Yang Chengran, Gorijala Priyanka, Timsina Jigyasha, Wang Lihua, Liu Menghan, Wang Ciyang, Brock William, Wang Yueyao, Urano Fumihiko, Sung Yun Ju, Cruchaga Carlos
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
Nat Commun. 2025 Aug 11;16(1):7412. doi: 10.1038/s41467-025-62463-w.
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.
在本研究中，我们将血浆蛋白质组学和代谢组学与超过2300名欧洲（EUR）和400名非洲（AFR）血统的基因型数据集相结合，以识别特定血统的多组学定量性状基因座（QTL）。我们总共绘制了954个AFR蛋白质定量性状基因座（pQTL）、2848个EUR pQTL、65个AFR代谢物定量性状基因座（mQTL）和490个EUR mQTL。我们进一步将这些QTL应用于按血统分层的2型糖尿病（T2D）风险研究，以确定疾病相关基因座背后的关键蛋白质和代谢物。使用结合了性状归因和共定位结果的INTACT方法，我们以血统分层的方式从EUR组中提名了270种蛋白质和72种代谢物；从AFR组中提名了7种蛋白质和1种代谢物作为T2D风险的分子效应物。在这里，我们表明，整合不同血统的遗传和组学研究可用于识别不同祖先群体中同一疾病背后不同的效应分子特征。
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