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Circulation. 2018 Nov 27;138(22):2469-2481. doi: 10.1161/CIRCULATIONAHA.118.036063.

Probing the Virtual Proteome to Identify Novel Disease Biomarkers.

Author information

1
Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN.
2
Cardiovascular Division, Brigham and Women's Hospital, Boston, MA (M.D.B.).
3
Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (M.D.B., M.S.H., R.E.G.).
4
Molecular Epidemiology and Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden (J.G.S., O.M.).
5
Department of Medicine and the Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (D.N.).
6
University of Minnesota Medical School, Duluth Campus (C.A.M.).
7
Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.).
8
Departments of Medicine (J.P.J., A.S.G., M.R.P., E.B.L.), University of Washington, Seattle.
9
Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle.
10
Kaiser Permanente Washington Health Research Institute, Seattle, WA (E.B.L., D.S.C.).
11
Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.).
12
Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.).
13
Biomedical Informatics Research Center (P.L.P., J.G.L.), Marshfield Clinic Research Institute, WI.
14
Center for Computational and Biomedical Informatics (M.H.B., T.E.K.), Marshfield Clinic Research Institute, WI.
15
Cincinnati Children's Hospital Medical Center and University of Cincinnati, OH (B.N.).
16
Genomic Medicine Institute (M.S.W.), Geisinger Health System, Danville, PA.
17
Departments of Bioinformatics and Genetics (M.D.R.), University of Pennsylvania, Philadelphia.
18
Biomedical and Translational Informatics (K.M.B.), Geisinger Health System, Danville, PA.
19
Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY (K.K.).
20
Center for Applied Genomics, Children's Hospital of Philadelphia, PA (F.D.M., P.M.S.).
21
Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (E.W.K.).
22
Perelman School of Medicine, Department of Genetics (S.S.V.), University of Pennsylvania, Philadelphia.
23
Department of Biostatistics, Boston University School of Public Health, MA (Y.Z., Q.Y.).
24
Department of Medicine, Boston University School of Medicine, MA (R.S.V.).
25
Biomedical Informatics (J.C.D., D.M.R.), Vanderbilt University Medical Center, Nashville, TN.
26
Department of Pharmacology (D.M.R.), Vanderbilt University Medical Center, Nashville, TN.

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.

KEYWORDS:

atherosclerosis; biomarkers; electronic health records; proteomics

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