Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network

Xinyuan Zhang^1,†, Yogasudha Veturi^2,†, Shefali Verma², William Bone¹, Anurag Verma², Anastasia Lucas², Scott Hebbring³, Joshua C. Denny⁴, Ian Stanaway⁵, Gail P. Jarvik⁵, David Crosslin⁵, Eric B. Larson⁶, Laura Rasmussen-Torvik⁷, Sarah A. Pendergrass⁸, Jordan W. Smoller⁹, Hakon Hakonarson¹⁰, Patrick Sleiman¹⁰, Chunhua Weng¹¹, David Fasel¹¹, Wei-Qi Wei¹², Iftikhar Kullo¹³, Daniel Schaid¹⁴, Wendy K. Chung¹⁵, Marylyn D. Ritchie^2,*

¹Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania
²Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
³Center for Human Genetics, Marshfield Clinic
⁴Department of Medicine, Vanderbilt University
⁵Departments of Medicine (Medical Genetics) and Genomic Sciences, University of Washington School of Medicine
⁶Kaiser Permanente Washington Health Research Institute
⁷Department of Preventive Medicine, Northwestern University Feinberg School of Medicine
⁸Biomedical and Translational Informatics Institute, Geisinger Health System
⁹Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital
¹⁰Center for Applied Genomics, Children's Hospital of Philadelphia
¹¹Department of Biomedical Informatics, Columbia University
¹²Department of Biomedical Informatics in School of Medicine, Vanderbilt University
¹³Division of Cardiovascular Diseases, Mayo Clinic
¹⁴Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic
¹⁵Department of Pediatrics, Columbia University
^†Authors contributed equally to this work
^*Corresponding author

Pacific Symposium on Biocomputing 24:272-283(2019)

© 2019 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.