Dataset Information

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms.

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.

SUBMITTER: Yet I

PROVIDER: S-EPMC4830611 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms.

Yet Idil I Menni Cristina C Shin So-Youn SY Mangino Massimo M Soranzo Nicole N Adamski Jerzy J Suhre Karsten K Spector Tim D TD Kastenmüller Gabi G Bell Jordana T JT

PloS one 20160413 4

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 ...[more]

PMID: 27073872

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Project description:We compared the performance of two commonly used genotyping platforms, genotyping-by-sequencing (GBS) and single nucleotide polymorphism-arrays (SNP), to investigate the extent and pattern of genetic variation within a collection of 1,000 diverse barley genotypes selected from the German Federal ex situ GenBank hosted at IPK Gatersleben. Each platform revealed equivalent numbers of robust bi-allelic SNPs (39,733 and 37,930 SNPs for the 50K SNP-array and GBS datasets respectively). A small overlap of 464 SNPs was common to both platforms, indicating that the methodologies we used selectively access informative polymorphism in different portions of the barley genome. Approximately half of the GBS dataset was comprised of SNPs with minor allele frequencies (MAFs) below 1%, illustrating the power of GBS to detect rare alleles in diverse germplasm collections. While desired for certain applications, the highly robust calling of alleles at the same SNPs across multiple populations is an advantage of the SNP-array, allowing direct comparisons of data from related or unrelated studies. Overall MAFs and diversity statistics (π) were higher for the SNP-array data, potentially reflecting the conscious removal of markers with a low MAF in the ascertainment population. A comparison of similarity matrices revealed a positive correlation between both approaches, supporting the validity of using either for entire GenBank characterization. To explore the potential of each dataset for focused genetic analyses we explored the outcomes of their use in genome-wide association scans for row type, growth habit and non-adhering hull, and discriminant analysis of principal components for the drivers of sub-population differentiation. Interpretation of the results from both types of analysis yielded broadly similar conclusions indicating that choice of platform used for such analyses should be determined by the research question being asked, group preferences and their capabilities to extract and interpret the different types of output data easily and quickly. Access to the requisite infrastructure for running, processing, analyzing, querying, storing, and displaying either datatype is an additional consideration. Our investigations reveal that for barley the cost per genotyping assay is less for SNP-arrays than GBS, which translates to a cost per informative datapoint being significantly lower for the SNP-array.

Dataset Information

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms.

Publications

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms.

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