The proposed research aims: to: a) Improve the understanding of the genetics of inherited diseases with unclear modes of transmission. Studies will evaluate the effectiveness of current methods of analysis, in particular linkage analysis and the use of measures of association, in understanding the underlying genetic mechanisms of such traits. Simulation studies will be used to generate data reflecting confounding factors thought to be a problem in linkage analysis of certain complex traits (for example psychiatric or behavioral disorders). Factors to be considered include assortative mating; genetic heterogeneity and trait determination by more than one locus. The ability of current methods to correctly analyze traits with these characteristics will be assessed and, where found lacking, alternative methods will be developed and tested. b) Carry out classical linkage analysis for specific genetic diseases. The diseases to be studied include tuberous sclerosis and Fanconi anemia, where evidence for genetic heterogeneity has already been detected. Methods to be tested in the simulation studies can be applied to these analyses in order to better understand the complete genetic picture, including identification of heterogeneity by detecting linkage of different disease forms to different marker loci. Such differentiation will help sharpen the clinical definition of various forms of the diseases. c) Refine strategies for ordering multiple linked loci on linkage maps using either pairwise recombination data or data generated from radiation hybrid experiments. d) Apply techniques of neural network pattern matching to problems of genetic systems. Applications include aid in phenotype definition for traits with multiple clinical characteristics, prediction of risk of disease based on phenotype, values of known risk factors and disease profiles in relatives and estimation of missing recombination data in multilocus data sets using information from other linkage relationships. As a result of advances from this work, better mathematical tools for the study of diseases with complex or ill-defined inheritance patterns will be available. Applications to specific diseases will increase understanding of interactions between clinical definition and predisposing genetic factors. This will increase the precision of genetic counseling and suggest useful approaches for studying the mechanisms involved in determining disease state.
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