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Probing the Virtual Proteome to Identify Novel Disease Biomarkers

Mosley, Jonathan D.; Benson, Mark D.; Smith, J. Gustav LU ; Melander, Olle LU ; Ngo, Debby; Shaffer, Christian M.; Ferguson, Jane F.; Herzig, Matthew S.; McCarty, Catherine A. and Chute, Christopher G., et al. (2018) In Circulation 138(22). p.2469-2481
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... (More)

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.

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Contribution to journal
publication status
published
subject
keywords
atherosclerosis, biomarkers, electronic health records, proteomics
in
Circulation
volume
138
issue
22
pages
13 pages
publisher
Lippincott Williams and Wilkins
external identifiers
  • scopus:85058922520
ISSN
1524-4539
DOI
10.1161/CIRCULATIONAHA.118.036063
language
English
LU publication?
yes
id
72cac14a-bfb1-4c22-b4a0-a3edd2c9d336
date added to LUP
2019-01-04 12:58:46
date last changed
2019-02-20 11:41:41
@article{72cac14a-bfb1-4c22-b4a0-a3edd2c9d336,
  abstract     = {<p>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 &gt;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&lt;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.</p>},
  author       = {Mosley, Jonathan D. and Benson, Mark D. and Smith, J. Gustav and Melander, Olle and Ngo, Debby 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 Namjou, Bahram and Williams, Marc S. and Ritchie, Marylyn D. and Borthwick, Kenneth M. and Kiryluk, Krzysztof and Mentch, Frank D. and Sleiman, Patrick M. and Karlson, Elizabeth W. and Verma, Shefali S. and Zhu, Yineng and Vasan, Ramachandran S. and Yang, Qiong and Denny, Josh C. and Roden, Dan M. and Gerszten, Robert E. and Wang, Thomas J.},
  issn         = {1524-4539},
  keyword      = {atherosclerosis,biomarkers,electronic health records,proteomics},
  language     = {eng},
  number       = {22},
  pages        = {2469--2481},
  publisher    = {Lippincott Williams and Wilkins},
  series       = {Circulation},
  title        = {Probing the Virtual Proteome to Identify Novel Disease Biomarkers},
  url          = {http://dx.doi.org/10.1161/CIRCULATIONAHA.118.036063},
  volume       = {138},
  year         = {2018},
}