Although the Positive and Negative Syndrome Scale (PANSS) was developed for use in schizophrenia (SZ), antipsychotic drug trials use the PANSS to measure symptom change also for bipolar (BP) and schizoaffective (SA) disorder, extending beyond its original indications. If the dimensions measured by the PANSS are different across diagnoses, then the same score change for the same drug condition may have different meanings depending on which group is being studied. Here, we evaluated whether the factor structure in the PANSS was consistent across schizophrenia (n = 3647), bipolar disorder (n = 858), and schizoaffective disorder (n = 592). Along with congruency coefficients, Hancock's H, and Jaccard indices, we used target rotations and statistical tests of invariance based on confirmatory factor models. We found the five symptom dimensions measured by the 30-item PANSS did not generalize well to schizoaffective and bipolar disorders. A model based on an 18-item version of the PANSS generalized better across SZ and BP groups, but significant problems remained in generalizing some of the factors to the SA sample. Schizophrenia and bipolar disorder showed greater similarity in factor structure than did schizophrenia and schizoaffective disorder. The Anxiety/Depression factor was the most consistent across disorders, while the Positive factor was the least consistent.

The Positive and Negative Syndrome Scale (PANSS) is frequently described with five latent factors, yet published factor models consistently fail to replicate across samples and related disorders. We hypothesize that (1) a subset of the PANSS, instead of the entire PANSS scale, would produce the most replicable five-factor models across samples, and that (2) the PANSS factor structure may be different depending on the treatment phase, influenced by the responsiveness of the positive symptoms to treatment. Using exploratory factor analysis, confirmatory factor analysis and cross validation on baseline and post-treatment observations from 3647 schizophrenia patients, we show that five-factor models fit best across samples when substantial subsets of the PANSS items are removed. The optimal model at baseline (five factors) omits 12 items: Motor Retardation, Grandiosity, Somatic Concern, Lack of Judgment and Insight, Difficulty in Abstract Thinking, Mannerisms and Posturing, Disturbance of Volition, Preoccupation, Disorientation, Excitement, Guilt Feelings and Depression. The PANSS factor models fit differently before and after patients have been treated. Patients with larger treatment response in positive symptoms have larger variations in factor structure across treatment stage than the less responsive patients. Negative symptom scores better predict the positive symptoms scores after treatment than before treatment. We conclude that sparse factor models replicate better on new samples, and the underlying disease structure of Schizophrenia changes upon treatment.

Objective:Common genetic variation spans schizophrenia, schizoaffective and bipolar disorders, but historically, these syndromes have been distinguished categorically. A symptom dimension shared across these syndromes, if such a general factor exists, might provide a clearer target for understanding and treating mental illnesses that share core biological bases. Method:We tested the hypothesis that a bifactor model of the Positive and Negative Syndrome Scale (PANSS), containing 1 general factor and 5 specific factors (positive, negative, disorganized, excited, anxiety), explains the cross-diagnostic structure of symptoms better than the traditional 5-factor model, and examined the extent to which a general factor reflects the overall severity of symptoms spanning diagnoses in 5094 total patients with a diagnosis of schizophrenia, schizoaffective, and bipolar disorder. Results:The bifactor model provided superior fit across diagnoses, and was closer to the "true" model, compared to the traditional 5-factor model (Vuong test; P < .001). The general factor included high loadings on 28 of the 30 PANSS items, omitting symptoms associated with the excitement and anxiety/depression domains. The general factor had highest total loadings on symptoms that are often associated with the positive and disorganization syndromes, but there were also substantial loadings on the negative syndrome thus leading to the interpretation of this factor as reflecting generalized psychosis. Conclusions:A bifactor model derived from the PANSS can provide a stronger framework for measuring cross-diagnostic psychopathology than a 5-factor model, and includes a generalized psychosis dimension shared at least across schizophrenia, schizoaffective, and bipolar disorder.

Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.

While 17% of US adults use tobacco regularly, smoking rates among persons with schizophrenia are upwards of 60%. Research supports a shared etiological basis for smoking and schizophrenia, including findings from genome-wide association studies (GWAS). However, few studies have directly tested whether the same or distinct genetic variants also influence smoking behavior among schizophrenia cases. Using data from the Psychiatric Genomics Consortium (PGC) study of schizophrenia (35476 cases, 46839 controls), we estimated genetic correlations between these traits and tested whether polygenic risk scores (PRS) constructed from the results of smoking behaviors GWAS were associated with schizophrenia risk or smoking behaviors among schizophrenia cases. Results indicated significant genetic correlations of schizophrenia with smoking initiation (r_g = 0.159; P = 5.05 × 10^-10), cigarettes-smoked-per-day (r_g = 0.094; P = 0.006), and age-of-onset of smoking (r_g = 0.10; P = 0.009). Comparing smoking behaviors among schizophrenia cases to the general population, we observe positive genetic correlations for smoking initiation (r_g = 0.624, P = 0.002) and cigarettes-smoked-per-day (r_g = 0.689, P = 0.120). Similarly, TAG-based PRS for smoking initiation and cigarettes-smoked-per-day were significantly associated with smoking initiation (P = 3.49 × 10^-5) and cigarettes-smoked-per-day (P = 0.007) among schizophrenia cases. We performed the first GWAS of smoking behavior among schizophrenia cases and identified a novel association with cigarettes-smoked-per-day upstream of the TMEM106B gene on chromosome 7p21.3 (rs148253479, P = 3.18 × 10^-8, n = 3520). Results provide evidence of a partially shared genetic basis for schizophrenia and smoking behaviors. Additionally, genetic risk factors for smoking behaviors were largely shared across schizophrenia and non-schizophrenia populations. Future research should address mechanisms underlying these associations to aid both schizophrenia and smoking treatment and prevention efforts.

The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.

Background

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

Aims

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Method

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Results

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = -0.34 years, s.e. = 0.08), major depression (β = -0.34 years, s.e. = 0.08), schizophrenia (β = -0.39 years, s.e. = 0.08), and educational attainment (β = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

Conclusions

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. CONCLUSIONS: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 × 10^-15), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 × 10^-6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 × 10^-11) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 × 10^-5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.