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BACKGROUND Psychiatric disorders are associated with cognitive impairment. We have developed a web-based, 9-task cognitive battery to measure the core domains affected in people with psychiatric disorders. To date, this assessment has been used to collect data on a clinical sample of participants with psychiatric disorders. OBJECTIVE The aims of this study were: (1) to establish a briefer version of the battery (called the Cardiff ONline Cognitive Assessment, CONCA) that can give a valid measure of cognitive ability (‘g’), and (2) to collect normative data and demonstrate CONCA’s application in a health population sample. METHODS Based on six criteria and data from our previous study, we selected 5 out of the original 9 tasks to include in CONCA. These included 3 core tasks that were sufficient to derive a measure of ‘g’ and 2 optional tasks. Participants from a web-based national cohort study (HealthWise Wales) were invited to complete CONCA. Completion rates, sample characteristics, performance distributions and associations between cognitive performance and demographic characteristics were examined. RESULTS A total of 3679 completed at least one CONCA task, of which, 3135 participants completed all three core CONCA tasks. Performance on CONCA was associated with age (B=-0.05, SE=0.002, P CONCLUSIONS CONCA provides a valid measure of ‘g’, which can be derived using as few as 3 tasks that take no more than 15 minutes. Performance on CONCA showed associations with demographic characteristics in the expected direction. The effect of device on cognitive performance is an important consideration for research utilising web-based assessments.

BackgroundThe nature and degree of cognitive impairments in schizoaffective disorder is not well established. The aim of this meta-analysis was to characterise cognitive functioning in schizoaffective disorder and compare it with cognition in schizophrenia and bipolar disorder. Schizoaffective disorder was considered both as a single category and as its two diagnostic subtypes, bipolar and depressive disorder.MethodsFollowing a thorough literature search (468 records identified), we included 31 studies with a total of 1685 participants with schizoaffective disorder, 3357 with schizophrenia and 1095 with bipolar disorder. Meta-analyses were conducted for seven cognitive variables comparing performance between participants with schizoaffective disorder and schizophrenia, and between schizoaffective disorder and bipolar disorder.ResultsParticipants with schizoaffective disorder performed worse than those with bipolar disorder (g = −0.30) and better than those with schizophrenia (g = 0.17). Meta-analyses of the subtypes of schizoaffective disorder showed cognitive impairments in participants with the depressive subtype are closer in severity to those seen in participants with schizophrenia (g = 0.08), whereas those with the bipolar subtype were more impaired than those with bipolar disorder (g = −0.23) and less impaired than those with schizophrenia (g = 0.29). Participants with the depressive subtype had worse performance than those with the bipolar subtype but this was not significant (g = 0.25, p = 0.05).ConclusionCognitive impairments increase in severity from bipolar disorder to schizoaffective disorder to schizophrenia. Differences between the subtypes of schizoaffective disorder suggest combining the subtypes of schizoaffective disorder may obscure a study's results and hamper efforts to understand the relationship between this disorder and schizophrenia or bipolar disorder.

There is controversy about the status of schizoaffective disorder depressive-type (SA-D), particularly whether it should be considered a form of schizophrenia or a distinct disorder. We aimed to determine whether individuals with SA-D differ from individuals with schizophrenia in terms of demographic, premorbid and lifetime clinical characteristics, and genetic liability to schizophrenia, depression and bipolar disorder. Participants were from the CardiffCOGS sample and met ICD-10 criteria for schizophrenia (n=713) or SA-D (n=151). Two samples, Cardiff Affected-sib (n=354) and Cardiff F-series (n=524), were used for replication. For all samples, phenotypic data were ascertained through structured interview, review of medical records, and an ICD-10 diagnosis made by trained researchers. Univariable and multivariable logistic regression models were used to compare individuals with schizophrenia and SA-D for demographic and clinical characteristics, and polygenic risk scores (PRS). In the CardiffCOGS, SA-D, compared to schizophrenia, was associated with female sex, childhood abuse, history of alcohol dependence, higher functioning Global Assessment Scale (GAS) score in worst episode of psychosis, lower functioning GAS score in worst episode of depression, and reduced lifetime severity of disorganised symptoms. Individuals with SA-D had higher depression PRS compared to those with schizophrenia. PRS for schizophrenia and bipolar disorder did not significantly differ between SA-D and schizophrenia. Compared to individuals with schizophrenia, individuals with SA-D had higher rates of environmental and genetic risk factors for depression and a similar genetic liability to schizophrenia. These findings are consistent with SA-D being a sub-type of schizophrenia resulting from elevated liability to both schizophrenia and depression.Key words: Polygenic risk score; depression; psychosis; diagnosis; phenotypes

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.

