American Journal of Medical Genetics Part B: Neuropsychiatric Genetics

Obsessive compulsive disorder (OCD) is significantly heritable, but only a fraction of the contributory genetic variation has been identified, and the molecular etiology involved remains obscure. Identifying rare contributory variants of large effect would be an important milestone in helping to elucidate the mechanisms involved. Analysis of densely affected pedigrees is a potentially useful strategy to bypass the sample size challenges of standard case-control approaches. Here we performed whole genome sequencing (WGS) of 25 individuals across two multiplex OCD pedigrees. We prioritised rare variants using a Bayesian inference approach which incorporates variant pathogenicity and co-segregation with OCD. In the first pedigree, we identified a highly deleterious missense variant in NPY5R, carried by the majority of affected individuals. This gene is brain-expressed and has previously been implicated in panic disorder and internet addiction GWAS studies. In the second pedigree, we identified a large deletion of DLGAP1 and a missense variant in MAPK8IP3, that perfectly co-segregated in a specific branch of the family: both genes have previously been implicated in OCD and autism. Both genes contribute to a protein interaction network including ERBB4 and RAPGEF1 which we had previously identified in a large Tourette Syndrome pedigree. Our analysis suggests that both energy homeostasis and downstream signalling from the post-synaptic density may both be important avenues for future research.

BackgroundObsessive-compulsive disorder (OCD) is a debilitating psychiatric condition influenced by both genetic and environmental risk factors. Epigenetic modifications, such as DNA methylation, may offer insights into biologically meaningful differences associated with the disorder. MethodsWe conducted the largest methylome-wide association study (MWAS) of OCD to date, analyzing saliva DNA samples from 414 individuals with OCD and 384 controls using the Illumina EPICv2 array. Differentially methylated positions (DMPs) and regions (DMRs) were identified, with additional analyses including sex-stratified comparisons, methylation quantitative trait loci (mQTL) mapping, and assessments of cell-type composition differences. ResultsWe identified 35 DMPs and 17 DMRs associated with OCD, mapping to genes involved in neurotransmission, neurodevelopment, synaptic function, and gene regulation. Sex-stratified analyses revealed additional sex-specific methylation signals, highlighting biological differences between males and females. Most associated loci were influenced by genetic variation (mQTLs). Differences in estimated cell composition and the identification of immune-related genes suggest a potential role for immune system processes. Correlation analyses between brain and saliva methylation indicated that several findings may reflect brain-relevant biology. ConclusionsOur findings emphasize the importance of integrating epigenetic, genetic, and sex-specific data to advance our understanding of OCD. DNA methylation may ultimately contribute to progress toward clinically relevant precision medicine approaches.

Trichotillomania and excoriation disorder are obsessive-compulsive related disorders that are often subclassified together as body-focused repetitive behavior (BFRB) disorders. While previous research suggests shared genetic factors, the genetic architecture of these BFRBs remains incompletely understood. Probands with trichotillomania and/or excoriation disorder and both of their biological parents were recruited for an ongoing genetic study of parent-offspring trios with BFRBs. Genome-wide array data were generated in 110 families (334 individuals total) to investigate the role of both common single-nucleotide polymorphisms and rare copy-number variants (CNVs). Polygenic risk scores were calculated using summary statistics from genome-wide association studies of related psychiatric conditions, including obsessive-compulsive disorder (OCD), depression, anxiety, and attention deficit/hyperactivity disorder. Using the polygenic transmission disequilibrium test, we observed a significant over-transmission of polygenic risk for OCD in probands of European ancestry from their parents (mean pTDT = 0.36, p = 0.01, n = 92), and a non-significant enrichment for the other conditions. Our results suggest that common variants associated with OCD may contribute to risk for BFRBs, consistent with their current classification as obsessive-compulsive related disorders. We also identified several rare CNVs in probands that overlapped genes intolerant to loss-of-function (LoF) mutations and those previously associated with neurodevelopmental disorders. The LoF-intolerant genes were enriched in biological processes relevant to synapse organization and neurodevelopment. This work provides new insight into the genetic underpinnings of these BFRB disorders, paving the way for larger genomic studies of these understudied conditions.

