ANX with comorbid MD]) were used for comparison. Genetic correlations were compared by comorbidity status, and Mendelian randomization was employed to assess causal relationships. RESULTS:MD-comorbid and ANX-comorbid PRSs showed a stronger association with ANX-MD cases than with their primary disorders, MD-only (z = -2.82, padjusted = .01) and ANX-only (z = -2.36, padjusted = .03), respectively. MD-only PRS was more strongly associated with MD-only than with ANX-only cases (z = 3.63, padjusted = 6.9 × 10-4). The genetic correlation (rg) was lower between ANX-only and MD-only (rg = 0.53, SE = 0.11) than between ANX-comorbid and MD-comorbid (rg = 0.91, SE = 0.01). Bidirectional causal effects observed in comorbidity-inclusive analyses were attenuated to null when comorbid states were excluded. Gene sets of MD-comorbid, ANX-comorbid, and MD-only, but not of ANX-only, were enriched for the immune regulation pathway-interleukin 21 production. CONCLUSIONS:The genetic distinction between ANX and MD becomes more pronounced when comorbid cases are excluded. The findings underscore the importance of disorder-specific genetic studies for advancing precision medicine.

The functional domain of the cerebellum has expanded beyond motor control to also include cognitive and affective functions. In line with this notion, cerebellar volume has increased over recent primate evolution, and cerebellar alterations have been linked to heritable mental disorders. To map the genetic architecture of human cerebellar morphology, we here studied a large imaging genetics sample from the UK Biobank (n discovery = 27,302; n replication: 11,264) with state-of-the art neuroimaging and biostatistics tools. Multivariate GWAS on regional cerebellar MRI features yielded 351 significant genetic loci (226 novel, 94% replicated). Lead SNPs showed positive enrichment for relatively recent genetic mutations over the last 20-40k years (i.e., overlapping the Upper Paleolithic, a period characterized by rapid cultural evolution), while gene level analyses revealed enrichment for human-specific evolution over the last ∼6-8 million years. Finally, we observed genetic overlap with major mental disorders, supporting cerebellar involvement in psychopathology.

Abstract Tourette Syndrome and other tic disorders (TD) are common, highly heritable neurodevelopmental conditions with complex genetic architectures. We conducted a genome-wide association study of 13,247 TD cases and 536,217 European ancestry controls and identified six independent genome-wide significant loci, including a pleiotropic signal at 3p21 shared with attention-deficit/hyperactivity disorder, among other traits. Gene prioritization highlighted 20 genes, including PCDH9, HCN1, NCKIPSD, WDR6, DALRD3 , and CELSR3 . Integrative analyses provide genetic support for the role of cortico-striato-thalamo-cortical circuits in TD pathophysiology and further localize TD genetic risk to specific cell types, including dopamine D1- and D2-receptor-positive medium spiny neurons, cortical pyramidal neurons, and oligodendrocyte-lineage cells. We further demonstrate extensive genetic correlations with neurodevelopmental and psychiatric traits, but not with neurological disorders. These findings advance our understanding of the genetic basis of TD, pinpointing specific genes and cell types that drive pathophysiology and providing a foundation for future mechanistic studies.

OBJECTIVES:The intrauterine environment may influence childhood immune-mediated disease risk. We investigated whether maternal prepregnancy body mass index (BMI), gestational weight gain (GWG) or hypertensive disorders during pregnancy (HDP) were associated with juvenile idiopathic arthritis (JIA) in children and whether genetic susceptibility modified associations. METHODS:We linked the Norwegian Mother, Father and Child Cohort Study (MoBa) to the Norwegian Patient Registry to identify children with JIA. Logistic regression estimated adjusted ORs (aORs) for associations with BMI, GWG and HDPs. Causal mediation analyses assessed whether hypertension mediated the GWG-JIA association. In a genotyped subsample, we tested interactions between exposures and a JIA Polygenic Risk Score (PRS). RESULTS:Among 78 901 eligible children, 265 developed JIA (genotyped subsample: 192 JIA/44 053 non-JIA). By the end of follow-up, all children were ≥14 years old and 85% were ≥16 years old. Maternal prepregnancy BMI was not associated with JIA. Each 1 SD (~6 kg) increase in GWG was associated with modestly higher odds of JIA (aOR 1.12, 95% CI 1.00 to 1.26), particularly among women with prepregnancy obesity (aOR 1.40, 95% CI 1.10 to 1.77). Maternal hypertension and pre-eclampsia were associated with higher JIA risk (aOR 1.43, 95% CI 1.02 to 1.99 and aOR 1.61, 95% CI 1.10 to 2.37). Hypertension did not mediate the GWG-JIA association (p>0.05). Among children with lower PRS, maternal obesity was associated with a lower JIA risk (aOR 0.37, 95% CI 0.17 to 0.80). CONCLUSION:Higher GWG and HDPs were independently associated with increased JIA risk. Genetic susceptibility may modify associations with maternal BMI, supporting a role for pregnancy health in JIA development.

