The age of onset can be significant for the genetics and severity of many complex human disorders. Examples are Alzheimer's disease, diabetes, breast cancer, and Huntington's disease. This project will apply survival analysis techniques to the modeling of disorders in which the age of onset is significant and a major gene may be involved. Previous work of the author and collaborators have developed survival- analysis models of this type with environmental covariates, a single genetic marker, and environmental or background-genetic familial correlations. The current proposal will extend this work in several ways, for example (1) by incorporating age of onset data and environmental risk factors into tests for genetic association, (2) incorporating gene- environment interactions and genomic imprinting into models for unclear families, (3) developing models to correct for sampling with possible ascertainment bias, (4) developing models for nuclear families where data from one or both parents may be missing, and (5) developing methods for multipoint genetic linkage analysis. Simulation studies will be carried out to investigate the power and efficiency of these methods and to compare then with other techniques. The methods will also be tested on real data for rheumatoid arthritis and systemic lupus erythematosus. Software to implement these methods will be developed and made available free-of-charge to interested researchers by means of posting on the Web. The distributed software will include well-documented computer sources and user-friendly executables.
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