Despite metabolomics transforming our understanding of risk factors and aetiology of metabolic diseases, profiling is rarely performed for people of non-European ancestries, on whom much of metabolic disease burden falls. Metabolome-wide association studies (MWAS) can be performed using genetic scores to predict metabolomic traits, helping address these inequities; however, their performance in populations of admixed American (AMR) ancestries is unexplored. We evaluated 141 genetic scores, developed in an INTERVAL Study sample of European (EUR) genetic ancestries, in the Mexico City Prospective Study (MCPS; n=132,336), obtaining a median predictive R2 of 0.027. Training Bayesian ridge models within MCPS substantially improved performance, with a median R2 of 0.083 on a withheld 20% subset. MCPS-trained models also outperformed INTERVAL-trained models among UK Biobank participants of AMR ancestries (n=600; median R2: 0.070 vs. 0.046). Finally, among AMR participants of the All of Us cohort, using MCPS-trained (vs. INTERVAL-trained) models to predict metabolomic traits yielded five times as many significant associations (FDR-corrected P<0.05) across three cardiometabolic diseases: ischaemic heart disease, type 2 diabetes, and chronic kidney disease. The genetic scores are openly available at the OmicsPred portal (www.OmicsPred.org), enabling better-powered analyses in diverse AMR cohorts and helping reduce global inequities in omics research.
T. Oreskovic, D. Jin, E. Trichia et al.· medRxiv· 0 citations
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.
Yu Xu, Douglas P. Loesch, H. Taylor et al.· medRxiv· 0 citations
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