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Gilly, A. ; Park, Y.-C. ; Tsafantakis, E.* ; Karaleftheri, M.* ; Dedoussis, G.* ; Zeggini, E.

Genome-wide meta-analysis of 92 cardiometabolic protein serum levels.

Mol. Metab. 78:101810 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
OBJECTIVES: Global cardiometabolic disease prevalence has grown rapidly over the years, making it the leading cause of death worldwide. Proteins are crucial components in biological pathways dysregulated in disease states. Identifying genetic components that influence circulating protein levels may lead to the discovery of biomarkers for early stages of disease or offer opportunities as therapeutic targets. METHODS: Here, we carry out a genome-wide association study (GWAS) utilising whole genome sequencing data in 3,005 individuals from the HELIC founder populations cohort, across 92 proteins of cardiometabolic relevance. RESULTS: We report 322 protein quantitative trait loci (pQTL) signals across 92 proteins, of which 76 are located in or near the coding gene (cis-pQTL). We link those association signals with changes in protein expression and cardiometabolic disease risk using colocalisation and Mendelian randomisation (MR) analyses. CONCLUSIONS: The majority of previously unknown signals we describe point to proteins or protein interactions involved in inflammation and immune response, providing genetic evidence for the contributing role of inflammation in cardiometabolic disease processes.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cardiometabolic Diseases ; Genome-wide Association Study ; Isolated Populations ; Proteomics ; Quantitative Trait Loci; Gene 6; Glucose; Gas6; Inflammation; Phenotype; Ligand
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2212-8778
e-ISSN 2212-8778
Zeitschrift Molecular Metabolism
Quellenangaben Band: 78, Heft: , Seiten: , Artikelnummer: 101810 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Translational Genomics (ITG)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-506700-001
Förderungen
UK Biobank Resource
European Research Council
Wellcome Trust
DOI 10.1016/j.molmet.2023.101810
WOS ID 001103871800001
Scopus ID 85173636063
PubMed ID 37778719
Erfassungsdatum 2023-11-28
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