Heritability Estimates for 361 Blood Metabolites Across 40 Genome-wide Association Studies

Overview

Journal Nat Commun

Specialty Biology

Date 2020 Jan 9

PMID 31911595

Citations 51

Authors

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

Affiliations

Soon will be listed here.

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

Citing Articles

Merging metabolomics and genomics provides a catalog of genetic factors that influence molecular phenotypes in pigs linking relevant metabolic pathways.

Bovo S, Ribani A, Fanelli F, Galimberti G, Martelli P, Trevisi P Genet Sel Evol. 2025; 57(1):11.

PMID: 40050712 PMC: 11887101. DOI: 10.1186/s12711-025-00960-8.

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits.

Akbary Moghaddam V, Acharya S, Schwaiger-Haber M, Liao S, Jung W, Thyagarajan B bioRxiv. 2025; .

PMID: 39896668 PMC: 11785221. DOI: 10.1101/2025.01.22.634403.

Genetic mapping of serum metabolome to chronic diseases among Han Chinese.

Cheng C, Xu F, Pan X, Wang C, Fan J, Yang Y Cell Genom. 2025; 5(2):100743.

PMID: 39837327 PMC: 11872534. DOI: 10.1016/j.xgen.2024.100743.

Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra.

Raffo M, Sarup P, Jensen J, Guo X, Jensen J, Orabi J Theor Appl Genet. 2025; 138(1):24.

PMID: 39786601 PMC: 11717810. DOI: 10.1007/s00122-024-04806-7.

Genome-wide association study reveals shared and distinct genetic architecture of fatty acids and oxylipins in the Hispanic Community Health Study/Study of Latinos.

Downie C, Highland H, Alotaibi M, Welch B, Howard A, Cheng S HGG Adv. 2024; 6(1):100390.

PMID: 39644095 PMC: 11751521. DOI: 10.1016/j.xhgg.2024.100390.

References

Willemsen G, Vink J, Abdellaoui A, den Braber A, van Beek J, Draisma H . The Adult Netherlands Twin Register: twenty-five years of survey and biological data collection. Twin Res Hum Genet. 2013; 16(1):271-81. PMC: 3739974. DOI: 10.1017/thg.2012.140. View

Fedko I, Hottenga J, Medina-Gomez C, Pappa I, van Beijsterveldt C, Ehli E . Estimation of Genetic Relationships Between Individuals Across Cohorts and Platforms: Application to Childhood Height. Behav Genet. 2015; 45(5):514-28. PMC: 4561077. DOI: 10.1007/s10519-015-9725-7. View

Boomsma D, de Geus E, Vink J, Stubbe J, Distel M, Hottenga J . Netherlands Twin Register: from twins to twin families. Twin Res Hum Genet. 2007; 9(6):849-57. DOI: 10.1375/183242706779462426. View

Wishart D, Djoumbou Feunang Y, Marcu A, Guo A, Liang K, Vazquez-Fresno R . HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2017; 46(D1):D608-D617. PMC: 5753273. DOI: 10.1093/nar/gkx1089. View

Haeussler M, Zweig A, Tyner C, Speir M, Rosenbloom K, Raney B . The UCSC Genome Browser database: 2019 update. Nucleic Acids Res. 2018; 47(D1):D853-D858. PMC: 6323953. DOI: 10.1093/nar/gky1095. View

Tukiainen T, Kettunen J, Kangas A, Lyytikainen L, Soininen P, Sarin A . Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci. Hum Mol Genet. 2011; 21(6):1444-55. DOI: 10.1093/hmg/ddr581. View

Willemsen G, de Geus E, Bartels M, van Beijsterveldt C, Brooks A, Estourgie-van Burk G . The Netherlands Twin Register biobank: a resource for genetic epidemiological studies. Twin Res Hum Genet. 2010; 13(3):231-45. DOI: 10.1375/twin.13.3.231. View

Kim S, Chen J, Cheng T, Gindulyte A, He J, He S . PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2018; 47(D1):D1102-D1109. PMC: 6324075. DOI: 10.1093/nar/gky1033. View

Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen A, Lee S . Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015; 47(10):1114-20. PMC: 4589513. DOI: 10.1038/ng.3390. View

10.

Chaleckis R, Murakami I, Takada J, Kondoh H, Yanagida M . Individual variability in human blood metabolites identifies age-related differences. Proc Natl Acad Sci U S A. 2016; 113(16):4252-9. PMC: 4843419. DOI: 10.1073/pnas.1603023113. View

11.

Yousri N, Fakhro K, Robay A, Rodriguez-Flores J, Mohney R, Zeriri H . Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population. Nat Commun. 2018; 9(1):333. PMC: 5780481. DOI: 10.1038/s41467-017-01972-9. View

12.

Nedic Erjavec G, Konjevod M, Nikolac Perkovic M, Strac D, Tudor L, Barbas C . Short overview on metabolomic approach and redox changes in psychiatric disorders. Redox Biol. 2017; 14:178-186. PMC: 5609866. DOI: 10.1016/j.redox.2017.09.002. View

13.

Sherry S, Ward M, Kholodov M, Baker J, Phan L, Smigielski E . dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2000; 29(1):308-11. PMC: 29783. DOI: 10.1093/nar/29.1.308. View

14.

Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V . ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Res. 2015; 44(D1):D1214-9. PMC: 4702775. DOI: 10.1093/nar/gkv1031. View

15.

Das S, Forer L, Schonherr S, Sidore C, Locke A, Kwong A . Next-generation genotype imputation service and methods. Nat Genet. 2016; 48(10):1284-1287. PMC: 5157836. DOI: 10.1038/ng.3656. View

16.

Bellis C, Kulkarni H, Mamtani M, Kent Jr J, Wong G, Weir J . Human plasma lipidome is pleiotropically associated with cardiovascular risk factors and death. Circ Cardiovasc Genet. 2014; 7(6):854-863. PMC: 4270876. DOI: 10.1161/CIRCGENETICS.114.000600. View

17.

Liu E, Li M, Wang W, Li Y . MaCH-admix: genotype imputation for admixed populations. Genet Epidemiol. 2012; 37(1):25-37. PMC: 3524415. DOI: 10.1002/gepi.21690. View

18.

Zuk O, Hechter E, Sunyaev S, Lander E . The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012; 109(4):1193-8. PMC: 3268279. DOI: 10.1073/pnas.1119675109. View

19.

Tsepilov Y, Shin S, Soranzo N, Spector T, Prehn C, Adamski J . Nonadditive Effects of Genes in Human Metabolomics. Genetics. 2015; 200(3):707-18. PMC: 4512538. DOI: 10.1534/genetics.115.175760. View

20.

Patti G, Yanes O, Siuzdak G . Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012; 13(4):263-9. PMC: 3682684. DOI: 10.1038/nrm3314. View