Nature MetabolismGeneticsPeer-reviewed journal article
Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases
Scott C. RitchieiD, Samuel A. LambertiD, Matthew ArnoldiD, Shu Mei TeoiD, Sol I. LimiD, Petar ŠćepanovićiD, Jonathan MarteniD, Sohail Zahid, Mark D. ChaffiniD, Yingying LiuiD, Gad AbrahamiD, Willem Hendrik OuwehandiD, David J. RobertsiD, Nicholas A. Watkins, Brian G. DrewiD, Anna C. CalkiniD, Emanuele Di AngelantonioiD, Nicole SoranzoiD, Stephen BurgessiD, Michael ChapmaniD, Sekar KathiresaniD, Amit V. KheraiD, John DaneshiD, Adam S. ButterworthiD, Michael InouyeiD
Abstract
Summary ParagraphCommon human diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGSs) aggregate these alleles into a metric which represents an individuals genetic predisposition to a specific disease. PGSs have shown promise for early risk prediction4-7, and there is potential to use PGSs to understand disease biology in parallel8. Here, we investigate the role plasma protein levels play in cardiometabolic disease risk in a cohort of 3,087 healthy individuals using PGSs. We found PGSs for coronary artery disease (CAD), type 2 diabetes (T2D), chronic kidney disease (CKD), and ischaemic stroke (IS) were associated with levels of 49 plasma proteins. These associations were polygenic in architecture, largely independent of cis protein QTLs, and robust to environmental variation. Over a median 7.7 years follow-up, 28 of these plasma proteins were associated with future myocardial infarction (MI) or T2D events, 16 of which were causal mediators between polygenic risk and incident disease. These protein mediators of polygenic disease risk included targets of approved therapies which may have repurposing potential. Our results demonstrate that PGSs can identify proteins with causal roles in disease, and may have utility in drug development.
- organism: human
- assay: proteomics
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 99
- FWCI
- 9.18
- Field percentile
- top 1%
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