Genome-wide characterization of circulating metabolic biomarkers
Minna K. Karjalainen, Savita Karthikeyan, Clare Oliver‐Williams, Eeva Sliz, Elias Allara, Wing Tung Fung, Praveen Surendran, Weihua Zhang, Pekka Jousilahti, Kati Kristiansson, Veikko Salomaa, Matt Goodwin, David A. Hughes, Michael Boehnke, Lilian Fernandes Silva, Xianyong Yin, Anubha Mahajan, Matt J. Neville, Natalie R. van Zuydam, Renée de Mutsert, Ruifang Li‐Gao, Dennis O. Mook‐Kanamori, Ayşe Demirkan, Jun Liu, Raymond Noordam, Stella Trompet, Zhengming Chen, Christiana Kartsonaki, Liming Li, Kuang Lin, Fiona A. Hagenbeek, Jouke‐Jan Hottenga, René Pool, M. Arfan Ikram, Joyce B. J. van Meurs, Toomas Haller, Yuri Milaneschi, Mika Kähönen, Pashupati P. Mishra, Peter K. Joshi, Erin Macdonald-Dunlop, Massimo Mangino, Jonas Zierer, İlhan E. Acar, Carel B. Hoyng, Yara Lechanteur, Lude Franke, Alexander Kurilshikov, Alexandra Zhernakova, Marian Beekman, Erik B. van den Akker, Ivana Kolčić, Ozren Polašek, Igor Rudan, Christian Gieger, Mélanie Waldenberger, Folkert W. Asselbergs, China Kadoorie Biobank Collaborative Group, FinnGen, Caroline Hayward, Jingyuan Fu, Anneke I. den Hollander, Cristina Menni, Tim D. Spector, James F. Wilson, Terho Lehtimäki, Olli T. Raitakari, Brenda W.J.H. Penninx, Tõnu Esko, Robin Walters, J. Wouter Jukema, Naveed Sattar, Mohsen Ghanbari, Ko Willems van Dijk, Fredrik Karpe, Mark I. McCarthy, Markku Laakso, Marjo‐Riitta Järvelin, Nicholas J. Timpson, Markus Perola, Jaspal S. Kooner, John C. Chambers, Cornelia M. van Duijn, P. Eline Slagboom, Dorret I. Boomsma, John Danesh, Mika Ala‐Korpela, Adam S. Butterworth, Johannes Kettunen
Abstract
Abstract Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism 1–7 . This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases 8–11 . 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.