Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Fiona A Hagenbeek; René Pool; Jenny van Dongen; Harmen H M Draisma; Jouke Jan Hottenga; Gonneke Willemsen; Abdel Abdellaoui; Iryna O Fedko; Anouk den Braber; Eco J C N de Geus; Meike Bartels; Michel G Nivard; Dorret I Boomsma; BBMRI Metabolomics Consortium

doi:10.1038/s41467-019-13770-6

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Fiona A Hagenbeek, René Pool, Jenny van Dongen, Harmen H M Draisma, Jouke Jan Hottenga, Gonneke Willemsen, Abdel Abdellaoui, Iryna O Fedko, Anouk den Braber, Eco J C N de Geus, Meike Bartels, Michel G Nivard, Dorret I Boomsma, BBMRI Metabolomics Consortium

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

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 (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-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.

Original language	English
Article number	39
Pages (from-to)	1-11
Number of pages	11
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-13770-6
Publication status	Published - 7 Jan 2020

Funding

We thank J.N. Vai\u00F1a, MSc, for excellent technical assistance, and Dr. K. Deisseroth for the generous sharing of tools and ChAT-cre rats. H.D.M. received funding for this work from the Netherlands Organization for Scientific Research (NWO; VICI grant 865.13.002), ERC StG \u201CBrainSignals,\u201D EU H2020 Framework Programme (Grant Agreement H2020 HBP 720270), and European Union\u2019s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and EraNet Neuron/NWO. C.P.J.d.K. received funding for this work from the Netherlands Organization for Scientific Research (NWO ALW #822.02.013). This manuscript is an outcome of efforts over three decades of hundreds of researchers across Amazonia as well as the sustained support of rural communities and Amazonian institutions and their funding agencies that make the RAINFOR network possible. We thank the following individuals in particular for their contribution to the long-term monitoring of South American forests: Alwyn Gentry, Sandra Pati\u00F1o, Samuel Almeida, Miguel Alexiades, Olaf Banki, Desmo Betian, Vincent Bezard, Ezequiel Chavez, Ren\u00E9 Guillen Villaroel, Antonio S. Lima, Irina Mendoza Polo, Petrus Naisso, Atila Alves de Oliveira, Alexander Parada Gutierrez, Jean Pierre Veillon, Freddy Ramirez Arevalo and Marc Steininger. The analysis undertaken here was largely funded by the NERC-funded TREMOR project (NE/N004655/1) to D.G., R.J.W.B., E.G. and O.L.P. A.E.-M. was funded by TREMOR and by two ERC awards (T-FORCES 291585, TreeMort 758873). D.G. acknowledges further support from a Newton-funded consortium award (ARBOLES, NE/S011811/1). O.L.P. was supported by an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award. T.A.M.P. was funded by the ERC award TreeMort 758873. This is paper number 47 of the Birmingham Institute of Forest Research. T.R.F., L.E.O.C.A. and O.L.P. were supported by NERC NE/N011570/1. Support for RAINFOR has come from the Natural Environment Research Council (NERC) Urgency Grants and NERC Consortium Grants AMAZONICA (NE/F005806/ 1), TROBIT (NE/D005590/1) and BIO-RED (NE/N012542/1), a European Research Council (ERC) grant T-FORCES (291585), the Gordon and Betty Moore Foundation (#1656), the European Union\u2019s Seventh Framework Programme (282664, AMAZA-LERT) and the Royal Society (CH160091). This is paper #47 of the Birmingham Institute of Forest Research (BIFoR). A.E.-M. thanks LES female writing group. Data from RAINFOR and other tropical networks are stored and curated by ForestPlots.net, a collaborative cyber-infrastructure initiative developed at the University of Leeds that unites permanent plot records and their contributing scientists from the world\u2019s tropical forests. All the bodies listed above have funded the development of ForestPlots.net and curation of data analysed here. This work is an outcome of the ForestPlots.net approved research project #8. We thank all organizers of the 2015 segmentation challenges who are not co-authoring this paper (a list is provided as Supplementary Note 3) and all participants of the international questionnaire (a list is provided as Supplementary Note 4). We further thank Angelika Laha, Diana Mindroc-Filimon, B\u00FCnyamin Pekdemir, and Jenshika Yoganathan (DKFZ, Germany) for helping with the comprehensive challenge capturing. Many thanks also go to Janina Dunning and Stefanie Strzysch (DKFZ, Germany) for their support of the project. Finally, we acknowledge support from the European Research Council (ERC) (ERC starting grant COMBIOSCOPY under the New Horizon Framework Programme grant agreement ERC-2015-StG-37960 as well as Seventh Framework Programme (FP7/2007-2013) under grant agreement no 318068 (VISCERAL)), the German Research Foundation (DFG) (grant MA 6340/10-1 and grant MA 6340/12-1), the Ministry of Science and Technology, Taiwan (MOST 106-3114-8-011-002, 106-2622-8-011-001-TE2, and 105-2221-E-011-121-MY2), the US National Institute of Health (NIH) (grants R01-NS070906, RG-1507-05243, and R01-EB017230 (NIBIB)), the Australian Research Council (DP140102794 and FT110100623), the Swiss National Science Foundation (grant 205321_157207), the Czech Science Foundation (grant P302/ 12/G157), the Czech Ministry of Education, Youth and Sports (grant LTC17016 in the frame of EU COST NEUBIAS project), the Engineering and Physical Sciences Research Council (EPSRC) (MedIAN UK Network (EP/N026993/1) and EP/P012841/1), the Wellcome Trust (NS/A000050/1), the Canadian Natural Science and Engineering Research Council (RGPIN-2015-05471), the UK Medical Research Council (MR/ P015476/1), and the Heidelberg Collaboratory for Image Processing (HCI) including matching funds from the industry partners of the HCI. 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\u2013786), 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\u20132013, 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\u20132013): ACTION Consortium (Aggression in Children: Unraveling gene\u2013environment 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). This work was funded by The Netherlands Organization for Scientific Research (NWO VICI 453-14-005). We thank the GIANT consortium, CARdioGRAMplusc4D, SSGAC, IGAP, ENIGMA, and PGC for providing GWAS summary statistics and GTEx Portal for RNA-seq data. We also thank Tabula Muris consortium and other individual groups for making scRNA-seq datasets publicly available. We thank all consortiums and all other individual laboratories for making GWAS summary statistics publicly available. We also thank P. Visscher and N. Wray for their thoughtful suggestions and discussions. We additionally thank A. Dale for his suggestions. This work was funded by the Netherlands Organization for Scientific Research (grant nos. NWO VICI 453-14-005 and NWO VIDI 452-12-014).

