1,722,089 research outputs found
Defining suicidality phenotypes for genetic studies: perspectives of the Psychiatric Genomics Consortium Suicide Working Group
Full text linkSuicidality phenotypes, consisting of suicidal ideation (SI), suicide attempt (SA), and suicide death (SD), are all heritable but present unique challenges in genome-wide association studies (GWAS) due to their individual complexity, overlap with each other and with related self-harm phenotypes, and varying associations with psychiatric disorders. GWAS have uncovered several loci associated with suicidality phenotypes by meta-analyzing data from multiple cohorts. However, combining datasets from many research groups, where each group may use different study designs, phenotyping instruments, and definitions of suicidality phenotypes, presents challenges. Heterogeneity resulting from these differences can limit genetic discovery; harmonizing phenotype definitions to ensure consistency will greatly improve results. Here, we describe a standardized phenotyping protocol that draws on the expertise of a subgroup of clinicians, researchers, and experts from the Psychiatric Genomics Consortium Suicide Working Group to propose consensus definitions for SI, SA, and SD for genetic studies
The Psychiatric Genomics Consortium: History, development, and the future
No full textPsychiatric genetics is in an era of discovery. Genome-wide association analyses and other genomic analyses are paving the way for rapid progress in understanding the biological etiology of psychiatric disorders. This chapter offers an overview of the Psychiatric Genomics Consortium, a large-scale collaborative initiated to decipher the genomic basis of psychiatric disorders. The Psychiatric Genomics Consortium is comprehensively evaluating common single-nucleotide polymorphisms, rare variants, gene sets and pathways, and other genetic variations, to reveal the cryptic genetic and biological basis of psychiatric illnesses. The Psychiatric Genomic Consortium’s history and development, contributions, and future directions are discussed. Findings for the key psychiatric disorders, including schizophrenia, bipolar disorder, autism spectrum disorders, attention-deficit/hyperactivity disorder, major depression, and anorexia nervosa are presented. The ultimate goal of the Psychiatric Genomics Consortium is to innovate better preventions and treatments, and to improve the lives of millions of people globally who are affected by mental illness
Psychiatric Genomics Consortium: Using Altmetric data for grant applications
No full textProfessor Patrick Sullivan from the Psychiatric Genomics Consortium explains how he is using Altmetric data in a grant application
Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium.
Full text linkDespite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).Positive genetic correlation was observed between MD and AD (rgMD-AD = + 0.47, P = 6.6 × 10-10). AC-quantity showed positive genetic correlation with both AD (rgAD-AC quantity = + 0.75, P = 1.8 × 10-14) and MD (rgMD-AC quantity = + 0.14, P = 2.9 × 10-7), while there was negative correlation of AC-frequency with MD (rgMD-AC frequency = -0.17, P = 1.5 × 10-10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10-6). There was no evidence for reverse causation.This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.AMSUNY DownstatePsychiatry and Behavioral SciencesInstitute for Genomics in HealthN/
Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways
No full textGenome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders
The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls
No full textBackground: Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction. Methods: To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424). Results: Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment—the relationship is positive in bipolar disorder but negative in major depressive disorder. Conclusions: The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum
Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders
Full text linkBACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype-by-sex (GxS) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. RESULTS: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 x 10(-8)), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 x 10(-8)) for cross-disorder GxS interaction (rs7302529, p = 1.6 x 10(-7); rs73033497, p = 8.8 x 10(-7); rs7914279, p = 6.4 x 10(-7)), implicating various functions. Gene-based analyses identified GxS interaction across disorders (p = 8.97 x 10(-7)) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 x 10(-7)), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 x 10(-7)) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant GxS interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). CONCLUSIONS: In the largest genome-wide GXS analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels
Converging evidence does not support GIT1 as an ADHD risk gene
Full text linkAttention-Deficit/Hyperactivity Disorder (ADHD) is a common neuropsychiatric disorder with a complex genetic background. The G protein-coupled receptor kinase interacting ArfGAP 1 (GIT1) gene was previously associated with ADHD. We aimed at replicating the association of GIT1 with ADHD and investigated its role in cognitive and brain phenotypes. Gene-wide and single variant association analyses for GIT1 were performed for three cohorts: (1) the ADHD meta-analysis data set of the Psychiatric Genomics Consortium (PGC, N=19,210), (2) the Dutch cohort of the International Multicentre persistent ADHD CollaboraTion (IMpACT-NL, N=225), and (3) the Brain Imaging Genetics cohort (BIG, N=1,300). Furthermore, functionality of the rs550818 variant as an expression quantitative trait locus (eQTL) for GIT1 was assessed in human blood samples. By using Drosophila melanogaster as a biological model system, we manipulated Git expression according to the outcome of the expression result and studied the effect of Git knockdown on neuronal morphology and locomotor activity. Association of rs550818 with ADHD was not confirmed, nor did a combination of variants in GIT1 show association with ADHD or any related measures in either of the investigated cohorts. However, the rs550818 risk-genotype did reduce GIT1 expression level. Git knockdown in Drosophila caused abnormal synapse and dendrite morphology, but did not affect locomotor activity. In summary, we could not confirm GIT1 as an ADHD candidate gene, while rs550818 was found to be an eQTL for GIT1. Despite GIT1's regulation of neuronal morphology, alterations in gene expression do not appear to have ADHD-related behavioral consequence
A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts
No full textBackground: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. 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 and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 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 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.Results:Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder 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 comparisons and are recommended in applications to psychiatric disorders
What Next in Schizophrenia Genetics for the Psychiatric Genomics Consortium?
Full text linkOver the last 8 years the Psychiatric Genomics Consortium (PGC; http://pgc.unc.edu) has fundamentally changed the landscape for psychiatric genetics research. This has been achieved through unprecedented teamwork, involving more than 900 investigators from 40 countries, allied to rigorous methodology. Significantly, the PGC has an open-source approach with the main findings freely available for unrestricted use (http://pgc.unc.edu/downloads). Dozens of groups around the world are using PGC data to develop better analytical methods and to perform secondary analyses on a dataset representing more than 400 000 human participants
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