Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes

Date
2015
Authors
Yao, C.
Chen, B. H.
Joehanes, R.
Otlu, B.
Zhang X.
Liu, C.
Huan, T.
Tastan, O.
Cupples, L. A.
Meigs, J. B.
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Supervisor
Co-Advisor
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Instructor
Source Title
Circulation
Print ISSN
Electronic ISSN
Publisher
Lippincott Williams & Wilkins
Volume
131
Issue
6
Pages
536 - 549
Language
English
Type
Article
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Volume Title
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Abstract

BACKGROUND - : Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown. METHODS AND RESULTS - : We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P≤5×10) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7), which in turn may promote interindividual variation in phenotypes. CONCLUSIONS - : Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.

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Keywords
Regulation network, Cardiovascular disease, Gene expression/regulation, Genetic variation, Adult, Cardiovascular disease, Cardiovascular risk, Cross linking, Disease course, Female, Gene expression, Genetic analysis, Genetic association, Genetic variability, Human, Integromic analysis, Major clinical study, Male, Middle aged, Phenotype, Priority journal, Quantitative trait locus, Single nucleotide polymorphism
Citation
Published Version (Please cite this version)