Title:?Efficient phylogeny based genome-wide association mapping method.


Abstract: The goal of genome wide association (GWA) mapping in modern 
genetics is to identify genes or narrow regions in the genome which 
contribute to genetically complex phenotypes such as morphology or 
disease. Existing methods include single-marker, haplotype and 
phylogeny-based association mapping. Phylogeny-based association mapping 
utilizes phylogenetic trees which are rich yet compact representations 
of the genetic relationships between samples. Thus phylogeny-based 
methods show obvious advantages over single marker-based and 
haplotype-based methods by incorporating richer information of the 
evolutionary history. However, most of the existing phylogeny-based 
methods are time-consuming and not scalable to genome-wide analysis. I 
developed two efficient phylogeny-based association mapping methods, 
TreeQA and TreeQA+, which utilize local perfect phylogenies constructed 
in genomic regions exhibiting no evidence of historical recombination. 
TreeQA is highly efficient because it uses linear-time algorithm to 
construct local perfect phylogeny trees, conducts effective permutation 
tests and maximizes the reuse of intermediate computation. Moreover, 
TreeQA is more robust and effective than previous phylogeny-based 
methods due to its ability to remove outliers induced by the tree 
topology and search for associations in sample subspaces. TreeQA+ 
inherits all advantages of TreeQA. Moreover, it improves TreeQA by 
utilizing the Brownian motion and maximum likelihood model to 
incorporate sample correlations induced by the topology of the 
phylogenetic trees (The correlations violate the sample independence 
assumption and are ignored by previous association methods.).

The method is also extended and applied to association analysis in high 
dimensional data of any domains, which is a general data mining problem.