A data-driven investigation of relationships between bipolar psychotic symptoms and schizophrenia genome-wide significant genetic loci.

The etiologies of bipolar disorder (BD) and schizophrenia include a large number of common risk alleles, many of which are shared across the disorders. BD is clinically heterogeneous and it has been postulated that the pattern of symptoms is in part determined by the particular risk alleles carried, and in particular, that risk alleles also confer liability to schizophrenia influence psychotic symptoms in those with BD. To investigate links between psychotic symptoms in BD and schizophrenia risk alleles we employed a data-driven approach in a genotyped and deeply phenotyped sample of subjects with BD. We used sparse canonical correlation analysis (sCCA) (Witten, Tibshirani, & Hastie, ) to analyze 30 psychotic symptoms, assessed with the OPerational CRITeria checklist, and 82 independent genome-wide significant single nucleotide polymorphisms (SNPs) identified by the Schizophrenia Working group of the Psychiatric Genomics Consortium for which we had data in our BD sample (3,903 subjects). As a secondary analysis, we applied sCCA to larger groups of SNPs, and also to groups of symptoms defined according to a published factor analyses of schizophrenia. sCCA analysis based on individual psychotic symptoms revealed a significant association (p = .033), with the largest weights attributed to a variant on chromosome 3 (rs11411529), chr3:180594593, build 37) and delusions of influence, bizarre behavior and grandiose delusions. sCCA analysis using the same set of SNPs supported association with the same SNP and the group of symptoms defined "factor 3" (p = .012). A significant association was also observed to the "factor 3" phenotype group when we included a greater number of SNPs that were less stringently associated with schizophrenia; although other SNPs contributed to the significant multivariate association result, the greatest weight remained assigned to rs11411529. Our results suggest that the canonical correlation is a useful tool to explore phenotype-genotype relationships. To the best of our knowledge, this is the first study to apply this approach to complex, polygenic psychiatric traits. The sparse canonical correlation approach offers the potential to include a larger number of fine-grained systematic descriptors, and to include genetic markers associated with other disorders that are genetically correlated with BD.

Epilepsy and bipolar disorder.

It is well recognized that mood disorders and epilepsy commonly co-occur. Despite this, our knowledge regarding the relationship between epilepsy and bipolar disorder is limited. Several shared features between the two disorders, such as their episodic nature and potential to run a chronic course, and the efficacy of some antiepileptic medications in the prophylaxis of both disorders, are often cited as evidence of possible shared underlying pathophysiology. The present paper aims to review the bidirectional associations between epilepsy and bipolar disorder, with a focus on epidemiological links, evidence for shared etiology, and the impact of these disorders on both the individual and wider society. Better recognition and understanding of these two complex disorders, along with an integrated clinical approach, are crucial for improved evaluation and management of comorbid epilepsy and mood disorders.

Cognitive Behaviour Therapy for Bulimia Nervosa and Eating Disorders Not Otherwise Specified: Translation from Randomized Controlled Trial to a Clinical Setting.

BACKGROUND: Enhanced Cognitive Behaviour Therapy (CBT-E) (Fairburn, Cooper and Shafran, 2003) was developed as a treatment approach for eating disorders focusing on both core psychopathology and additional maintenance mechanisms. AIMS: To evaluate treatment outcomes associated with CBT-E in a NHS Eating Disorders Service for adults with bulimia and atypical eating disorders and to make comparisons with a previously published randomized controlled trial (Fairburn et al., 2009) and "real world" evaluation (Byrne, Fursland, Allen and Watson, 2011). METHOD: Participants were referred to the eating disorder service between 2002 and 2011. They were aged between 18-65 years, registered with a General Practitioner within the catchment area, and had experienced symptoms fulfilling criteria for BN or EDNOS for a minimum of 6 months. RESULTS: CBT-E was commenced by 272 patients, with 135 completing treatment. Overall, treatment was associated with significant improvements in eating disorder and associated psychopathology, for both treatment completers and the intention to treat sample. CONCLUSIONS: Findings support dissemination of CBT-E in this context, with significant improvements in eating disorder psychopathology. Improvements to global EDE-Q scores were higher for treatment completers and lower for the intention to treat sample, compared to previous studies (Fairburn et al., 2009; Byrne et al., 2011). Level of attrition was found at 40.8% and non-completion of treatment was associated with higher levels of anxiety. Potential explanations for these findings are discussed.

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.

Association Between Schizophrenia-Related Polygenic Liability and the Occurrence and Level of Mood-Incongruent Psychotic Symptoms in Bipolar Disorder.

Importance: Bipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes and mechanisms. Objective: To investigate the association between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRSs), and psychotic presentations of BD. Design, Setting, and Participants: This case-control study in the United Kingdom used multinomial logistic regression to estimate differential PRS associations across categories of cases and controls. Participants included in the final analyses were 4436 cases of BD from the Bipolar Disorder Research Network. These cases were compared with the genotypic data for 4976 cases of schizophrenia and 9012 controls from the Type 1 Diabetes Genetics Consortium study and the Generation Scotland study. Data were collected between January 1, 2000, and December 31, 2013. Data analysis was conducted from March 1, 2016, to February 28, 2017. Exposures: Standardized PRSs, calculated using alleles with an association threshold of P < .05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, were adjusted for the first 10 population principal components and genotyping platforms. Main Outcomes and Measures: Multinomial logit models estimated PRS associations with BD stratified by Research Diagnostic Criteria subtypes of BD, by lifetime occurrence of psychosis, and by lifetime mood-incongruent psychotic features. Ordinal logistic regression examined PRS associations across levels of mood incongruence. Ratings were derived from the Schedules for Clinical Assessment in Neuropsychiatry interview and the Bipolar Affective Disorder Dimension Scale. Results: Of the 4436 cases of BD, 2966 (67%) were female patients, and the mean (SD) age at interview was 46 [12] years. Across clinical phenotypes, there was an exposure-response gradient, with the strongest PRS association for schizophrenia (risk ratio [RR] = 1.94; 95% CI, 1.86-2.01), followed by schizoaffective BD (RR = 1.37; 95% CI, 1.22-1.54), bipolar I disorder subtype (RR = 1.30; 95% CI, 1.24-1.36), and bipolar II disorder subtype (RR = 1.04; 95% CI, 0.97-1.11). Within BD cases, there was an effect gradient, indexed by the nature of psychosis. Prominent mood-incongruent psychotic features had the strongest association (RR = 1.46; 95% CI, 1.36-1.57), followed by mood-congruent psychosis (RR = 1.24; 95% CI, 1.17-1.33) and BD with no history of psychosis (RR = 1.09; 95% CI, 1.04-1.15). Conclusions and Relevance: For the first time to date, a study shows a polygenic-risk gradient across schizophrenia and BD, indexed by the occurrence and level of mood-incongruent psychotic symptoms.