Cross-platform genetic discovery of small molecule products of metabolism and clinical outcomes

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Pan-Cohort metabolomics – The future of population health
Virtual event 2nd February 2020
Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Part 6:
Prof. Dr. Claudia Langenberg
MRC Epidemiology Unit, University of Cambridge, UK
Computational Medicine, Berlin Institute of Health (BIH), Germany

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p 4.9×10−10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a functional variant (rs17681684) in GLP2R associated with citrulline levels, impaired insulin secretion and type 2 diabetes risk. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 (odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10−30)). We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease (area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)), for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.



More information on the database Prof. Langenberg and colleagues created can be found here:
https://omicscience.org
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