SOFTWARE FOR INTEGRATED LINKAGE AND ASSOCIATION ANALYSIS

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The genetic analysis of complex diseases holds enormous promise for increasing knowledge about the biologic mechanisms leading to common diseases such as heart disease, cancer, diabetes, and Alzheimer disease. However, the genetic analysis of complex disease is challenging because these diseases are likely due to a complex interplay of multiple genetic and environmental factors. As a result of this complexity, studies of common diseases require very large samples. New quantitative methods are needed which maximize the information obtained from each study by extending currently available methods and combining them with novel methods. Studies of complex disease require the use of multiple analytic approaches and an understanding of the advantage and disadvantage of each method as well as the ways in which the different analytic methods can complement each other to enhance understanding of genetic factors for a complex disease. The goal of this proposal is to develop new methods of linkage and association analysis, and to combine the new methods with existing linkage and association methods for nuclear families into single software package. Specifically we propose to: 1) extend existing affected-sib-pair linkage mapping software (Siblink) to allow consideration of phenotypic and environmental covariates, additional unlinked genes, and genotyping error; 2) provide enhancements to Siblink to allow for seamless estimation of empirical p-values and power, interval estimates of disease gene location, examination of robustness to model misspecification and incorporation of additional sibs; 3) develop methods for family-based association tests when parental genotyping information is missing; 4) develop methods to combine families with and without parents in a single analysis and for the use of multiple families from extended pedigrees; 5) perform simulation studies to examine optimal study design in the context of simultaneous linkage and association studies; 6) provide guidance as to the analysis method of choice under various conditions. The study of the genetics of a large number of common diseases currently underway at Duke University Center for Human Genetics and the University of Michigan provides a rich resource for application and assessment of these new methods to real data.