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Impaired cognition in schizophrenia is associated with worse functional outcomes. While genetic factors are known to contribute to variation in cognition in schizophrenia, few rare coding variants with strong effects have been identified, and the relative effects from de novo, inherited and non-transmitted alleles are unknown. We used array and exome sequencing data from 656 proband-parent trios to examine the contribution of rare and common variants to school performance, and by implication cognitive function, in schizophrenia. Probands who performed worse at school were enriched for damaging de novo coding variants in genes associated with developmental disorders (DD) (p-value = 0.00026; OR = 11.6). Damaging, ultra-rare coding variants in DD genes that were transmitted or non-transmitted from parents, had no effects on school performance. 13 probands had damaging de novocoding variants in DD genes, of whom 12 had lower school performance. Among probands with lower school performance, those with damaging de novocoding variants in DD genes had a higher rate of comorbid mild intellectual disability (p-value = 0.0002; OR = 15.6). Parental transmission of common genetic liability for educational attainment and intelligence, but not for schizophrenia, was associated with proband school performance, but no significant effects were seen for non-transmitted parental alleles. Overall, we provide evidence for rare and common genetic contributions to school performance in schizophrenia. The strong effects for damaging de novocoding variants in DD genes provide further evidence that cognitive impairment in schizophrenia has a shared aetiology with developmental disorders. Furthermore, we report no evidence in this sample that non-transmitted parental alleles for cognitive traits contributed to school performance in schizophrenia via indirect effects on the environment.

ImportanceImpaired cognitive function in schizophrenia is associated with poor functional outcomes, but the role of rare coding variants is unclear.ObjectiveTo determine whether ultrarare constrained variants (URCVs) are associated with cognition in patients with schizophrenia.Design, Setting, and ParticipantsLinear regression was used to perform a within-case genetic association study of URCVs and current cognition and premorbid cognitive ability. A multivariable linear regression analysis of the outcomes associated with URCVs, schizophrenia polygenic risk score, polygenic risk score for intelligence and schizophrenia associated copy number variants on cognitive ability was performed. Exome sequencing data from 802 participants with schizophrenia were assessed for current cognition using the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery and for estimated premorbid IQ using the National Adult Reading Test. Individuals were recruited from clinical and voluntary mental health services in the UK. Those with a diagnosis of intellectual disability or a neurological disorder known to affect cognition were excluded. Data collection occurred between 2007 and 2015. Data were analyzed between April 2020 and March 2022.Main Outcomes and MeasuresAssociation between URCVs, current cognition, and current cognition adjusted for premorbid IQ.ResultsOf the 802 participants, 499 (62%) were men and 303 (38%) were women; mean (SD) age at interview was 43.36 (11.87) years. Ultrarare constrained variants (n = 400) were associated with lower current cognition scores (β = −0.18; SE = 0.07; P = .005). In the univariable analysis, premorbid IQ was associated with URCVs (β = −0.12; SE = 0.05; P = .02) and partly attenuated the association with current cognition (β = −0.09; SE = 0.05; P = .08). Multivariable analysis showed that measured genetic factors combined accounted for 6.2% of variance in current cognition, 10.3% of variance in premorbid IQ, and supported outcomes of URCVs associated with current cognition independent of premorbid IQ (β = −0.10; SE = 0.05; P = .03).Conclusions and RelevanceThe findings of this study suggest that URCVs contribute to variance in cognitive function in schizophrenia, with partly independent associations before and after onset of the disorder. Although the estimated effect sizes were small, future studies may show that the effect sizes will be greater with better annotation of pathogenic variants. Genomic data may contribute to identifying those at particularly high risk of cognitive impairment in whom early remedial or preventive measures can be implemented.

The spectrum of non-motor symptoms in dystonia remains unclear. Using UK Biobank data, we analysed clinical phenotypic and genetic information in the largest dystonia cohort reported to date. Case–control comparison of dystonia and matched control cohort was undertaken to identify domains (psychiatric, pain, sleep and cognition) of increased symptom burden in dystonia. Whole exome data were used to determine the rate and likely pathogenicity of variants in Mendelian inherited dystonia causing genes and linked to clinical data. Within the dystonia cohort, phenotypic and genetic single-nucleotide polymorphism (SNP) data were combined in a mixed model analysis to derive genetically informed phenotypic axes. A total of 1572 individuals with dystonia were identified, including cervical dystonia (n = 775), blepharospasm (n = 131), tremor (n = 488) and dystonia, unspecified (n = 154) groups. Phenotypic patterns highlighted a predominance of psychiatric symptoms (anxiety and depression), excess pain and sleep disturbance. Cognitive impairment was limited to prospective memory and fluid intelligence. Whole exome sequencing identified 798 loss of function variants in dystonia-linked genes, 67 missense variants (MPC > 3) and 305 other forms of non-synonymous variants (including inframe deletion, inframe insertion, stop loss and start loss variants). A single loss of function variant (ANO3) was identified in the dystonia cohort. Combined SNP and clinical data identified multiple genetically informed phenotypic axes with predominance of psychiatric, pain and sleep non-motor domains. An excess of psychiatric, pain and sleep symptoms were evident across all forms of dystonia. Combination with genetic data highlights phenotypic subgroups consistent with the heterogeneity observed in clinical practice.

