TY - JOUR
T1 - Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network
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
AU - Schaid, Daniel
AU - Chung, Wendy K.
AU - Ritchie, Marylyn Deriggi
N1 - Funding Information:
The eMERGE Network was initiated and funded by NHGRI through the following grants:
Publisher Copyright:
© 2018 The Authors.
PY - 2019
Y1 - 2019
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.
AB - 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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M3 - Conference article
C2 - 30864329
AN - SCOPUS:85062764154
SN - 2335-6928
VL - 24
SP - 272
EP - 283
JO - Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing
IS - 2019
T2 - 24th Pacific Symposium on Biocomputing, PSB 2019
Y2 - 3 January 2019 through 7 January 2019
ER -