Structural variants, including copy number variants (CNVs), confer substantial risk for neurodevelopmental and psychiatric disorders (NPDs), yet whether their cortical effects relate to those observed in the psychiatric conditions they predispose to remains unclear. Here, we present the first systematic comparison of cortical phenotypes across 18 NPD-associated CNVs and aneuploidies, disorder-associated common variants, and 8 psychiatric disorders. Rare CNVs preferentially affected total surface area (SA), with 11-fold larger effects than psychiatric diagnoses, while NPDs preferentially affected mean cortical thickness (CT), with most CT effects observed in medicated subgroups, suggesting non-genetic contributions. NPD-associated common variants showed enrichment in SA but not CT associations. Regionally, both rare and common genetic variants showed larger effects in sensorimotor regions, aligning with the sensorimotor-to-association cortical gradient as well as regional heritability estimates. In contrast, psychiatric diagnoses showed larger effects in association regions. Individual NPD-associated variants were evenly split between those increasing and decreasing surface area. This heterogeneity likely explains why aggregating variants using polygenic scores shows only weak associations with SA. Overall, cortical signatures of psychiatric diagnoses diverge from those associated with genetic risk. Genetic variants preferentially impact SA and sensorimotor regions through early developmental mechanisms, while psychiatric diagnoses are associated with CT and association regions likely reflecting medication, illness chronicity, and environmental factors.

BACKGROUND:Obesity, which is common in bipolar disorder (BD), is associated with smaller hippocampal volumes. We do not know the role of weight/weight gain in relation to longitudinal hippocampal changes among individuals with BD. METHODS:In collaboration with the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-BD Working Group, we obtained T1-weighted magnetic resonance imaging and clinical data from 233 participants with BD and 701 healthy control participants (HCs) scanned twice, 2.84 ± 1.63 years apart on average. We estimated subcortical volumes using FreeSurfer longitudinal image processing stream and used linear mixed models to assess the bidirectional relationship between baseline body mass index (BMI) or BMI change and hippocampal volume or volume change. While the hippocampus was our a priori region of interest, we repeated these analyses in other subcortical regions. RESULTS:Baseline BMI predicted future hippocampal atrophy, but baseline brain structure did not predict future weight changes. BMI increased significantly over time (F1,1085 = 15.98, p < .001). Individuals with lower baseline BMI experienced greater weight gain (F1,922 = 105.12, p < .001). Greater weight gain was associated with greater hippocampal atrophy over time (F1,899 = 16.33, p = .001), more so in participants with BD than HCs (F1,898 = 6.91, p = .009). Consequently, lower baseline BMI predicted greater future hippocampal volume loss (F1,904 = 14.77, p < .001). These associations were not observed in other subcortical regions. CONCLUSIONS:Our findings suggest that weight gain is a modifiable risk factor for hippocampal atrophy, especially in individuals with lower BMI and those with BD. Prevention of weight gain in general, but especially in people with BD, could provide neuroprotective benefits.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

BACKGROUND:Genetic studies have shown associations between genetic risk for schizophrenia and brain imaging phenotypes. However, prior studies focused on a single neuroimaging modality and/or employed methods that do not fully elucidate the shared genetic architecture between them, limiting our understanding of their complex genetic relationship. STUDY DESIGN:We used genome-wide association study summary statistics for schizophrenia alongside 37 brain measurements, selected based on adequate SNP-based heritability and representing structural, microstructural, and functional brain features derived from T1, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI). These were integrated with a clinical cohort (1065 cases, 1037 controls) to examine the polygenic overlap between schizophrenia and brain measurements. Polygenic overlap was assessed at genome-wide and individual locus levels through linkage disequilibrium score regression, polygenic scoring (PGS), bivariate MiXeR, and conjunctional false discovery rate. STUDY RESULTS:Schizophrenia showed weak genetic correlations with all brain measures (rg = -0.131 to 0.146; PFDR = .069 to .019), and no significant correlation with brain PGS. Nonetheless, a substantial proportion of causal variants with mixed effect direction were shared between schizophrenia and brain traits. Genetic correlations and polygenic scores showed significant positive associations with the proportion of shared variants with concordant effect direction. Additionally, we identified 218 loci shared with schizophrenia in T1, 138 in DTI, and 24 in rs-fMRI measures. CONCLUSIONS:Our findings indicate shared genetic underpinnings between schizophrenia and brain structure and functional connectivity, emphasizing the necessity for complementary methodologies to investigate the genetic overlap between complex polygenic traits.

