Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing

0571761 - ÚMG 2024 RIV US eng J - Článek v odborném periodiku
Png, G. - Gerlini, R. - Hatzikotoulas, K. - Barysenka, A. - Rayner, N. W. - Klaric, L. - Rathkolb, B. - Aguilar-Pimentel, J. A. - Rozman, Jan - Fuchs, H. - Gailus-Durner, V. - Tsafantakis, E. - Karaleftheri, M. - Dedoussis, G. - Pietrzik, C. - Wilson, J. F. - Angelis, M. H. - Becker-Pauly, C. - Gilly, A. - Zeggini, E.
Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing.
Human Molecular Genetics. Roč. 32, č. 8 (2023), s. 1266-1275. ISSN 0964-6906. E-ISSN 1460-2083
Institucionální podpora: RVO:68378050
Klíčová slova: METAANALYSIS * CHOLESTEROL * EXPRESSION * RECEPTORS * DISCOVERY * PLATFORM
Obor OECD: Biochemistry and molecular biology
Impakt faktor: 3.5, rok: 2022
Způsob publikování: Open access
https://academic.oup.com/hmg/article/32/8/1266/6812863?login=true

Cardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 248 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analyzing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356, 22.5x WGS) and Pomak (n = 1537, 18.4x WGS), we detect 301 independently associated pQTL variants for 170 proteins, including 12 rare variants (minor allele frequency < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations but have drifted up in the frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including Mep1b for high-density lipoprotein (HDL) levels, and describe a knock-out (KO) Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships and demonstrate the importance of isolated populations in pQTL analysis.
Trvalý link: https://hdl.handle.net/11104/0343045