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Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine

Reyhan Sönmez Flitman, Bita Khalili, Zoltan Kutalik, Rico Rueedi, Sven Bergmann
doi: https://doi.org/10.1101/2020.05.22.110197
Reyhan Sönmez Flitman
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
3Swiss Institute of Bioinformatics, Lausanne, Switzerland
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  • For correspondence: reyhan.sonmez@unil.ch sven.bergmann@unil.ch
Bita Khalili
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
3Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Zoltan Kutalik
2University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland
3Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Rico Rueedi
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
3Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Sven Bergmann
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
3Swiss Institute of Bioinformatics, Lausanne, Switzerland
4Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
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  • For correspondence: reyhan.sonmez@unil.ch sven.bergmann@unil.ch
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Summary

In this study we investigate the results of a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome. We used RNAseq data from lymphoblastoid cell lines (LCLs) derived from 555 Caucasian individuals to characterize their transcriptome. As for the metabolome we took an untargeted approach using binned features from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples from the same subjects allowing for data-driven discovery of associated compounds (rather than working with a limited set of quantified metabolites).

Using pairwise linear regression we identified 21 study-wide significant associations between metabolome features and gene expression levels. We observed the most significant association between the gene ALMS1 and two adjacent metabolome features at 2.0325 and 2.0375 ppm. By using our previously developed metabomatching methodology, we found N-Acetylaspartate (NAA) as the potential underlying metabolite whose urine concentration is correlated with ALMS1 expression. Indeed, a number of metabolome- and genome-wide association studies (mGWAS) had already suggested the locus of this gene to be involved in regulation of N-acetylated compounds, yet were not able to identify unambiguously the exact metabolite, nor to disambiguate between ALMS1 and NAT8, another gene found in the same locus as the mediator gene. The second highest significant association was observed between HPS1 and two metabolome features at 2.8575 and 2.8725 ppm. Metabomatching of the association profile of HPS1 with all metabolite features pointed at trimethylamine (TMA) as the most likely underlying metabolite. mGWAS had previously implicated a locus containing HPS1 to be associated with TMA concentrations in urine but could not disambiguate this association signal from PYROXD2, a gene in the same locus. We used Mendelian randomization to show for both ALMS1 and HPS1 that their expression is causally linked to the respective metabolite concentrations.

Our study provides evidence that the integration of metabolomics with gene expression data can support mQTL analysis, helping to identify the most likely gene involved in the modulation of the metabolite concentration.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 25, 2020.
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Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine
Reyhan Sönmez Flitman, Bita Khalili, Zoltan Kutalik, Rico Rueedi, Sven Bergmann
bioRxiv 2020.05.22.110197; doi: https://doi.org/10.1101/2020.05.22.110197
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Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine
Reyhan Sönmez Flitman, Bita Khalili, Zoltan Kutalik, Rico Rueedi, Sven Bergmann
bioRxiv 2020.05.22.110197; doi: https://doi.org/10.1101/2020.05.22.110197

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