TreeQA: quantitative genome wide association mapping using local perfect phylogeny trees.
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 paper, 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. We conducted extensive experiments on both simulated datasets and mouse inbred lines to demonstrate the efficiency and effectiveness of TreeQA.
Pan
Feng
F
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. panfeng@cs.unc.edu
McMillan
Leonard
L
Pardo-Manuel De Villena
Fernando
F
Threadgill
David
D
Wang
Wei
W
D000465:Algorithms; D000818:Animals; D001699:Biometry; D008076:Cholesterol, HDL / Q000097:blood / Q000235:genetics; D030541:Databases, Genetic; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D005838:Genotype; D051379:Mice; D008815:Mice, Inbred Strains / Q000235:genetics; D010641:Phenotype; D010802:Phylogeny; D020641:Polymorphism, Single Nucleotide
D008076:Cholesterol, HDL