BACKGROUND Cognitive impairments are features of many psychiatric disorders and affect functioning. A barrier to cognitive research on psychiatric disorders is the lack of large cross-disorder data sets. However, the collection of cognitive data can be logistically challenging and expensive. Web-based collection may be an alternative; however, little is known about who does and does not complete web-based cognitive assessments for psychiatric research. OBJECTIVE The aims of this study are to develop a web-based cognitive battery for use in psychiatric research, validate the battery against the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery, and compare the characteristics of the participants who chose to take part with those of the individuals who did not participate. METHODS Tasks were developed by The Many Brains Project and selected to measure the domains specified by the MATRICS initiative. We undertook a cross-validation study of 65 participants with schizophrenia, bipolar disorder, depression, or no history of psychiatric disorders to compare the web-based tasks with the MATRICS Consensus Cognitive Battery. Following validation, we invited participants from 2 large ongoing genetic studies, which recruited participants with psychiatric disorders to complete the battery and evaluated the demographic and clinical characteristics of those who took part. RESULTS Correlations between web-based and MATRICS tasks ranged between 0.26 and 0.73. Of the 961 participants, 887 (92.3%) completed at least one web-based task, and 644 (67%) completed all tasks, indicating adequate completion rates. Predictors of web-based participation included being female (odds ratio [OR] 1.3, 95% CI 1.07-1.58), ethnicity other than White European (OR 0.66, 95% CI 0.46-0.96), higher levels of education (OR 1.19, 95% CI 1.11-1.29), diagnosis of an eating disorder (OR 2.17, 95% CI 1.17-4) or depression and anxiety (OR 5.12, 95% CI 3.38-7.83), and absence of a diagnosis of schizophrenia (OR 0.59, 95% CI 0.35-0.94). Lower performance on the battery was associated with poorer functioning (B=−1.76, SE 0.26; P CONCLUSIONS Our findings offer valuable insights into the advantages and disadvantages of testing cognitive function remotely for mental health research.

Background Cognitive impairments are features of many psychiatric disorders and affect functioning. A barrier to cognitive research on psychiatric disorders is the lack of large cross-disorder data sets. However, the collection of cognitive data can be logistically challenging and expensive. Web-based collection may be an alternative; however, little is known about who does and does not complete web-based cognitive assessments for psychiatric research. Objective The aims of this study are to develop a web-based cognitive battery for use in psychiatric research, validate the battery against the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery, and compare the characteristics of the participants who chose to take part with those of the individuals who did not participate. Methods Tasks were developed by The Many Brains Project and selected to measure the domains specified by the MATRICS initiative. We undertook a cross-validation study of 65 participants with schizophrenia, bipolar disorder, depression, or no history of psychiatric disorders to compare the web-based tasks with the MATRICS Consensus Cognitive Battery. Following validation, we invited participants from 2 large ongoing genetic studies, which recruited participants with psychiatric disorders to complete the battery and evaluated the demographic and clinical characteristics of those who took part. Results Correlations between web-based and MATRICS tasks ranged between 0.26 and 0.73. Of the 961 participants, 887 (92.3%) completed at least one web-based task, and 644 (67%) completed all tasks, indicating adequate completion rates. Predictors of web-based participation included being female (odds ratio [OR] 1.3, 95% CI 1.07-1.58), ethnicity other than White European (OR 0.66, 95% CI 0.46-0.96), higher levels of education (OR 1.19, 95% CI 1.11-1.29), diagnosis of an eating disorder (OR 2.17, 95% CI 1.17-4) or depression and anxiety (OR 5.12, 95% CI 3.38-7.83), and absence of a diagnosis of schizophrenia (OR 0.59, 95% CI 0.35-0.94). Lower performance on the battery was associated with poorer functioning (B=−1.76, SE 0.26; P Conclusions Our findings offer valuable insights into the advantages and disadvantages of testing cognitive function remotely for mental health research.

