Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR

Alexey A Shadrin*, Guy Hindley, Espen Hagen, Nadine Parker, Markos Tesfaye, Piotr Jaholkowski, Zillur Rahman, Gleda Kutrolli, Vera Fominykh, Srdjan Djurovic, Olav B Smeland, Kevin S O'Connell, Dennis van der Meer, Oleksandr Frei, Ole A Andreassen*, Anders M Dale*

*Corresponding author for this work

Research output: Working paper / PreprintPreprint

Abstract

Comorbidities are an increasing global health challenge. Accumulating evidence suggests overlapping genetic architectures underlying comorbid complex human traits and disorders. The bivariate causal mixture model (MiXeR) can quantify the polygenic overlap between complex phenotypes beyond global genetic correlation. Still, the pattern of genetic overlap between three distinct phenotypes, which is important to better characterize multimorbidities, has previously not been possible to quantify. Here, we present and validate the trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three phenotypes using summary statistics from genome-wide association studies (GWAS). Our simulations show that the trivariate MiXeR can reliably reconstruct different patterns of genetic overlap. We further demonstrate how the tool can be used to estimate the proportions of genetic overlap between three phenotypes using real GWAS data, providing examples of complex patterns of genetic overlap between diverse human traits and diseases that could not be deduced from bivariate analyses. This contributes to a better understanding of the etiology of complex phenotypes and the nature of their relationship, which may aid in dissecting comorbidity patterns and their biological underpinnings.
Original languageEnglish
PublisherMedRxiv
DOIs
Publication statusPublished - 27 Feb 2024

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