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

Fiona A. Hagenbeek; Rene Pool; Jenny van Dongen; Harmen H. M. Draisma; Jouke Jan Hottenga; Gonneke Willemsen; Abdel Abdellaoui; Iryna O. Fedko; Anouk den Braber; Pieter Jelle Visser; Eco J. C. N. de Geus; Ko Willems van Dijk; Aswin Verhoeven; H. Eka Suchiman; Marian Beekman; P. Eline Slagboom; Cornelia M. van Duijn; Amy C. Harms; Thomas Hankemeier; Meike Bartels; Michel G. Nivard; Dorret I. Boomsma; J. J. H. Barkey Wolf; D. Cats; N. Amin; J. W. Beulens; J. A. van der Bom; N. Bomer; A. Demirkan; J. A. van Hilten; J. M. T. A. Meessen; M. H. Moed; J. Fu; G. L. J. Onderwater; F. Rutters; C. So-Osman; W. M. van der Flier; A. A. W. A. van der Heijden; A. van der Spek; F. W. Asselbergs; E. Boersma; P. M. Elders; J. M. Geleijnse; R. G. H. H. Nelissen; C. D. A. Stehouwer; C. E. Teunissen; I. C. C. van der Horst; C. J. H. van der Kallen; M. M. J. van Greevenbroek; M. J. T. Reinders; 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^*, Rene Pool, Jenny van Dongen, Harmen H. M. Draisma, Jouke Jan Hottenga, Gonneke Willemsen, Abdel Abdellaoui, Iryna O. Fedko, Anouk den Braber, Pieter Jelle Visser, Eco J. C. N. de Geus, Ko Willems van Dijk, Aswin Verhoeven, H. Eka Suchiman, Marian Beekman, P. Eline Slagboom, Cornelia M. van Duijn, Amy C. Harms, Thomas Hankemeier, Meike BartelsMichel G. Nivard^*, Dorret I. Boomsma^*, J. J. H. Barkey Wolf, D. Cats, N. Amin, J. W. Beulens, J. A. van der Bom, N. Bomer, A. Demirkan, J. A. van Hilten, J. M. T. A. Meessen, M. H. Moed, J. Fu, G. L. J. Onderwater, F. Rutters, C. So-Osman, W. M. van der Flier, A. A. W. A. van der Heijden, A. van der Spek, F. W. Asselbergs, E. Boersma, P. M. Elders, J. M. Geleijnse, R. G. H. H. Nelissen, C. D. A. Stehouwer, C. E. Teunissen, I. C. C. van der Horst, C. J. H. van der Kallen, M. M. J. van Greevenbroek, M. J. T. Reinders, BBMRI Metabolomics Consortium

^*Corresponding author for this work

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 (h(total)(2)), and the proportion of heritability captured by known metabolite loci (h(Metabolite-hits)(2)) for 309 lipids and 52 organic acids. Our study reveals significant differences in h(Metabolite-hits)(2) among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h(Metabolite-hits)(2) 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
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

Keywords

NETHERLANDS TWIN REGISTER
MISSING HERITABILITY
GENOTYPE IMPUTATION
VARIANCE-ESTIMATION
METABOLOMICS
SELECTION
DATABASE
BIOBANK

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1 Erratum / corrigendum / retractions

Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies
Stehouwer, C., van der Kallen, C., van Greevenbroek, M. & BBMRI Metabolomics Consortium, 31 Mar 2020, In: Nature Communications. 11, 1, 1 p., 1702.
Research output: Contribution to journal › Erratum / corrigendum / retractions › Academic

Open Access

Cite this

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., Visser, P. J., de Geus, E. J. C. N., Willems van Dijk, K., Verhoeven, A., Suchiman, H. E., Beekman, M., Slagboom, P. E., van Duijn, C. M., Harms, A. C., Hankemeier, T., ... BBMRI Metabolomics Consortium (2020). Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nature Communications, 11(1), Article 39. https://doi.org/10.1038/s41467-019-13770-6

@article{c340eb69fc024639a40db44f4d3116e9,

title = "Heritability estimates for 361 blood metabolites across 40 genome-wide association studies",

