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Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Research output: Contribution to journalArticle

Idil Yet, Cristina Menni, So-Youn Shin, Massimo Mangino, Nicole Soranzo, Jerzy Adamski, Karsten Suhre, Tim D Spector, Gabi Kastenmüller, Jordana T Bell

Original languageEnglish
Article numbere0153672
JournalPLoS ONE
Volume11
Issue number4
DOIs
Publication statusPublished - 13 Apr 2016

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  • journal.pone.0153672

    journal.pone.0153672.PDF, 493 KB, application/pdf

    27/04/2016

    Final published version

    CC BY

King's Authors

Abstract

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.

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