Viewpoint: using gene-environment interactions to dissect the effects of complex mixtures.
Teasing out the health effects of constituents of complex mixtures poses formidable statistical challenges owing to the problem of multicollinearity. While statistical devices such as regression on principal components, model selection, and model averaging offer some approaches to this problem, incorporation of external information is likely to be more helpful. I explore a general hierarchical modeling framework that would allow such information as sources, genetic interactions, and toxicology to be included in the higher levels of the model.
Thomas
Duncan C
DC
Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089-9011, USA. dthomas@usc.edu
D000704:Analysis of Variance; D000818:Animals; D004347:Drug Interactions; D004785:Environmental Pollutants / Q000633:toxicity*; D005838:Genotype; D015386:Hazardous Substances / Q000633:toxicity*; D006801:Humans; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D025341:Principal Component Analysis; D012044:Regression Analysis; D018570:Risk Assessment; D014116:Toxicology
D004785:Environmental Pollutants; D015386:Hazardous Substances