. 2015 Feb 10;131(6):536-49.

doi: 10.1161/CIRCULATIONAHA.114.010696. Epub 2014 Dec 22.

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

Chen Yao¹, Brian H Chen¹, Roby Joehanes¹, Burcak Otlu¹, Xiaoling Zhang¹, Chunyu Liu¹, Tianxiao Huan¹, Oznur Tastan¹, L Adrienne Cupples¹, James B Meigs¹, Caroline S Fox¹, Jane E Freedman¹, Paul Courchesne¹, Christopher J O'Donnell¹, Peter J Munson¹, Sunduz Keles¹, Daniel Levy²

Affiliations

¹ From the National Heart, Lung, and Blood Institute's Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., L.A.C., C.S.F., P.C., C.J.O'D., D.L.); Population Sciences Branch, National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., P.C., D.L.); Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD (R.J., P.J.M.); Department of Computer Engineering, Middle East Technical University, Ankara, Turkey (B.O.); Department of Computer Engineering, Bilkent University, Ankara, Turkey (O.T.); Department of Biostatistics, Boston University School of Public Health, Boston, MA (L.A.C.); Harvard Medical School, Boston, MA (J.B.M.); Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (C.S.F.); Department of Medicine, University of Massachusetts Medical School, Worchester (J.E.F.); Division of Cardiology, Massachusetts General Hospital, Boston, MA (C.J.O'D.); and Departments of Statistics and of Biostatistics and Medical Informatics, University of Wisconsin-Madison (S.K.).
² From the National Heart, Lung, and Blood Institute's Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., L.A.C., C.S.F., P.C., C.J.O'D., D.L.); Population Sciences Branch, National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., P.C., D.L.); Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD (R.J., P.J.M.); Department of Computer Engineering, Middle East Technical University, Ankara, Turkey (B.O.); Department of Computer Engineering, Bilkent University, Ankara, Turkey (O.T.); Department of Biostatistics, Boston University School of Public Health, Boston, MA (L.A.C.); Harvard Medical School, Boston, MA (J.B.M.); Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (C.S.F.); Department of Medicine, University of Massachusetts Medical School, Worchester (J.E.F.); Division of Cardiology, Massachusetts General Hospital, Boston, MA (C.J.O'D.); and Departments of Statistics and of Biostatistics and Medical Informatics, University of Wisconsin-Madison (S.K.). LevyD@nih.gov.

PMID: 25533967
PMCID: PMC4369387
DOI: 10.1161/CIRCULATIONAHA.114.010696

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

Chen Yao et al. Circulation. 2015.

. 2015 Feb 10;131(6):536-49.

doi: 10.1161/CIRCULATIONAHA.114.010696. Epub 2014 Dec 22.

Authors

Affiliations

¹ From the National Heart, Lung, and Blood Institute's Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., L.A.C., C.S.F., P.C., C.J.O'D., D.L.); Population Sciences Branch, National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., P.C., D.L.); Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD (R.J., P.J.M.); Department of Computer Engineering, Middle East Technical University, Ankara, Turkey (B.O.); Department of Computer Engineering, Bilkent University, Ankara, Turkey (O.T.); Department of Biostatistics, Boston University School of Public Health, Boston, MA (L.A.C.); Harvard Medical School, Boston, MA (J.B.M.); Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (C.S.F.); Department of Medicine, University of Massachusetts Medical School, Worchester (J.E.F.); Division of Cardiology, Massachusetts General Hospital, Boston, MA (C.J.O'D.); and Departments of Statistics and of Biostatistics and Medical Informatics, University of Wisconsin-Madison (S.K.).
² From the National Heart, Lung, and Blood Institute's Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., L.A.C., C.S.F., P.C., C.J.O'D., D.L.); Population Sciences Branch, National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., P.C., D.L.); Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD (R.J., P.J.M.); Department of Computer Engineering, Middle East Technical University, Ankara, Turkey (B.O.); Department of Computer Engineering, Bilkent University, Ankara, Turkey (O.T.); Department of Biostatistics, Boston University School of Public Health, Boston, MA (L.A.C.); Harvard Medical School, Boston, MA (J.B.M.); Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (C.S.F.); Department of Medicine, University of Massachusetts Medical School, Worchester (J.E.F.); Division of Cardiology, Massachusetts General Hospital, Boston, MA (C.J.O'D.); and Departments of Statistics and of Biostatistics and Medical Informatics, University of Wisconsin-Madison (S.K.). LevyD@nih.gov.