To date, four genome-wide association studies (GWAS) of obsessive-compulsive disorder (OCD) have been published, reporting a high single-nucleotide polymorphism (SNP)-heritability of 28% but finding only one significant SNP. A sub-stantial increase in sample size will likely lead to further identification of SNPs, genes, and biological pathways mediating the susceptibility to OCD. We conducted a GWAS meta-analysis with a 2-3-fold increase in case sample size (OCD cases: N = 37,015, controls: N = 948,616) compared to the last OCD GWAS, including six previously published cohorts (OCGAS, IOCDF-GC, IOCDF-GC-trio, NORDiC-nor, NORDiC-swe, and iPSYCH) and unpublished self-report data from 23andMe Inc. We explored the genetic architecture of OCD by conducting gene-based tests, tissue and celltype enrichment analyses, and estimating heritability and genetic correlations with 74 pheno-types. To examine a potential heterogeneity in our data, we conducted multivariable GWASs with MTAG. We found support for 15 independent genome-wide significant loci (14 new) and 79 protein-coding genes. Tissue enrichment analyses implicate multiple cortical regions, the amygdala, and hypothalamus, while cell type analyses yielded 12 cell types linked to OCD (all neurons). The SNP-based heritability of OCD was estimated to be 0.08. Using MTAG we found evidence for specific genetic underpinnings characteristic of different cohort-ascertainment and identified additional significant SNPs. OCD was genetically correlated with 40 disorders or traits-positively with all psychiatric disorders and negatively with BMI, age at first birth and multiple autoimmune diseases. The GWAS meta-analysis identified several biologically informative genes as important contributors to the aetiology of OCD. Overall, we have begun laying the groundwork through which the biology of OCD will be understood and described.

Obsessive-compulsive disorder (OCD) is a chronic, serious psychiatric disorder that affects 2-3% of the population and is associated with high personal and societal costs. Genetic factors are estimated to explain roughly half the risk of developing OCD, and genomic studies are just beginning to identify common and rare genetic variants mediating this risk. A major goal of genomic studies is to yield insights into the etiology of OCD and identify molecular targets for the development of novel therapeutics. However, the overwhelming majority of subjects in existing genetic studies are of European ancestry, limiting the generalizability of these findings. To address this gap in understanding, we established the Black EquaLity in OCD NeuroGenomics (BELONG) study (https://belongocd.com/). BELONG aims to collect DNA and clinical data from 1,250 richly phenotyped OCD cases of African ancestry in a culturally sensitive manner. In addition, BELONG includes the collection of parental DNA samples for trio-based analyses and unrelated matched controls for case-control analysis. DNA samples will be sequenced using optimized approaches that will allow us to examine both rare and common genome-wide variation to identify OCD risk genes. We will also meta-analyze these data with other existing OCD genomic data. Overall, BELONG will increase the representation of Black Americans in OCD genetic research, which is necessary to generalize precision medicine discoveries in psychiatric genetics.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, which in some cases occurs comorbid with aggressive and antisocial behavior (AGG; ASB). The three externalizing behaviors are moderately to highly heritable and are genetically correlated. However, the genomic regions underlying this correlation are unknown. In this study, we aimed to localize genetic loci shared between ADHD, AGG, and ASB, using two complementary approaches. GWAS summary statistics for ADHD, AGG, and ASB were used for (1) cross-trait gene-based meta-analysis association analyses and (2) local genetic correlation analyses to identify shared genetic loci. Results of both complementary methods were combined to retrieve overlapping genes. Biological functionality of prioritized genes was assessed by exploring gene expression patterns in brain tissues and testing for gene-based association with (subcortical) brain regions. We confirmed previous findings that ADHD, AGG, and ASB were positively genetically correlated at a global level. We identified eleven significant genes in cross-trait gene-based meta-analyses, 31 loci shared between traits; 34 genes were identified when both approaches were combined. This study emphasizes the complex genetic architecture underlying global genetic correlations at the locus level. Converging evidence from these cross-trait analyses highlights novel candidate genes underlying biological mechanisms shared by ADHD, AGG, and ASB.

ObjectivesObsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its substantial burden and moderate heritability, its etiology remains poorly understood, and treatments are often suboptimal. While recent genome-wide association studies (GWAS) have identified some risk loci, yet OCD remains in the linear phase of sample collection to variant association, with many more OCD-associated variants left to discover. This study aimed to develop and validate an electronic health record (EHR)-based algorithm to identify OCD cases and facilitate large-scale genetic studies. MethodsWe leveraged EHR-linked biobank data from two large hospital systems, namely Vanderbilt University Medical Center (VUMC) and Mass General Brigham (MGB), to develop a high-throughput phenotyping algorithm integrating diagnostic codes, medication records, and natural language processing (NLP) of clinical notes. Algorithm performance was evaluated through expert chart review, and genetic validation was performed using OCD polygenic risk scores (PRS). ResultsExpert chart reviews found that our algorithm combining both ICD codes and NLP achieved higher positive predictive values (PPV) for OCD cases (0.84 at VUMC and 0.91 at MGB) compared to using either ICD codes or NLP alone, albeit with a lower case yield. Furthermore, at both sites, algorithm-determined cases exhibited significantly elevated PRS derived from the latest OCD GWAS, providing genetic validation of our phenotyping approach. ConclusionOur study demonstrates a scalable and cost-efficient approach for EHR-based ascertainment of OCD cases, facilitating large-scale genetic studies and advancing understanding of the disorders complex etiology.

This scoping review examines sex and gender differences in obsessive-compulsive disorder (OCD) to generate hypotheses about sources of heterogeneity. A PubMed search (2009-2025) using terms related to sex, gender, and OCD identified 7,497 records. Records were screened by two reviewers, and 1,446 articles were assessed at the full-text level by four co-authors. Of these, 855 studies were included: 61 meta-analyses, 13 systematic reviews, 39 narrative reviews, and 742 original reports. Original studies were grouped by focus--symptoms (96), epidemiology (217), comorbidities (187), human genetics (89), neurocognitive and neurobiological functioning (56), treatment (66), animal models (55)--and evaluated using CASP checklists. Across domains, sex- and gender-related patterns in OCD have been reported, but findings remain fragmented and rarely synthesized. Methodological limitations include inconsistent assessment of sex and gender, variability in symptom measurement, limited consideration of menstrual and reproductive factors, and few genome-wide or whole-brain imaging studies. Biases may also arise from gender differences in insight, help-seeking behavior, and comorbidity. Synthesizing these findings, we hypothesize that two OCD subtypes may contribute to observed sex and gender patterns. One subtype (OCD-I) is characterized by earlier onset and stronger familial or neurodevelopmental loading and shows a male preponderance that may reflect a female protective effect. A second subtype (OCD-II) is more often stress-precipitated, emerges later, and may be more common in women due to greater exposure to interpersonal, reproductive, and traumatic stressors rather than greater innate susceptibility. Appreciating sex and gender effects may clarify OCD heterogeneity and inform research, prevention, and treatment.

