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

Xinyuan Zhang, Yogasudha Veturi, Shefali Verma, William Bone, Anurag Verma, Anastasia Lucas, Scott Hebbring, Joshua C. Denny, Ian B. Stanaway, Gail P. Jarvik, David Crosslin, Eric B. Larson, Laura Rasmussen-Torvik, Sarah A. Pendergrass, Jordan W. Smoller, Hakon Hakonarson, Patrick Sleiman, Chunhua Weng, David Fasel, Wei Qi WeiIftikhar Kullo, Daniel Schaid, Wendy K. Chung, Marylyn D. Ritchie

Research output: Contribution to journalConference article

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)272-283
Number of pages12
JournalPacific Symposium on Biocomputing
Volume24
Issue number2019
StatePublished - 2019
Event24th Pacific Symposium on Biocomputing, PSB 2019 - Kohala Coast, United States
Duration: Jan 3 2019Jan 7 2019

Keywords

  • Bivariate Analysis
  • Cardiovascular Diseases
  • Colocalization
  • EQTL
  • Multivariate Analysis
  • Neurological Disorders
  • Pleiotropy
  • Univariate Analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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    Zhang, X., Veturi, Y., Verma, S., Bone, W., Verma, A., Lucas, A., Hebbring, S., Denny, J. C., Stanaway, I. B., Jarvik, G. P., Crosslin, D., Larson, E. B., Rasmussen-Torvik, L., Pendergrass, S. A., Smoller, J. W., Hakonarson, H., Sleiman, P., Weng, C., Fasel, D., ... Ritchie, M. D. (2019). Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network. Pacific Symposium on Biocomputing, 24(2019), 272-283.