Bayesian analysis of haplotypes for linkage disequilibrium mapping
Bayesian analysis of haplotypes for linkage disequilibrium mapping
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DOI：
10.1101/gr.194801
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发表时间：
2001-10-01
期刊：
影响因子：
7
通讯作者：
Risch, N
中科院分区：
文献类型：
--
作者：
Liu, JS;Sabatti, C;Risch, N
疾病染色体的单倍型分析可以帮助识别可能的历史重组事件和定位疾病突变。大多数可用的分析仅使用边缘和成对等位基因频率信息。我们已经开发了一个贝叶斯框架，利用完整的单倍型信息，以克服各种并发症，如多个创始人，unphased染色体，数据污染，和不完整的标记数据。一个随机模型被用来描述几个变量之间的依赖结构表征观察到的单倍型，例如，祖先的单倍型和它们的年龄，突变率，重组事件，和疾病突变的位置。一个有效的马尔可夫链蒙特卡罗算法计算的估计量的兴趣。该方法在真实的数据集（囊性纤维化数据和Friedreich共济失调数据）和模拟数据集上都表现良好。实现所提出的方法的程序BLADE以及两个真实的数据集可以从http://www.fas.harvard.edu/similar到junliu/TechRept/Olfolder/diseq_tar.gz获得。
Haplotype analysis of disease chromosomes can help identify probable historical recombination events and localize disease mutations. Most available analyses use only marginal and pairwise allele frequency information. We have developed a Bayesian framework that utilizes full haplotype information to overcome various complications such as multiple founders, unphased chromosomes, data contamination, and incomplete marker data. A stochastic model is used to describe the dependence structure among several variables characterizing the observed haplotypes, for example, the ancestral haplotypes and their ages, mutation rate, recombination events, and the location of the disease mutation. An efficient Markov chain Monte Carlo algorithm was developed for computing the estimates of the quantities of interest. The method is shown to perform well in both real data sets (cystic fibrosis data and Friedreich ataxia data) and simulated data sets. The program that implements the proposed method, BLADE, as well as the two real datasets, can be obtained from http://www.fas.harvard.edu/similar to junliu/TechRept/Olfolder/diseq_prog.tar.gz.