Sevon Petteri, Toivonen Hannu, Ollikainen Vesa
Department of Computer Science, PO Box 68, FI-00014 University of Helsinki, Finland.
IEEE/ACM Trans Comput Biol Bioinform. 2006 Apr-Jun;3(2):174-85. doi: 10.1109/TCBB.2006.28.
We describe TreeDT, a novel association-based gene mapping method. Given a set of disease-associated haplotypes and a set of control haplotypes, TreeDT predicts likely locations of a disease susceptibility gene. TreeDT extracts, essentially in the form of haplotype trees, information about historical recombinations in the population: A haplotype tree constructed at a given chromosomal location is an estimate of the genealogy of the haplotypes. TreeDT constructs these trees for all locations on the given haplotypes and performs a novel disequilibrium test on each tree: Is there a small set of subtrees with relatively high proportions of disease-associated chromosomes, suggesting shared genetic history for those and a likely disease gene location? We give a detailed description of TreeDT and the tree disequilibrium tests, we analyze the algorithm formally, and we evaluate its performance experimentally on both simulated and real data sets. Experimental results demonstrate that TreeDT has high accuracy on difficult mapping tasks and comparisons to other methods (EATDT, HPM, TDT) show that TreeDT is very competitive.
我们描述了TreeDT，一种基于关联的新型基因定位方法。给定一组疾病相关单倍型和一组对照单倍型，TreeDT可预测疾病易感基因的可能位置。TreeDT主要以单倍型树的形式提取群体中历史重组的信息：在给定染色体位置构建的单倍型树是单倍型谱系的一种估计。TreeDT为给定单倍型上的所有位置构建这些树，并对每棵树进行一项新型不平衡测试：是否存在一小部分子树，其疾病相关染色体比例相对较高，这表明这些子树具有共同的遗传历史以及可能的疾病基因位置？我们详细描述了TreeDT和树不平衡测试，对该算法进行了形式化分析，并在模拟数据集和真实数据集上对其性能进行了实验评估。实验结果表明，TreeDT在困难的定位任务上具有很高的准确性，与其他方法（EATDT、HPM、TDT）的比较表明，TreeDT具有很强的竞争力。
