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

References

Camacho, D., A. d. l. Fuente and P. Mendes (2005): “The origin of correlations in metabolomics data,” Metabolomics, 1, 53–63.10.1007/s11306-005-1107-3Suche in Google Scholar

Ellis, J. K., T. J. Athersuch, L. D. Thomas, F. Teichert, M. Perez-Trujillo, C. Svendsen, D. J. Spurgeon, R. Singh, L. Jarup, J. G. Bundy and H. C. Keun (2012): “Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population,” BMC Medicine, 10, 61. doi:10.1186/1741-7015-10-61.10.1186/1741-7015-10-61Suche in Google Scholar PubMed PubMed Central

Gelman, A., X. li Meng and H. Stern (1996): “Posterior predictive assessment of model fitness via realized discrepancies,” Stat. Sinica, 6, 733–807.Suche in Google Scholar

Hoff, P. D. and X. Niu (2012): “A covariance regression model,” Stat. Sinica, 22, 729–753.Suche in Google Scholar

Jarup, L. and A. Akesson (2009): “Current status of cadmium as an environmental health problem,” Toxicol. Appl. Pharm., 238, 201–208.Suche in Google Scholar

Jeong, H., B. Tombor, R. Albert, Z. N. Oltvai and A. L. Barabasi (2000): “The large-scale organization of metabolic networks,” Nature, 407, 651–654.10.1038/35036627Suche in Google Scholar PubMed

Lever, M., P. M. George, W. J. Dellow, R. S. Scott and S. T. Chambers (2005): “Homocysteine, glycine betaine, and n,n-dimethylglycine in patients attending a lipid clinic,” Metabolism, 54, 1–14, URL http://www.sciencedirect.com/science/article/pii/S0026049504003014.Suche in Google Scholar

Nawrot, T. S., J. A. Staessen, H. A. Roels, E. Munters, A. Cuypers, T. Richart, A. Ruttens, K. Smeets, H. Clijsters and J. Vangronsveld (2010): “Cadmium exposure in the population: from health risks to strategies of prevention,” Biometals., 23, 769–782.Suche in Google Scholar

Noonan, C. W., S. M. Sarasua, D. Campagna, S. J. Kathman, J. A. Lybarger and P. W. Mueller (2002): “Effects of exposure to low levels of environmental cadmium on renal biomarkers,” Environ. Health Persp., 110, 151–155.Suche in Google Scholar

Pare, G., N. R. Cook, P. M. Ridker and D. I. Chasman (2010): “On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the women’s genome health study,” PLoS Genetics, 6, e1000981.10.1371/journal.pgen.1000981Suche in Google Scholar PubMed PubMed Central

Plomin, R., C. M. A. Haworth and O. S. P. Davis (2009): “Common disorders are quantitative traits,” Nat. Rev. Genet. 10, 872–878, URL http://dx.doi.org/10.1038/nrg2670, m3: 10.1038/nrg2670; 10.1038/nrg2670.10.1038/nrg2670Suche in Google Scholar PubMed

Plummer, M. (2003, March). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). March (pp. 20–22).Suche in Google Scholar

Rao, C. R. (1970): “Estimation of heteroscedastic variances in linear models,” J. Am. Stat. Assoc., 65, 161–172.Suche in Google Scholar

Rutemiller, H. C. and D. A. Bowers (1968): “Estimation in a heteroscedastic regression model,” J. Am. Stat. Assoc., 63, 552–557.Suche in Google Scholar

Spiegelhalter, D. J., N. G. Best, B. P. Carlin and A. Van Der Linde (2002): “Bayesian measures of model complexity and fit,” J. R. Stat. Soc. B., 64, 583–639.Suche in Google Scholar

Steuer, R. (2006): “Review: on the analysis and interpretation of correlations in metabolomic data,” Brief. Bioinform., 7, 151–158.Suche in Google Scholar

Struchalin, M. V., A. Dehghan, J. C. Witteman, C. van Duijn and Y. S. Aulchenko (2010): “Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations,” BMC Genetics, 11, 92. doi:10.1186/1471-2156-11-92.10.1186/1471-2156-11-92Suche in Google Scholar PubMed PubMed Central

Thomas, L. D., S. Hodgson, M. Nieuwenhuijsen and L. Jarup (2009): “Early kidney damage in a population exposed to cadmium and other heavy metals,” Environ. Health Persp., 117, 181–184.Suche in Google Scholar

Valcarcel, B., P. Wurtz, a. B. Seich, T. Tukiainen, A. J. Kangas, P. Soininen, M.-R. Jarvelin, M. Ala-Korpela, T. M. Ebbels and M. de Iorio (2011): “A differential network approach to exploring differences between biological states: an application to prediabetes,” PLoS ONE, 6, e24702.10.1371/journal.pone.0024702Suche in Google Scholar PubMed PubMed Central

Wood, A. P., F. J. Warren and D. P. Kelly (2010): “Methylotrophic bacteria in trimethylaminuria and bacterial vaginosis.” In: Timmis, K. (Ed.), Handbook of Hydrocarbon and Lipid Microbiology, Biomedical and Life Sciences, volume Part 29, Heidelberg: Springer, pp. 3227–3240.10.1007/978-3-540-77587-4_245Suche in Google Scholar

Zeisel, S. H., J. S. Wishnok and J. K. Blusztajn (1983): “Formation of methylamines from ingested choline and lecithin,” J Pharmacol. Exp. Therap., 225, 320–324.Suche in Google Scholar