Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility.
Fengzhe XuEvan Yi-Wen YuXue CaiLiang YueLi-Peng JingXinxiu LiangYuanqing FuZelei MiaoMin YangMenglei ShuaiWanglong GouCongmei XiaoZhangzhi XueYuting XieSainan LiSha LuMeiqi ShiXuhong WangWensheng HuClaudia LangenbergJian YangYu-Ming ChenTiannan GuoJu-Sheng ZhengPublished in: Nature communications (2023)
Identification of protein quantitative trait loci (pQTL) helps understand the underlying mechanisms of diseases and discover promising targets for pharmacological intervention. For most important class of drug targets, genetic evidence needs to be generalizable to diverse populations. Given that the majority of the previous studies were conducted in European ancestry populations, little is known about the protein-associated genetic variants in East Asians. Based on data-independent acquisition mass spectrometry technique, we conduct genome-wide association analyses for 304 unique proteins in 2,958 Han Chinese participants. We identify 195 genetic variant-protein associations. Colocalization and Mendelian randomization analyses highlight 60 gene-protein-phenotype associations, 45 of which (75%) have not been prioritized in Europeans previously. Further cross-ancestry analyses uncover key proteins that contributed to the differences in the obesity-induced diabetes and coronary artery disease susceptibility. These findings provide novel druggable proteins as well as a unique resource for the trans-ancestry evaluation of protein-targeted drug discovery.
Keyphrases
- genome wide
- coronary artery disease
- mass spectrometry
- protein protein
- dna methylation
- type diabetes
- amino acid
- genome wide association
- randomized controlled trial
- high resolution
- drug discovery
- binding protein
- genome wide association study
- heart failure
- metabolic syndrome
- oxidative stress
- insulin resistance
- physical activity
- percutaneous coronary intervention
- skeletal muscle
- artificial intelligence
- deep learning
- endothelial cells
- high glucose
- liquid chromatography
- big data
- atrial fibrillation
- cardiovascular events
- case control
- high performance liquid chromatography