Large-scale genomic studies of schizophrenia implicate genes involved in the epigenetic regulation of transcription by histone methylation and genes encoding components of the synapse. However, the interactions between these pathways in conferring risk to psychiatric illness are unknown. Loss-of-function (LoF) mutations in the gene encoding histone methyltransferase, SETD1A, confer substantial risk to schizophrenia. Among several roles, SETD1A is thought to be involved in the development and function of neuronal circuits. Here, we employed a multi-omics approach to study the effects of heterozygous Setd1a LoF on gene expression and synaptic composition in mouse cortex across five developmental timepoints from embryonic day 14 to postnatal day 70. Using RNA sequencing, we observed that Setd1a LoF resulted in the consistent downregulation of genes enriched for mitochondrial pathways. This effect extended to the synaptosome, in which we found age-specific disruption to both mitochondrial and synaptic proteins. Using large-scale patient genomics data, we observed no enrichment for genetic association with schizophrenia within differentially expressed transcripts or proteins, suggesting they derive from a distinct mechanism of risk from that implicated by genomic studies. This study highlights biological pathways through which SETD1A LOF may confer risk to schizophrenia. Further work is required to determine whether the effects observed in this model reflect human pathology.

Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors.\ud\udLASSO and ridge-penalised logistic regression, support vector machines (SVM), random forests, boosting, neural networks and stacked models were trained to predict schizophrenia, using PRS for schizophrenia (PRSSZ), sex, parental depression, educational attainment, winter birth, handedness and number of siblings as predictors. Models were evaluated for discrimination using area under the receiver operator characteristic curve (AUROC) and relative importance of predictors using permutation feature importance (PFI). In a secondary analysis, fitted models were tested for association with schizophrenia-related traits which had not been used in model development.\ud\udFollowing learning curve analysis, 738 cases and 3690 randomly sampled controls were selected from the UK Biobank. ML models combining all predictors showed the highest discrimination (linear SVM, AUROC = 0.71), but did not significantly outperform logistic regression. AUROC was robust over 100 random resamples of controls. PFI identified PRSSZ as the most important predictor. Highest variance in fitted models was explained by schizophrenia-related traits including fluid intelligence (most associated: linear SVM), digit symbol substitution (RBF SVM), BMI (XGBoost), smoking status (XGBoost) and deprivation (linear SVM).\ud\udIn conclusion, ML approaches did not provide substantial added value for prediction of schizophrenia over logistic regression, as indexed by AUROC; however, risk scores derived with different ML approaches differ with respect to association with schizophrenia-related traits.

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3–50, P < 2.14 × 10−6) and 32 genes at a false discovery rate of 1, epilepsy and severe neurodevelopmental disorders2, although different mutation types are implicated in some shared genes. Most genes described here, however, are not implicated in neurodevelopment. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk3, suggesting that common and rare genetic risk factors converge at least partially on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, which indicates that more risk genes await discovery using this approach.

The primary aim of precision medicine is to tailor healthcare more closely to the needs of individual patients. This requires progress in two areas: the development of more precise treatments and the ability to identify patients or groups of patients in the clinic for whom such treatments are likely to be the most effective. There is widespread optimism that advances in genomics will facilitate both of these endeavors. It can be argued that of all medical specialties psychiatry has most to gain in these respects, given its current reliance on syndromic diagnoses, the minimal foundation of existing mechanistic knowledge, and the substantial heritability of psychiatric phenotypes. Here, we review recent advances in psychiatric genomics and assess the likely impact of these findings on attempts to develop precision psychiatry. Emerging findings indicate a high degree of polygenicity and that genetic risk maps poorly onto the diagnostic categories used in the clinic. The highly polygenic and pleiotropic nature of psychiatric genetics will impact attempts to use genomic data for prediction and risk stratification, and also poses substantial challenges for conventional approaches to gaining biological insights from genetic findings. While there are many challenges to overcome, genomics is building an empirical platform upon which psychiatry can now progress towards better understanding of disease mechanisms, better treatments, and better ways of targeting treatments to the patients most likely to benefit, thus paving the way for precision psychiatry.

