Dataset Information

Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities.

ABSTRACT: Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.

SUBMITTER: Mallard TT

PROVIDER: S-EPMC9264403 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities.

Mallard Travis T TT Linnér Richard Karlsson RK Grotzinger Andrew D AD Sanchez-Roige Sandra S Seidlitz Jakob J Okbay Aysu A de Vlaming Ronald R Meddens S Fleur W SFW Palmer Abraham A AA Davis Lea K LK Tucker-Drob Elliot M EM Kendler Kenneth S KS Keller Matthew C MC Koellinger Philipp D PD Harden K Paige KP

Cell genomics 20220608 6

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabil ...[more]

PMID: 35812988

Similar Datasets

Project description:Background: Epidemiological research has reported that attention-deficit hyperactivity disorder (ADHD), anxiety, bipolar disorder (BD), schizophrenia (SCZ), and unipolar depression (UD) are multimorbid conditions that are typically accompanied by cognitive advantages or deficits, suggesting that common biological mechanisms may underlie these phenotypes. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with psychiatric disorders and cognitive functioning. However, the mechanisms by which these SNPs contribute to multimorbidities amongst psychiatric and cognitive phenotypes remains largely unknown. Objective: To identify shared regulatory mechanisms amongst multimorbid psychiatric disorders and cognitive functioning. Methods: We integrated data on 3D genome organization, expression quantitative trait loci (eQTLs), and pathway analyses to identify shared and specific regulatory impacts of 2,893 GWAS SNPs (p < 1 × 10-6) associated with ADHD, anxiety, BD, SCZ, UD, and cognitive functioning on genes and biological pathways. Drug-gene interaction analysis was performed to identify potential pharmacological impacts on these genes and pathways. Results: The analysis revealed 33 genes and 62 pathways that were commonly affected by tissue-specific gene regulatory interactions associated with all six phenotypes despite there being no common SNPs in our original dataset. The analysis of brain-specific regulatory connections revealed similar patterns at eQTL and eGene levels, but no pathways shared by all six phenotypes. Instead, pairwise overlaps and individualized pathways were identified for psychiatric and cognitive phenotypes in brain tissues. Conclusions: This study offers insight into the shared genes and biological pathways that are affected by tissue-specific regulatory impacts resulting from psychiatric- and cognition-associated genetic variants. These results provide limited support for the "p-factor" hypothesis for psychiatric disorders and potential mechanisms that explain drug side-effects. Our results highlight key biological pathways for development of therapies that target single or multiple psychiatric and cognitive phenotypes.

Project description:BackgroundRecent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.MethodsWe analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.ResultsWe confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.ConclusionsOur study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Dataset Information

Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities.

Publications

Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets