Techniques will be developed for genetic analysis of complex diseases. Diseases such as coronary artery disease and late-onset Alzheimer's disease have both environmental and genetic components. However, identification of genes contributing to increased risk of such diseases has eluded investigators. Part of the difficulty in such studies stems from the need to use techniques designed for genetically simpler traits. Computational constraints prevent full use of even these techniques for analysis of complex disorders, severely limiting the power of genetic analysis to identify relevant genes with clinically feasible sample sizes. The combination of current computer technology and novel simulation approaches to statistical estimation have made timely the development of Monte Carlo methods of likelihood analysis which can be used for genetic analysis of complex traits. Over the last four years this approach has been used to develop techniques for fitting models for genetically complex traits on extended pedigrees, including methods for jointly performing segregation and linkage analysis, and for estimating the parameters of complex genetic models. The research now proposed is directed towards linkage analysis methods for complex traits. Methods will be developed which make use of data on multiple genetic markers, including techniques that make use of the increasingly dense genetic maps that are becoming readily available. These techniques will be evaluated by analyses on several data sets, including both data sets consisting of Mendelian disorders with large numbers of marker loci, as well as data sets with complex disorders, including pedigrees segregating for abnormal lipid levels associated with risk of coronary heart disease, and pedigrees segregating for Alzheimer's disease. Additionally, software will be developed to the point where it can be used by practitioners in the field, incorporating current methods and those now to be developed, and including support of this software for feedback and use by interested researchers.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Mammalian Genetics Study Section (MGN)
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University of Washington
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Blue, Elizabeth M; Brown, Lisa A; Conomos, Matthew P et al. (2016) Estimating relationships between phenotypes and subjects drawn from admixed families. BMC Proc 10:357-362
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Koepke, Hoyt; Thompson, Elizabeth (2013) Efficient identification of equivalences in dynamic graphs and pedigree structures. J Comput Biol 20:551-70
Sverdlov, Serge; Thompson, Elizabeth A (2013) Correlation between relatives given complete genotypes: from identity by descent to identity by function. Theor Popul Biol 88:57-67

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