Genetic and Phenotypic Features of Schizophrenia in the UK Biobank.

Importance: Large-scale biobanks provide important opportunities for mental health research, but selection biases raise questions regarding the comparability of individuals with those in clinical research settings. Objective: To compare the genetic liability to psychiatric disorders in individuals with schizophrenia in the UK Biobank with individuals in the Psychiatric Genomics Consortium (PGC) and to compare genetic liability and phenotypic features with participants recruited from clinical settings. Design, Setting, and Participants: This cross-sectional study included participants from the population-based UK Biobank and schizophrenia samples recruited from clinical settings (CLOZUK, CardiffCOGS, Cardiff F-Series, and Cardiff Affected Sib-Pairs). Data were collected between January 1993 and July 2021. Data analysis was conducted between July 2021 and June 2023. Main Outcomes and Measures: A genome-wide association study of UK Biobank schizophrenia case-control status was conducted, and the results were compared with those from the PGC via genetic correlations. To test for differences with the clinical samples, polygenic risk scores (PRS) were calculated for schizophrenia, bipolar disorder, depression, and intelligence using PRS-CS. PRS and phenotypic comparisons were conducted using pairwise logistic regressions. The proportions of individuals with copy number variants associated with schizophrenia were compared using Firth logistic regression. Results: The sample of 517 375 participants included 1438 UK Biobank participants with schizophrenia (550 [38.2%] female; mean [SD] age, 54.7 [8.3] years), 499 475 UK Biobank controls (271 884 [54.4%] female; mean [SD] age, 56.5 [8.1] years), and 4 schizophrenia research samples (4758 [28.9%] female; mean [SD] age, 38.2 [21.0] years). Liability to schizophrenia in UK Biobank was highly correlated with the latest genome-wide association study from the PGC (genetic correlation, 0.98; SE, 0.18) and showed the expected patterns of correlations with other psychiatric disorders. The schizophrenia PRS explained 6.8% of the variance in liability for schizophrenia case status in UK Biobank. UK Biobank participants with schizophrenia had significantly lower schizophrenia PRS than 3 of the clinically ascertained samples and significantly lower rates of schizophrenia-associated copy number variants than the CLOZUK sample. UK Biobank participants with schizophrenia had higher educational attainment and employment rates than the clinically ascertained schizophrenia samples, lower rates of smoking, and a later age of onset of psychosis. Conclusions and Relevance: Individuals with schizophrenia in the UK Biobank, and likely other volunteer-based biobanks, represent those less severely affected. Their inclusion in wider studies should enhance the representation of the full spectrum of illness severity.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Ketamine or ECT? What Have We Learned From the KetECT and ELEKT-D Trials?

1. Two recent clinical trials, KetECT and ELEKT-D, compared the effectiveness of ketamine and electroconvulsive therapy (ECT) for major depressive disorder. Notably, these trials reported marked differences in ECT's clinical outcomes of, with remission rates of 63% for KetECT and a strikingly lower rate of 22% for ELEKT-D, while the remission rates for ketamine were 46% and 38%, respectively. Considering that the primary objective of both trials was to compare the standard treatment (ECT) with an experimental intervention (ketamine), it is crucial to highlight the pronounced disparities in ECT's clinical outcomes. This article offers a comprehensive comparison of these trials while also exploring how patient characteristics, treatment protocols, and study designs may contribute to such pronounced outcome discrepancies. These differences highlight the heterogeneous nature of depression and underscore the need for personalized treatments. These studies also provide valuable insights into identifying the most suitable candidates for ketamine and ECT.

Copy Number Variants Increasing Risk for Schizophrenia: Shared and Distinct Effects on Brain Morphometry and Cognitive Performance.