Background Schizoaffective disorder depressed type (SAD) has been considered a distinct diagnostic entity since 1987, yet it is rarely recognised in clinical and research settings as it is considered analogous to schizophrenia with comorbid depression. However, these assumptions are often based on anecdotal evidence, as little research has attempted to examine differences between the two disorders. We aimed to establish the validity of SAD as a diagnosis, by investigating phenotypic and genotypic differences between schizophrenia and SAD. Methods Participants were from the Cardiff Cognition study and included if they met ICD-10 criteria for schizophrenia (n=713) or SAD (n=151). Diagnosis was determined via consensus assessment by trained researchers using lifetime symptom data from the SCAN interview alongside patient medical records. Polygenic risk scores were derived for schizophrenia and major depressive disorder using PRSice. Logistic regressions were used to measure the association between diagnosis and lifetime clinical characteristics across five categories: demographics, premorbid functioning, illness progression, psychosis, and depression. Sex and age at interview were included as covariates. Logistic regressions were used to measure the association between diagnosis and polygenic risk scores for schizophrenia and depression at a threshold of p Results Compared to schizophrenia, SAD was significantly associated with female sex (OR= 3.19, 95% CI= 2.23 - 4.59, p=5.0x10-9), lower global assessment score in worst episode of depression (OR= 0.47, 95% CI= 0.37 – 0.59, p=5.0x10-9), higher global assessment score in worst episode of psychosis (OR= 1.44, 95% CI= 1.20 – 1.72, p= 6.8x10-4), a greater likelihood of an admission for depression (OR= 2.24, 95% CI= 1.48 – 3.40, p= 1.2x10-3), greater alcohol abuse or dependence (OR= 2.12, 95% CI= 1.41 – 3.20, p= 2.3x10-3), longer duration of depression (OR= 1.46, 95% CI= 1.19 - 1.84, p= 3.5x10-3), experiencing childhood abuse (OR= 2.07, 95% CI = 1.35 – 3.17, p= 3.9x10-3), a reduced likelihood of an involuntary admission for psychosis (OR= 0.40, 95% CI= 0.22 – 0.75, p= 0.01), depression onset occurring prior to psychosis onset (OR= 2.40, 95% CI= 1.37 – 4.43, p= 0.01), having a higher number of children (OR= 1.34, 95% CI= 1.08 – 1.67, p= 0.03), and better cognitive functioning (OR= 1.20, 95% CI= 1.04 – 1.40, p= 0.05). Depression polygenic risk score was higher in participants with SAD (OR= 1.33, 95% CI= 1.06 – 1.66, R2= 0.015, p= 0.01); schizophrenia polygenic risk score was not associated with diagnosis (OR= 0.94, 95% CI= 0.75 – 1.17, R2= 0.001, p= 0.58). Secondary analyses were conducted to investigate sex-specific effects. A significant difference in schizophrenia polygenic risk was observed between males and females, which indicated that males with SAD have lower schizophrenia polygenic risk scores than females with SAD (OR= 0.46, 95% CI= 0.28 – 0.74, R2= 0.143, p=1.5x10-3). Sex-specific effects were not observed for depression PRS or phenotypes. Discussion Our results indicate a number of differences between individuals with schizophrenia and SAD. Primarily, the results demonstrate a greater burden of depression in those with SAD, both phenotypically and genotypically. Our results also suggest that the genetic contribution to SAD may differ between males and females. The findings indicate that schizophrenia research, particularly genetic research, needs to consider the impacts of sex-specific effects and comorbid depression before combining patient samples for analysis.

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.

Background Cognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases. Methods We combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases. Results PRS for both population IQ (P = 4.39 × 10–28) and EA (P = 1.27 × 10–26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS. Conclusions Cognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.

Background Cognition is impaired in patient with psychosis and is predictive of functional outcomes. Despite this, cognitive function is not routinely assessed in clinical services in the United Kingdom. Collecting cognitive data for research is also labour-intensive and expensive. Web-based assessments may be a solution for these issues but to date, these have not been utilised in patients with psychosis or other psychiatric disorders. Methods We have developed an online cognitive battery for use in psychosis research (and broader mental health research) in collaboration with The Many Brains Project, website developers, patients and clinicians (Cardiff ONline Cognitive Assessment, CONCA). Tasks were selected to measure the domains outlined by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative. We have undertaken a cross-validation study in those with schizophrenia (N=15), bipolar disorder (N=16), depression (N=15) and healthy controls (N=19) to compare the online tasks with the MATRICS battery. Following validation, we invited participants from the Cardiff Cognition in Schizophrenia Study (CardiffCOGS) and the National Centre for Mental Health (NCMH) to complete CONCA. Results Correlations between CONCA and MATRICS tasks ranged from 0.25 to 0.73 in our validation sample (N=65). A total of 6960 individuals were invited to participate and 1227 consented to take part. There was a better response rate from NCMH participants (who were recruited more recently) compared to those from CardiffCOGS. Online participants recruited from NCMH were more highly educated (W=1171600, p Discussion Web-based cognitive testing is a suitable method for collecting large-scale data in psychiatric populations, although there was some evidence of recruitment bias. The results of the validation and recruitment phases were used to inform selection of the final battery. We consulted with patients and health professionals from a youth psychosis service and NCMH’s patient involvement group to create a user-friendly interface and will continue to work with these groups to develop clinically useful feedback to facilitate patient monitoring in early intervention psychosis services.

