全基因组关联研究
孟德尔随机化
疾病
数量性状位点
生物
SNP公司
遗传学
基因
弗雷明翰心脏研究
计算生物学
遗传关联
弗雷明翰风险评分
单核苷酸多态性
生物信息学
医学
遗传变异
内科学
基因型
作者
Chen Yao,George Chen,Ci Song,Joshua A. Keefe,Michael Mendelson,Tianxiao Huan,Benjamin B. Sun,Annika Laser,Joseph Maranville,Hongsheng Wu,Jennifer E. Ho,Paul Courchesne,Asya Lyass,Martin G. Larson,Christian Gieger,Johannes Graumann,Andrew D. Johnson,John Danesh,Heiko Runz,Shih-Jen Hwang
标识
DOI:10.1038/s41467-018-05512-x
摘要
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. Genetic variation can influence levels of disease-related plasma proteins and, thus, contribute to the pathogenesis of complex diseases. Here, the authors perform genome-wide QTL analysis for 71 plasma proteins to identify causal proteins for coronary heart disease and provide a molecular QTL browser.
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