Introduction Bipolar disorder (BD) is a severe psychiatric disorder characterized by shifting of mood patterns from manic to depressive episodes. The molecular mechanisms underlying BD have not been fully elucidated, and research into biomarkers is important for prevention and early intervention. The Na+, K+-ATPase is a metalloprotein that interacts with many chemical elements. It was demonstrated that the interactions of Na+, K+-ATPase with endogenous cardiac steroids is involved in BD. It was hypothesized that these interactions are mimicked by chemical elements which may participate in BD etiology. We have recently demonstrated that the concentration of Aluminum (Al), Boron (B), Cupper (Cu), Potassium (K), Magnesium (Mg) and Vanadium (V) were significantly lower in the pre-frontal cortex of individuals with BD compared with controls. We hypothesized that differences in the levels of chemical elements between BD and healthy controls would also be reflected in scalp hair.MethodsTo test this hypothesis, the levels of 25 chemical elements were determined by Inductively coupled plasma mass spectrometry (ICP-MS) in the scalp hair of 30 individuals with BD and 30 sex- and age-matched controls.ResultsWe found that the levels of Al, Cu, Nickel (Ni) and Thallium (Tl) are elevated in the hair of BD patients compared to controls. In addition, the concentrations of Ni levels in hair samples were correlated with the severity of the mental illness as quantified by the Global Assessment of Functioning Scale.ConclusionAlthough interpretations are tentative due to the limited sample size, our results suggest that changes in chemical elements may be involved either in the etiology of BD or altered due to the disease progression, which needs to be clarified further in larger independent samples.

The neuropeptides oxytocin and vasotocin are predominantly produced in the supraoptic and paraventricular nuclei of the anterior-inferior, anterior-superior and tubular-superior hypothalamic subunits. Evidence suggests that oxytocin and vasotocin signaling play a role in both physiology and behavior, and that dysfunction of these signaling systems may contribute to the co-occurrence of metabolic and psychiatric conditions. The genetic pathways, however, that may underlie the connection between these physiological and behavioral traits are yet to be clearly delineated. We deployed bivariate mixture models and conjunctional FDR to estimate the global and local genetic overlap between three oxytocinergic-vasotocinergic hypothalamus subunits and ten psychiatric and metabolic traits related to oxytocin and vasotocin signaling. We show that these three subunits share moderate-to-extensive genetic overlap with the tested traits, therein stronger overlap with psychiatric than metabolic traits. We found most complete overlap between the anterior subunits and systolic blood pressure. Across all subunit and trait combinations, we pinpoint 95 novel, unique associated loci. The genes associated with these loci were enriched in gene sets linked to neuroimaging and neurodegeneration as well as metabolic markers, and were up-/down-regulated in tissues such as blood vessel and the liver. These findings help shed light on the genetic architecture of the hypothalamic subunits implicated in oxytocin and vasotocin and selected traits, and provide new avenues for future research.

