Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

Yurong Cheng, Pascal Schlosser, Johannes Hertel, Peggy Sekula, Peter J. Oefner, Ute Spiekerkoetter, Johanna Mielke, Daniel F. Freitag, Miriam Schmidts, Florian Kronenberg, Kai-Uwe Eckardt, Ines Thiele, Yong Li and Anna Köttgen ()
Additional contact information
Yurong Cheng: Faculty of Medicine and Medical Center - University of Freiburg
Pascal Schlosser: Faculty of Medicine and Medical Center - University of Freiburg
Johannes Hertel: School of Medicine, National University of Ireland, Galway
Peggy Sekula: Faculty of Medicine and Medical Center - University of Freiburg
Peter J. Oefner: University of Regensburg
Ute Spiekerkoetter: Medical Center and Faculty of Medicine - University of Freiburg
Johanna Mielke: Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies
Daniel F. Freitag: Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies
Miriam Schmidts: Medical Center and Faculty of Medicine - University of Freiburg
Florian Kronenberg: Medical University of Innsbruck
Kai-Uwe Eckardt: University of Erlangen-Nürnberg
Ines Thiele: School of Medicine, National University of Ireland, Galway
Yong Li: Faculty of Medicine and Medical Center - University of Freiburg
Anna Köttgen: Faculty of Medicine and Medical Center - University of Freiburg

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: Abstract Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e−7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-20877-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20877-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-20877-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().