Probing the Virtual Proteome to Identify Novel Disease Biomarkers

Jonathan D. Mosley; Mark D. Benson; J. Gustav Smith; Olle Melander; Debby Ngo; Christian M. Shaffer; Jane F. Ferguson; Matthew S. Herzig; Catherine A. McCarty; Christopher G. Chute; Gail P. Jarvik; Adam S. Gordon; Melody R. Palmer; David R. Crosslin; Eric B. Larson; David S. Carrell; Iftikhar J. Kullo; Jennifer A. Pacheco; Peggy L. Peissig; Murray H. Brilliant; Terrie E. Kitchner; James G. Linneman; Bahram Namjou; Marc S. Williams; Marylyn D. Ritchie; Kenneth M. Borthwick; Krzysztof Kiryluk; Frank D. Mentch; Patrick M. Sleiman; Elizabeth W. Karlson; Shefali S. Verma; Yineng Zhu; Ramachandran S. Vasan; Qiong Yang; Josh C. Denny; Dan M. Roden; Robert E. Gerszten; Thomas J. Wang

doi:10.1161/CIRCULATIONAHA.118.036063

Probing the Virtual Proteome to Identify Novel Disease Biomarkers

Jonathan D. Mosley, Mark D. Benson, J. Gustav Smith, Olle Melander, Debby Ngo, Christian M. Shaffer, Jane F. Ferguson, Matthew S. Herzig, Catherine A. McCarty, Christopher G. Chute, Gail P. Jarvik, Adam S. Gordon, Melody R. Palmer, David R. Crosslin, Eric B. Larson, David S. Carrell, Iftikhar J. Kullo, Jennifer A. Pacheco, Peggy L. Peissig, Murray H. BrilliantTerrie E. Kitchner, James G. Linneman, Bahram Namjou, Marc S. Williams, Marylyn D. Ritchie, Kenneth M. Borthwick, Krzysztof Kiryluk, Frank D. Mentch, Patrick M. Sleiman, Elizabeth W. Karlson, Shefali S. Verma, Yineng Zhu, Ramachandran S. Vasan, Qiong Yang, Josh C. Denny, Dan M. Roden, Robert E. Gerszten, Thomas J. Wang

Cardiovascular Medicine

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

14 Scopus citations

Abstract

Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Original language	English (US)
Pages (from-to)	2469-2481
Number of pages	13
Journal	Circulation
Volume	138
Issue number	22
DOIs	https://doi.org/10.1161/CIRCULATIONAHA.118.036063
State	Published - Nov 27 2018

Keywords

atherosclerosis
biomarkers
electronic health records
proteomics

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine
Physiology (medical)

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10.1161/CIRCULATIONAHA.118.036063

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Mosley, J. D., Benson, M. D., Smith, J. G., Melander, O., Ngo, D., Shaffer, C. M., Ferguson, J. F., Herzig, M. S., McCarty, C. A., Chute, C. G., Jarvik, G. P., Gordon, A. S., Palmer, M. R., Crosslin, D. R., Larson, E. B., Carrell, D. S., Kullo, I. J., Pacheco, J. A., Peissig, P. L., ... Wang, T. J. (2018). Probing the Virtual Proteome to Identify Novel Disease Biomarkers. Circulation, 138(22), 2469-2481. https://doi.org/10.1161/CIRCULATIONAHA.118.036063

Mosley, JD, Benson, MD, Smith, JG, Melander, O, Ngo, D, Shaffer, CM, Ferguson, JF, Herzig, MS, McCarty, CA, Chute, CG, Jarvik, GP, Gordon, AS, Palmer, MR, Crosslin, DR, Larson, EB, Carrell, DS, Kullo, IJ, Pacheco, JA, Peissig, PL, Brilliant, MH, Kitchner, TE, Linneman, JG, Namjou, B, Williams, MS, Ritchie, MD, Borthwick, KM, Kiryluk, K, Mentch, FD, Sleiman, PM, Karlson, EW, Verma, SS, Zhu, Y, Vasan, RS, Yang, Q, Denny, JC, Roden, DM, Gerszten, RE & Wang, TJ 2018, 'Probing the Virtual Proteome to Identify Novel Disease Biomarkers', Circulation, vol. 138, no. 22, pp. 2469-2481. https://doi.org/10.1161/CIRCULATIONAHA.118.036063