BackgroundThe underdiagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) in females, particularly those with the inattentive presentation, highlights a critical gap in clinical care. Although DNA methylation is often tissue-specific, accessible peripheral tissues like cord blood provide valuable insights into early-life risk and can serve as biomarkers. We aimed to identify sex-specific DNA methylation signatures in cord blood associated with childhood ADHD symptoms, which could illuminate early-life risk markers and inform improved detection strategies. MethodWe conducted sex-stratified epigenome-wide association study (EWAS) of cord blood DNA methylation in relation to ADHD symptoms in children (n=2,391; 48.1% females) from the Norwegian Mother, Father, and Child cohort study (MoBa). We tested for sex interaction effects and analyzed inattention and hyperactivity/impulsivity symptoms separately. ResultsWe identified five differentially methylated CpG positions (DMPs) and 22 differentially methylated regions (DMRs). In females, two DMPs, cg13084029 (ARRB1) and cg15676735 (ZBTB3), and seven DMRs were associated with inattention symptoms. The identified DMPs exhibited significant sex interaction effects (adjusted p-value < 0.05). There was no overlap between the DMPs or DMRs identified in females and males, and the epigenetic signatures for inattention and hyperactivity/impulsivity were largely disparate. Several annotated genes (e.g., ARRB1, PNPO, KDM5B, HOXA2, and HOXC4) have recognized roles in neurotransmission and neurodevelopment. ConclusionOur findings demonstrate that the peripheral DNA methylation signatures at birth associated with later ADHD symptoms are distinct between females and males. This highlights the value of sex-stratified analyses and suggests that peripheral epigenetic profiles hold promise for the development of tools for early detection.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder, with symptoms that remit or persist over time. Biological mechanisms underlying symptom change remain poorly understood, though epigenetic processes, like DNA methylation (DNAm), may serve as dynamic biomarkers of clinical outcomes. We examined change in DNAm in children and adolescents with and without ADHD, including differences between remitted and persistent ADHD. MethodsWe analyzed 219 saliva samples from 94 participants (aged 9.5-14.5 years) attending 2 to 3 waves of NICAP. DNAm was profiled using the Infinium MethylationEPIC BeadChip. Linear mixed models adjusted for age, sex, medication, batch and cell-proportion assessed longitudinal change. Comparisons included (1) all ADHD cases versus controls, (2) persistent or remitted ADHD versus controls, and (3) persistent versus remitted ADHD. False discovery rate correction controlled for multiple testing (FDR p<0.05). ResultsNo CpGs were statistically different between ADHD and controls, after correction. Compared to controls, the average DNAm at 5 sites was statistically different in persistent or remitted ADHD, and methylation of 3 additional CpGs differentially changed over time in persistent ADHD. Remitted differed from persistent ADHD at four CpGs, including cg21443143 (ZFAT), which showed a unique decline over time. Gene enrichment links findings to brain structures and function, though these did not survive multiple testing. ConclusionOur study identified several epigenetic differences between remitted and persistent ADHD outcomes from typical development, and from each other. Given the early stage of this research, our findings warrant further prospective epigenome-wide studies into these diagnostic trajectories.

Attention-deficit/hyperactivity disorder (ADHD) is a heritable neurodevelopmental disorder for which genetic factors explain up to 75% of the variance. In this study, we performed a genome-wide association meta-analysis (GWAMA) of ADHD symptom measures, with an effective sample size of 120,092 (71,733 unique individuals from 28 population-based cohorts, with 288,887 quantitative ADHD symptom measures). Next, we meta-analyzed the results with a genome-wide association study (GWAS) of ADHD diagnosis. The GWAMA of ADHD symptoms returned no genome-wide significant variants. However, we estimated strong genetic correlations between our study of quantitative ADHD symptoms and the earlier study of ADHD diagnosis (rg= 1.00, SE= 0.06). Moderate negative genetic correlations (rg< -0.40) were observed with several cognitive traits. Genetic correlations between ADHD and aggressive behavior and antisocial behavior were around 1. This provides further evidence of the wide pleiotropic effects of genetic variants and the role that genetic variants play in the co-occurrence with (mental) health traits. The GWAMAs of ADHD symptoms and diagnosis identified 2,039 genome-wide significant variants, representing 39 independent loci, of which 17 were new. Using a novel fine-mapping and functional annotation method, we identified 22 potential effector genes which implicate several new potential biological processes and pathways that may play a role in ADHD. Our findings support the notion that clinical ADHD is at the extreme end of a continuous liability that is indexed by ADHD symptoms. We show that including ADHD symptom counts in large-scale GWAS can be useful to identify novel genes implicated in ADHD and related symptoms.

Anxiety disorders are common and can be debilitating, with effective treatments remaining hampered by an incomplete understanding of the underlying genetic etiology. Improvements have been made in understanding the genetic influences on mouse behavioral models of anxiety, yet it is unclear the extent to which genes identified in these experimental systems contribute to genetic variation in human anxiety phenotypes. Leveraging new and existing large-scale human genome-wide association studies, we tested whether sets of genes previously identified in mouse anxiety-like behavior studies contribute to a range of human anxiety disorders. When tested as individual genes, thirteen mouse-identified genes were associated with human anxiety phenotypes, suggesting an overlap of individual genes contributing to both mouse models of anxiety-like behaviors and human anxiety traits. When genes were tested as sets, we did identify fourteen significant associations between mouse gene sets and human anxiety, but the majority of gene sets showed no significant association with human anxiety phenotypes. These few significant associations indicate a need to identify and develop more translatable mouse models by identifying sets of genes that match between model systems and specific human phenotypes of interest. We suggest that continuing to develop improved behavioral paradigms and finer-scale experimental data, for instance from individual neuronal subtypes or cell-type-specific expression data, is likely to improve our understanding of the genetic etiology and underlying functional changes in anxiety disorders.

