Whole genome sequencing of 91 multiplex schizophrenia families reveals increased burden of rare, exonic copy number variation in schizophrenia probands and genetic heterogeneity

The importance of genomic copy number variants (CNVs) has long been recognized in the etiology of neurodevelopmental diseases. We report here the results from the CNV analysis of whole-genome sequences from 91 multiplex schizophrenia families. Employing four algorithms (CNVnator, Cn.mops, DELLY and LUMPY) to identify CNVs, we find 1231 rare deletions and 287 rare duplications in 300 individuals (77 with schizophrenia (SZ), 32 with schizoaffective disorder (SAD), 82 with another neuropsychiatric diagnosis and 109 unaffected). The size of the CNVs ranges from a few hundred base-pairs to about 1.3Mb. The total burden of CNVs does not differ significantly between affected (SZ and SAD) and unaffected individuals. Parent-to-child transmission rate for rare CNVs affecting exonic regions is significantly higher for affected (SZ and SAD) probands as compared to their siblings, but rates for all CNVs is not. We observe heterogeneity between families in terms of genes involved in CNVs, and find several CNVs involving genes previously implicated in either schizophrenia or other neuropsychiatric disorders

naklada - Zagreb, Croatia USE OF THE COMMUNICATION CHECKLIST - SELF REPORT (CC-SR) IN SCHIZOPHRENIA: LANGUAGE IMPAIRMENTS CORRELATE WITH POOR PREMORBID SOCIAL ADJUSTMENT

Background: The present study reports preliminary results from the multicentre project on the approbation of the Russian language version of the “The Communication Checklist-Self Report” (RL-CC-SR) and its first use in schizophrenia (SZ), aiming to evaluate the contribution of language disturbances in the pathogenesis of this heterogeneous disorder. Subjects and methods: The study evaluated patients’ clinical state with the Diagnostic Interview for Psychoses (DIP), and assessed language and communication disturbances (LCD) with the RL-CC-SR in all participants (213 healthy controls (HC), 83 SZ patients, 31 SZ first-degree relatives). Data from the current sample of SZ (n=50), and HC (n=213) was analysed to calculate the relationships between LCD, social and clinical variables using descriptive statistics methods, T-test and Pearson’s correlations (SPSS-26, 2019). Results: The quotient scores (<6) and raw scores on all three CC-SR subscales demonstrated prominent LCD in SZ: (i) language structure (LS) (SZ:11.92±8.01, HC:7.54±5.91; <0.001), (ii) pragmatic skills (PS) (SZ:11.30±10.07, HC:8.71±7.39; =0.040), (iii) social engagement (SE) (SZ:31.94±11.76, HC:19.42±10.35; <0.001). In SZ, Pearson correlations of LS scores were significant for the DIP-items Odd Speech (p=0.033), and Social Engagement - Blunted Affect (p=0.042). PS was related to early disease onset (=0.027), poor premorbid work adjustment (p=0.003), along with LS (p=0.005), and was also linked to poor premorbid social adjustment (p=0.005). Conclusions: SZ patients are aware of their LCD at all levels of language structure, pragmatics, and nonverbal communication, but are unable to compensate. Disturbances of LS and PS in SZ patients relate to their poor social adjustment and functioning, and may prove to be associated with the primary negative symptoms domain of the disorder and its generally poor outcome

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the “Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders” consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals

Genetic Overlap Profiles of Cognitive Ability in Psychotic and Affective Illnesses::A Multi-Site Study of Multiplex Pedigrees

