Rare brain disorders often present with changes in brain volume, and variation in brain volume is known to be highly heritable. Recent work studying brain volume variation has largely focused on common variants and structural variants. Rare variants often have large effect sizes and clearer connections to biological mechanisms, but the role of rare variants has not been extensively studied. We performed rare-variant gene aggregation analysis for total brain volume and 43 regional brain volume phenotypes (n = 50,061) to identify genes associated with brain volume variation through loss-of-function and missense variants. We identified and replicated mutations in DISP1 and SCUBE2 that were associated with reduced cerebellar volume and suggest that this was mediated by modifying sonic hedgehog signaling. Additionally, we found an association between mutations in PTEN and macrocephaly that are likely mediated through the PI3K/mTOR pathway and hypothesize that mutations in FA2H influence cerebral white matter volume. Further, we identified 7 genes associated with volume variation in the population and rare brain diseases in ClinVar, supporting the role of mutations in these genes causing diseases and related subclinical phenotypes. Overall, we showed that rare-variant analysis can provide clarity on the biological processes connecting brain volume and disease.

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.

Genome-wide association studies (GWAS) help to identify disease-linked genetic variants, but pinpointing the most likely causal genes in GWAS loci remains challenging. Existing GWAS gene prioritization tools are powerful but often use complex black box models trained on datasets containing biases. Here, we used a data-driven approach to construct a truth set of causal genes in 200 GWAS loci. We found that a simple logistic regression model performed as well as a more complex XGBoost model, and that many commonly-used gene prioritization features could be removed without meaningfully affecting performance (e.g., expression quantitative trait locus colocalization and Mendelian randomization). We present CALDERA, a gene prioritization tool that uses a logistic regression model and uses just four input features. In independent benchmarking datasets of resolved GWAS loci, CALDERA achieved state-of-the-art performance in comparison with other methods (FLAMES, L2G, and cS2G). CALDERA outputs causal gene probabilities for all genes in a given GWAS locus and we show that these probabilities are well-calibrated. Applying CALDERA to 93 UK Biobank traits, we predicted 11,956 putative causal genes, potentially resolving up to 52% of loci. Overall, CALDERA provides a powerful solution for prioritizing potentially causal genes in GWAS loci that minimizes the data processing required to construct input features and generates an easily-interpretable output score.

Investigating the genetic underpinnings of functional brain connectivity is essential to understand how genetic variation influences brain health and disease. Here, a mass-univariate approach was adopted to study the genetic architecture of functional brain circuitry (Ntotal = 28,159 subjects) with high spatial resolution (82 brain regions). Common genetic variants explained individual differences in 33% of all 3321 inter-regional functional pathways with 72 significant associations reflecting widespread, pleiotropic effects across the connectome. These associations were mapped to five genes-PAX8, EphA3, SLC39A12, THBS1 and APOE-with known associations with brain phenotypes and which converged in biological processes related to neurodevelopment and cardiovascular and cognitive traits (enrichment minimum p = 3.0 × 10-6 and p = 1.6 × 10-5, respectively). Our findings show that the genetic component of individual differences in functional brain connectivity is largely shared throughout the brain, highlighting the importance of genetic variation in large-scale brain organisation and its relationship with cognitive function and overall health.

