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      MWASTools: an R/bioconductor package for metabolome-wide association studies

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          Abstract

          Summary

          MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results.

          Availability and implementation

          The MWASTools R package is implemented in R (version  > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references6

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

          Yoav Benjamini, Yosef Hochberg (1995)
          Article 0 comments Cited 23615 times – based on 0 reviews     Review now
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            Metabonomics: a platform for studying drug toxicity and gene function.

            Jeremy Nicholson, John C Connelly, John C. Lindon … (2002)
            The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.
            Article 0 comments Cited 309 times – based on 0 reviews     Review now
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              Human metabolic phenotype diversity and its association with diet and blood pressure.

              Elaine Holmes, Ruey Leng Loo, Jeremiah Stamler … (2008)
              Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.
              Article 0 comments Cited 303 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Jonathan Wren: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 March 2018
                Publication date (Electronic): 26 July 2017
                Publication date PMC-release: 26 July 2017
                Volume: 34
                Issue: 5
                Pages: 890-892
                Affiliations
                [1 ]Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
                [2 ]Division of Myocardial Function, National Heart and Lung Institute, Imperial College London, UK
                [3 ]Duke-NUS Medical School, Singapore
                [4 ]Bristol Heart Institute, University of Bristol, UK
                Author notes
                To whom correspondence should be addressed. Email: m.dumas@ 123456imperial.ac.uk
                Author information
                http://orcid.org/0000-0002-4971-9003
                http://orcid.org/0000-0001-9523-7024
                Article
                Publisher ID: btx477
                DOI: 10.1093/bioinformatics/btx477
                PMC ID: 6049002
                PubMed ID: 28961702
                SO-VID: 40111e45-2620-40ca-81db-e34b5aed5ddc
                Copyright © © The Author 2017. Published by Oxford University Press.
                License:

                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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 27 February 2017
                Date revision received : 22 June 2017
                Date accepted : 24 July 2017
                Page count
                Pages: 3
                Funding
                Funded by: FCT 10.13039/100006129
                Award ID: BD/52036/2012
                Funded by: British Heart Foundation 10.13039/501100000274
                Award ID: RG/15/5/31446
                Funded by: BHF 10.13039/501100000274
                Award ID: CH/15/31199
                Funded by: European Commission 10.13039/501100000780
                Award ID: LSHG-CT-2006-037683
                Categories
                Subject: Applications Notes
                Subject: Systems Biology

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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