Background: Copy number variations (CNVs) conferring risk for mental disorders are associated with brain changes and cognitive deficits. However, whether these effects are shared or distinct across CNVs remains untested. Here we compared the effects on brain morphometry and cognitive performance across CNVs with shared psychiatric liability. Methods: Unaffected and unrelated participants of White British and Irish ancestry were drawn from the UK Biobank. After quality control, we retained 31,941 participants not carrying any damaging CNVs and 202 participants carrying one CNV increasing risk for schizophrenia. Using regression analyses, we tested the association between brain morphometry and cognitive performance with CNV carrying status and compared these effect sizes across CNVs using z test for the equality of regression coefficients. Equation modeling was used to examine the mediation of brain phenotypes on the association between CNVs and cognitive performance. Results: We detected different patterns of association between CNVs and brain morphometry and cognitive abilities. Comparing across CNVs, 1q21.1 deletion showed the strongest association with surface area in frontal lobe (ß = -1.03, p = 4 × 10-8; ß = -0.81, p = .00001) and performance in digit memory (ß = -1.58, p = .00003), while 1q21.1 duplication showed the strongest association with volume of the putamen (ß = -0.70, p = .0004) and reaction time (ß = -1.14, p = .000002). We also showed that even when 2 CNVs were associated with performance in the same cognitive ability, these associations were mediated by different brain changes. Conclusions: Despite sharing similar psychiatric liability, the CNVs under study appeared to have different effects on brain morphometry and on performance in cognitive abilities, suggesting the existence of distinctive neurobiological pathways into the same clinical phenotypes.

Family-based analysis of the contribution of rare and common genetic variants to school performance in schizophrenia.

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 common and rare variants to school performance, and by implication cognitive function, in schizophrenia. Parental transmission of common alleles contributing to higher educational attainment (p value = 0.00015; OR = 2.63) and intelligence (p value = 0.00009; OR = 2.80), but not to schizophrenia, were associated with higher proband school performance. No significant effects were seen for non-transmitted parental common alleles. Probands with lower school performance 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. Among probands with lower school performance, those with damaging de novo coding variants in DD genes had a higher rate of comorbid mild intellectual disability (p value = 0.0002; OR = 15.6). Overall, we provide evidence for rare and common genetic contributions to school performance in schizophrenia. The strong effects for damaging de novo coding 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 common alleles for cognitive traits contributed to school performance in schizophrenia via indirect effects on the environment.

DRAGON-Data: a platform and protocol for integrating genomic and phenotypic data across large psychiatric cohorts.

BACKGROUND: Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood. AIMS: Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research. METHOD: As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant. RESULTS: We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation. CONCLUSIONS: DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.

Characterising heart rhythm abnormalities associated with Xp22.31 deletion.

BACKGROUND: Genetic deletions at Xp22.31 are associated with the skin condition X linked ichthyosis (XLI), and with a substantially increased risk of atrial fibrillation/flutter (AF), in males. AF is associated with elevated thrombosis, heart failure, stroke and dementia risk. METHODS: Through: (a) examining deletion carriers with a diagnosis of AF in UK Biobank, (b) undertaking an online survey regarding abnormal heart rhythms (AHRs) in men/boys with XLI and female carriers of XLI-associated deletions and (c) screening for association between common genetic variants within Xp22.31 and idiopathic AF-related conditions in UK Biobank, we have investigated how AHRs manifest in deletion carriers, and have identified associated risk factors/comorbidities and candidate gene(s). Finally, we examined attitudes towards heart screening in deletion carriers. RESULTS: We show that AHRs may affect up to 35% of deletion carriers (compared with <20% of age-matched non-carriers), show no consistent pattern of onset but may be precipitated by stress, and typically resolve quickly and respond well to intervention. Gastrointestinal (GI) conditions and asthma/anaemia were the most strongly associated comorbidities in male and female deletion carriers with AHR, respectively. Genetic analysis indicated significant enrichment of common AF risk variants around STS (7 065 298-7 272 682 bp in GRCh37/hg19 genome build) in males, and of common GI disorder and asthma/anaemia risk variants around PNPLA4 (7 866 804-7 895 780 bp) in males and females, respectively. Deletion carriers were overwhelmingly in favour of cardiac screening implementation. CONCLUSION: Our data suggest AHRs are frequently associated with Xp22.31 deletion, and highlight subgroups of deletion carriers that may be prioritised for screening. Examining cardiac function further in deletion carriers, and in model systems lacking steroid sulfatase, may clarify AF pathophysiology.

Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.

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. LASSO 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. Following 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). In 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 variants in ten genes confer substantial risk for schizophrenia.

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 <5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure and function of the synapse. The associations of the NMDA (N-methyl-D-aspartate) receptor subunit GRIN2A and AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptor subunit GRIA3 provide support for dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We observe an overlap of rare variant risk among schizophrenia, autism spectrum disorders1, 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.

Reduced reproductive success is associated with selective constraint on human genes.

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. Although genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2. Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily owing to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is probably mediated by genetically associated cognitive and behavioural traits, which may mean that male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. Although this genetic association may only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

Electroconvulsive Therapy Reverses Cerebral Hypoperfusion in a Patient With Psychotic Depression and Catatonia.

ABSTRACT: A 72-year-old man suffering with severe depression with psychotic symptoms, marked apathy, and psychomotor retardation was treated with electroconvulsive therapy (ECT) after resistance to treatment with psychotropic medications. His age, comorbidities, and dysexecutive syndrome prompted consideration of a diagnosis of frontotemporal dementia (FTD), and a 99mTechnetium-labeled hexamethyl propylene amine oxime single-photon emission computed tomography (SPECT) brain perfusion scan showed bilateral perfusion defects that were most pronounced in the frontal regions of the brain. The scan was judged to be abnormal and in keeping with a neurodegenerative dementia. We reasoned that the fluctuation in symptoms was inconsistent with a diagnosis of FTD and that his severe depression could be improved with ECT even if he had FTD, so we decided to proceed with this treatment. A course of 12 sessions of ECT resulted in remission of his psychiatric symptoms and improvement in cognitive performance. A repeat SPECT scan 5 weeks after the last ECT demonstrated a substantial improvement in cerebral blood flow, favoring the diagnosis of depression, rather than dementia. Similar case reports from the literature suggest that ECT does reverse brain hypoperfusion in severe cases of depression and catatonia. Clinicians should be aware that abnormal SPECT findings are nonspecific and can be caused by various conditions, including psychiatric illness, and are not necessarily diagnostic of a neurodegenerative disease.

Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders.

BACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. RESULTS: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10-8), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10-6) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10-7; rs73033497, p = 8.8 × 10-7; rs7914279, p = 6.4 × 10-7), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10-7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10-7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10-7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). CONCLUSIONS: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.

Schizophrenia, autism spectrum disorders and developmental disorders share specific disruptive coding mutations.

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.

Analysis of Diffusion Tensor Imaging Data From the UK Biobank Confirms Dosage Effect of 15q11.2 Copy Number Variation on White Matter and Shows Association With Cognition.

BACKGROUND: Copy number variations at the 15q11.2 BP1-BP2 locus are present in 0.5%-1.0% of the population, and the deletion is associated with several neurodevelopmental disorders. Previously, we showed a reciprocal effect of 15q11.2 copy number variation on fractional anisotropy, with widespread increases in deletion carriers. We aim to expand these findings using a larger sample of participants (N = 29,166) and higher resolution imaging and by examining the implications for cognitive performance. METHODS: Diffusion tensor imaging measures from participants with no neurological or psychiatric diagnoses were obtained from the UK Biobank database. We compared 15q11.2 BP1-BP2 deletion (n = 102) and duplication (n = 113) carriers to a large cohort of control individuals with no neuropsychiatric copy number variants (n = 28,951). Additionally, we assessed how changes in white matter mediated the association between carrier status and cognitive performance. RESULTS: Deletion carriers showed increases in fractional anisotropy in the internal capsule and cingulum and decreases in the posterior thalamic radiation compared with both duplication carriers and control subjects (who had intermediate values). Compared with control subjects, deletion carriers had lower scores across cognitive tasks, which were partly influenced by white matter. Reduced fractional anisotropy in the posterior thalamic radiation partially contributed to worse cognitive performance in deletion carriers. CONCLUSIONS: These results, together with our previous findings, provide convergent evidence for an effect of 15q11.2 BP1-BP2 on white matter microstructure, this being more pronounced in deletion carriers. Additionally, changes in white matter were found to partially mediate cognitive ability in deletion carriers, providing a link between white matter changes in 15q11.2 BP1-BP2 carriers and cognitive function.

