Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing. Issue 8 (9th November 2022)

• Record Type: Journal Article
• Authors: Png, Grace; Gerlini, Raffaele; Hatzikotoulas, Konstantinos; Barysenka, Andrei; Rayner, N William; Klarić, Lucija; Rathkolb, Birgit; Aguilar-Pimentel, Juan A; Rozman, Jan; Fuchs, Helmut; Gailus-Durner, Valerie; Tsafantakis, Emmanouil; Karaleftheri, Maria; Dedoussis, George; Pietrzik, Claus; Wilson, James F; de Angelis, Martin Hrabe; Becker-Pauly, Christoph; Gilly, Arthur; Zeggini, Eleftheria
• Is Part Of: Human molecular genetics. Volume 32:Issue 8(2023)
• Page Start: 1266
• Page End: 1275
• Publication Date: 2022-11-09
• Subjects: Human molecular genetics -- Periodicals; Human chromosome abnormalities -- Periodicals; 572.8
• DOI: 10.1093/hmg/ddac275
• Languages: English
• ISSNs: 0964-6906

Abstract

Abstract: 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; 22.5× WGS) and Pomak ( n = 1537; 18.4× 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.