Schizophrenia is a debilitating psychiatric condition that manifests itself in the early adult life and is often associated with poor quality of life and decreased life expectancy. It is both highly heritable and polygenic, with risk variants spanning the full spectrum of population frequencies. There has been a long-standing debate, from an evolutionary perspective, as to how risk alleles persist in the human gene pool, particularly given the decreased fecundity associated with schizophrenia. It has been repeatedly show that rare risk alleles of strong effect persist due to mutation-selection balance, a mechanism which implies that they are removed from the population by natural selection at the same rate that they appear by mutation. However, an explanation for the persistence of common risk alleles of small effect has not been explicitly given, though this is often attributed to various evolutionary scenarios. One of the most popular hypotheses links this persistence to positive selection, arguing that current common risk alleles for schizophrenia might have been beneficial at some point during human evolution, and these benefits resulted in the allele frequencies we observe today. While detecting selection signals in genomic data has been traditionally challenging, recent advances in the statistical and population genetics communities have made possible to systematically test for the presence of selected alleles in GWAS summary statistics. To demonstrate the possibilities of such a procedure, this work uses a large-scale meta-analysis of schizophrenia GWAS data (40,675 cases and 64,643 controls) to test four different metrics of positive selection, each tailored to a particular timeframe of human evolution. The LD-Score framework shows that none of these metrics are significantly associated with common genetic variation, raising doubts into the validity of positive selection as a driver for the persistence of schizophrenia risk alleles. Furthermore, strong association is shown for a metric of negative selection, background selection (h2 enrichment = 1.801; p=0.001), which recurrently affects regions of low recombination and reduces genetic diversity. An examination of the mechanism of action of background selection, coupled with a simulation analysis, allows us to establish that regions with reduced genetic diversity are more likely to harbour common risk alleles in schizophrenia, and that this is compatible with our current knowledge about the epidemiology of the disorder. This also suggests a genetic model for the action of natural selection in schizophrenia, which relates our findings to the nearly-neutral theory of molecular evolution (Ohta 1973) and could be extended to other psychiatric complex traits.

BACKGROUND:\ud Cognitive impairments are well-established features of schizophrenia, but there is ongoing debate about the nature and degree of cognitive impairment in patients with schizoaffective disorder and bipolar disorder. We hypothesized that there is a spectrum of increasing impairment from bipolar disorder to schizoaffective disorder bipolar type, to schizoaffective disorder depressive type and schizophrenia.\ud \ud METHODS:\ud We compared performance on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery between participants with schizophrenia (n = 558), schizoaffective disorder depressive type (n = 112), schizoaffective disorder type (n = 76), bipolar disorder (n = 78) and healthy participants (n = 103) using analysis of covariance with post hoc comparisons. We conducted an ordinal logistic regression to examine whether cognitive impairments followed the hypothesized spectrum from bipolar disorder (least severe) to schizophrenia (most severe). In addition to categorical diagnoses, we addressed the influence of symptom domains, examining the association between cognition and mania, depression and psychosis.\ud \ud RESULTS:\ud Cognitive impairments increased in severity from bipolar disorder to schizoaffective disorder bipolar type, to schizophrenia and schizoaffective disorder depressive type. Participants with schizophrenia and schizoaffective disorder depressive type showed equivalent performance (d = 0.07, p = 0.90). The results of the ordinal logistic regression were consistent with a spectrum of deficits from bipolar disorder to schizoaffective disorder bipolar type, to schizophrenia/schizoaffective disorder depressive type (odds ratio = 1.98, p < 0.001). In analyses of the associations between symptom dimensions and cognition, higher scores on the psychosis dimension were associated with poorer performance (B = 0.015, standard error = 0.002, p < 0.001).\ud \ud LIMITATIONS:\ud There were fewer participants with schizoaffective disorder and bipolar disorder than schizophrenia. Despite this, our analyses were robust to differences in group sizes, and we were able to detect differences between groups.\ud \ud CONCLUSION:\ud Cognitive impairments represent a symptom dimension that cuts across traditional diagnostic boundaries.