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A Statistical Model for Assessing Genetic Susceptibility as a Risk Factor in Multifactorial Diseases: Lessons from Occupational Asthma

Personal Authors: Demchuk, Eugene ; Yucesoy, Berran ; Johnson, Victor J. ; Andrew, Michael ; Weston, Ainsley ; Germolec, Dori R. ; De Rosa, Christopher T. ; Luster, Michael I.
Published Date: Nov 13 2006
Document Type: Journal Article
File Type:

Environ Health Perspect. 2007; 115(2):231-234.
Keywords: [+]

921Z4FP/FP/OFP OPHS HHS/United States
Y1-ES-69277266/ES/NIEHS NIH HHS/United States

Incorporating the influence of genetic variation in the risk assessment process is often considered, but no generalized approach exists. Many common human diseases such as asthma, cancer, and cardiovascular disease are complex in nature, as they are influenced variably by environmental, physiologic, and genetic factors. The genetic components most responsible for differences in individual disease risk are thought to be DNA variants (polymorphisms) that influence the expression or function of mediators involved in the pathological processes.


The purpose of this study was to estimate the combinatorial contribution of multiple genetic variants to disease risk.


We used a logistic regression model to help estimate the joint contribution that multiple genetic variants would have on disease risk. This model was developed using data collected from molecular epidemiology studies of allergic asthma that examined variants in 16 susceptibility genes.


Based on the product of single gene variant odds ratios, the risk of developing asthma was assigned to genotype profiles, and the frequency of each profile was estimated for the general population. Our model predicts that multiple disease variants broaden the risk distribution, facilitating the identification of susceptible populations. This model also allows for incorporation of exposure information as an independent variable, which will be important for risk variants associated with specific exposures.


The present model provided an opportunity to estimate the relative change in risk associated with multiple genetic variants. This will facilitate identification of susceptible populations and help provide a framework to model the genetic contribution in probabilistic risk assessment.

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