The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Gustav Smith

Associate professor

Default user image.

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

Summary, in English

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.


  • EpiHealth: Epidemiology for Health
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • Molecular Epidemiology and Cardiology
  • Cardiovascular Research - Hypertension

Publishing year












Document type

Journal article


Lippincott Williams & Wilkins


  • Cardiac and Cardiovascular Systems


  • atherosclerosis
  • biomarkers
  • electronic health records
  • proteomics



Research group

  • Molecular Epidemiology and Cardiology
  • Cardiovascular Research - Hypertension


  • ISSN: 1524-4539