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从时变规模总体中抽取的大样本的合并计算。

Coalescence computations for large samples drawn from populations of time-varying sizes.

作者信息

Polanski Andrzej, Szczesna Agnieszka, Garbulowski Mateusz, Kimmel Marek

机构信息

Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.

The Linnaeus Centre for Bioinformatics, Uppsala University, BMC, Uppsala, Sweden.

出版信息

PLoS One. 2017 Feb 7;12(2):e0170701. doi: 10.1371/journal.pone.0170701. eCollection 2017.

Abstract

We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.

摘要

我们给出了关于合并树中时间的概率分布以及大样本量合并情况下预期等位基因频率的新结果。所获得的结果基于计算方法,该方法涉及将合并时间尺度变化与积分变换技术相结合,并使用无穷乘积的解析公式。我们展示了所提出方法在计算合并树中时间的概率分布及其极限、评估合并树中时间和预期等位基因频率的近似表达式的准确性以及分析大型人类线粒体DNA数据集方面的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03a8/5295683/04668eaa7337/pone.0170701.g001.jpg

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