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title = "Probing the Virtual Proteome to Identify Novel Disease Biomarkers",

abstract = "Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a {"}virtual proteomic{"} approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malm{\"o} Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.",

keywords = "atherosclerosis, biomarkers, electronic health records, proteomics",

author = "Mosley, {Jonathan D.} and Benson, {Mark D.} and Smith, {J. Gustav} and Olle Melander and Debby Ngo and Shaffer, {Christian M.} and Ferguson, {Jane F.} and Herzig, {Matthew S.} and McCarty, {Catherine A.} and Chute, {Christopher G.} and Jarvik, {Gail P.} and Gordon, {Adam S.} and Palmer, {Melody R.} and Crosslin, {David R.} and Larson, {Eric B.} and Carrell, {David S.} and Kullo, {Iftikhar J.} and Pacheco, {Jennifer A.} and Peissig, {Peggy L.} and Brilliant, {Murray H.} and Kitchner, {Terrie E.} and Linneman, {James G.} and Bahram Namjou and Williams, {Marc S.} and Ritchie, {Marylyn D.} and Borthwick, {Kenneth M.} and Krzysztof Kiryluk and Mentch, {Frank D.} and Sleiman, {Patrick M.} and Karlson, {Elizabeth W.} and Verma, {Shefali S.} and Yineng Zhu and Vasan, {Ramachandran S.} and Qiong Yang and Denny, {Josh C.} and Roden, {Dan M.} and Gerszten, {Robert E.} and Wang, {Thomas J.}",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors. Circulation is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.",

year = "2018",

month = nov,

day = "27",

doi = "10.1161/CIRCULATIONAHA.118.036063",

language = "English (US)",

volume = "138",

pages = "2469--2481",

journal = "Circulation",

issn = "0009-7322",

publisher = "Lippincott Williams and Wilkins",

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TY - JOUR

T1 - Probing the Virtual Proteome to Identify Novel Disease Biomarkers

AU - Mosley, Jonathan D.

AU - Benson, Mark D.

AU - Smith, J. Gustav

AU - Melander, Olle

AU - Ngo, Debby

AU - Shaffer, Christian M.

AU - Ferguson, Jane F.

AU - Herzig, Matthew S.

AU - McCarty, Catherine A.

AU - Chute, Christopher G.

AU - Jarvik, Gail P.

AU - Gordon, Adam S.

AU - Palmer, Melody R.

AU - Crosslin, David R.

AU - Larson, Eric B.

AU - Carrell, David S.

AU - Kullo, Iftikhar J.

AU - Pacheco, Jennifer A.

AU - Peissig, Peggy L.

AU - Brilliant, Murray H.

AU - Kitchner, Terrie E.

AU - Linneman, James G.

AU - Namjou, Bahram

AU - Williams, Marc S.

AU - Ritchie, Marylyn D.

AU - Borthwick, Kenneth M.

AU - Kiryluk, Krzysztof

AU - Mentch, Frank D.

AU - Sleiman, Patrick M.

AU - Karlson, Elizabeth W.

AU - Verma, Shefali S.

AU - Zhu, Yineng

AU - Vasan, Ramachandran S.

AU - Yang, Qiong

AU - Denny, Josh C.

AU - Roden, Dan M.

AU - Gerszten, Robert E.

AU - Wang, Thomas J.

N1 - Publisher Copyright: © 2018 The Authors. Circulation is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.

PY - 2018/11/27

Y1 - 2018/11/27

N2 - Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

AB - Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

KW - atherosclerosis

KW - biomarkers

KW - electronic health records

KW - proteomics

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Probing the Virtual Proteome to Identify Novel Disease Biomarkers

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Keywords

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