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

Xinyuan Zhang1,†, Yogasudha Veturi2,†, 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. Ritchie2,*

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.


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.

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