Use of pathway information in molecular epidemiology

<p>Abstract</p> <p>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 o...

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Main Authors: Thomas Duncan C, Conti David V, Baurley James, Nijhout Frederik, Reed Michael, Ulrich Cornelia M
Format: Article
Language:English
Published: BMC 2009-10-01
Series:Human Genomics
Subjects:
Online Access:http://www.humgenomics.com/content/4/1/21
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spelling doaj-838ba303339646db8efe74263a63ceed2020-11-24T20:56:04ZengBMCHuman Genomics1479-73642009-10-0141214210.1186/1479-7364-4-1-21Use of pathway information in molecular epidemiologyThomas Duncan CConti David VBaurley JamesNijhout FrederikReed MichaelUlrich Cornelia M<p>Abstract</p> <p>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.</p> http://www.humgenomics.com/content/4/1/21colorectal cancercomplex diseasesfolategene-environment interactionsgene-gene interactions
collection DOAJ
language English
format Article
sources DOAJ
author Thomas Duncan C
Conti David V
Baurley James
Nijhout Frederik
Reed Michael
Ulrich Cornelia M
spellingShingle Thomas Duncan C
Conti David V
Baurley James
Nijhout Frederik
Reed Michael
Ulrich Cornelia M
Use of pathway information in molecular epidemiology
Human Genomics
colorectal cancer
complex diseases
folate
gene-environment interactions
gene-gene interactions
author_facet Thomas Duncan C
Conti David V
Baurley James
Nijhout Frederik
Reed Michael
Ulrich Cornelia M
author_sort Thomas Duncan C
title Use of pathway information in molecular epidemiology
title_short Use of pathway information in molecular epidemiology
title_full Use of pathway information in molecular epidemiology
title_fullStr Use of pathway information in molecular epidemiology
title_full_unstemmed Use of pathway information in molecular epidemiology
title_sort use of pathway information in molecular epidemiology
publisher BMC
series Human Genomics
issn 1479-7364
publishDate 2009-10-01
description <p>Abstract</p> <p>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.</p>
topic colorectal cancer
complex diseases
folate
gene-environment interactions
gene-gene interactions
url http://www.humgenomics.com/content/4/1/21
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