QUANTITATIVE ASSOCIATION METHODS FOR GENE MAPPING

Determining the causes of common complex human diseases and traits that are influenced by several genetic and environmental factors has immense public health benefits, ranging from prevention to earlier detection and treatment. However, the current quantitative methods used to analyze complex diseases are limited in their power, their flexibility to account for multiple genetic and environmental factors, and their robustness to departures from sometimes untestable assumptions. The overall objectives of this proposed research program are to facilitate "The Future of Genetic Studies of Complex Human Diseases" by developing innovative statistical methods and software that can be used by biomedical researchers to design and analyze family-based association studies that take advantage of both linkage and linkage disequilibrium. The four specific aims of this proposed research program encompass the development of the necessary quantitative tools: 1. Development of robust semi-parametric statistical methods that will be used to analyze family-based genetic association studies, and which account for many of the complexities of family-based studies of common diseases, such as different types of traits (e.g., binary, polytomous, ordinal, censored age-dependent, and quantitative traits), environmental risk factors, gene-gene and gene-environment interactions, and residual correlations among pedigree members; 2. Development of statistical methods and simulation routines that will be used to determine the design of genetic association studies; 3. Development of user-friendly computer software that implements these procedures; 4. Development of documentation that describes the implemented statistical methodology, how to use the developed software, and how to interpret the results from the analyses. The advantages of this proposed research, over those currently used to identify and characterize genes of complex diseases, are that it will offer a more powerful and flexible method to find these types of genes.