Lin Li, Wong Limsoon, Leong Tze-Yun, Lai Poh San
School of Computing, National University of Singapore, Singapore.
J Bioinform Comput Biol. 2010 Dec;8 Suppl 1:127-46. doi: 10.1142/s0219720010005142.
Effective identification of disease-causing gene locations can have significant impact on patient management decisions that will ultimately increase survival rates and improve the overall quality of health care. Linkage disequilibrium mapping is the process of finding disease gene locations through comparisons of haplotype frequencies between disease chromosomes and normal chromosomes. This work presents a new method for linkage disequilibrium mapping. The main advantage of the proposed algorithm, called LinkageTracker, is its consistency in producing good predictive accuracy under different conditions, including extreme conditions where the occurrence of disease samples with the mutation of interest is very low and there is presence of error or noise. We compared our method with some leading methods in linkage disequilibrium mapping such as HapMiner, Blade, GeneRecon, and Haplotype Pattern Mining (HPM). Experimental results show that for a substantial class of problems, our method has good predictive accuracy while taking reasonably short processing time. Furthermore, LinkageTracker does not require any population ancestry information about the disease and the genealogy of the haplotypes. Therefore, it is useful for linkage disequilibrium mapping when the users do not have such information about their datasets.
有效识别致病基因位置会对患者管理决策产生重大影响，这些决策最终将提高生存率并改善整体医疗保健质量。连锁不平衡作图是通过比较疾病染色体和正常染色体之间的单倍型频率来寻找疾病基因位置的过程。这项工作提出了一种新的连锁不平衡作图方法。所提出的算法称为LinkageTracker，其主要优点是在不同条件下，包括在感兴趣突变的疾病样本出现频率非常低且存在误差或噪声的极端条件下，都能始终产生良好的预测准确性。我们将我们的方法与连锁不平衡作图中的一些领先方法进行了比较，如HapMiner、Blade、GeneRecon和单倍型模式挖掘（HPM）。实验结果表明，对于相当一部分问题，我们的方法具有良好的预测准确性，同时处理时间合理较短。此外，LinkageTracker不需要任何关于疾病的群体祖先信息和单倍型的谱系信息。因此，当用户没有关于其数据集的此类信息时，它对于连锁不平衡作图很有用。