Advances in genomics have resulted in a rapid expansion of the number of known rare genetic disorders (RGDs). However, the low frequency of RGDs presents a challenge for accurately describing the phenotypic spectrum of a given disorder. Remote phenotyping strategies are uniquely poised to address this knowledge gap. Here, we have piloted remote evaluation of cognitive ability and psychosis spectrum symptoms in 21 individuals with 3q29 deletion syndrome (3q29del) (57% male, mean age=14.3{+/-}8.6 years), a hallmark RGD. We find that remote cognitive assessment using the Penn Computerized Neurocognitive Battery and the Peabody Picture Vocabulary Test accurately captured full scale (r=0.710, p=0.001) and verbal IQ (r=0.637, p=0.003), respectively, as compared to in-person assessment with gold-standard instruments. Psychosis spectrum symptoms measured using the Structured Interview for Prodromal Syndromes were significantly correlated between in-person and remote evaluations (total score r=0.753, p=0.003; positive domain score r=0.806, p=0.0009). Based on the successful pilot of remote phenotyping in 3q29del, we designed a protocol for remote phenotyping of individuals with 3q29del. The phenotyping battery is comprised of caregiver-report and direct assessments to capture the spectrum of neurodevelopmental, neuropsychiatric, and medical features associated with the 3q29 deletion. While we designed the battery based on specific areas of concern for 3q29del, the high degree of phenotypic overlap between 3q29del and other RGDs renders this protocol amenable for implementation across a variety of RGDs, facilitating deeper understanding of the phenotypic spectrum and cross-disorder comparison. Ultimately, we hope that the increased utilization of remote phenotyping strategies will help to expand our understanding of RGDs at large, which will lead to improved clinical management strategies and better long-term outcomes for affected individuals and their families.

ImportanceObsessive-compulsive disorder (OCD) affects 2-3% of the population with often disabling obsessions and compulsions. Despite its high heritability, genetic studies of OCD have lagged other psychiatric disorders, particularly in understanding the role of rare genetic variants. ObjectiveTo identify rare coding genetic variants contributing to OCD risk and examine genetic overlap with chronic tic disorders (CTD) and other psychiatric conditions. DesignFamily-based and case-control whole-exome sequencing (WES) study. SettingsWES data were aggregated from 11 independent cohorts across Sweden, the United States, and the United Kingdom. ParticipantsA total of 47,194 individuals were available, and 44,089 passed quality control for analysis. The final sample included 6,071 individuals with OCD, comprising 1,202 probands from family-based trios and 4,869 cases, and 38,018 controls. ExposuresRare damaging coding variants identified by WES. Main Outcomes and MeasuresIdentification of OCD risk genes through rare variant analyses, meta-analysis with CTD data, gene-set enrichment analyses, and evaluation of cross-disorder genetic overlap using curated gene sets. ResultsThe analysis provided an estimate of approximately 470 autosomal genes contributing to OCD risk through rare genetic variation. CHD8 reached genome-wide significance (q < 0.05). Meta-analysis with CTD data revealed additional risk genes, including CELSR3 (q < 0.05), QRICH1, and WWC1 (q < 0.1). We observed significant genetic overlap between OCD, autism spectrum disorder (ASD), and developmental delay: 33% of ASD genes with FDR < 0.1 showed association with OCD (p < 0.001), and 36% showed possible associations in the shared OCD-CTD genetic architecture (p < 0.001), but minimal rare-variant overlap with bipolar disorder and schizophrenia risk genes. We also found that CHD8-regulated genes were enriched for both rare and common variant associations with OCD. Conclusions and RelevanceIn this largest study to date of rare coding variation in OCD, we confirm CHD8 as the first genome-wide significant rare-variant risk gene, show that genes that are targets of CHD8 can carry rare and common variant risk for OCD, and identify multiple additional genes and pathways contributing to risk. Taken together, the findings show that OCD shares substantially greater genetic overlap with neurodevelopmental conditions than with adult-onset psychiatric disorders, refining the developmental framework of OCD and informing future mechanistic and clinical research. Key PointsO_ST_ABSQuestionC_ST_ABSHow do rare genetic variants contribute to OCD, and how do they overlap with variants linked to chronic tic disorders (CTD) and other neurodevelopmental conditions? FindingsExome sequencing of 6,071 OCD cases demonstrated significant enrichment of rare damaging variants in evolutionarily conserved genes, and CHD8 emerged as the first genome-wide significant OCD risk gene. Rare variant patterns in OCD and CTD aligned with those seen in neurodevelopmental, but not adult-onset psychiatric disorders, indicating shared neurodevelopmental pathways. MeaningThese findings clarify OCDs neurodevelopmental genetic architecture, identify CHD8 as a key risk pathway, and reveal overlap with CTD and other conditions.

