title: Use of pathway information in molecular epidemiology.
abstract: Candidate gene studies are generally motivated by some form of pathway reasoning in the selection of genes to be studied, but seldom has the logic of the approach been carried through to the analysis. Marginal effects of polymorphisms in the selected genes, and occasionally pairwise gene–gene or gene–environment interactions,are often presented, but a unified approach to modelling the entire pathway has been lacking. In this review, a variety of approaches to this problem is considered, focusing on hypothesis-driven rather than purely exploratory methods. Empirical modelling strategies are based on hierarchical models that allow prior knowledge about the structure of the pathway and the various reactions to be included as ‘prior covariates’. By contrast, mechanistic models aim to describe the reactions through a system of differential equations with rate parameters that can vary between individuals, based on their genotypes. Some ways of combining the two approaches are suggested and Bayesian model averaging methods for dealing with uncertainty about the true model form in either framework is discussed. Biomarker measurements can be incorporated into such analyses, and two-phase sampling designs stratified on some combination of disease, genes and exposures can be an efficient way of obtaining data that would be too expensive or difficult to obtain on a full candidate gene sample. The review concludes with some thoughts about potential uses of pathways in genome-wide association studies.
authors.affiliation: Department of Preventive Medicine, University of Southern California, 1540 Alcazar St., CHP-220, Los Angeles, CA 90089-9011, USA. dthomas@usc.edu
authors.forename: Duncan C
authors.initials: DC
authors.lastname: Thomas
authors.forename: David V
authors.initials: DV
authors.lastname: Conti
authors.forename: James
authors.initials: J
authors.lastname: Baurley
authors.forename: Frederik
authors.initials: F
authors.lastname: Nijhout
authors.forename: Michael
authors.initials: M
authors.lastname: Reed
authors.forename: Cornelia M
authors.initials: CM
authors.lastname: Ulrich
mesh_terms: D001499:Bayes Theorem; D015179:Colorectal Neoplasms / Q000235:genetics; D004843:Epistasis, Genetic; D005492:Folic Acid / Q000378:metabolism; D005819:Genetic Markers; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D053858:Metabolic Networks and Pathways; D017720:Molecular Epidemiology / Q000379:methods*; D011110:Polymorphism, Genetic; D012306:Risk
chemical_list: D005819:Genetic Markers; D005492:Folic Acid