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Karjalainen, M.K.* ; Karthikeyan, S.* ; Oliver-Williams, C.* ; Sliz, E.* ; Allara, E.* ; Fung, W.T.* ; Surendran, P.* ; Zhang, W.* ; Jousilahti, P.* ; Kristiansson, K.* ; Salomaa, V.* ; Goodwin, M.* ; Hughes, D.A.* ; Boehnke, M.* ; Fernandes Silva, L.* ; Yin, X.* ; Mahajan, A.* ; Neville, M.J.* ; van Zuydam, N.R.* ; de Mutsert, R.* ; Li-Gao, R.* ; Mook-Kanamori, D.O.* ; Demirkan, A.* ; Liu, J.* ; Noordam, R.* ; Trompet, S.* ; Chen, Z.* ; Kartsonaki, C.* ; Li, L.* ; Lin, K.* ; Hagenbeek, F.A.* ; Hottenga, J.J.* ; Pool, R.* ; Ikram, M.A.* ; van Meurs, J.* ; Haller, T.* ; Milaneschi, Y.* ; Kähönen, M.* ; Mishra, P.P.* ; Joshi, P.K.* ; Macdonald-Dunlop, E.* ; Mangino, M.* ; Zierer, J.* ; Acar, I.E.* ; Hoyng, C.B.* ; Lechanteur, Y.T.E.* ; Franke, L.* ; Kurilshikov, A.* ; Zhernakova, A.* ; Beekman, M.* ; van den Akker, E.B.* ; Kolcic, I.* ; Polasek, O.* ; Rudan, I.* ; Gieger, C. ; Waldenberger, M. ; Asselbergs, F.W.* ; Hayward, C.* ; Fu, J.* ; den Hollander, A.I.* ; Menni, C.* ; Spector, T.D.* ; Wilson, J.F.* ; Lehtimäki, T.* ; Raitakari, O.T.* ; Penninx, B.W.J.H.* ; Esko, T.* ; Walters, R.G.* ; Jukema, J.W.* ; Sattar, N.* ; Ghanbari, M.* ; Willems van Dijk, K.* ; Karpe, F.* ; McCarthy, M.I.* ; Laakso, M.* ; Järvelin, M.R.* ; Timpson, N.J.* ; Perola, M.* ; Kooner, J.S.* ; Chambers, J.C.* ; van Duijn, C.M.* ; Slagboom, P.E.* ; Boomsma, D.I.* ; Danesh, J.* ; Ala-Korpela, M.* ; Butterworth, A.S.* ; Kettunen, J.*

Genome-wide characterization of circulating metabolic biomarkers.

Nature 628, 130-138 (2024)
DOI PMC
Creative Commons Lizenzvertrag
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Mendelian Randomization; Fatty-acids; Genetic Inhibition; Aging Research; Ketone-bodies; Association; Loci; Variants; Heart; Risk
ISSN (print) / ISBN 0028-0836
e-ISSN 1476-4687
Zeitschrift Nature
Quellenangaben Band: 628, Heft: 8006, Seiten: 130-138 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology II (EPI2)