Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification.

TitleMetabolic profiling and the metabolome-wide association study: significance level for biomarker identification.
Publication TypeJournal Article
Year of Publication2010
AuthorsChadeau-Hyam M, Ebbels TMD, Brown IJ, Chan Q, Stamler J, Huang CChing, Daviglus ML, Ueshima H, Zhao L, Holmes E, Nicholson JK, Elliott P, De Iorio M
JournalJ Proteome Res
Volume9
Issue9
Pagination4620-7
Date Published2010 Sep 3
ISSN1535-3907
KeywordsBiological Markers, Cluster Analysis, Computer Simulation, High-Throughput Screening Assays, Humans, Least-Squares Analysis, Metabolome, Metabolomics, Models, Biological, Nuclear Magnetic Resonance, Biomolecular, Reference Values, ROC Curve, Urine
Abstract

High throughput metabolic profiling via the metabolome-wide association study (MWAS) is a powerful new approach to identify biomarkers of disease risk, but there are methodological challenges: high dimensionality, high level of collinearity, the existence of peak overlap within metabolic spectral data, multiple testing, and selection of a suitable significance threshold. We define the metabolome-wide significance level (MWSL) as the threshold required to control the family wise error rate through a permutation approach. We used 1H NMR spectroscopic profiles of 24 h urinary collections from the INTERMAP study. Our results show that the MWSL primarily depends on sample size and spectral resolution. The MWSL estimates can be used to guide selection of discriminatory biomarkers in MWA studies. In a simulation study, we compare statistical performance of the MWSL approach to two variants of orthogonal partial least-squares (OPLS) method with respect to statistical power, false positive rate and correspondence of ranking of the most significant spectral variables. Our results show that the MWSL approach as estimated by the univariate t test is not outperformed by OPLS and offers a fast and simple method to detect disease-related discriminatory features in human NMR urinary metabolic profiles.

DOI10.1021/pr1003449
Alternate JournalJ. Proteome Res.
PubMed ID20701291
PubMed Central IDPMC2941198
Grant ListG0801056 / / Medical Research Council / United Kingdom
P09870_DFHM / / Biotechnology and Biological Sciences Research Council / United Kingdom
R01 HL050490 / HL / NHLBI NIH HHS / United States
R01 HL050490-10 / HL / NHLBI NIH HHS / United States
R01 HL084228 / HL / NHLBI NIH HHS / United States
R01 HL084228 / HL / NHLBI NIH HHS / United States
R01 HL084228-02 / HL / NHLBI NIH HHS / United States
R01 HL50490 / HL / NHLBI NIH HHS / United States