In this study, we generated and integrated plasma proteomics and metabolomics with the genotype datasets of over 2300 European (EUR) and 400 African (AFR) ancestries to identify ancestry-specific multi-omics quantitative trait loci (QTLs). In total, we mapped 954 AFR pQTLs, 2848 EUR pQTLs, 65 AFR mQTLs, and 490 EUR mQTLs. We further applied these QTLs to ancestry-stratified type-2 diabetes (T2D) risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. Using INTACT that combined trait-imputation and colocalization results, we nominated 270 proteins and 72 metabolites from the EUR set; seven proteins and one metabolite from the AFR set as molecular effectors of T2D risk in an ancestry-stratified manner. Here, we show that the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse ancestral groups.
© 2025. The Author(s).