Abstract Background and Aims Comorbidity between ischaemic heart disease (IHD) and depression (DEP) is prevalent and more pronounced in woman than in men. The biological basis of these sex differences, however, remains unclear. We aimed to assess the contribution of genetic and biological risk factors to sex differences in IHD–DEP comorbidity. Methods We analysed sex-stratified genome-wide association study summary statistics from 1.14 million individuals of European ancestry across multiple large-scale cohorts and international consortia. Global and local genetic correlations ( r g ), pleiotropic loci, and IHD-DEP shared genes were identified using LDSC, MiXeR, LAVA, conjunctional FDR, and FLAMES. We conducted conditional analyses using genetic and phenotypic data for 331 putative risk factors. Results The r g between IHD and DEP was twice as high in females ( r g =.43) compared to males ( r g =.21), explaining a greater proportion of comorbidity in females (21% vs 13%). Pleiotropy analyses identified sex-specific genomic regions and genes contributing to IHD–DEP comorbidity. Genetic conditional analysis indicated that behavioural traits (alcohol use, insomnia, social deprivation) contributed more to male IHD–DEP comorbidity, whereas asthma and female-specific health traits contributed more to female IHD–DEP comorbidity. Phenotypic mediation largely reflected the same pattern. Conclusions Higher IHD-DEP comorbidity in females compared to males is partly attributable to greater shared genetic liability. Distinct genes and differing contributions of behavioural, metabolic, immunological, and reproductive factors further shape these sex differences. These results support sex-aware risk stratification—targeting alcohol, sleep, and loneliness in males and endocrine status and asthma control in females.

Schizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome. To more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). We extracted genes that 1) are targeted by existing drugs that could potentially be repurposed for schizophrenia, 2) are predicted to be druggable, or 3) may be testable in rodent models. We prioritized 101 schizophrenia genes, including 15 that are targeted by approved or investigational drugs (e.g., DRD2, GRIN2A, CACNA1C, GABBR2). Of these, 7 have never been tested in clinical trials for schizophrenia or other psychiatric disorders (e.g., AKT3). Seven genes are not targeted by any existing small molecule drugs, but are predicted to be druggable (e.g., GRM1). We prioritized two potentially druggable genes in loci that are shared with an addiction GWAS (PDE4B and VRK2). We curated a high-quality list of 101 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.

Astrocytes are increasingly implicated in the pathophysiology of schizophrenia (SCZ), yet how astrocytic dysfunction contributes to disease-relevant neuronal abnormalities remains unclear. Here, we used mass spectrometry-based proteomics to profile lysates (proteome) and secreted proteins (secretome) from iPSC-derived astrocytes originating from 9 SCZ patients and 8 healthy controls. Compartment-specific analyses showed that lysates were enriched for mitochondrial and nuclear pathways, whereas astrocyte-conditioned media (ACM) were enriched for extracellular matrix (ECM) and vesicle-associated proteins. Differential expression analysis revealed minimal overlap between dysregulated proteins in lysates and ACM, suggesting modality-specific effects of SCZ-associated donor background. Interestingly, ECM proteins and key secreted cues involved in synaptic development, including MFGE8 and SEMA3C, were selectively reduced in SCZ ACM, whereas RNA-processing proteins were aberrantly increased. This is in line with previously reported microRNA enrichment in extracellular vesicles (EV) derived from SCZ patients. Gene set analyses further identified the alteration in secretion and nuclear processes as well as the potential involvement of autophagy-dependent release mechanism in SCZ astrocytes. Together, these findings suggest disrupted astrocytic protein homeostasis and extracellular signalling in SCZ iPSC-derived astrocytes, providing mechanistic insight into astrocyte-mediated contributions to synaptic and circuit deficits in the disorder.

Externalizing spectrum disorders-spanning attention-deficit/hyperactivity disorder, conduct disorder, substance use disorders, and other disorders characterized by disinhibition-frequently co-occur within individuals due, in part, to shared genetic etiology. To advance understanding of this genetic architecture, we conducted a multi-ancestry, multivariate genome-wide association analysis of more than 4 million individuals, identifying 1,294 genomic regions linked to an externalizing factor. Fine-mapping and gene prioritization efforts identified 961 effector genes, with the putative causal variant associations showing robust replication in the All of Us Research Program sample. Bioinformatic analyses revealed a broadly distributed neural architecture with early and sustained involvement of GABAergic and glutamatergic neurons. Drug repurposing analyses further highlighted the role of GABAA receptors, as well as dopaminergic signaling, excitatory-inhibitory balance, and neurosteroid pathways. A genome-wide polygenic index predicted ~12% of the variance in externalizing in independent cohorts of individuals with European-like ancestry, compared to ~3% in individuals with African-like ancestry, and was associated with myriad health and life outcomes. Together, these findings map the shared genetic etiology of externalizing psychopathology and identify neurodevelopmental and synaptic mechanisms with translational relevance.

