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

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.

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Publication Details

Journal
Nature Communications
Published
2025-08-11
DOI
https://doi.org/10.1038/s41467-025-62463-w
Citations
3
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
5.45
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article

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

Fumihiko Urano, Chengran Yang, Carlos C Cruchaga, Ciyang Wang et al.
3 citations
Nature Communications
Genetic Associations and Epidemiology
5.45
article

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

Fumihiko Urano, Chengran Yang, Carlos C Cruchaga, Ciyang Wang, Yueyao Wang, Yun Ju Sung, Priyanka Gorijala, Lihua Wang, Menghan Liu, Jigyasha Timsina, William Brock
article en
3 citations

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.

Nature CommunicationsVol. 16(1)
Washington University in St. Louis (US), Hope Center for Neurological Disorders (US)
Good health and well-being
Openalex Percentile: Top 4%
Genetic Associations and Epidemiology
5.45
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European and African ancestry-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites — Fumihiko Urano, Chengran Yang, et al. · Nature Communications (2025) | TGRS Research Map | TGRS