Genes encoding the mRNA targets of Fragile X mental retardation protein (FMRP) are enriched for genetic association with psychiatric disorders. However, many FMRP targets possess functions that are themselves genetically associated with psychiatric disorders, including synaptic transmission and plasticity, making it unclear whether the genetic risk is truly related to binding by FMRP or is alternatively mediated by the sampling of genes better characterised by another trait or functional annotation. Using published common variant, rare coding variant and copy number variant data, we examined the relationship between FMRP binding and genetic association with schizophrenia, major depressive disorder and bipolar disorder. We then explored the partitioning of genetic association between overrepresented functional categories. High-confidence targets of FMRP were enriched for common schizophrenia risk alleles, as well as rare loss-of-function andde novononsynonymous variants in cases. Similarly, through common variation, FMRP targets were associated with major depressive disorder, and we present novel evidence of association with bipolar disorder. These relationships could not be explained by membership of other functional annotations known to be associated with psychiatric disorders, including those related to synaptic structure and function. This study reinforces the evidence that targeting by FMRP captures a subpopulation of genes enriched for genetic association with a range of psychiatric disorders, across traditional diagnostic boundaries.

Background Cognitive impairment in schizophrenia is a major contributor to poor outcomes, yet its causes are poorly understood. Some rare copy number variants (CNVs) are associated with schizophrenia risk and affect cognition in healthy populations, but their contribution to cognitive impairment in schizophrenia has not been investigated. We examined the effect of 12 schizophrenia CNVs on cognition in those with schizophrenia. Methods General cognitive ability was measured using the Measurement and Treatment Research to Improve Cognition in Schizophrenia composite z score in 875 patients with schizophrenia and in a replication sample of 519 patients with schizophrenia using Wechsler Adult Intelligence Scale Full Scale IQ. Using linear regression, we tested for association between cognition and schizophrenia CNV status, covarying for age and sex. In addition, we tested whether CNVs hitting genes in schizophrenia-enriched gene sets (loss-of-function intolerant and synaptic gene sets) were associated with cognitive impairment. Results A total of 23 schizophrenia CNV carriers were identified. Schizophrenia CNV carriers had lower general cognitive ability than nonschizophrenia CNV carriers in discovery (β = −0.66, 95% confidence interval [CI] = −1.31 to −0.01) and replication samples (β = −0.91, 95% CI = −1.71 to −0.11) and after meta-analysis (β = −0.76, 95% CI = −1.26 to −0.25, p = .003). CNVs hitting loss-of-function intolerant genes were associated with lower cognition (β = −0.15, 95% CI = −0.29 to −0.001, p = .048). Conclusions In those with schizophrenia, cognitive ability in schizophrenia CNV carriers is 0.5–1.0 standard deviations below non-CNV carriers, which may have implications for clinical assessment and management. We also demonstrate that rare CNVs hitting genes intolerant to loss-of-function variation lead to more severe cognitive impairment, above and beyond the effect of known schizophrenia CNVs.

Schizophrenia is a complex highly heritable disorder. Genome-wide association studies (GWAS) have identified multiple loci that influence the risk of developing schizophrenia, although the causal variants driving these associations and their impacts on specific genes are largely unknown. We identify a significant correlation between schizophrenia risk and expression at 89 genes in the dorsolateral prefrontal cortex (P ≤ 9.43 × 10−6), including 20 novel genes. Genes whose expression correlate with schizophrenia were enriched for those involved in abnormal CNS synaptic transmission (PFDR = 0.02) and antigen processing and presentation of peptide antigen via MHC class I (PFDR = 0.02). Within the CNS synaptic transmission set, we identify individual significant candidate genes to which we assign direction of expression changes in schizophrenia. The findings provide strong candidates for experimentally probing the molecular basis of synaptic pathology in schizophrenia.

Recent studies have demonstrated genetic differences between monozygotic (MZ) twins. To test the hypothesis that early post-twinning mutational events associate with phenotypic discordance, we investigated a cohort of 13 twin pairs (n = 26) discordant for various clinical phenotypes using whole-exome sequencing and screened for copy number variation (CNV). We identified a de novo variant in PLCB1, a gene involved in the hydrolysis of lipid phosphorus in milk from dairy cows, associated with lactase non-persistence, and a variant in the mitochondrial complex I gene MT-ND5 associated with amyotrophic lateral sclerosis (ALS). We also found somatic variants in multiple genes (TMEM225B, KBTBD3, TUBGCP4, TFIP11) in another MZ twin pair discordant for ALS. Based on the assumption that discordance between twins could be explained by a common variant with variable penetrance or expressivity, we screened the twin samples for known pathogenic variants that are shared and identified a rare deletion overlapping ARHGAP11B, in the twin pair manifesting with either schizotypal personality disorder or schizophrenia. Parent-offspring trio analysis was implemented for two twin pairs to assess potential association of variants of parental origin with susceptibility to disease. We identified a de novo variant in RASD2 shared by 8-year-old male twins with a suspected diagnosis of autism spectrum disorder (ASD) manifesting as different traits. A de novo CNV duplication was also identified in these twins overlapping CD38, a gene previously implicated in ASD. In twins discordant for Tourette's syndrome, a paternally inherited stop loss variant was detected in AADAC, a known candidate gene for the disorder.

