Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

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 metab...

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Main Authors:	Yet, Idil, Menni, Cristina, Shin, So-Youn, Mangino, Massimo, Soranzo, Nicole, Adamski, Jerzy, Suhre, Karsten, Spector, Tim D., Kastenmüller, Gabi, Bell, Jordana T.
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830611/

id	pubmed-4830611
recordtype	oai_dc
spelling	pubmed-48306112016-04-22 Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms Yet, Idil Menni, Cristina Shin, So-Youn Mangino, Massimo Soranzo, Nicole Adamski, Jerzy Suhre, Karsten Spector, Tim D. Kastenmüller, Gabi Bell, Jordana T. Research Article 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. Public Library of Science 2016-04-13 /pmc/articles/PMC4830611/ /pubmed/27073872 http://dx.doi.org/10.1371/journal.pone.0153672 Text en © 2016 Yet et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Yet, Idil Menni, Cristina Shin, So-Youn Mangino, Massimo Soranzo, Nicole Adamski, Jerzy Suhre, Karsten Spector, Tim D. Kastenmüller, Gabi Bell, Jordana T.
spellingShingle	Yet, Idil Menni, Cristina Shin, So-Youn Mangino, Massimo Soranzo, Nicole Adamski, Jerzy Suhre, Karsten Spector, Tim D. Kastenmüller, Gabi Bell, Jordana T. Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
author_facet	Yet, Idil Menni, Cristina Shin, So-Youn Mangino, Massimo Soranzo, Nicole Adamski, Jerzy Suhre, Karsten Spector, Tim D. Kastenmüller, Gabi Bell, Jordana T.
author_sort	Yet, Idil
title	Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
title_short	Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
title_full	Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
title_fullStr	Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
title_full_unstemmed	Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
title_sort	genetic influences on metabolite levels: a comparison across metabolomic platforms
description	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.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830611/
_version_	1613565857400619008

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

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