| A Statistical Model for Assessing Genetic Susceptibility as a Risk Factor in Multifactorial Diseases: Lessons from Occupational Asthma Eugene Demchuk,1,2* Berran Yucesoy,2* Victor J. Johnson,2* Michael Andrew,3 Ainsley Weston,2 Dori R. Germolec,4 Christopher T. De Rosa,1 and Michael I. Luster2 1Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA; 2Toxicology and Molecular Biology Branch, and 3Biostatistics and Epidemiology Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, West Virginia, USA; 4Toxicology Operations Branch, Environmental Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA Abstract
Background: 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. Objective: The purpose of this study was to estimate the combinatorial contribution of multiple genetic variants to disease risk. Methods: 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. Results: 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. Conclusion: 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. Key words: asthma, genetics, polygenic diseases, risk assessment, susceptibility genes. Environ Health Perspect 115:231–234 (2007) . doi:10.1289/ehp.8870 available via http://dx.doi.org/ [Online 13 November 2006]
Address correspondence to B. Yucesoy, Chronic Inflammatory and Immune Disease Team, Toxicology and Molecular Biology Branch, Health Effects Laboratory Division, NIOSH/CDC, 1095 Willowdale Rd., M/S 3014, Morgantown, WV 26505-2888 USA. Telephone: (304) 285-5993. Fax: (304) 285-5708. E-mail: byucesoy@cdc.gov *These authors provided equal contributions. These studies were supported in part by an interagency agreement with the NIEHS, Division of Intramural Research (Y1-ES-69277266) , and a grant from the CDC Office of Genomics and Disease Prevention (921Z4FP) . The findings and conclusions in this report are those of the authors and do not necessarily represent the views of NIOSH. The authors declare they have no competing financial interests. Received 21 November 2005 ; accepted 13 November 2006. An erratum was published in Environ HealthPerspect 115:A241 (2007) .
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