Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics.
Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics.
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发表时间：
1996-06
影响因子：
9.8
通讯作者：
E. Sobel;K. Lange
中科院分区：
文献类型：
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作者：
E. Sobel;K. Lange
随机方法在系谱分析中的引入使遗传学家能够处理标准确定性方法难以处理的计算。到目前为止，这些随机技术的工作原理是在一个谱系的遗传下降状态集合上运行马尔可夫链。每个下降状态指定的路径的基因流的系谱和创始人等位基因下降到每个path.The目前的文件后续伊丽莎白·汤普森的建议，遗传下降图提供了一个更合适的空间来执行马尔可夫链。下降图指定了基因流动的路径，但不是沿着路径行进的特定创始者等位基因。本文探讨了算法实现汤普森的建议，共显性标记的自动单倍型分析，估计位置分数，并计算基因聚类统计强大的连锁分析的背景下。数值算例验证了算法的可行性。
The introduction of stochastic methods in pedigree analysis has enabled geneticists to tackle computations intractable by standard deterministic methods. Until now these stochastic techniques have worked by running a Markov chain on the set of genetic descent states of a pedigree. Each descent state specifies the paths of gene flow in the pedigree and the founder alleles dropped down each path. The current paper follows up on a suggestion by Elizabeth Thompson that genetic descent graphs offer a more appropriate space for executing a Markov chain. A descent graph specifies the paths of gene flow but not the particular founder alleles traveling down the paths. This paper explores algorithms for implementing Thompson's suggestion for codominant markers in the context of automatic haplotyping, estimating location scores, and computing gene-clustering statistics for robust linkage analysis. Realistic numerical examples demonstrate the feasibility of the algorithms.