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.

Many traits show small global sex differences in genetic correlations and heritability. However, how these differences are distributed across the genome remains unknown. Here, we use LAVA to test for local genetic sex differences in genetic correlations, heritabilities, and the magnitude of genetic effects across 157 quantitative traits in the UK Biobank. Nearly every trait shows evidence for sex-dimorphic effects in at least one locus. We find that such loci can flag biological differences between the sexes. Moreover, we test for differences in the magnitude of genetic effects on the raw and the standardized scale. We show these have complementary interpretations, where only the latter scale is informative for heritability. Our results show how average metrics of genetic correlation and heritability across the whole genome can mask important variability between loci and that the scale of genetic effects needs to be considered carefully when comparing their magnitudes.

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium (PGC) SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS (N=41,917-173,140 cases; total N=1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N=11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest for test sample size ratios of 25% for SCZ/BIP/MDD/control, and 0.65 for SCZ vs. rest, 0.61 for BIP vs. rest and 0.64 for MDD vs. rest for case-only analyses with test sample size ratios of 33.3% for SCZ/BIP/MDD (and 0% for controls). DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying appreciable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.

Many drug targets in ongoing Parkinson’s disease (PD) clinical trials have strong genetic links. While genome-wide association studies (GWAS) nominate regions associated with disease, pinpointing causal genes is challenging. Our aim was to prioritize additional druggable genes underlying PD GWAS signals. The polygenic priority score (PoPS) integrates genome-wide information from MAGMA gene-level associations and over 57,000 gene-level features. We applied PoPS to East Asian and European PD GWAS data and prioritized genes based on PoPS, distance to the GWAS signal, and non-synonymous credible set variants. We prioritized 46 genes, including well-established PD genes (SNCA, LRRK2, GBA1, TMEM175, VPS13C), genes with strong literature evidence supporting a mechanistic link to PD (RIT2, BAG3, SCARB2, FYN, DYRK1A, NOD2, CTSB, SV2C, ITPKB), and genes relatively unexplored in PD. Many hold potential for drug repurposing or development. We prioritized high-confidence genes with strong links to PD pathogenesis that may represent our next-best candidates for developing disease-modifying therapeutics.

Children can be reliably diagnosed with autism as early as 3 years of age, and early interventions are initiated. There is often a significant gap between the age of onset of symptoms (2–3 years) and diagnosis (8–10 years) in Africa. We conducted a study to validate the Social Communication Questionnaire (SCQ) as a screening instrument in a rural setting in Kenya. The study was conducted along the Kenyan Coast. Study participants included 172 children with a neurodevelopmental disorder (NDD) diagnosis (84 of which were autism) and 112 controls. Internal consistency was evaluated through the use of Cronbach’s alpha, confirmatory factor analysis (CFA) with maximum likelihood procedure to assess the conceptual model for the SCQ. Additionally, the sensitivity and specificity of cut-off scores using ROC analysis and item difficulties and discrimination quality using an IRT framework were also assessed. Factor analysis revealed an adequate fitting model for the three-factor DSM-IV-TR (root mean squared error of approximation (RMSEA) = 0.050; Comparative Fit Index (CFI) = 0.974; Tucker-Lewis Index (TLI) = 0.973) and two-factor DSM-5 factor structure (RMSEA = 0.050; CFI = 0.972; TLI = 0.974). The reliability coefficient alphas for the whole group for all items (Cronbach’s α = 0.90) and all three domains (Cronbach’s α = 0.68–0.84) were acceptable to excellent. The recommended cut-off score of 15 yielded 72

