Genome-wide characterization of circulating metabolic biomarkers
Karjalainen, Minna K; Karthikeyan, Savita; Oliver-Williams, Clare; Sliz, Eeva; Allara, Elias; Fung, Wing Tung; Surendran, Praveen; Zhang, Weihua; Jousilahti, Pekka; Kristiansson, Kati; Salomaa, Veikko; Goodwin, Matt; Hughes, David A; Boehnke, Michael; Fernandes Silva, Lilian; Yin, Xianyong; Mahajan, Anubha; Neville, Matt J; van Zuydam, Natalie R; de Mutsert, Renée; Li-Gao, Ruifang; Mook-Kanamori, Dennis O; Demirkan, Ayse; Liu, Jun; Noordam, Raymond; Trompet, Stella; Chen, Zhengming; Kartsonaki, Christiana; Li, Liming; Lin, Kuang; Hagenbeek, Fiona A; Hottenga, Jouke Jan; Pool, René; Ikram, M Arfan; van Meurs, Joyce; Haller, Toomas; Milaneschi, Yuri; Kähönen, Mika; Mishra, Pashupati P; Joshi, Peter K; Macdonald-Dunlop, Erin; Mangino, Massimo; Zierer, Jonas; Acar, Ilhan E; Hoyng, Carel B; Lechanteur, Yara T E; Franke, Lude; Kurilshikov, Alexander; Zhernakova, Alexandra; Beekman, Marian; van den Akker, Erik B; Kolcic, Ivana; Polasek, Ozren; Rudan, Igor; Gieger, Christian; Waldenberger, Melanie; Asselbergs, Folkert W; China Kadoorie Biobank Collaborative Group; Estonian Biobank Research Team; FinnGen; Hayward, Caroline; Fu, Jingyuan; den Hollander, Anneke I; Menni, Cristina; Spector, Tim D; Wilson, James F; Lehtimäki, Terho; Raitakari, Olli T; Penninx, Brenda W J H; Esko, Tonu; Walters, Robin G; Jukema, J Wouter; Sattar, Naveed; Ghanbari, Mohsen; Willems van Dijk, Ko; Karpe, Fredrik; McCarthy, Mark I; Laakso, Markku; Järvelin, Marjo-Riitta; Timpson, Nicholas J; Perola, Markus; Kooner, Jaspal S; Chambers, John C; van Duijn, Cornelia; Slagboom, P Eline; Boomsma, Dorret I; Danesh, John; Ala-Korpela, Mika; Butterworth, Adam S; Kettunen, Johannes (2024-03-06)
Springer
06.03.2024
Karjalainen, M.K., Karthikeyan, S., Oliver-Williams, C. et al. Genome-wide characterization of circulating metabolic biomarkers. Nature 628, 130–138 (2024). https://doi.org/10.1038/s41586-024-07148-y
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© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404052571
https://urn.fi/URN:NBN:fi:oulu-202404052571
Tiivistelmä
Abstract
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8,9,10,11. 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.
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8,9,10,11. 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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