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

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 quant...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077504/
https://www.ncbi.nlm.nih.gov/pubmed/36349687
http://dx.doi.org/10.1093/hmg/ddac275
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author 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
author_facet 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
author_sort Png, Grace
collection PubMed
description 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.
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spelling pubmed-100775042023-04-07 Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing 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 Hum Mol Genet Original Article 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. Oxford University Press 2022-11-09 /pmc/articles/PMC10077504/ /pubmed/36349687 http://dx.doi.org/10.1093/hmg/ddac275 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
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
Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
title Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
title_full Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
title_fullStr Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
title_full_unstemmed Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
title_short Identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
title_sort identifying causal serum protein–cardiometabolic trait relationships using whole genome sequencing
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077504/
https://www.ncbi.nlm.nih.gov/pubmed/36349687
http://dx.doi.org/10.1093/hmg/ddac275
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