Graphical abstract Identification of neuropsychiatric CNVs. A) Schematic of a deletion and duplication. B) Example of a locus enriched for deletions in cases. C) Number of risk CNVs implicated in published studies of neuropsychiatric disorders. The number of cases included in each study is shown on the x axis, and larger points represent studies with more control samples (see Table 1 for further details). SCZ = schizophrenia, ASD = autism spectrum disorder, ID = intellectual disability, MDD = major depressive disorder, ADHD = attention-deficit hyperactivity disorder, TS = Tourette syndrome, OCD = obsessive compulsive disorder, BD = bipolar disorder., Copy number variants (CNVs) at specific loci have been identified as important risk factors for several neuropsychiatric disorders, such as schizophrenia, autism spectrum disorder, intellectual disability (ID) and depression. These CNVs are individually rare (

Brain development requires a complex choreography of cell proliferation, specialisation, migration and network formation, guided by the activation and repression of gene expression programs. It remains unclear how this process is disrupted in neuropsychiatric disorders. Here we integrate human genetics with transcriptomic data from the differentiation of human embryonic stem cells into cortical excitatory neurons. This reveals a cascade of transcriptional programs, activated during early corticoneurogenesis in vitro and in vivo, in which genetic variation is robustly associated with neuropsychiatric disorders and cognitive function. Within these early neurogenic programs, genetic risk is concentrated in loss-of-function intolerant (LoFi) genes, capturing virtually all LoFi disease association. Down-regulation of these programs in DLG2 knockout lines delays expression of cell-type identity alongside marked deficits in neuronal migration, morphology and action potential generation, validating computational predictions. These data implicate specific cellular pathways and neurodevelopmental processes in the aetiology of multiple neuropsychiatric disorders and cognition.

Background Individuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia. Aims To link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability. Method We linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores. Results Individuals with schizophrenia in SAIL had increased rates of epilepsy (standardised rate ratio (SRR) = 5.34), intellectual disability (SRR = 3.11), type 2 diabetes (SRR = 2.45), congenital disorders (SRR = 1.77), ischaemic heart disease (SRR = 1.57) and smoking (SRR = 1.44) in comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015–0.017), with carriers being 7.5–7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder. Conclusions This study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.

People with schizophrenia are enriched for rare coding variants in genes associated with neurodevelopmental disorders, particularly autism spectrum disorders and intellectual disability. However, it is unclear if the same changes to gene function that increase risk to neurodevelopmental disorders also do so for schizophrenia. Using data from 3444 schizophrenia trios and 37,488 neurodevelopmental disorder trios, we show that within shared risk genes, de novo variants in schizophrenia and neurodevelopmental disorders are generally of the same functional category, and that specific de novo variants observed in neurodevelopmental disorders are enriched in schizophrenia (P = 5.0 × 10−6). The latter includes variants known to be pathogenic for syndromic disorders, suggesting that schizophrenia be included as a characteristic of those syndromes. Our findings imply that, in part, neurodevelopmental disorders and schizophrenia have shared molecular aetiology, and therefore likely overlapping pathophysiology, and support the hypothesis that at least some forms of schizophrenia lie on a continuum of neurodevelopmental disorders. Overlapping genes have been implicated in schizophrenia and neurodevelopmental disorders. Here, the authors overlap de novo variants in the two types of disorders and find variants in these genes with the same functional effect and in some cases the same specific variants.

BackgroundRecent case-control genetic studies of attention deficit hyperactivity disorder (ADHD) have implicated common and rare genetic risk alleles, highlighting the polygenic and complex aetiology of this neurodevelopmental disorder. Studies of other neurodevelopmental disorders, such as autism spectrum disorder (ASD), Tourette disorder, developmental delay/intellectual disability, and schizophrenia indicate that identification of specific risk alleles and additional insights into disorder biology can be gained by studying non-inherited de novo variation. In this study, we aimed to identify large de novo copy number variants (CNVs) in children with ADHD.MethodsChildren with a confirmed diagnosis of ADHD and their parents were genotyped and included in this sample. We used PennCNV to call large (>200kb) CNVs and identified those calls that were present in the proband and absent in both biological parents.ResultsIn 305 parent-offspring trios, we detected 14 de novo CNVs in 13 probands, giving a mutation rate of 4.6% and a per individual rate of 4.3%. This rate is higher than published reports in controls and similar to those observed for ASD, schizophrenia and Tourette disorder. We also identified de novo mutations at 4 genomic loci (15q13.1-13.2 duplication, 16p13.11 duplication, 16p12.2 deletion and 22q11.21 duplication) that have previously been implicated in other neurodevelopmental disorders, two of which (16p13.11 and 22q11.21) have also been implicated in case-control ADHD studies.ConclusionsOur study complements ADHD case-control genomic analyses and demonstrates the need for larger parent-offspring trio genetic studies to gain further insights into the complex aetiology of ADHD.

