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

Journal Article (2019)
Author(s)

Fiona A. Hagenbeek (Amsterdam Public Health, Vrije Universiteit Amsterdam)

N. Bomer (University Medical Center Groningen)

J. A. van Hilten (Sanquin Research)

J. Fu (University Medical Center Groningen)

A. A.W.A. van der Heijden (Amsterdam UMC)

A. van der Spek (Erasmus MC)

E. Boersma (Erasmus MC, Thorax Centre)

E. B. van den Akker (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)

M. J.T. Reinders (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

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Contributor(s)

Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.1038/s41467-019-13770-6
More Info
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Publication Year
2019
Language
English
Research Group
Pattern Recognition and Bioinformatics
Journal title
Nature Communications
Issue number
1
Volume number
11
Article number
39
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314
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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 (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.