Pan Feng, McMillan Leonard, Pardo-Manuel De Villena Fernando, Threadgill David, Wang Wei
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Pac Symp Biocomput. 2009:415-26.
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.
现代遗传学中全基因组关联（GWA）图谱绘制的目标是识别基因组中有助于形成形态或疾病等遗传复杂表型的基因或狭窄区域。在现有方法中，基于树的关联图谱绘制方法相对于基于单标记和基于单倍型的方法具有明显优势，因为它们将有关基因组进化历史的信息纳入了分析。然而，现有的基于树的方法主要是为病例/对照研究得出的二元表型设计的，或者无法进行全基因组规模的分析。在本文中，我们介绍了一种定量GWA图谱绘制算法TreeQA。TreeQA利用在没有历史重组证据的基因组区域构建的局部完美系统发育树。通过高效的算法设计与实现，TreeQA能够有效地进行全基因组定量关联分析，并且比以前的方法更有效。我们在模拟数据集和小鼠近交系上都进行了广泛的实验，以证明TreeQA的效率和有效性。
