Abstract
Original language | English |
---|---|
Article number | 39 |
Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | Nature communications |
Volume | 11 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2020 |
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In: Nature communications, Vol. 11, No. 1, 39, 01.12.2020, p. 1-11.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Heritability estimates for 361 blood metabolites across 40 genome-wide association studies
AU - Hagenbeek, Fiona A.
AU - Pool, René
AU - van Dongen, Jenny
AU - Draisma, Harmen H. M.
AU - Jan Hottenga, Jouke
AU - Willemsen, Gonneke
AU - Abdellaoui, Abdel
AU - Fedko, Iryna O.
AU - den Braber, Anouk
AU - Visser, Pieter Jelle
AU - de Geus, Eco J. C. N.
AU - Willems van Dijk, Ko
AU - Verhoeven, Aswin
AU - Suchiman, H. Eka
AU - Beekman, Marian
AU - Slagboom, P. Eline
AU - van Duijn, Cornelia M.
AU - Barkey Wolf, J. J. H.
AU - Cats, D.
AU - Amin, N.
AU - Beulens, J. W.
AU - van der Bom, J. A.
AU - Bomer, N.
AU - Demirkan, A.
AU - van Hilten, J. A.
AU - Meessen, J. M. T. A.
AU - Moed, M. H.
AU - Fu, J.
AU - Onderwater, G. L. J.
AU - Rutters, F.
AU - So-Osman, C.
AU - van der Flier, W. M.
AU - van der Heijden, A. A. W. A.
AU - van der Spek, A.
AU - Asselbergs, F. W.
AU - Boersma, E.
AU - Elders, P. M.
AU - Geleijnse, J. M.
AU - Ikram, M. A.
AU - Kloppenburg, M.
AU - Meulenbelt, I.
AU - Mooijaart, S. P.
AU - Nelissen, R. G. H. H.
AU - Netea, M. G.
AU - Penninx, B. W. J. H.
AU - Stehouwer, C. D. A.
AU - Teunissen, C. E.
AU - Terwindt, G. M.
AU - Zwinderman, A. H.
AU - BBMRI Metabolomics Consortium
AU - Zwinderman, A. H.
AU - Reinders, M. J. T.
AU - 't Hart, L. M.
AU - Reinders, M. J. T.
AU - Harms, Amy C
AU - Hankemeier, Thomas
AU - Bartels, Meike
AU - Nivard, Michel G
AU - Boomsma, Dorret I
N1 - Funding Information: We thank all twins and family members for their participation. We thank P.M. Visscher (University of Queensland) for his helpful comments and C.V. Dolan (Vrije Universiteit Amsterdam) for critically reading and commenting on the final version of the paper. Preliminary analyses of this paper were included in a presentation at the 46th Annual Meeting of the Behavioral Genetics Association (BGA) in June 2016 (abstract in Behav. Genet. (2016) 46:785–786), and a presentation at the 49th Annual Meeting of the BGA in June 2019 (abstract forthcoming). This work was performed within the framework of the BBMRI Metabolomics Consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO, nos. 184.021.007 and 184.033.111). The European Network of Genomic and Genetic Epidemiology (ENGAGE) contributed to funding to perform the Biocrates Absolute-IDQTM p150 metabolomics measurements (European Union Seventh Framework Program: FP7/2007–2013, grant number 201413). Analyses were supported by the Netherlands Organization for Scientific Research: Netherlands Twin Registry Repository: researching the interplay between genome and environment (480-15-001/674); the European Union Seventh Framework Program (FP7/ 2007–2013): ACTION Consortium (Aggression in Children: Unraveling gene–environment interplay to inform Treatment and InterventiON strategies; Grant number 602768). Genotyping was made possible by grants from NWO/SPI 56-464-14192, Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health, Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls (USA) and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995) and European Research Council (ERC-230374). EMIF-AD has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking EMIF grant agreement no. 115372. DIB acknowledges her KNAW Academy Professor Award (PAH/6635). M. Bartels is supported by an ERC consolidator grant (WELL-BEING 771057 PI Bartels). Jv.D. is supported by the NWO-funded X-omics project (184.034.019). Publisher Copyright: © 2020, The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2 total), and the proportion of heritability captured by known metabolite loci (h2 Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2 Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2 Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.
AB - Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2 total), and the proportion of heritability captured by known metabolite loci (h2 Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2 Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2 Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.
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U2 - https://doi.org/10.1038/s41467-019-13770-6
DO - https://doi.org/10.1038/s41467-019-13770-6
M3 - Article
C2 - 31911595
SN - 2041-1723
VL - 11
SP - 1
EP - 11
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 39
ER -