A fundamental challenge in life sciences is the characterization of genetic factors that underlie phenotypic differences. Thanks to the advanced sequencing technologies, an enormous amount of genetic variants have been identified and cataloged. Such data hold great potential to understand how genes affect phenotypes and contribute to the susceptibility to environmental stimulus. However, the existing computational methods for analyzing and interpreting the high?throughput genetic data are still in their infancy. We propose to systematically investigate the computational and statistical principles in modeling and discovering genetic basis of complex phenotypes. The proposed research provides answers to the following fundamental questions in genetic association study: (1) How to effectively and efficiently assess statistical significance of the findings? (2) How to account for the relatedness between samples in genetic association study? (3) How to accurately capture possible interactions between multiple genetic factors and their joint contribution to phenotypic variation? In particular, we will develop data structures and efficient algorithms for accurate and robust significance assessment that account for local population structure and joint effect of multiple genetic factors. The proposed computational tools will be integrated into software packages under common application framework adopted by the broad scientific community.
A fundamental challenge in life sciences is the characterization of genetic factors that underlie phenotypic differences. Existing methods are not able to adequately address the complexity of high throughput data. Innovative computational models and methods developed in this project will enable scientists more effectively analyze the research data, thus further understanding of human diseases and speed the development diagnostic tools, cures, and therapies.
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