Principal Investigators: Tzeng, Jung-Ying Proposal Number: DMS-0504726 Institution: North Carolina State University
Proposal Title: Haplotype-based Association Modeling for Whole-Genome Scan and Candidate Gene Studies
Identifying genes responsible for human diseases can illuminate significant insight to the detection, treatment and prevention of these diseases. In the search for genes underlying human complex diseases, haplotype-based association analysis has been recognized as a tool with high resolution and, more importantly, potentially great power for identifying modest etiological effects of genes. However, in practice, its efficacy has not been as successful as expected in theory; one primary cause is that such analysis requires a large number of parameters in order to capture the abundant haplotype varieties. While high degrees of freedom can hinder the power of identifying modest genetic effects on complex diseases, the need to incorporate covariates of other risk factors further worsens the degrees-of-freedom problem in mapping genes for complex diseases. To tackle this issue, the proposed work constructs an efficient and powerful model-based framework for association analysis at haplotype level. The central focus of the methods development is on the efficient use of haplotype information in a model-based framework, and different strategies of reducing haplotype complexity are considered at different research stages to optimize efficiency. For screening-stage analyses, the PI constructs approaches based on haplotype similarity of pair-wise comparison in a regression platform to detect chromosomal regions that are likely to harbor disease genes. For refinement-stage analyses, the PI develops evolutionary-based methods of haplotype grouping to identify specific disease-associated haplotypes, and integrate new dimension-reduction techniques into existing regression methods.
The proposed work aims not only to provide novel association tools in complex gene mapping, but also develop a routine method for case-control studies and offer a methodological foundation for future advancement. With the availability of a better statistical tool, scientists can advance their understanding of complex diseases, and design better diagnostic and therapeutic strategies that improve human health.
