Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data

Jonathan D. Mosley; Sara L. Van Driest; Quinn S. Wells; Christian M. Shaffer; Todd L. Edwards; Lisa Bastarache; Catherine A. McCarty; Will Thompson; Christopher G. Chute; Gail P. Jarvik; David R. Crosslin; Eric B. Larson; Iftikhar J. Kullo; Jennifer A. Pacheco; Peggy L. Peissig; Murray H. Brilliant; James G. Linneman; Josh C. Denny; Dan M. Roden

doi:10.1161/CIRCGENETICS.116.001530

Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data

Jonathan D. Mosley, Sara L. Van Driest, Quinn S. Wells, Christian M. Shaffer, Todd L. Edwards, Lisa Bastarache, Catherine A. McCarty, Will Thompson, Christopher G. Chute, Gail P. Jarvik, David R. Crosslin, Eric B. Larson, Iftikhar J. Kullo, Jennifer A. Pacheco, Peggy L. Peissig, Murray H. Brilliant, James G. Linneman, Josh C. Denny, Dan M. Roden

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Background-Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. Methods and Results-We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. Conclusions-The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

Original language	English (US)
Pages (from-to)	521-530
Number of pages	10
Journal	Circulation: Cardiovascular Genetics
Volume	9
Issue number	6
DOIs	https://doi.org/10.1161/CIRCGENETICS.116.001530
State	Published - Dec 1 2016

Bibliographical note

Funding Information:
This work was supported by a career development award from the Vanderbilt Faculty Research Scholars Fund (Dr Mosley), American Heart Association (15MCPRP25620006 and 16FTF30130005; Dr Mosley), and PGRN (P50-GM115305) and R01 LM010685. BioVU is supported by institutional funding and by Clinical and Translational Science Awards grant UL1 TR000445 from National Center for Advancing Translational Sciences/National Institutes of Health. The Electronic Medical Records and Genomics (eMERGE) Network is funded by National Human Genome Research Institute and National Institute of General Medical Sciences: U01-HG8672 and U01-HG006378 (VUMC); U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and VUMC); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-006378 and U01-HG-04603 (VUMC Coordinating Center); U01HG004438 (Center for Inherited Disease Research); and U01HG004424 (the Broad Institute) serving as Genotyping Centers. ARIC study (Atherosclerosis Risk in Communities) is supported by National Heart, Lung and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268 201100007C, HHSN268201100008C, HHSN268201100009C, HHSN 268201100010C, HHSN268201100011C, andHHSN268201100012C). Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle).

Publisher Copyright:
© 2016 American Heart Association, Inc.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

atherosclerosis
blood pressure
coronary artery disease
risk factors
triglycerides

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1161/CIRCGENETICS.116.001530

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Mosley, J. D., Van Driest, S. L., Wells, Q. S., Shaffer, C. M., Edwards, T. L., Bastarache, L., McCarty, C. A., Thompson, W., Chute, C. G., Jarvik, G. P., Crosslin, D. R., Larson, E. B., Kullo, I. J., Pacheco, J. A., Peissig, P. L., Brilliant, M. H., Linneman, J. G., Denny, J. C., & Roden, D. M. (2016). Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data. Circulation: Cardiovascular Genetics, 9(6), 521-530. https://doi.org/10.1161/CIRCGENETICS.116.001530

Mosley, JD, Van Driest, SL, Wells, QS, Shaffer, CM, Edwards, TL, Bastarache, L, McCarty, CA, Thompson, W, Chute, CG, Jarvik, GP, Crosslin, DR, Larson, EB, Kullo, IJ, Pacheco, JA, Peissig, PL, Brilliant, MH, Linneman, JG, Denny, JC & Roden, DM 2016, 'Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data', Circulation: Cardiovascular Genetics, vol. 9, no. 6, pp. 521-530. https://doi.org/10.1161/CIRCGENETICS.116.001530

