Sourcing Bivariate Genetic Overlap for Polygenic Prediction using MiXeR-Pred

The past two decades have seen the advent and mass application of genome-wide association studies (GWAS). The observation that complex phenotypes are polygenic has contributed to the development of the polygenic score (PGS) for understanding individual-level genetic predisposition. There have been substantial advances in PGS methodology in recent years. However, few methods leverage the pleiotropic nature of complex phenotypes for polygenic prediction. Here, we present MiXeR-Pred, a novel approach for polygenic prediction that builds on an established MiXeR framework to source genetic overlap with a secondary phenotype to inform PGS prediction of a primary phenotype. We apply MiXeR-Pred using both bipolar disorder and schizophrenia as complex primary phenotypes along with the following secondary phenotypes: education attainment, major depressive disorder, and measures of cortical brain morphology. We compare MiXeR-Pred predictions to the PGS derived from each primary phenotype's GWAS in addition to the multi-trait analysis of GWAS (MTAG) approach, which can use correlated secondary phenotypes to boost discovery and prediction for a primary phenotype. We show that MiXeR-Pred improves prediction performance when compared to both the primary GWAS and MTAG PGS, regardless of the secondary phenotype. Not only can MiXeR-Pred be used to further our understanding of pleiotropy among complex phenotypes, but it also provides a novel conceptualization of how one can source pleiotropy to improve PGS performance which can ultimately contribute to advancements in personalized medicine. The MiXeR-Pred tool is available at https://github.com/precimed/mixer-pred. ### Competing Interest Statement A.M.D. is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. A.M.D. is a member of the Scientific Advisory Board of Human Longevity, Inc. (HLI), and the Mohn Medical Imaging and Visualization Centre in Bergen, Norway. A.M.D. receives funding through a research agreement with General Electric Healthcare (GEHC). For A.M.D. all terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. O.A.A. has received speaker fees from Lundbeck, Janssen, Otsuka, and Sunovion and is a consultant to Cortechs.ai. ### Funding Statement Funding was provided by the Research Council of Norway [grants 223273, 300309, 324252, 326813, 324499, 334920], the South-East Regional Health Authority [grant 2022-073], EEA and Norway [grant EEA-RO-NO-2018-0573], European Union's Horizon 2020 Research and Innovation Programme [Grant 847776, 964874, 801133], and the National Institutes of Health [grants U24DA041123, U24DA055330]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Regional Committee for Medical and Health Research Ethics of South-East Norway. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes GWAS summary statistics used in this study are publicly available with the exception of summary statistics excluding the TOP sample which were provided by the psychiatric genomics consortium. The TOP sample data are not publicly available due to national data privacy regulations. The MiXeR-Pred tool presented in this article is available at https://github.com/precimed/mixer-pred. The remaining software are also publicly available: PRSice-2, https://choishingwan.github.io/PRSice/; MTAG, https://github.com/JonJala/mtag; PLINK, https://www.cog-genomics.org/plink/; cleansumstats pipeline used for harmonizing GWAS summary statistics: https://github.com/precimed/python_convert (v0.9.1).