Neuropsychiatric disorders have long been known to have a strong genetic component, but until about 10 years ago, their molecular pathology was largely unknown. A greater understanding of the genetic risk underlying these conditions was driven by advances in microarray and sequencing technologies and large-scale genome-wide association studies. We now know that these disorders are highly polygenic, and common risk alleles at multiple loci have been associated with schizophrenia (SZ), bipolar disorder, and major depression. Thirteen large, rare copy number variants (CNVs) have been confidently implicated in SZ, although their role in bipolar disorder and depression is much smaller. High-throughput sequencing studies in patients with SZ have shown rare coding variants to be enriched among neuronally expressed genes, such as the activity-regulated cytoskeleton-associated protein and N-methyl- d -aspartate receptor synaptic protein complexes. Cross-disorder studies have also indicated a strong genetic overlap between various neuropsychiatric disorders.

Key Points Question Are phenotypic dimensions in schizophrenia associated with genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence? Findings In this cross-sectional genetic association study of 1220 individuals with schizophrenia, analyses indicated that higher levels of disorganized symptoms, but not other symptom dimensions, and lower levels of current cognitive ability were significantly associated with schizophrenia polygenic risk scores. Current cognitive ability was also associated with intelligence polygenic risk scores. Meaning The findings of this study suggest that variation in disorganized symptoms and cognitive ability in schizophrenia are markers of schizophrenia common genetic liability; cognitive performance in schizophrenia may reflect distinct contributions from genetic liabilities to both intelligence and schizophrenia., Importance Schizophrenia is a clinically heterogeneous disorder. It is currently unclear how variability in symptom dimensions and cognitive ability is associated with genetic liability for schizophrenia. Objective To determine whether phenotypic dimensions within schizophrenia are associated with genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence. Design, Setting, and Participants In a genetic association study, 3 cross-sectional samples of 1220 individuals with a diagnosis of schizophrenia were recruited from community, inpatient, and voluntary sector mental health services across the UK. Confirmatory factor analysis was used to create phenotypic dimensions from lifetime ratings of the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, and the MATRICS Consensus Cognitive Battery. Analyses of polygenic risk scores (PRSs) were used to assess whether genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence were associated with these phenotypic dimensions. Data collection for the cross-sectional studies occurred between 1993 and 2016. Data analysis for this study occurred between January 2019 and March 2021. Main Outcomes and Measures Outcome measures included phenotypic dimensions defined from confirmatory factor analysis relating to positive symptoms, negative symptoms of diminished expressivity, negative symptoms of motivation and pleasure, disorganized symptoms, and current cognitive ability. Exposure measures included PRSs for schizophrenia, bipolar disorder, major depression, attention-deficit/hyperactivity disorder, autism spectrum disorder, and intelligence. Results Of the 1220 study participants, 817 were men (67.0%). Participants’ mean (SD) age at interview was 43.10 (12.74) years. Schizophrenia PRS was associated with increased disorganized symptom dimension scores in both a 5-factor model (β = 0.14; 95% CI, 0.07-0.22; P = 2.80 × 10−4) and a 3-factor model across all samples (β = 0.10; 95% CI, 0.05-0.15; P = 2.80 × 10−4). Current cognitive ability was associated with genetic liability to schizophrenia (β = −0.11; 95% CI, −0.19 to −0.04; P = 1.63 × 10−3) and intelligence (β = 0.23; 95% CI, 0.16-0.30; P = 1.52 × 10−10). After controlling for estimated premorbid IQ, current cognitive performance was associated with schizophrenia PRS (β = −0.08; 95% CI, −0.14 to −0.02; P = 8.50 × 10−3) but not intelligence PRS. Conclusions and Relevance The findings of this study suggest that genetic liability for schizophrenia is associated with higher disorganized dimension scores but not other symptom dimensions. Cognitive performance in schizophrenia appears to reflect distinct contributions from genetic liabilities to both intelligence and schizophrenia., This genetic association study examines the associations between phenotypic dimensions in schizophrenia and genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence in patients with schizophrenia.