A key component of Mendels work is what we now refer to as pleiotropy - when variation in one gene gives rise to variation in multiple phenotypes. This study focuses on aiding genetic discovery in depression by revisiting the depressed phenotype and developing a quantitative trait in a large mixed family and population study, using analyses built upon the theory which underpins Mendels pleiotropic observations - the relationship between phenotypic variation and genetic variation. Measures of genetic covariation were used to evaluate and rank ten measures of mood, personality, and cognitive ability as endophenotypes for depression. The highest-ranking traits were subjected to principal component analysis, and the first principal component used to create multivariate measures of depression. Four traits fulfilled most endophenotype criteria, however, only two traits (neuroticism and the general health questionnaire) consistently ranked highest across all measures of covariation. As such, three composite traits were derived incorporating two, three, or four traits. Composite traits were compared to the binary classification of depression and to their constituent univariate traits in terms of their coheritability, their ability to identify risk loci in a genome-wide association analysis, and phenotypic variance explained by polygenic profile scores for depression. Association analyses of binary depression, univariate traits, and composite traits yielded no genome-wide significant results. However, composite traits were more heritable and more highly correlated with depression than their constituent traits, suggesting that analysing candidate endophenotypes in combination captures more of the heritable component of depression and may in part be limited by sample size in the current study.

BackgroundTransdiagnostic genetic factor models organize shared liability across psychiatric disorders, but they may leave systematic pairwise genetic overlap unexplained. MethodsUsing publicly available PGC cross-disorder LD score regression genetic correlations and published five-factor genomic SEM parameters, we computed model-implied disorder correlations and derived edge-level residual genetic correlations (observed minus model-implied) for all disorder pairs. We summarized residual misfit by ranking the largest residual edges and by aggregating residual edges into disorder-level hub indices. As a parsimonious comparison, we constructed a p-factor-augmented baseline and repeated the residual mapping. Uncertainty was propagated via Monte Carlo sampling using reported standard errors. ResultsResidual structure was concentrated in a subset of disorders rather than being uniformly distributed. The largest positive residual edge was OCD-anxiety ([~]0.35), followed by prominent residual links including OCD-Tourette syndrome, ADHD-cannabis use disorder, and ASD-ADHD. At the node level, OCD emerged as the most consistent residual hub, with ADHD typically second. Under the p-factor baseline, the overall residual pattern persisted. Hub rankings did not map one-to-one onto disorder uniqueness, indicating complementary information captured by node-level and edge-level residuals. ConclusionsHigher-order genetic factors explain broad shared liability but leave meaningful, structured residual links between specific disorder pairs. OCD and ADHD are robust residual hubs, highlighting candidate cross-disorder connections for targeted phenotypic harmonization, cross-phenotype GWAS, and theory-guided model refinements.

Genome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed "mental health phenotypes") using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

