Year: 2025
Type: review
Abstract: Psychiatric disorders have complex genetic architectures with substantial genetic overlap across conditions, which may partially explain their high levels of comorbidity. This presents significant challenges to research. Genome-wide association studies (GWASs) have uncovered hundreds of loci associated with single disorders, but the genetic landscape of psychiatric disorders has remained largely obscure. Moving beyond the conventional infinitesimal model, uni-, bi-, and trivariate MiXeR tools, applied to GWAS summary statistics, has enabled us to more comprehensively describe the genetic architecture of complex disorders and traits and their overlap. Furthermore, the GSA-MiXeR tool improves biological interpretation of GWAS findings to better elucidate causal mechanisms. Here, we outline the methodology that underlies the MiXeR tools together with instructions for their optimal use. We review results from studies that have investigated the genetic architecture of psychiatric disorders and their overlap using the MiXeR toolset. These studies have revealed generally high polygenicity and low discoverability among psychiatric disorders, particularly in contrast to somatic disorders. There is also pervasive genetic overlap across psychiatric disorders and behavioral traits, while their overlap with somatic traits is smaller, consistent with differences in polygenicity. Finally, GSA-MiXeR has quantified the contribution of gene sets to the heritability of psychiatric disorders, prioritizing small, biologically coherent gene sets. Together, these findings have implications for our understanding of the complex relationships between psychiatric disorders and related traits. MiXeR tools have provided new insights into the genetic architecture of psychiatric disorders, generating a better understanding of their underlying biological mechanisms and potential for clinical utility.
Source: Biological Psychiatry
Authors: Dennis van der Meer, Guy Hindley, Alexey A. Shadrin, Olav B. Smeland, Nadine Parker, Anders M. Dale, Oleksandr Frei, Ole A. Andreassen
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $5,489
Funders: Sunovion, European Commission, Universitetet i Oslo, Norges Forskningsråd, H. Lundbeck A/S, National Institutes of Health, University of California, San Diego, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse
Awards: Modern Signal Processing Methods for Ultra-realistic Light-Field Displays, G3096137445, G3346251958, G3761261155, G434337789, G514026569, G5479941350, G5836115955, G5908129080, G6838422206, Predicting comorbid cardiovascular disease in individuals with mental disorder by decoding disease mechanisms, G7984825499, G8130263415, Using real-world big data from eHealth, biobanks and national registries, integrated with clinical trial data to improve outcome of severe mental disorders, SCIENTIA-FELLOWS II: International Postdoctoral Fellowship Programme, The VETSA Longitudinal MRI Twin Study of Aging (VETSA MRI 4), Healthy Brain and Child Development National Consortium Data Coordinating Center, ABCD-USA Consortium: Data Analysis, Informatics and Resource Center, OTA-21-015A Post-Acute Sequelae of SARS-CoV-2 Infection Initiative: NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core
OpenAlex ID: W4407739353