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Genome-wide characterization of circulating metabolic biomarkers

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Title: Genome-wide characterization of circulating metabolic biomarkers
Authors: Karjalainen, MK, Karthikeyan, S, Oliver-Williams, C, Sliz, E, Allara, E, Fung, WT, Surendran, P, Zhang, W, Jousilahti, P, Kristiansson, K, Salomaa, V, Goodwin, M, Hughes, DA, Boehnke, M, Fernandes Silva, L, Yin, X, Mahajan, A, Neville, MJ, van Zuydam, NR, de Mutsert, R, Li-Gao, R, Mook-Kanamori, DO, Demirkan, A, Liu, J, Noordam, R, Trompet, S, Chen, Z, Kartsonaki, C, Li, L, Lin, K, Hagenbeek, FA, Hottenga, JJ, Pool, R, Ikram, MA, van Meurs, J, Haller, T, Milaneschi, Y, Kähönen, M, Mishra, PP, Joshi, PK, Macdonald-Dunlop, E, Mangino, M, Zierer, J, Acar, IE, Hoyng, CB, Lechanteur, YTE, Franke, L, Kurilshikov, A, Zhernakova, A, Beekman, M, Walters, RG, Karpe, F, McCarthy, MI, van Duijn, C, Kettunen, J
Publisher Information: Springer Nature
Publication Year: 2024
Collection: Oxford University Research Archive (ORA)
Description: Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases. 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.
Document Type: article in journal/newspaper
Language: English
DOI: 10.1038/s41586-024-07148-y
Availability: https://doi.org/10.1038/s41586-024-07148-y
https://ora.ox.ac.uk/objects/uuid:2fd4aff0-4c63-4370-89d0-5a5020e0c452
Rights: info:eu-repo/semantics/openAccess ; CC Attribution (CC BY)
Accession Number: edsbas.70052ACF
Database: BASE
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  – Url: https://doi.org/10.1038/s41586-024-07148-y#
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  Data: Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases. 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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