European and African ancestry-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites

Chengran Yang, Priyanka Gorijala, Jigyasha Timsina, Lihua Wang, Menghan Liu, Ciyang Wang, William Brock, Yueyao Wang, Fumihiko Urano, Yun Ju Sung and Carlos Cruchaga ()
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Chengran Yang: Washington University School of Medicine
Priyanka Gorijala: Washington University School of Medicine
Jigyasha Timsina: Washington University School of Medicine
Lihua Wang: Washington University School of Medicine
Menghan Liu: Washington University School of Medicine
Ciyang Wang: Washington University School of Medicine
William Brock: Washington University School of Medicine
Yueyao Wang: Washington University School of Medicine
Fumihiko Urano: MSC 8127-0021-09
Yun Ju Sung: Washington University School of Medicine
Carlos Cruchaga: Washington University School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-62463-w

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