Year: 2025
Type: article
Abstract: BACKGROUND: Multimorbidities are a global health challenge. Accumulating evidence indicates that overlapping genetic architectures underlie comorbid complex human traits and disorders. This can be quantified for a pair of phenotypes using various techniques. Still, the pattern of genetic overlap between three distinct complex phenotypes, which is important for understanding multimorbidities, has not been possible to quantify. METHODS: Here, we present and validate the novel trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three complex phenotypes using summary statistics from genome-wide association studies. Our simulations show that trivariate MiXeR can reliably reconstruct different patterns of genetic overlap and estimate the proportions of genetic overlap between three phenotypes. RESULTS: We found substantial genetic overlap between gastro-intestinal and brain diseases supporting a genetic basis of the gut-brain axis-the pattern consistent with pairwise analysis. However, the pattern of genetic overlap between three diverse cardiometabolic and renal health indicators and three immune-linked disorders revealed a much larger genomic component shared between all phenotypes than expected from separate pairwise analyses. This suggests the existence of core pathways underlying distinct but related chronic conditions. CONCLUSIONS: Overall, trivariate MiXeR offers a novel and efficient tool for investigating patterns of genetic overlap among three complex phenotypes. This contributes to a better understanding of genetic relationships between complex traits and disorders, potentially providing new insights into the mechanisms underlying common multimorbidities. Trivariate MiXeR is freely available at https://github.com/precimed/mix3r .
Source: Genome Medicine
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,290
Awards: G1160300623, G1350562830, G1420817852, Modern Signal Processing Methods for Ultra-realistic Light-Field Displays, G2195690467, G2230720350, G2621189761, G3096137445, G3277956970, G3346251958, G3433676821, G3761261155, G4261582792, G434337789, G4493028280, G5479941350, User-friendly ICT tools to enhance self-management and effective treatment of depression in the EU, G6517481153, G67063899, 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, G8371668726, SCIENTIA-FELLOWS II: International Postdoctoral Fellowship Programme, G857621426, G9000012900, G9000112882, G9000218109, G9000318151, 3/7 Psychiatric Genomics Consortium: Advancing Discovery and Impact, 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: W4414613893