The goal of genome wide association (GWA) mapping in modern genetics is to identify genes or narrow regions in the genome that contribute to genetically complex phenotypes such as morphology or disease. Among the existing methods, tree-based association mapping methods show obvious advantages over single marker-based and haplotype-based methods because they incorporate information about the evolutionary history of the genome into the analysis. However, existing tree-based methods are designed primarily for binary phenotypes derived from case/control studies or fail to scale genome-wide.
In this project, we introduce TreeQA, a quantitative GWA mapping algorithm. TreeQA utilizes local perfect phylogenies constructed in genomic regions exhibiting no evidence of historical recombination. By efficient algorithm design and implementation, TreeQA can efficiently conduct quantitative genom-wide association analysis and is more effective than the previous methods. [paper]
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NSF IIS 0448392: “CAREER: Mining Salient Localized Patterns in Complex Data”
NSF IIS 0812464: ” III-Core: Discovering and Exploring Patterns in Subspaces”
NIH GM 076468: “The Center for Genome Dynamics at Jackson Laboratory:An NIGMS National Center of Systems Biology”
UCRF: “University Cancer Research Fund”