Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Chen Yao(National Institutes of Health), George Chen(National Institutes of Health), Ci Song(Uppsala University), Joshua A. Keefe(National Institutes of Health), Michael Mendelson(Boston Children's Hospital), Tianxiao Huan(National Institutes of Health), Benjamin B. Sun(University of Cambridge), Annika Laser(Helmholtz Zentrum München), Joseph Maranville(Merck & Co., Inc., Rahway, NJ, USA (United States)), Hongsheng Wu(Wentworth Institute of Technology), Jennifer E. Ho(Massachusetts General Hospital), Paul Courchesne(National Institutes of Health), Asya Lyass(Boston University), Martin G. Larson(Boston University), Christian Gieger(Helmholtz Zentrum München), Johannes Graumann(Max Planck Institute for Heart and Lung Research), Andrew D. Johnson(National Institutes of Health), John Danesh(University of Cambridge), Heiko Runz(Merck & Co., Inc., Rahway, NJ, USA (United States)), Shih-Jen Hwang(National Institutes of Health), Chunyu Liu(National Institutes of Health), Adam S. Butterworth(University of Cambridge), Karsten Suhre(Weill Cornell Medical College in Qatar), Daniel Levy(National Institutes of Health)
Nature Communications
August 9, 2018
Cited by 458Open Access
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Abstract

Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment.


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