Many common human diseases originate in part from the complicated effects of multiple genetic variants found throughout the genome. Given the enormous impact of such diseases on public health, it is imperative to map relevant genetic variants to improve our understanding of the molecular basis of such diseases, as well as improve screening techniques for disease prevention. To this end, successful human gene mapping of complex diseases requires the development and application of powerful statistical methods that fully utilize the resources of the Human Genome Project. This grant proposes a set of such statistical methods that either address novel problems or improve existing solutions to problems in human gene mapping studies. These proposed methods are applicable to a variety of genetic studies as they address topics in linkage, linkage disequilibrium, and high-dimensional genetic analyses of complex diseases and disease-related quantitative traits. The methods can be partitioned into the two general groups: mixed-modeling procedures and case-control likelihood procedures. The mixed-modeling procedures considered include a general variance-component (VC) mapping framework for continuous and discrete trait data and a modified VC mapping framework that allows for haplotypes. Also considered is a novel linear-mixed-model framework that identifies a large combination of genetic variants that influence a quantitative trait using support-vector- machine regression. The case-control likelihood procedures considered are extensions of the approach of Epstein and Satten (2003) for haplotype inference on disease. Extensions considered include allowing for covariates and haplotype-covariate interactions, and also categorical disease outcomes. The proposed statistical methods in this grant have the potential to increase the power to identify genetic variants that influence complex diseases and disease-related quantitative traits. This project will evaluate the performance of these methods using simulations based on the study design and data from an existing gene mapping study of type 2 diabetes. Also, this grant will implement the proposed statistical methods in user- friendly, efficient software for public distribution. Finally, this project will be opportunistic in identifying and addressing unforseen statistical problems arising in human gene mapping studies.
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