Research by the Psychiatric Genomics Consortium (PGC) has advanced the discovery of common and rare genetic variations that contribute to the susceptibility to many psychiatric disorders and neurodevelopmental conditions. This Review reflects on major findings from the past 5 years of research by the PGC in five priority areas: discovery of common variants using genome-wide association studies; rare variation and its interplay with polygenic risk; using genetics to go beyond diagnostic boundaries; ascribing functional attributes to genomic discoveries; and developing and implementing processes for data sharing, outreach to various communities, and training. The insights gained in these domains frame the agenda for the next phase of PGC research. In addition to accelerating integrative findings of common and rare variants within, and across, multiple psychiatric disorders and neurodevelopmental conditions, the next phase will use multiple populations to elucidate genetic causes, integrate results with rapidly accumulating multimodal functional genomics data to gain mechanistic understanding, convert genetic findings to clinically actionable phenotypes, such as treatment response, and address the emerging use of polygenic scores. Together, these next steps will highlight the biological underpinnings of psychiatric disorders and neurodevelopmental conditions, which continue to contribute to global morbidity and mortality.

Insomnia (INS), major depression (DEP) and anxiety disorders (ANX) frequently co-occur and share a substantial proportion of their genetic risk. Most genetic studies have focused on shared risk between DEP and ANX, often omitting INS. Other studies that include INS have been limited to genome-wide correlations or overlap. Identifying the specific genetic factors that influence these three conditions could offer a route to identify therapeutic targets with potential transdiagnostic benefits. Realizing this potential requires pinpointing the likely causal variants and genes that are shared, estimating how pleiotropic effects are mediated, as well as identifying the biological processes they affect. Here we conduct a multi-level trivariate genetic analysis of INS, DEP and ANX using genome-wide association studies (GWAS) of more than one million individuals from multiple ancestries. We show that 55% of the genetic signal is shared across all three conditions. Supporting its importance, INS shares a significant fraction of the genetic overlap (75%) and correlation (7%) between DEP and ANX. We identify 195 genomic loci with shared signal for at least two conditions, many of which are likely arising from the same causal variant (at least 50%) or effector gene (60-80%). Pairwise mediation analyses suggest that these shared likely causal variants are more consistent with models of vertical pleiotropy where DEP and INS are risk factors towards ANX, rather than with models of horizontal pleiotropy. We find convergence of shared effector genes on biological processes in inhibitory synaptic transmission, neuronal organization, and axonal development. These results reveal an interconnected but mechanistically diverse basis for shared genetic risk across INS, DEP and ANX, and offer potential candidates for future pathway-tailored therapeutic targets with transdiagnostic benefits.

Bipolar disorder is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 bipolar disorder risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci and prioritized 17 likely causal SNPs for bipolar disorder. We mapped these SNPs to genes and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci and results from rare variant exome sequencing in bipolar disorder. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment, including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, FKBP2, RASGRP1, FURIN, FES, MED24 and THRA among others in bipolar disorder. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of bipolar disorder polygenic risk scores across diverse populations and present a high-throughput fine-mapping pipeline.

Insomnia disorder, major depressive disorder and anxiety disorders are the most common mental health conditions, often co-occurring and sharing genetic risk factors, suggesting possible common brain mechanisms. Here we analyzed multimodal magnetic resonance imaging data from over 25,604 UK Biobank participants to identify shared versus symptom-specific brain features associated with symptom severity of these disorders. Smaller total cortical surface area, smaller thalamic volumes and weaker functional connectivity were linked to more severe symptoms of all three disorders. Disorder-specific symptom severity associations were also observed: smaller reward-related subcortical regions were associated with more severe insomnia symptoms; thinner cortices in language, reward and limbic regions with more severe depressive symptoms; and weaker amygdala reactivity and functional connectivity of dopamine-, glutamate- and histamine-enriched regions with more severe anxiety symptoms. These symptom-specific associations were often in parts of the amygdala-hippocampal-medial prefrontal circuit, highlighting the interconnectedness of these disorders and suggesting new pathways for research and treatment.