Profile Url: richard-bruggeman
Researcher at University of Groningen, University Medical Center Groningen, Department of Neuroscience
Nature Genetics, 2016-11-21
Genomic copy number variants (CNVs) have been strongly implicated in the etiology of schizophrenia (SCZ). However, apart from a small number of risk variants, elucidation of the CNV contribution to risk has been difficult due to the rarity of risk alleles, all occurring in less than 1% of cases. We sought to address this obstacle through a collaborative effort in which we applied a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. We observed a global enrichment of CNV burden in cases (OR=1.11, P=5.7e-15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7e-6). CNV burden is also enriched for genes associated with synaptic function (OR = 1.68, P = 2.8e-11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3e-5). We identified genome-wide significant support for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. We find support at a suggestive level for nine additional candidate susceptibility and protective loci, which consist predominantly of CNVs mediated by non-allelic homologous recombination (NAHR).
Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2x10-6) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6x10-6) and manic symptoms (BD, p=2x10-5), 2) BD subphenotypes: psychotic features (SCZ p=1.2x10-10, BD p=5.3x10-5) and age of onset (SCZ p=7.9x10-4). Finally, we show that psychotic features in BD has significant SNP-heritability (h2snp=0.15, SE=0.06), and a significant genetic correlation with SCZ (rg=0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes (DARS2, ARFGEF2, DCAKD and GATAD2A) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.
Introduction To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its (sub)clinical symptoms though there is no systematic review. Aims To summarize the evidence from cluster- and trajectory-based studies of positive, negative and cognitive symptoms in patients with schizophrenia spectrum disorders, their siblings and healthy people. Additionally, we aimed to highlight knowledge gaps and point out future directions to optimize the translatability of cluster- and trajectory-based studies. Methods A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE, and Web of Science electronic databases. Both cross-sectional and longitudinal studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. Results Of 2,285 studies retrieved, 50 studies (17 longitudinal and 33 cross-sectional) conducted in 30 countries were selected for review. Longitudinal studies discovered two to five trajectories of positive and negative symptoms in patient, and four to five trajectories of cognitive deficits in patient and sibling. In cross-sectional studies, three clusters of positive and negative symptoms in patient, four clusters of positive and negative schizotypy in sibling, and three to five clusters of cognitive deficits in patient and sibling were identified. These studies also reported multidimensional predictors of clusters and trajectories. Conclusions Our findings indicate that (sub)clinical symptoms of schizophrenia are more heterogeneous than currently recognized. Identified clusters and trajectories can be used as a basis for personalized psychiatry.