Complex disorders arise from the actions and interaction of many genetic and non-genetic factors. Such disorders, including asthma, diabetes, cardiovascular disease, and psychiatric disorders, account for a huge proportion of public health care expenditures, and yet we remain largely ignorant of their basic defects. The identification of genetic variation influencing susceptibility to complex disorders would yield a better understanding of the basic defects, leading to more effective and specific treatments, and would also improve our ability to identify and characterize non-genetic risk factors that might prove cost-effective targets for prevention strategies. It is clear, however, that the paradigms that were used so successfully in the identification of genes for simple, Mendelian disorders must be modified for studies in complex disorders. In our initial cycle of funding, we proposed to develop approaches for the identification of genetic variation affecting susceptibility to complex disorders at three levels of resolution: linkage mapping, fine-scale linkage disequilibrium (LD) mapping, and positional cloning. We have published manuscripts describing approaches we developed at each of these levels of resolution and have made the resulting software freely available. These published manuscripts include description of an approach for identifying and characterizing gene-gene interaction in the context of robust multipoint linkage analysis (Cox et al., 1999), description of a likelihood-based method of LD mapping using the Decay in Haplotype Sharing (DHS) (McPeek and Strahs, 1999), and description of approaches for the analytic component to the positional cloning of complex disorders (Horikawa et al., 2000). We now propose to extend these studies.
Our specific aims are: (1) To extend the approaches we have developed for identifying and characterizing gene-gene interactions between unlinked regions to enable us to better model gene-gene and gene-environment interaction within the framework of robust, allele-sharing methods for multipoint linkage analysis; (2) To extend the DHS approach with (a) development of improved models of background LD that can be utilized in these analyses and (b) development of computationally-feasible algorithms that allow the use of genotypes in unrelated individuals (rather than unambiguously established haplotypes); and (3) To develop robust, flexible approaches that test whether genetic variation adequately accounts for the evidence for linkage in the context of realistically complex molecular genetic models. Our long-term research goal is to provide a robust statistical and analytic framework that can be integrated with molecular genetic studies to identify genetic variation influencing complex phenotypes.
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