Objective: Mental disorders are heritable and polygenic, and genome-wide genetic correlations (r) have indicated widespread shared genetic risk across multiple disorders and related traits, mirroring their overlapping clinical characteristics. However, r may underestimate the shared genetic underpinnings of mental disorders and related traits because it does not differentiate mixtures of concordant and discordant genetic effects from an absence of genetic overlap. Using novel statistical genetics tools, the authors aimed to evaluate the genetic overlap between mental disorders and related traits when accounting for mixed effect directions.
Methods: The authors applied the bivariate causal mixture model (MiXeR) to summary statistics for four mental disorders, four related mental traits, and height from genome-wide association studies (Ns ranged from 53,293 to 766,345). MiXeR estimated the number of "causal" variants for a given trait ("polygenicity"), the number of variants shared between traits, and the genetic correlation of shared variants (r). Local r was investigated using LAVA.
Results: Among mental disorders, ADHD was the least polygenic (5.6K "causal" variants), followed by bipolar disorder (8.6K), schizophrenia (9.6K), and depression (14.5K). Most variants were shared across mental disorders (4.4K-9.3K) and between mental disorders and related traits (5.2K-12.8K), but with disorder-specific variations in r and r. Overlap with height was small (0.7K-1.1K). MiXeR estimates correlated with LAVA local r (r=0.88, p<0.001).
Conclusions: There is extensive genetic overlap across mental disorders and related traits, with mixed effect directions and few disorder-specific variants. This suggests that genetic risk for mental disorders is predominantly differentiated by divergent effect distributions of pleiotropic genetic variants rather than disorder-specific variants. This represents a conceptual advance in our understanding of the landscape of shared genetic architecture across mental disorders, which may inform genetic discovery, biological characterization, nosology, and genetic prediction.
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http://dx.doi.org/10.1176/appi.ajp.21101051 | DOI Listing |
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Foot and Ankle Division, Department of Orthopaedic Surgery, NYU Langone Health, New York City, NY 10002, USA. Electronic address:
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Neurosci Biobehav Rev
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Department of Psychology, Sapienza University of Rome, Rome, Italy; Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy. Electronic address:
Introduction: Brain and sleep development in childhood shapes emotional and cognitive growth, including the ability to recall dreams. In line with the continuity hypothesis of dreaming, several findings suggest a link between clinical symptoms and nightmare frequency. Sleep disorders and anxiety are among the most frequently co-occurring conditions in children and adolescents with autism spectrum disorder (ASD).
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Anhui Province, Hefei 230022, China; Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China. Electronic address:
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View Article and Find Full Text PDFJ Affect Disord
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Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA. Electronic address:
Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this exploratory study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive-Behavioral Therapy (CBT) in 163 treatment-naïve outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways.
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