The traditional methods of linkage analysis, which test for cosegregation of disease and marker, were developed for Mendelian diseases. These methods may be misleading or inconclusive when applied to complex non- Mendelian diseases, because they require simplifying assumptions about the inheritance of the disease in order to infer disease gene location. In other words, an incorrect model of the disease may lead to incorrect conclusions. Model-free linkage analysis methods avoid this problem by directly testing for nonrandom segregation of markers to affected individuals. The model-free methods have a crucial and important role in the search for disease susceptibility loci involved in genetically complex diseases. One promising model-free method is the affected pedigree member (APM) method of linkage analysis. The APM method has the advantage of using marker information on all the affected relatives in each family, unlike the model-free sib-pair methods which only use information on the siblings and their parents. By the research proposed here, the APM method will be evaluated, improved, and extended, making it a more useful and efficient method for mapping genes involved in complex human diseases. Extensive computer simulations will be carried out to evaluate the number of families needed to detect linkage and the most efficient way to ascertain them. A model-free method to test for linkage heterogeneity will be developed and evaluated. These studies will provide a greater understanding of the potential and limitations of the APM method. An extension of the APM method that uses marker information from both unaffected and affected individuals will be developed and evaluated. Also, the APM statistic will be modified so that closely related pairs of affected relatives contribute more to the overall statistic than distantly related pairs of relatives, in effect, subcategorizing the disease into a more familial form. Our extended and modified methods will be evaluated by applying them to both simulated data and to a collection of pedigrees segregating for Alzheimer disease. In addition, an APM method for X-linked traits will be developed and evaluated. Ultimately, these studies will lead to better APM statistics with an improved ability to map a wide range of genetically complex diseases.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
First Independent Research Support & Transition (FIRST) Awards (R29)
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Genome Study Section (GNM)
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University of Pittsburgh
Schools of Public Health
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