A proteome-wide association study of cardiovascular diseases in 640,000 participants of multiple ancestries
Proteomics holds great promise for identifying potentially druggable effectors of common diseases, yet its application at population-scale across diverse ancestries, remains challenging. Here, we developed genetic imputation models for 2,594 plasma proteins using proteomic and genetic data from 54,219 UK Biobank participants, validating their performance across multiple ancestry groups and in an independent cohort. Plasma proteomes were then imputed for over 640,000 participants in the UK Biobank and the All of Us Research Program. To assess its aetiological value at population-scale, a further proteome-wide association study of cardiovascular diseases was performed across six genetic ancestries. We identified ~9000 protein-disease associations across 89 cardiovascular conditions (PheCodes), the majority of which show consistent effects across ancestries and biobanks, with many comprising known targets of drugs either approved or under development. The associations reveal both shared and distinct proteomic signatures across cardiovascular conditions and defined clusters of distinct pathophysiology with shared underlying molecular pathways. Integration of data on tissue specificity and single-cell transcriptomics prioritised liver-derived proteins in circulation as candidate effectors of coronary artery disease, highlighting inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) as a putative effector. Using a liver-targeted CRISPR gene-editing platform, we show that in vivo disruption of ITIH4 reduces plasma cholesterol and pro-atherogenic lipid species in a preclinical model, consistent with a causal role in cardiovascular disease. Our study enables study of large-scale proteomics in diverse populations, provides a systematic map of protein associations of cardiovascular diseases, and demonstrates the utility of genetically imputed proteomes for target discovery and experimental validation. To facilitate proteomic analyses for the research community, the resultant models and association results have been made freely available through the OmicsPred platform.