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 Jan Kullo, Daniel J Schaid, Wendy K. Chung, Marylyn D. Ritchie

Research output: Contribution to journalArticle

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. Pacific Symposium on Biocomputing
Volume24
StatePublished - Jan 1 2019

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Electronic Health Records
Genomics
Cardiovascular Diseases
Phenotype
Nervous System Diseases
Single Nucleotide Polymorphism
Drug Repositioning
Heart Diseases
Gene Expression
Population
Therapeutics

ASJC Scopus subject areas

  • Medicine(all)

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Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network. / Zhang, Xinyuan; Veturi, Yogasudha; Verma, Shefali; Bone, William; Verma, Anurag; Lucas, Anastasia; Hebbring, Scott; Denny, Joshua C.; Stanaway, Ian B.; Jarvik, Gail P.; Crosslin, David; Larson, Eric B.; Rasmussen-Torvik, Laura; Pendergrass, Sarah A.; Smoller, Jordan W.; Hakonarson, Hakon; Sleiman, Patrick; Weng, Chunhua; Fasel, David; Wei, Wei Qi; Kullo, Iftikhar Jan; Schaid, Daniel J; Chung, Wendy K.; Ritchie, Marylyn D.

In: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, Vol. 24, 01.01.2019, p. 272-283.

Research output: Contribution to journalArticle

Zhang, X, Veturi, Y, Verma, S, Bone, W, Verma, A, Lucas, A, Hebbring, S, Denny, JC, Stanaway, IB, Jarvik, GP, Crosslin, D, Larson, EB, Rasmussen-Torvik, L, Pendergrass, SA, Smoller, JW, Hakonarson, H, Sleiman, P, Weng, C, Fasel, D, Wei, WQ, Kullo, IJ, Schaid, DJ, Chung, WK & Ritchie, MD 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. Pacific Symposium on Biocomputing, vol. 24, pp. 272-283.
Zhang, Xinyuan ; Veturi, Yogasudha ; Verma, Shefali ; Bone, William ; Verma, Anurag ; Lucas, Anastasia ; Hebbring, Scott ; Denny, Joshua C. ; Stanaway, Ian B. ; Jarvik, Gail P. ; Crosslin, David ; Larson, Eric B. ; Rasmussen-Torvik, Laura ; Pendergrass, Sarah A. ; Smoller, Jordan W. ; Hakonarson, Hakon ; Sleiman, Patrick ; Weng, Chunhua ; Fasel, David ; Wei, Wei Qi ; Kullo, Iftikhar Jan ; Schaid, Daniel J ; Chung, Wendy K. ; Ritchie, Marylyn D. / Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network. In: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2019 ; Vol. 24. pp. 272-283.
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AU - Zhang, Xinyuan

AU - Veturi, Yogasudha

AU - Verma, Shefali

AU - Bone, William

AU - Verma, Anurag

AU - Lucas, Anastasia

AU - Hebbring, Scott

AU - Denny, Joshua C.

AU - Stanaway, Ian B.

AU - Jarvik, Gail P.

AU - Crosslin, David

AU - Larson, Eric B.

AU - Rasmussen-Torvik, Laura

AU - Pendergrass, Sarah A.

AU - Smoller, Jordan W.

AU - Hakonarson, Hakon

AU - Sleiman, Patrick

AU - Weng, Chunhua

AU - Fasel, David

AU - Wei, Wei Qi

AU - Kullo, Iftikhar Jan

AU - Schaid, Daniel J

AU - Chung, Wendy K.

AU - Ritchie, Marylyn D.

PY - 2019/1/1

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N2 - 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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