Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data.
Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data.
复制标题
根据群体 SNP 数据联合估计基因转化率和平均转化区长度。
DOI：
10.1093/bioinformatics/btp229
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Song,YunS
中科院分区：
文献类型：
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作者：
Yin,Junming;Jordan,MichaelI;Song,YunS
动机：两种已知的减数分裂重组类型是交叉和基因转换。尽管它们在基因组中留下了不同的足迹，但梳理它们对观察到的遗传变异的相对贡献是一项具有挑战性的任务。特别是对于给定的群体SNP数据集，交叉率、基因转换率和平均转换束长度的联合估计被广泛认为是一个非常困难的问题。结果：在本文中，我们设计了一种基于似然的方法，使用交错隐马尔可夫模型（HMM），可以联合估计上述重组的三个基本参数。我们的方法显著改进了最近提出的基于阶乘HMM的方法。研究表明，对重叠基因转换进行建模对于提高基因转换率和平均转换束长度的联合估计至关重要。我们在模拟数据上测试了我们方法的性能。然后，我们应用我们的方法分析了黑腹果蝇x染色体端粒的真实生物学数据，并表明该区域的基因转换率与交叉率的比率可能并不像之前声称的那么高。可用性：本文中讨论的算法的软件实现可在http://www.cs.berkeley.edu/ ~ yss/software.html.联系：yss@eecs.berkeley.edu
Motivation:Two known types of meiotic recombination are crossovers and gene conversions. Although they leave behind different footprints in the genome, it is a challenging task to tease apart their relative contributions to the observed genetic variation. In particular, for a given population SNP dataset, the joint estimation of the crossover rate, the gene conversion rate and the mean conversion tract length is widely viewed as a very difficult problem.Results:In this article, we devise a likelihood-based method using an interleaved hidden Markov model (HMM) that can jointly estimate the aforementioned three parameters fundamental to recombination. Our method significantly improves upon a recently proposed method based on a factorial HMM. We show that modeling overlapping gene conversions is crucial for improving the joint estimation of the gene conversion rate and the mean conversion tract length. We test the performance of our method on simulated data. We then apply our method to analyze real biological data from the telomere of theXchromosome ofDrosophila melanogaster, and show that the ratio of the gene conversion rate to the crossover rate for the region may not be nearly as high as previously claimed.Availability:A software implementation of the algorithms discussed in this article is available at http://www.cs.berkeley.edu/∼yss/software.html.Contact:yss@eecs.berkeley.edu