Importance Psychotic experiences, such as hallucinations and delusions, are reported by approximately 5% to 10% of the general population, although only a small proportion develop psychotic disorders such as schizophrenia. Studying the genetic causes of psychotic experiences in the general population, and its association with the genetic causes of other disorders, may increase the understanding of their pathologic significance.Objectives To determine whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits and to identify genetic loci associated with psychotic experiences.Design, Setting and Participants Analyses of genetic correlation, polygenic risk scores, and copy number variation were performed using data from participants in the UK Biobank from April 1, 2018, to March 20, 2019, to assess whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits. Genome-wide association studies of psychotic experience phenotypes were conducted to identify novel genetic loci. Participants in the final analyses after exclusions included 6123 individuals reporting any psychotic experience, 2143 individuals reporting distressing psychotic experiences, and 3337 individuals reporting multiple occurrences of psychotic experiences. A total of 121 843 individuals who did not report a psychotic experience formed the comparator group. Individuals with a psychotic disorder were excluded from all analyses.Main Outcomes and Measures Genetic associations with psychotic experience phenotypes.Results The study included a total of 127 966 participants (56.0% women and 44.0% men; mean [SD] age, 64.0 [7.6] years). Psychotic experiences were genetically correlated with major depressive disorder, schizophrenia, autism spectrum disorder, and attention-deficit/hyperactivity disorder. Analyses of polygenic risk scores identified associations between psychotic experiences and genetic liability for major depressive disorder, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. Individuals reporting psychotic experiences had an increased burden of copy number variations previously associated with schizophrenia (odds ratio [OR], 2.04; 95% CI, 1.39-2.98; P = 2.49 × 10−4) and neurodevelopmental disorders more widely (OR, 1.75; 95% CI, 1.24-2.48; P = 1.41 × 10−3). Genome-wide association studies identified 4 significantly associated loci, including a locus in Ankyrin-3 (ANK3 [GenBank NM_020987]) (OR, 1.16; 95% CI, 1.10-1.23; P = 3.06 × 10−8) with any psychotic experience, and a locus in cannabinoid receptor 2 gene (CNR2 [GenBank NM_001841]) (OR, 0.66; 95% CI, 0.56-0.78; P = 3.78 × 10−8) with distressing psychotic experiences. The genome-wide association study of any psychotic experience had a low single-nucleotide polymorphism–based heritability estimate (h2 = 1.71%; 95% CI, 1.02%-2.40%).Conclusions and Relevance A large genetic association study of psychotic experiences from the population-based UK Biobank sample found support for a shared genetic liability between psychotic experiences and schizophrenia, major depressive disorder, bipolar disorder, and neurodevelopmental disorders.

Rare genetic variants may play a prominent role in schizophrenia. We report on the to date largest whole exome sequencing study of schizophrenia case-control samples from related populations and combine with other available sequence data, analysing in total 34,084 individuals (14,302 cases). Three genes showed significant association at FDR < 0.10 (SETD1A, TAF13 and MKI67) and gene-set analyses highlighted the involvement of the synaptome and excitatory neurons, and demonstrated shared architecture with high-functioning autism.

Genetic studies of schizophrenia (SCZ) have now implicated numerous genomic loci that contribute to risk including several copy number variants (CNV) of large effect and hundreds of associated loci of small effect. However, in only a few cases has a specific gene been clearly identified. Rare CNV that affect only a single gene offer a potential avenue to discovering specific SCZ risk genes. Here, we use CNV generated from exome-sequencing of 4,913 SCZ cases and 6,188 controls in a homogenous Swedish cohort to assess the contribution of single-gene deletions and duplications to SCZ risk. As previously seen, we found an excess of rare deletions (p = 0.0004) and duplications (p = 0.0006) in SCZ cases compared to controls. When limiting to only single-gene CNV we identified nominally significant excess of deletions (p = 0.04) and duplications (p = 0.03). In an effort to increase the number of single-gene CNV, we reduced strict filtering criteria but required support from two independent CNV calling methods to create an expanded set that showed a significant burden of deletions in 11 out of 22 gene sets previously implicated in SCZ and in the combined set of genes across those sets (p = 0.008). Finally, for the significantly enriched set of voltage-gated calcium channels, we performed an extensive validation of all deletions generated from exome-sequencing as well as any deletion with evidence from previously analyzed genotyping arrays. In total, 4 exonic, single-gene deletions validated in cases and none in controls (p = 0.039), of which all were identified by exome-sequencing. Broadly, these results point to the potential contribution of single-gene CNV to SCZ and the added value of a deeper dive into CNV calls from exome-sequencing.

BackgroundPathogenic copy number variants (CNVs) increase risk for medical disorders, even among carriers free from neurodevelopmental disorders. The UK Biobank recruited half a million adults who provided samples for biochemical and haematology tests which have recently been released. We wanted to assess how the presence of pathogenic CNVs affects these biochemical test results.MethodsWe called all CNVs from the Affymetrix microarrays and selected a set of 54 CNVs implicated as pathogenic (including their reciprocal deletions/duplications) and present in five or more persons. We used linear regression analysis to establish their association with 28 biochemical and 23 haematology tests.ResultsWe analysed 421k participants who passed our CNV quality control filters and self-reported as white British or Irish descent. There were 268 associations between CNVs and biomarkers that were significant at a false discovery rate of 0.05. Deletions at 16p11.2 had the highest number of significant associations, but several rare CNVs had higher effect sizes indicating that the lack of significance was likely due to the reduced statistical power for rarer events. The distribution of values can be visualised on our interactive website: http://kirov.psycm.cf.ac.uk/.ConclusionsCarriers of many pathogenic CNVs have changes in biochemical and haematology tests, and many of those are associated with adverse health consequences. These changes did not always correlate with increases in diagnosed medical disorders in this population. Carriers should have regular blood tests in order to identify and treat adverse medical consequences early. Levels of cholesterol and related lipids were unexpectedly lower in carriers of CNVs associated with increased weight gain, most likely due to the use of statins by such people.

