Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure

Salamanca Beatriz Valcarcel, Ebbels Timothy M.D. and Iorio Maria De ()
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Salamanca Beatriz Valcarcel: Rheumatology Unit, Institute of Child Health, University College, 30 Guilford Street, London WC1N 1EH, UK
Ebbels Timothy M.D.: Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, UK
Iorio Maria De: Department of Statistical Science, University College, Gower Street, London WC1E 6BT, UK

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 2, 191-201

Abstract: In this study, we propose a novel statistical framework for detecting progressive changes in molecular traits as response to a pathogenic stimulus. In particular, we propose to employ Bayesian hierarchical models to analyse changes in mean level, variance and correlation of metabolic traits in relation to covariates. To illustrate our approach we investigate changes in urinary metabolic traits in response to cadmium exposure, a toxic environmental pollutant. With the application of the proposed approach, previously unreported variations in the metabolism of urinary metabolites in relation to urinary cadmium were identified. Our analysis highlights the potential effect of urinary cadmium on the variance and correlation of a number of metabolites involved in the metabolism of choline as well as changes in urinary alanine. The results illustrate the potential of the proposed approach to investigate the gradual effect of pathogenic stimulus in molecular traits.

Keywords: correlation networks; covariance regression; genomics; metabolomics; variance regression (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2013-0041

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