Funders	Funder number
Avera Institute
Dutch Government	184.033.111, 184.021.007
ERC Consolidator	WELL-BEING 771057
EU/EFPIA Innovative Medicines Initiative Joint Undertaking EMIF	115372
European Network of Genomic and Genetic Epidemiology
European Union Seventh Framework Program
FP7/2007	201413
NIMH U24 MH068457-06	U24 MH068457-06
NWO-funded	184.034.019
Netherlands Organization for Scientific Research	602768, FP7/ 2007–2013, 480-15-001/674, NWO/SPI 56-464-14192
National Institutes of Health	1RC2 MH089995, MH081802, R01 HD042157-01A1
Foundation for the National Institutes of Health
National Institute of Mental Health	RC2MH089951
European Research Council	ERC-230374

Cohort Studies

Netherlands Twin Register (NTR)

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Hagenbeek, F. A., Pool, R., van Dongen, J., Draisma, H. H. M., Jan Hottenga, J., Willemsen, G., Abdellaoui, A., Fedko, I. O., den Braber, A., de Geus, E. J. C. N., Bartels, M., Nivard, M. G., Boomsma, D. I., & BBMRI Metabolomics Consortium (2020). Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nature Communications, 11(1), 1-11. Article 39. https://doi.org/10.1038/s41467-019-13770-6

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abstract = "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 (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-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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Hagenbeek, FA , Pool, R , van Dongen, J, Draisma, HHM, Jan Hottenga, J , Willemsen, G, Abdellaoui, A, Fedko, IO, den Braber, A , de Geus, EJCN , Bartels, M , Nivard, MG , Boomsma, DI & BBMRI Metabolomics Consortium 2020, 'Heritability estimates for 361 blood metabolites across 40 genome-wide association studies', Nature Communications, vol. 11, no. 1, 39, pp. 1-11. https://doi.org/10.1038/s41467-019-13770-6

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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 (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-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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Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

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