Rare Copy Number Variants Are Associated With Poorer Cognition in Schizophrenia.

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 (ß = -0.66, 95% confidence interval [CI] = -1.31 to -0.01) and replication samples (ß = -0.91, 95% CI = -1.71 to -0.11) and after meta-analysis (ß = -0.76, 95% CI = -1.26 to -0.25, p = .003). CNVs hitting loss-of-function intolerant genes were associated with lower cognition (ß = -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.

Copy number variation and neuropsychiatric illness.

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 (<0.5% frequency), have high effect sizes, and show pleiotropic effects for multiple neuropsychiatric disorders, which implies a shared aetiology. Neuropsychiatric CNVs are also associated with cognitive impairment and other medical morbidities, such as heart defects and obesity. As most neuropsychiatric CNVs are multigenic, it has been challenging to map their effects onto specific biological processes, although gene-set analyses have implicated genes related to the synapse and chromatin regulation. However, future whole-genome sequencing studies have potential for identifying novel single-gene CNV associations, which could provide insights into the pathophysiology underlying neuropsychiatric disorders.

Electroconvulsive therapy for depression: 80 years of progress.

Electroconvulsive therapy is the most effective treatment for severe, psychotic or treatment-resistant depression. However, its effectiveness continues to be questioned, both in mainstream media and narratives within the scientific literature. In this analysis, we use an evidence-based approach to demonstrate the efficacy and safety of modern electroconvulsive therapy.

Effects of genomic copy number variants penetrant for schizophrenia on cortical thickness and surface area in healthy individuals: analysis of the UK Biobank.

BACKGROUND: Schizophrenia is a highly heritable disorder with undetermined neurobiological causes. Understanding the impact on brain anatomy of carrying genetic risk for the disorder will contribute to uncovering its neurobiological underpinnings. AIMS: To examine the effect of rare copy number variants (CNVs) associated with schizophrenia on brain cortical anatomy in a sample of unaffected participants from the UK Biobank. METHOD: We used regression analyses to compare cortical thickness and surface area (total and across gyri) between 120 unaffected carriers of rare CNVs associated with schizophrenia and 16 670 participants without any pathogenic CNV. A measure of cortical thickness and surface area covariance across gyri was also compared between groups. RESULTS: Carrier status was associated with reduced surface area (ß = -0.020 mm2, P < 0.001) and less robustly with increased cortical thickness (ß = 0.015 mm, P = 0.035), and with increased covariance in thickness (carriers z = 0.31 v. non-carriers z = 0.22, P < 0.0005). Associations were mainly present in frontal and parietal areas and driven by a limited number of rare risk alleles included in our analyses (mainly 15q11.2 deletion for surface area and 16p13.11 duplication for thickness covariance). CONCLUSIONS: Results for surface area conformed with previous clinical findings, supporting surface area reductions as an indicator of genetic liability for schizophrenia. Results for cortical thickness, though, argued against its validity as a potential risk marker. Increased structural thickness covariance across gyri also appears related to risk for schizophrenia. The heterogeneity found across the effects of rare risk alleles suggests potential different neurobiological gateways into schizophrenia's phenotype.

Genetic association of FMRP targets with psychiatric disorders.

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. High-confidence targets of FMRP, derived from studies of multiple tissue types, were enriched for common schizophrenia risk alleles, as well as rare loss-of-function and de novo nonsynonymous variants in schizophrenia 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 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.