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abstract = "Background-Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. Methods and Results-We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. Conclusions-The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.",

keywords = "atherosclerosis, blood pressure, coronary artery disease, risk factors, triglycerides",

author = "Mosley, {Jonathan D.} and {Van Driest}, {Sara L.} and Wells, {Quinn S.} and Shaffer, {Christian M.} and Edwards, {Todd L.} and Lisa Bastarache and McCarty, {Catherine A.} and Will Thompson and Chute, {Christopher G.} and Jarvik, {Gail P.} and Crosslin, {David R.} and Larson, {Eric B.} and Kullo, {Iftikhar J.} and Pacheco, {Jennifer A.} and Peissig, {Peggy L.} and Brilliant, {Murray H.} and Linneman, {James G.} and Denny, {Josh C.} and Roden, {Dan M.}",

note = "Funding Information: This work was supported by a career development award from the Vanderbilt Faculty Research Scholars Fund (Dr Mosley), American Heart Association (15MCPRP25620006 and 16FTF30130005; Dr Mosley), and PGRN (P50-GM115305) and R01 LM010685. BioVU is supported by institutional funding and by Clinical and Translational Science Awards grant UL1 TR000445 from National Center for Advancing Translational Sciences/National Institutes of Health. The Electronic Medical Records and Genomics (eMERGE) Network is funded by National Human Genome Research Institute and National Institute of General Medical Sciences: U01-HG8672 and U01-HG006378 (VUMC); U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and VUMC); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-006378 and U01-HG-04603 (VUMC Coordinating Center); U01HG004438 (Center for Inherited Disease Research); and U01HG004424 (the Broad Institute) serving as Genotyping Centers. ARIC study (Atherosclerosis Risk in Communities) is supported by National Heart, Lung and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268 201100007C, HHSN268201100008C, HHSN268201100009C, HHSN 268201100010C, HHSN268201100011C, andHHSN268201100012C). Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle). Publisher Copyright: {\textcopyright} 2016 American Heart Association, Inc. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",

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doi = "10.1161/CIRCGENETICS.116.001530",

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TY - JOUR

T1 - Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data

AU - Mosley, Jonathan D.

AU - Van Driest, Sara L.

AU - Wells, Quinn S.

AU - Shaffer, Christian M.

AU - Edwards, Todd L.

AU - Bastarache, Lisa

AU - McCarty, Catherine A.

AU - Thompson, Will

AU - Chute, Christopher G.

AU - Jarvik, Gail P.

AU - Crosslin, David R.

AU - Larson, Eric B.

AU - Kullo, Iftikhar J.

AU - Pacheco, Jennifer A.

AU - Peissig, Peggy L.

AU - Brilliant, Murray H.

AU - Linneman, James G.

AU - Denny, Josh C.

AU - Roden, Dan M.

N1 - Funding Information: This work was supported by a career development award from the Vanderbilt Faculty Research Scholars Fund (Dr Mosley), American Heart Association (15MCPRP25620006 and 16FTF30130005; Dr Mosley), and PGRN (P50-GM115305) and R01 LM010685. BioVU is supported by institutional funding and by Clinical and Translational Science Awards grant UL1 TR000445 from National Center for Advancing Translational Sciences/National Institutes of Health. The Electronic Medical Records and Genomics (eMERGE) Network is funded by National Human Genome Research Institute and National Institute of General Medical Sciences: U01-HG8672 and U01-HG006378 (VUMC); U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and VUMC); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-006378 and U01-HG-04603 (VUMC Coordinating Center); U01HG004438 (Center for Inherited Disease Research); and U01HG004424 (the Broad Institute) serving as Genotyping Centers. ARIC study (Atherosclerosis Risk in Communities) is supported by National Heart, Lung and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268 201100007C, HHSN268201100008C, HHSN268201100009C, HHSN 268201100010C, HHSN268201100011C, andHHSN268201100012C). Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle). Publisher Copyright: © 2016 American Heart Association, Inc. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Background-Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. Methods and Results-We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. Conclusions-The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

AB - Background-Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. Methods and Results-We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. Conclusions-The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

KW - atherosclerosis

KW - blood pressure

KW - coronary artery disease

KW - risk factors

KW - triglycerides

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JO - Circulation: Cardiovascular Genetics

JF - Circulation: Cardiovascular Genetics

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ER -

Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data

Abstract

Bibliographical note

Keywords

UN SDGs

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