AimAttention-deficit/hyperactivity disorder (ADHD) is a highly heritable psychiatric disorder. The objective of this study was to define ADHD-associated candidate genes, and their associated molecular modules and biological themes, based on the analysis of rare genetic variants.\n\nMethodsWe combined data from 11 published copy number variation (CNV) studies in 6176 individuals with ADHD and 25026 controls and prioritized genes by applying an integrative strategy based on criteria including recurrence in ADHD individuals, absence in controls, complete coverage in copy number gains, and presence in the minimal region common to overlapping CNVs, as well as on protein-protein interactions and information from cross-species genotype-phenotype annotation.\n\nResultsWe localized 2241 eligible genes in the 1532 reported CNVs, of which we classified 432 as high-priority ADHD candidate genes. The high-priority ADHD candidate genes were significantly co-expressed in the brain. A network of 66 genes was supported by ADHD-relevant phenotypes in the cross-species database. In addition, four significantly interconnected protein modules were found among the high-priority ADHD genes. A total of 26 genes were observed across all applied bioinformatic methods. Look-up in the latest genome-wide association study for ADHD showed that among those 26, POLR3C and RBFOX1 were also supported by common genetic variants.\n\nConclusionsIntegration of a stringent filtering procedure in CNV studies with suitable bioinformatics approaches can identify ADHD candidate genes at increased levels of credibility. Our pipeline provides additional insight in the molecular mechanisms underlying ADHD and allows prioritization of genes for functional validation in validated model organisms.

Minimal phenotyping refers to the reliance on the use of a small number of self-report items for disease case identification. This strategy has been applied to genome-wide association studies (GWAS) of major depressive disorder (MDD). Here we report that the genotype derived heritability (h2SNP) of depression defined by minimal phenotyping (14%, SE = 0.8%) is lower than strictly defined MDD (26%, SE = 2.2%). This cannot be explained by differences in prevalence between definitions or including cases of lower liability to MDD in minimal phenotyping definitions of depression, but can be explained by misdiagnosis of those without depression or with related conditions as cases of depression. Depression defined by minimal phenotyping is as genetically correlated with strictly defined MDD (rG = 0.81, SE = 0.03) as it is with the personality trait neuroticism (rG = 0.84, SE = 0.05), a trait not defined by the cardinal symptoms of depression. While they both show similar shared genetic liability with neuroticism, a greater proportion of the genome contributes to the minimal phenotyping definitions of depression (80.2%, SE = 0.6%) than to strictly defined MDD (65.8%, SE = 0.6%). We find that GWAS loci identified in minimal phenotyping definitions of depression are not specific to MDD: they also predispose to other psychiatric conditions. Finally, while highly predictive polygenic risk scores can be generated from minimal phenotyping definitions of MDD, the predictive power can be explained entirely by the sample size used to generate the polygenic risk score, rather than specificity for MDD. Our results reveal that genetic analysis of minimal phenotyping definitions of depression identifies non-specific genetic factors shared between MDD and other psychiatric conditions. Reliance on results from minimal phenotyping for MDD may thus bias views of the genetic architecture of MDD and may impede our ability to identify pathways specific to MDD.

IntroductionAttention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. Up to 50% of children with ADHD may also experience symptoms of emotional dysregulation, such as anger, irritability, and aggression. Emotional dysregulation contributes to adverse health outcomes such as depression and peer problems, yet it is poorly understood, and effective treatment options are lacking. Emerging evidence suggests that sensory processing may play a role in emotional dysregulation. Forty to 50% of children with ADHD may also experience sensory modulation dysfunction, or SMD. SMD is characterized by hypo- or hyperreactivity to pain and sensation. Only one study investigated the relationship of SMD and emotional dysregulation in ADHD; they found a correlation of r=0.45. If SMD drives emotional dysregulation in ADHD, treating SMD has the potential to improve emotional regulation. Further evaluating the relationship between SMD and emotional dysregulation in ADHD is the crucial first step in developing effective treatment options. MethodsData for this analysis are derived from the baseline assessment of a multi-site, randomized, controlled trial: The Micronutrients for ADHD in Youth (MADDY) Study. The study enrolled children aged 6-12 with a diagnosis of ADHD and symptoms of emotional dysregulation. Using a cross-sectional study design, we will measure the association between emotional dysregulation and SMD at baseline. Emotional dysregulation was measured using the Strengths and Difficulties Questionnaire (SDQ) and a composite score from the Child and Adolescent Symptom Inventory, Version-5 (CASI-5). SMD will be assessed using two subscales from the Temperament in Middle Childhood Questionnaire (TMCQ). To test our hypothesis, we will use simple linear regression. Models will be adjusted for potential confounding variables. ConclusionOur results will serve to better characterize the relationship between SMD and emotional dysregulation in children with ADHD, which may inform treatment options and diminish adverse health outcomes.