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• W2138775195 abstract "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." @default.
• W2138775195 created "2016-06-24" @default.
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• W2138775195 date "2008-11-01" @default.
• W2138775195 modified "2023-09-27" @default.
• W2138775195 title "TREEQA: QUANTITATIVE GENOME WIDE ASSOCIATION MAPPING USING LOCAL PERFECT PHYLOGENY TREES" @default.
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• W2138775195 doi "https://doi.org/10.1142/9789812836939_0039" @default.
• W2138775195 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2739990" @default.
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