OBJECTIVE:Bipolar disorder, major depressive disorder (MDD), and schizophrenia are severe mental disorders and are each associated with poor cardiometabolic health. Mapping genetic relationships of these heritable disorders with blood markers of metabolic activity may uncover biological pathways underlying this important shared clinical feature. METHODS:The authors charted genetic overlap of the three disorders, type 2 diabetes, coronary artery disease, and body mass index (BMI) with 249 circulating metabolites through linkage disequilibrium score regression and bivariate Gaussian mixture modeling. Causal relationships and functionally annotated shared genetic variants were estimated, and enrichment across brain and body tissues was investigated. RESULTS:All three disorders had extensive overlap with the metabolites. The pattern of genetic correlations was similar between MDD, type 2 diabetes, coronary artery disease, and BMI (Spearman's correlation rs>0.93), opposite in direction to the pattern found for schizophrenia and bipolar disorder (MDD-bipolar disorder rs=-0.74; MDD-schizophrenia rs=-0.83). Notably, this genetic divergence contrasted with phenotypic associations, which were similar across all three disorders. The metabolites had widespread, robust causal effects on the disorders and cardiometabolic traits. The authors mapped 1,056 genes shared between the individual disorders and metabolites to disorder-specific processes related to metabolic activity, mitochondrial function, and synaptic processes. These genes were expressed throughout the brain, heart, and liver. CONCLUSIONS:Severe mental disorders have strong associations with metabolites, and MDD has a distinctly different genetic relationship than bipolar disorder and schizophrenia do. The study findings suggest that metabolic pathways are involved in the development of severe mental disorders and can play a central role in disentangling disorder-specific etiologies. The "metabolic psychiatry" approach applied here has high potential to guide development of targeted interventions.

Lockdowns and social restrictions imposed in response to the Covid-19 pandemic intensified the proximity and reciprocal exposure among members of nuclear families. It is unclear how variation in mental distress during this period is attributed to potential influences of family members. This study used genetic data from adolescents (n = 4 388), mothers (n = 27 852) and fathers (n = 25 953), to disentangle the contributions of parent-driven, child-driven, and partner-driven components to mental distress during the first two months of the Covid-19 lockdown. Separate models also included adolescents’ non-pandemic mental distress as outcomes (n = 13 484). Trio genome-wide complex trait analyses separated two types of genetic components; direct–how an individual’s genotype is associated with their own mental distress, and indirect–how an individual’s genotype is associated with the mental distress of family members. A trio polygenic score (PGS) design was used to investigate associations of specific genetic liability factors with mental distress, and whether these changed over time (PGS×time). Results suggest that family-level genetic factors contribute to mental distress; variance components capturing indirect genetic effects accounted for 10% of adolescent mental distress (mother-driven), 2–3% of maternal (partner-driven), and 5% of paternal mental distress (child-driven). Mothers’ depression and ADHD PGS were positively associated with fathers’ mental distress. No PGS×time interactions were found. Direct genetic effects accounted for 9–10% variance in mental distress across family members, partly explained by genetic variants associated with anxiety, depression, ADHD and neuroticism. These findings highlight the importance of family dynamics and emphasize the potential value of including family members in mental health interventions.

Background:Depression is a highly heterogeneous condition. Depression with an onset in childhood and early adolescence has a worse clinical course, is more heritable, and shows a lower genetic correlation with other depression subtypes, than does later-onset depression. It is also more strongly associated with neurodevelopmental (ND) comorbidities and genetic liability to attention-deficit hyperactivity disorder. Thus, we hypothesised that early-onset depression represents a distinctive 'neurodevelopmental' depression subtype associated with an increased burden of rare copy number variants (CNVs) that are enriched in ND conditions. We tested this hypothesis using four population cohorts across the UK, Norway, and Sweden. Methods:Participants were ascertained from four population cohorts across the UK, Norway, and Sweden. Early-onset depression was defined as a score >11 on the self-reported Short Mood and Feelings Questionnaire between ages 10 and 14 years (cases n = 5994 vs. controls n = 26,388) and, for secondary analyses, using ICD-10 criteria for major depressive disorder (MDD) with onset  ≤ 14 years (cases n = 856 vs. controls n = 96,769). Carriers of large, rare (>500 kb, <1% frequency) CNVs and known ND CNVs were identified. Primary analyses tested associations between early-onset depression and (i) large, rare CNVs, and (ii) ND CNVs. Secondary analyses investigated parent-reported measures of early-onset depression. Results:Meta-analysis did not identify any robust associations between early-onset depression (SMFQ-defined) and large, rare CNVs (OR = 0.92 [95% CI = 0.84-1.02], p = 0.12) or ND CNVs (OR = 1.06 [0.85-1.31], p = 0.60). No robust associations were observed between early-onset depression, defined using ICD-10 MDD criteria, and large rare CNVs (OR = 1.08 [0.86-1.36], p = 0.49) or ND CNVs (OR = 0.69 [0.34-1.39], p = 0.30). Conclusion:Our findings did not support the hypothesis that individuals with early-onset depression show enrichment for large, rare or known ND CNVs.