Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

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Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

Aims/hypothesis: Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European.

Methods: Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations.

Results: We identified three, 44 and one protein asso ciated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development. Conclusions/interpretation: Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations. Data availability: The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub (https://github.com/Arthur1021/MESA-1K-PWAS). Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Authors : Liu S.; Zhu J.; Zhong H.; Wu C.; Xue H.; Darst B.F.; Guo X.; Durda P.; Tracy R.P.; Liu Y.; Johnson W.C.; Taylor K.D.; Manichaikul A.W.; Goodarzi M.O.; Gerszten R.E.; Clish C.B.; Chen Y.-D.I.; Highland H.; Haiman C.A.; Gignoux C.R.; Lange L.; Conti D.V.; Raffield L.M.; Wilkens L.; Marchand L.L.; North K.E.; Young K.L.; Loos R.J.; Buyske S.; Matise T.; Peters U.; Kooperberg C.; Reiner A.P.; Yu B.; Boerwinkle E.; Sun Q.; Rooney M.R.; Echouffo-Tcheugui J.B.; Daviglus M.L.; Qi Q.; Mancuso N.; Li C.; Deng Y.; Manning A.; Meigs J.B.; Rich S.S.; Rotter J.I.; Wu L.

Source : Springer Science and Business Media Deutschland GmbH