Leveraging Shared Genetic Architecture Between Psychiatric and Other Medical Disorders Increases Power and Reveals Novel Psychiatric Risk Loci
- 1Purdue University
Abstract
Psychiatric disorders frequently co-occur with a broad range of non-psychiatric conditions, suggesting shared biological mechanisms that transcend traditional diagnostic boundaries. Although recent studies have demonstrated substantial genetic overlap between psychiatric and other medical disorders, the genomic architecture underlying these relationships and the extent to which shared genetic liability can be leveraged for psychiatric gene discovery remain incompletely understood. Here, we systematically investigated the shared genetic architecture between eleven major psychiatric disorders and thirty-nine non-psychiatric disorders spanning cardiovascular, metabolic, autoimmune, neurological, gastrointestinal, respiratory, and other disease categories. To maximize statistical power, we meta-analyzed genome-wide association study (GWAS) summary statistics from large biobanks and international consortia.
Genome-wide analyses identified 298 significant genetic correlations between psychiatric and non-psychiatric disorders, revealing pervasive shared liability across disease domains. Local genetic correlation analyses identified 180 genomic regions contributing to cross-disorder risk, while colocalization analyses revealed 1,470 candidate shared variants. Several highly pleiotropic loci, including regions containing FURIN and TCF4, contributed to risk across multiple psychiatric and other medical conditions. Genomic structural equation modeling identified recurrent dimensions of shared liability, including cardiovascular-kidney-metabolic and neuroimmune-related dimensions, suggesting that psychiatric-medical comorbidity is organized into biologically coherent systems.
Leveraging these shared genetic architectures through cross-trait analyses identified 1,569 significant loci, including 382 psychiatric risk loci not reported in the individual psychiatric GWAS used in our analyses. Integrative positional and functional gene-mapping approaches prioritized genes involved in neuronal development, synaptic organization, neurotrophic signaling, and cellular homeostasis, with LRFN5, MED19, PITPNM2, CTNND1, TMEM106B, GLYCTK, and PCLO as the top implicated genes. Pathway and tissue-enrichment analyses further demonstrated convergence on neurodevelopmental and synaptic processes and significant enrichment across multiple brain regions. Our results provide a comprehensive characterization of the genetic architecture underlying comorbidity between psychiatric and other medical disorders and demonstrate that leveraging genetically correlated medical conditions across domains can substantially increase power for psychiatric risk-locus discovery.
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BibTeX
@misc{zhong2026leveraging,
title = {{Leveraging Shared Genetic Architecture Between Psychiatric and Other Medical Disorders Increases Power and Reveals Novel Psychiatric Risk Loci}},
author = {Q. Zhong and A. Topaloudi and P. Paschou},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.26364453},
url = {https://www.medrxiv.org/content/10.64898/2026.10.01.26364453v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Leveraging Shared Genetic Architecture Between Psychiatric and Other Medical Disorders Increases Power and Reveals Novel Psychiatric Risk Loci
AU - Q. Zhong
AU - A. Topaloudi
AU - P. Paschou
PY - 2026
DA - 2026/10/01
PB - medRxiv
DO - 10.64898/2026.10.01.26364453
UR - https://www.medrxiv.org/content/10.64898/2026.10.01.26364453v1
AB - Psychiatric disorders frequently co-occur with a broad range of non-psychiatric conditions, suggesting shared biological mechanisms that transcend traditional diagnostic boundaries. Although recent studies have demonstrated substantial genetic overlap between psychiatric and other medical disorders, the genomic architecture underlying these relationships and the extent to which shared genetic liability can be leveraged for psychiatric gene discovery remain incompletely understood. Here, we systematically investigated the shared genetic architecture between eleven major psychiatric disorders and thirty-nine non-psychiatric disorders spanning cardiovascular, metabolic, autoimmune, neurological, gastrointestinal, respiratory, and other disease categories. To maximize statistical power, we meta-analyzed genome-wide association study (GWAS) summary statistics from large biobanks and international consortia. Genome-wide analyses identified 298 significant genetic correlations between psychiatric and non-psychiatric disorders, revealing pervasive shared liability across disease domains. Local genetic correlation analyses identified 180 genomic regions contributing to cross-disorder risk, while colocalization analyses revealed 1,470 candidate shared variants. Several highly pleiotropic loci, including regions containing FURIN and TCF4, contributed to risk across multiple psychiatric and other medical conditions. Genomic structural equation modeling identified recurrent dimensions of shared liability, including cardiovascular-kidney-metabolic and neuroimmune-related dimensions, suggesting that psychiatric-medical comorbidity is organized into biologically coherent systems. Leveraging these shared genetic architectures through cross-trait analyses identified 1,569 significant loci, including 382 psychiatric risk loci not reported in the individual psychiatric GWAS used in our analyses. Integrative positional and functional gene-mapping approaches prioritized genes involved in neuronal development, synaptic organization, neurotrophic signaling, and cellular homeostasis, with LRFN5, MED19, PITPNM2, CTNND1, TMEM106B, GLYCTK, and PCLO as the top implicated genes. Pathway and tissue-enrichment analyses further demonstrated convergence on neurodevelopmental and synaptic processes and significant enrichment across multiple brain regions. Our results provide a comprehensive characterization of the genetic architecture underlying comorbidity between psychiatric and other medical disorders and demonstrate that leveraging genetically correlated medical conditions across domains can substantially increase power for psychiatric risk-locus discovery.
ER -