BackgroundAround 30% of individuals with schizophrenia remain symptomatic and significantly impaired despite antipsychotic treatment and are considered to be treatment resistant. Clinicians are currently unable to predict which patients are at higher risk of treatment resistance.AimsTo determine whether genetic liability for schizophrenia and/or clinical characteristics measurable at illness onset can prospectively indicate a higher risk of treatment-resistant psychosis (TRP).MethodIn 1070 individuals with schizophrenia or related psychotic disorders, schizophrenia polygenic risk scores (PRS) and large copy number variations (CNVs) were assessed for enrichment in TRP. Regression and machine-learning approaches were used to investigate the association of phenotypes related to demographics, family history, premorbid factors and illness onset with TRP.ResultsYounger age at onset (odds ratio 0.94,P= 7.79 × 10−13) and poor premorbid social adjustment (odds ratio 1.64,P= 2.41 × 10−4) increased risk of TRP in univariate regression analyses. These factors remained associated in multivariate regression analyses, which also found lower premorbid IQ (odds ratio 0.98,P= 7.76 × 10−3), younger father's age at birth (odds ratio 0.97,P= 0.015) and cannabis use (odds ratio 1.60,P= 0.025) increased the risk of TRP. Machine-learning approaches found age at onset to be the most important predictor and also identified premorbid IQ and poor social adjustment as predictors of TRP, mirroring findings from regression analyses. Genetic liability for schizophrenia was not associated with TRP.ConclusionsPeople with an earlier age at onset of psychosis and poor premorbid functioning are more likely to be treatment resistant. The genetic architecture of susceptibility to schizophrenia may be distinct from that of treatment outcomes.

The role of large, rare copy number variants (CNVs) in neuropsychiatric disorders is well established, but their association with common psychiatric disorders, such as depression, remains unclear.To examine the association of a group of 53 CNVs associated with neurodevelopmental disorders and burden of rare CNVs with risk of depression.This case-control study used data from the UK Biobank study sample, which comprised 502 534 individuals living in the United Kingdom. Individuals with autism spectrum disorder, intellectual disability, attention-deficit/hyperactivity disorder, schizophrenia, or bipolar affective disorder diagnoses were excluded. Analyses were further restricted to individuals of European genetic ancestry (n = 407 074). The study was conducted from January 2017 to September 2018.CNV carrier status.For the primary outcome, individuals who reported that a physician had told them they had a depression diagnosis were defined as cases. Analyses were repeated using 2 alternative depression definitions: self-reported lifetime depression with current antidepressant prescription at the time of visit 1, and hospital discharge diagnosis of depression.Copy number variants were identified in 488 366 individuals aged 37 to 73 years. In total, 407 074 individuals with European genetic ancestry (220 201 female [54.1%]; mean [SD] age of 56.9 [8.0] years) were included in the study. Of these individuals, 23 979 (5.9%) had self-reported lifetime depression and 383 095 (94.1%) reported no lifetime depression. The group of 53 neurodevelopmental CNVs was associated with self-reported depression (odds ratio [OR], 1.34; 95% CI, 1.19-1.49, uncorrected P = 1.38 × 10-7), and these results were consistent when using 2 alternative definitions of depression. This association was partially explained by physical health, educational attainment, social deprivation, smoking status, and alcohol consumption. A strong independent association remained between the neurodevelopmental CNVs and depression in analyses that incorporated these other measures (OR, 1.26; 95% CI, 1.11-1.43; P = 2.87 × 10-4). Eight individual CNVs were nominally associated with risk of depression, and 3 of these 8 CNVs (1q21.1 duplication, Prader-Willi syndrome duplication, and 16p11.2 duplication) survived Bonferroni correction for the 53 CNVs tested. After the exclusion of carriers of neurodevelopmental CNVs, no association was found between measures of CNV burden and depression.Neurodevelopmental CNVs appear to be associated with depression, extending the spectrum of clinical phenotypes that are associated with CNV carrier status.

