Predictors of change in social networks, support and satisfaction following a first episode psychosis: A cohort study

Background Diminished social networks are common in psychosis but few studies have measured these comprehensively and prospectively to determine how networks and support evolve during the early phase. There is little information regarding perceived support in the early phase of illness. The aim of this study was to describe social support, networks and perceived satisfaction, explore the clinical correlates of these outcomes and examine whether phases of untreated psychosis are linked with social network variables to determine potential opportunities for intervention. Methods During the study period, we assessed 222 people with first-episode psychosis at entry into treatment using valid and reliable measures of diagnosis, positive and negative symptoms, periods of untreated psychosis and prodrome and premorbid adjustment. For follow-up we contacted participants to conduct a second assessment (n = 158). There were 97 people who participated which represented 61% of those eligible. Social network and support information obtained at both time points included the number of friends, self-reported satisfaction with support and social network size and clinician’s evaluation of the degree of support received through networks. Mixed effects modelling determined the contribution of potential explanatory variables to social support measured. Results A number of clinical variables were linked with social networks, support and perceived support and satisfaction. The size of networks did not change over time but those with no friends and duration of untreated psychosis was significantly longer for those with no friends at entry into treatment (n = 129, Median = 24.5mths, IQR = 7.25 – 69.25; Mann-Whitney U = 11.78, p = 0.008). Social support at baseline and at one year was predicted by homelessness (t = -2.98, p = 0.001, CI -4.74 to -1.21), duration of untreated psychosis (t = -0.86, p = 0.031, CI - 1.65 to -0.08) and premorbid adjustment (t = -2.26, p = 0.017, CI -4.11 to -0.42). Social support improved over time but the duration of untreated psychosis was not linked with the rate of improvement in this outcome. Conclusions Improved social support could indicate greater reliance on social support or becoming more adept at mobilising resources to meet social needs. Particularly vulnerable groups with very long duration of untreated psychosis confirm the need for earlier intervention or targeted social network interventions to preserve social connectedness

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

A. Palotie on työryhmän Schizophrenia Working Grp Psychiat jäsen.We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.Peer reviewe

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

Predictors of change in social networks, support and satisfaction following a first episode psychosis: A cohort study

Background Diminished social networks are common in psychosis but few studies have measured these comprehensively and prospectively to determine how networks and support evolve during the early phase. There is little information regarding perceived support in the early phase of illness. The aim of this study was to describe social support, networks and perceived satisfaction, explore the clinical correlates of these outcomes and examine whether phases of untreated psychosis are linked with social network variables to determine potential opportunities for intervention. Methods During the study period, we assessed 222 people with first-episode psychosis at entry into treatment using valid and reliable measures of diagnosis, positive and negative symptoms, periods of untreated psychosis and prodrome and premorbid adjustment. For follow-up we contacted participants to conduct a second assessment (n = 158). There were 97 people who participated which represented 61% of those eligible. Social network and support information obtained at both time points included the number of friends, self-reported satisfaction with support and social network size and clinician’s evaluation of the degree of support received through networks. Mixed effects modelling determined the contribution of potential explanatory variables to social support measured. Results A number of clinical variables were linked with social networks, support and perceived support and satisfaction. The size of networks did not change over time but those with no friends and duration of untreated psychosis was significantly longer for those with no friends at entry into treatment (n = 129, Median = 24.5mths, IQR = 7.25 – 69.25; Mann-Whitney U = 11.78, p = 0.008). Social support at baseline and at one year was predicted by homelessness (t = -2.98, p = 0.001, CI -4.74 to -1.21), duration of untreated psychosis (t = -0.86, p = 0.031, CI - 1.65 to -0.08) and premorbid adjustment (t = -2.26, p = 0.017, CI -4.11 to -0.42). Social support improved over time but the duration of untreated psychosis was not linked with the rate of improvement in this outcome. Conclusions Improved social support could indicate greater reliance on social support or becoming more adept at mobilising resources to meet social needs. Particularly vulnerable groups with very long duration of untreated psychosis confirm the need for earlier intervention or targeted social network interventions to preserve social connectedness

Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases.

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg2) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg2 from imputed SNPs (5.1× enrichment; p = 3.7 × 10−17) and 38% (SE = 4%) of hg2 from genotyped SNPs (1.6× enrichment, p = 1.0 × 10−4). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg2 despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease

A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared. Results: Compared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders