gms | German Medical Science

50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie (dae)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Deutsche Arbeitsgemeinschaft für Epidemiologie

12. bis 15.09.2005, Freiburg im Breisgau

Genetic susceptibility to air pollution: candidate genes, gene-environment and gene-gene interactions, pathways, and beyond

Meeting Abstract

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  • Duncan C. Thomas - University of Southern California, Los Angeles

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Deutsche Arbeitsgemeinschaft für Epidemiologie. 50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie. Freiburg im Breisgau, 12.-15.09.2005. Düsseldorf, Köln: German Medical Science; 2005. Doc05gmds659

The electronic version of this article is the complete one and can be found online at: http://www.egms.de/en/meetings/gmds2005/05gmds129.shtml

Published: September 8, 2005

© 2005 Thomas.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

A leading hypothesis for the mechanism by which air pollution causes asthma and other respiratory effects is through oxidative stress, a complex pathway involving a host of genes for the metabolic activation and detoxification of oxidizying agents [1]. I will review the epidemiologic evidence from the Southern California Childrens Health Study for the modifying effects of family history, various candidate genes, and other host susceptibility factors on ozone, particulates and other pollutants, and show how these could be organized in a unified statistical model. This motivates the development of new statistical approaches to looking a gene-environment and gene-gene interactions with multiple genes and multiple environmental or host factors in combination, including empirical approaches combining hierarchical models with Bayesian model averaging techniques and Bayesian pharmacokinetic modeling of the entire pathway [2]. Recurring themes involve allowing for uncertainty about model form in estimating model parameters and incorporation of prior knowledge in the form of covariates for some parts of the model. While traditional linkage analysis and candidate gene studies have identified a number of genes or chrosomal regions that may be involved, there probably remain many still to be discovered. Recent genotyping technology advances have now made it possible to consider genome-wide association scans involving, say, 600,000 SNPs on large case-control samples. Cost-efficient study designs and analysis methods that would extract the maximum possible information out of such studies are an active area of research [3].


References

1.
Gilliland, Environ Health Perspect 1999;107(suppl 3):403-7
2.
Conti et al. Hum Hered 2003;56:83-93
3.
Thomas, Haile & Duggan. Am J Hum Genet 2005;77:337-345