The 2016 World Health Organization (WHO) classification of primary central nervous system (CNS) tumors includes numerous uncommon (representing ≤1% of tumors) low-grade (grades I-II) brain neoplasms with varying clinical behaviors and outcomes. Generally, gross tumor or maximal safe resection is the primary treatment. Adjuvant treatments, though their exact role is unknown, may be considered individually based on pathological subtypes and a proper assessment of risks and benefits. Targetable mutations such as BRAF (proto-oncogene B-Raf), TRAIL (tumor necrosis factor apoptosis inducing ligand), and PDGFR (platelet derived growth factor receptor) have promising roles in future management.

Background Huntington’s disease (HD) is caused by an expanded CAG repeat in the HTT gene on chromosome 4 with longer repeats being associated with earlier onset disease. However, recent genetic studies have shown that other genes also have a significant effect on onset and progression of HD. Many of these ‘modifier’ genes are involved in DNA repair and are thought to influence HD by affecting the stability of the CAG repeat in the DNA of striatal neurons. Aims To identify coding variants in DNA repair genes associated with significantly earlier or later onset HD. Methods We stratified over 6000 HD patients from HD-REGISTRY by the difference between their predicted age at disease onset (based on CAG length alone) and their actual age at motor onset. We then sequenced the exomes of 500 patients with particularly early or late onset disease to look for enrichment of rare coding variants in DNA repair genes. Results We have identified multiple rare, coding, and predicted damaging variants in DNA repair genes such as FAN1, MSH3, and EXO1 associated with particularly early or late onset HD. Mapping these variants onto protein domains and structures gives insight into pathogenesis. For example, damaging variants in the FAN1 DNA-binding and nuclease domains are predominantly associated with early onset disease, suggesting that FAN1 usually functions to prevent HD onset, possibly through inhibition of CAG repeat expansion. Conclusions Rare, deleterious variants in DNA repair genes are associated with large (10–20 year) differences in age at HD onset. We are now taking these human variants into model cell and animal systems to probe the molecular mechanisms of HD further. This work might uncover novel therapeutic targets for disease modification.

Schizophrenia is a highly heritable disorder for which anatomical brain alterations have been repeatedly reported in clinical samples. Unaffected at-risk groups have also been studied in an attempt to identify brain changes that do not reflect reverse causation or treatment effects. However, no robust associations have been observed between neuroanatomical phenotypes and known genetic risk factors for schizophrenia. We tested subcortical brain volume differences between 49 unaffected participants carrying at least one of the 12 copy number variants associated with schizophrenia in UK Biobank and 9,063 individuals who did not carry any of the 93 copy number variants reported to be pathogenic. Our results show that CNV carriers have reduced volume in some of the subcortical structures previously shown to be reduced in schizophrenia. Moreover, these associations were partially accounted for by the association between pathogenic copy number variants and cognitive impairment, which is one of the features of schizophrenia.

Schizophrenia is a complex highly heritable disorder. Genome-wide association studies have identified multiple loci that influence the risk of developing schizophrenia, although the causal variants driving these associations and their impacts on specific genes are largely unknown. Here we link genetic findings to gene expression in the human brain by performing a transcriptome-wide association study (TWAS) in which we integrate the largest published genome-wide association dataset of schizophrenia, with publically available post mortem expression data from the dorsolateral prefrontal cortex (DLPFC). We identify a significant correlation between schizophrenia risk and expression at eighty-nine genes in DLPFC, including forty-two genes not identified in earlier TWAS of this transcriptomic resource. Genes whose expression correlate with schizophrenia were enriched for those involved in nervous system development, abnormal synaptic transmission, reduced long term potentiation, and calcium-dependent cell-cell adhesion. Previous genetic studies have implicated post-synaptic glutamatergic and gabaergic processes in schizophrenia; here we extend this to include molecules that regulate presynaptic transmitter release. We identify specific candidate genes to which we assign predicted directions of effect in terms of expression level, facilitating downstream experimental studies geared towards a better mechanistic understanding of schizophrenia pathogenesis.

The role of large, rare copy number variants (CNVs) in neurodevelopmental disorders is well-established,1–5 but their contribution to common psychiatric disorders, such as depression, remains unclear. We have previously shown that a substantial proportion of CNV enrichment in schizophrenia is explained by CNVs associated with neurodevelopmental disorders.6, 7 Depression shares genetic risk with schizophrenia8, 9 and is frequently comorbid with neurodevelopmental disorders10, 11, suggesting to us the hypothesis that if CNVs play a role in depression, neurodevelopmental CNVs are those most likely to be associated. We confirmed this in UK Biobank by showing that neurodevelopmental CNVs were associated with depression (24,575 cases, 5.87%; OR=1.36, 95% CI 1.22-1.51, p=1.61×10-8), whilst finding no evidence implicating other CNVs. Four individual neurodevelopmental CNVs increased risk of depression (1q21.1 duplication, PWS duplication, 16p13.11 deletion, 16p11.2 duplication). The association between neurodevelopmental CNVs and depression was partially explained by social deprivation but not by education attainment or physical illness.