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 (h(total)(2)), and the proportion of heritability captured by known metabolite loci (h(Metabolite-hits)(2)) for 309 lipids and 52 organic acids. Our study reveals significant differences in h(Metabolite-hits)(2) among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h(Metabolite-hits)(2) 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.",

keywords = "NETHERLANDS TWIN REGISTER, MISSING HERITABILITY, GENOTYPE IMPUTATION, VARIANCE-ESTIMATION, METABOLOMICS, SELECTION, DATABASE, BIOBANK",

author = "Hagenbeek, \{Fiona A.\} and Rene Pool and \{van Dongen\}, Jenny and Draisma, \{Harmen H. M.\} and \{Jan Hottenga\}, Jouke and Gonneke Willemsen and Abdel Abdellaoui and Fedko, \{Iryna O.\} and \{den Braber\}, Anouk and Visser, \{Pieter Jelle\} and \{de Geus\}, \{Eco J. C. N.\} and \{Willems van Dijk\}, Ko and Aswin Verhoeven and Suchiman, \{H. Eka\} and Marian Beekman and Slagboom, \{P. Eline\} and \{van Duijn\}, \{Cornelia M.\} and Harms, \{Amy C.\} and Thomas Hankemeier and Meike Bartels and Nivard, \{Michel G.\} and Boomsma, \{Dorret I.\} and Wolf, \{J. J. H. Barkey\} and D. Cats and N. Amin and Beulens, \{J. W.\} and \{van der Bom\}, \{J. A.\} and N. Bomer and A. Demirkan and \{van Hilten\}, \{J. A.\} and Meessen, \{J. M. T. A.\} and Moed, \{M. H.\} and J. Fu and Onderwater, \{G. L. J.\} and F. Rutters and C. So-Osman and \{van der Flier\}, \{W. M.\} and \{van der Heijden\}, \{A. A. W. A.\} and \{van der Spek\}, A. and Asselbergs, \{F. W.\} and E. Boersma and Elders, \{P. M.\} and Geleijnse, \{J. M.\} and Nelissen, \{R. G. H. H.\} and Stehouwer, \{C. D. A.\} and Teunissen, \{C. E.\} and \{van der Horst\}, \{I. C. C.\} and \{van der Kallen\}, \{C. J. H.\} and \{van Greevenbroek\}, \{M. M. J.\} and Reinders, \{M. J. T.\} and \{BBMRI Metabolomics Consortium\}",

note = "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: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = jan,

day = "7",

doi = "10.1038/s41467-019-13770-6",

language = "English",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

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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, Visser, PJ, de Geus, EJCN, Willems van Dijk, K, Verhoeven, A, Suchiman, HE, Beekman, M, Slagboom, PE, van Duijn, CM, Harms, AC, Hankemeier, T, Bartels, M, Nivard, MG, Boomsma, DI, Wolf, JJHB, Cats, D, Amin, N, Beulens, JW, van der Bom, JA, Bomer, N, Demirkan, A, van Hilten, JA, Meessen, JMTA, Moed, MH, Fu, J, Onderwater, GLJ, Rutters, F, So-Osman, C, van der Flier, WM, van der Heijden, AAWA, van der Spek, A, Asselbergs, FW, Boersma, E, Elders, PM, Geleijnse, JM, Nelissen, RGHH, Stehouwer, CDA, Teunissen, CE, van der Horst, ICC , van der Kallen, CJH , van Greevenbroek, MMJ, Reinders, MJT & BBMRI Metabolomics Consortium 2020, 'Heritability estimates for 361 blood metabolites across 40 genome-wide association studies', Nature Communications, vol. 11, no. 1, 39. https://doi.org/10.1038/s41467-019-13770-6

TY - JOUR

T1 - Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

AU - Hagenbeek, Fiona A.

AU - Pool, Rene

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 - Harms, Amy C.

AU - Hankemeier, Thomas

AU - Bartels, Meike

AU - Nivard, Michel G.

AU - Boomsma, Dorret I.

AU - Wolf, J. J. H. Barkey

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 - Nelissen, R. G. H. H.

AU - Stehouwer, C. D. A.

AU - Teunissen, C. E.

AU - van der Horst, I. C. C.

AU - van der Kallen, C. J. H.

AU - van Greevenbroek, M. M. J.

AU - Reinders, M. J. T.

AU - BBMRI Metabolomics Consortium

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).

PY - 2020/1/7

Y1 - 2020/1/7

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 (h(total)(2)), and the proportion of heritability captured by known metabolite loci (h(Metabolite-hits)(2)) for 309 lipids and 52 organic acids. Our study reveals significant differences in h(Metabolite-hits)(2) among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h(Metabolite-hits)(2) 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 (h(total)(2)), and the proportion of heritability captured by known metabolite loci (h(Metabolite-hits)(2)) for 309 lipids and 52 organic acids. Our study reveals significant differences in h(Metabolite-hits)(2) among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h(Metabolite-hits)(2) 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.

KW - NETHERLANDS TWIN REGISTER

KW - MISSING HERITABILITY

KW - GENOTYPE IMPUTATION

KW - VARIANCE-ESTIMATION

KW - METABOLOMICS

KW - SELECTION

KW - DATABASE

KW - BIOBANK

U2 - 10.1038/s41467-019-13770-6

DO - 10.1038/s41467-019-13770-6

M3 - Article

C2 - 31911595

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 39

ER -

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

Abstract

Keywords

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

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