PMID: 25533967
PMCID: PMC4369387
DOI: 10.1161/CIRCULATIONAHA.114.010696

Erratum in

Correction.
[No authors listed] [No authors listed] Circulation. 2015 May 12;131(19):e474. doi: 10.1161/CIR.0000000000000213. Circulation. 2015. PMID: 25964287 No abstract available.

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(-8)) 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.

Keywords: cardiovascular disease; gene expression/regulation network; genetic variation.

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Figures

**Figure 1**
Flowchart of integromic analysis. A total of 1512 single nucleotide polymorphisms (SNPs) associated with 21 cardiovascular disease (CVD) traits (at P ≤ 5×10⁻⁸) were derived from database of Genotypes and Phenotypes and the National Human Genome Research Institute genome-wide association studies (GWAS) catalog. We built a CVD phenotype network by connecting 2 traits if they share the same GWAS SNP. Whole blood samples were collected from 5257 FHS participants. Genome-wide genotyping and mRNA expression levels were assayed. We correlated 1077 SNPs (after genotyping quality control of 1512 SNPs) with 17873 gene expression values to assess expression quantitative trait loci (eQTLs). We replicated these eQTLs in 2 large databases. We then built an eQTL network by connecting eQTLs to their associated genes and traits. We identified modules associated with different CVD traits within the network. Finally, we conducted mediation analyses to test whether the genetic effect appears to influence the CVD phenotype through effects of the eQTL (ie, GWAS SNP) on gene expression. BMI indicates body mass index; FHS, Framingham Heart Study; HDL-C, high-density lipoprotein cholesterol; and LDL-C, low-density lipoprotein cholesterol.

**Figure 2**
Cardiovascular disease phenotype network by virtue of shared genome-wide association study single nucleotide polymorphisms. Each node represents a cardiovascular disease trait, and 2 traits are connected if they share at least 1 single nucleotide polymorphism in genome-wide association studies. The width of each line is weighted by the proportion of shared single nucleotide polymorphisms between 2 connected traits. HDL indicates high-density lipoprotein; and LDL, low-density lipoprotein.

**Figure 3**
Reference and single nucleotide polymorphism (rs7528684) allele matches to the Nfkb sequence logo (Encyclopedia of DNA Elements [ENCODE] motif logo NFKB_disci from http://compbio.mit.edu/encode-motifs/).

**Figure 4**
Modules in the cardiovascular disease (CVD) expression quantitative trait loci (eQTL) network. Gray nodes represent CVD traits. Blue nodes represent single nucleotide polymorphisms (SNPs) associated with CVD traits in genome-wide association studies. Orange nodes represent genes whose expression is associated with SNPs in Framingham Heart Study participants. Gray edges represent SNP-trait associations. Red edges represent *cis* associations between SNPs and gene expression. Green edges represent *trans* associations between SNPs and gene expressions. A, Type 1 diabetes mellitus eQTL module. B, rs964184 pleiotropic eQTL module. C, Lipids eQTL module. D, Coronary artery disease and smoking eQTL module. E, eQTLs associated with *FDFT1*. HDL indicates high-density lipoprotein; LDL, low-density lipoprotein; and LDLR, low-density lipoprotein receptor.

**Figure 5**
Example of triangular relations among phenotype, single nucleotide polymorphism, and gene expression. rs174546 (in *FADS1*) was associated with high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides in genome-wide association studies (GWAS). This single nucleotide polymorphism was significantly associated with expression of *LDLR* in Framingham Heart Study participants (P = 2.9×10⁻⁷). The expression of *LDLR* was also significantly associated with HDL-C, LDL-C, and triglyceride levels in Framingham Heart Study participants. eQTL indicates expression quantitative trait loci.

**Figure 6**
Cardiovascular disease phenotype and metabolite network by virtue of shared genome-wide association study single nucleotide polymorphisms. Gray nodes represent cardiovascular disease traits. Red nodes represent metabolites. Two traits are connected if they share at least 1 single nucleotide polymorphism in genome-wide association studies. HDL-C indicates high-density lipoprotein cholesterol; and LDL, low-density lipoprotein.

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Comment in

Making sense of genome-wide association studies: integrating genetic variation with gene expression to derive functional mechanisms underlying disease risk.
Chen HH, Stewart AF. Chen HH, et al. Circulation. 2015 Feb 10;131(6):519-21. doi: 10.1161/CIRCULATIONAHA.114.014634. Epub 2014 Dec 22. Circulation. 2015. PMID: 25533968 No abstract available.

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

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

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