||5R03CA106081-02 Interpret this number
||Ut Md Anderson Cancer Ctr
||Statistical Methods for Gene-Gene and Gene-Environment *
DESCRIPTION (provided by applicant):
With the completion of the Human Genome Project, determining how genes interact with other genes and with environmental factors to produce diseases, an important epidemiologic and public health research priority, is now feasible. Complex human diseases such as cancer are often the result of the interaction of genetic susceptibility factors and possibly modifiable environmental factors. Case-control studies are widely used in epidemiology for studying associations between diseases and potential genetic and environmental risk factors. However, concerns over the selection of an appropriate control groups have led to the development of alternative epidemiologic study designs. One particular approach, the case only design, has been shown to be an efficient and valid method for testing for gene-environment interactions under the assumption of independence between the genetic and environment factor. However, it is impossible to test the assumption of independence of risk factors based on the case only data.
Therefore, the objective of this proposal is to develop novel statistical tools that will be useful in studying the interactions among genes and the environment in the case only study design without the assumption of independence between gene and environment or linkage equilibrium between genes. Particularly, we are proposing a Bayesian likelihood-based approach, using a Markov chain Monte Carlo technique that does not require the assumption of independence of risk factors. We plan to examine the robustness of the proposed method. The methods developed in this proposal will be applied to data from a series of more than 1700 comprehensively characterized patients with histologically confirmed lung cancer enrolled through the thoracic surgery clinics at M.D. Anderson Cancer Center in an ongoing study of genetic susceptibility to lung cancer.
Comparison of haplotype inference methods using genotypic data from unrelated individuals.
Xu H, Wu X, Spitz MR, Shete S
Hum Hered, 2004;58(2), p. 63-8.
Complex segregation analysis reveals a multigene model for lung cancer.
Xu H, Spitz MR, Amos CI, Shete S
Hum Genet, 2005 Jan;116(1-2), p. 121-7.
2004 Nov 16.