An algorithm for inferring complex haplotypes in a region of copy-number variation
An algorithm for inferring complex haplotypes in a region of copy-number variation
复制标题
DOI：
10.1016/j.ajhg.2008.06.021
复制
发表时间：
2008-08-08
影响因子：
9.8
通讯作者：
Tsunoda, Tatsuhiko
中科院分区：
文献类型：
--
作者：
Kato, Mamoru;Nakamura, Yusuke;Tsunoda, Tatsuhiko
最近的研究广泛地研究了人类基因组中被称为拷贝数变异（拷贝数变异）的大规模遗传变异，并且越来越多地认识到拷贝数变异在正常个体中的普遍性及其功能重要性。然而，缺乏一种从高通量实验数据中准确推断CNV区域内等位基因或单倍型的方法，阻碍了对CNV特性的更精细分析以及在疾病相关研究中的应用。在这里，我们开发了一种算法，通过使用高通量实验平台获得的数据来推断CNV区域内的复杂单倍型。我们将该算法应用于实验数据，并估计了单倍型的种群频率，可以获得DNA拷贝序列和数量的信息。这些结果表明，分析这种复杂的单倍型对于准确检测群体间CNV区域内的遗传差异至关重要。
Recent studies have extensively examined the large-scale genetic variants in the human genome known as copy-number variations (CNVs), and the universality of CNVs in normal individuals, along with their functional importance, has been increasingly recognized. However, the absence of a method to accurately infer alleles or haplotypes within a CNV region from high-throughput experimental data hampers the finer analyses of CNV properties and applications to disease-association studies. Here we developed an algorithm to infer complex haplotypes within a CNV region by using data obtained from high-throughput experimental platforms. We applied this algorithm to experimental data and estimated the population frequencies of haplotypes that can yield information on both sequences and numbers of DNA copies. These results suggested that the analysis of such complex haplotypes is essential for accurately detecting genetic differences within a CNV region between population groups.