BACKGROUND: Cognitive impairment is a key feature of psychiatric illness, making cognition an important tool for exploring of the genetics of illness risk. It remains unclear which measures should be prioritized in pleiotropy-guided research. Here, we generate profiles of genetic overlap between psychotic and affective disorders and cognitive measures in Caucasian and Hispanic groups. METHODS: Data were from four samples of extended pedigrees (N = 3046). Coefficient of relationship analyses were used to estimate genetic overlap between illness risk and cognitive ability. Results were meta-analyzed. FINDINGS: Psychosis was characterized by cognitive impairments on all measures with a generalized profile of genetic overlap. General cognitive ability shared greatest genetic overlap with psychosis risk (average Endophenotype Ranking Value (ERV) across samples from a random-effects meta-analysis = 0.32) followed by Verbal Memory (ERV = 0.24), Executive Function (ERV = 0.22), and Working Memory (ERV = 0.21). For bipolar disorder, there was genetic overlap with Processing Speed (ERV = 0.05) and Verbal Memory (ERV = 0.11), but these were confined to select samples. Major depression was characterized by enhanced Working and Face Memory performance, as reflected in significant genetic overlap in two samples. INTERPRETATION: There is substantial genetic overlap between risk for psychosis and a range of cognitive abilities (including general intelligence). Most of these effects are largely stable across of ascertainment strategy and ethnicity. Genetic overlap between affective disorders and cognition, on the other hand, tend to be specific to ascertainment strategy, ethnicity, and cognitive test battery

Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders

BACKGROUND: There is increasing evidence for shared genetic susceptibility between schizophrenia and bipolar disorder. Although genetic variants only convey subtle increases in risk individually, their combination into a polygenic risk score constitutes a strong disease predictor.AimsTo investigate whether schizophrenia and bipolar disorder polygenic risk scores can distinguish people with broadly defined psychosis and their unaffected relatives from controls. METHOD: Using the latest Psychiatric Genomics Consortium data, we calculated schizophrenia and bipolar disorder polygenic risk scores for 1168 people with psychosis, 552 unaffected relatives and 1472 controls. RESULTS: Patients with broadly defined psychosis had dramatic increases in schizophrenia and bipolar polygenic risk scores, as did their relatives, albeit to a lesser degree. However, the accuracy of predictive models was modest. CONCLUSIONS: Although polygenic risk scores are not ready for clinical use, it is hoped that as they are refined they could help towards risk reduction advice and early interventions for psychosis.Declaration of interestR.M.M. has received honoraria for lectures from Janssen, Lundbeck, Lilly, Otsuka and Sunovian.Funding: This work was funded by the Medical Research Council (G0901310), the Wellcome Trust (grants 085475/B/08/Z, 085475/Z/08/Z), the European Union’s Seventh Framework Programme for research, technological development and demonstration (grant 602450). This study was also supported by the NIHR Biomedical Research Centre at University College London (mental health theme) and by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry – Kings College London. Further support: NHIR Academic Clinical fellowship awarded to M.S.C.. E.B. acknowledges research funding from: BMA Margaret Temple grants 2016 and 2006, MRC – Korean Health Industry Development Institute Partnering Award (MC_PC_16014), MRC New Investigator Award and a MRC Centenary Award (G0901310), National Institute of Health Research UK post-doctoral fellowship, the Psychiatry Research Trust, the Schizophrenia Research Fund, the Brain and Behaviour Research foundation’s NARSAD Young Investigator Awards 2005, 2008, Wellcome Trust Research Training Fellowship and the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry Kings College London. The Brain and Behaviour Research foundation’s (NARSAD’s) Young Investigator Award (Grant 22604, awarded to C.I.). The BMA Margaret Temple grant 2016 to J. H.T. European Research Council Marie Curie award to A.D.-R. The infrastructure for the GROUP consortium is funded through the Geestkracht programme of the Dutch Health Research Council (ZON-MW, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental healthcare organisations. Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGZ Eindhoven en de kempen, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan Zorggroep, Prins Clauscentrum Sittard, RIAGG Roermond, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psychomedical center (The Hague). Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal, Riagg Amersfoort and Delta. The sample from Spain was collected at the Hospital Universitario Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Carlos III Health Institute PI020499, PI050427, PI060507, Plan Nacional de Drugs Research Grant 2005- Orden sco/3246/2004, SENY Fundació Research Grant CI 2005-0308007 and Fundación Marqués de Valdecilla API07/011. The present data were obtained at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: MINECO Exp.: SAF2013-46292-R