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首页> 外文期刊>Annals of Human Genetics >Intercoalescence Time Distribution of Incomplete Gene Genealogies in Temporally Varying Populations, and Applications in Population Genetic Inference
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Intercoalescence Time Distribution of Incomplete Gene Genealogies in Temporally Varying Populations, and Applications in Population Genetic Inference

机译：时变群体中不完整基因谱系的居间发光时间分布及其在群体遗传推断中的应用

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摘要

Tracing back to a specific time T in the past, the genealogy of a sample of haplotypes may not have reached their common ancestor and may leave m lineages extant. For such an incomplete genealogy truncated at a specific time T in the past, the distribution and expectation of the intercoalescence times conditional on T are derived in an exact form in this paper for populations of deterministically time-varying sizes, specifically, for populations growing exponentially. The derived intercoalescence time distribution can be integrated to the coalescent-based joint allele frequency spectrum (JAFS) theory, and is useful for population genetic inference from large-scale genomic data, without relying on computationally intensive approaches, such as importance sampling and Markov Chain Monte Carlo (MCMC) methods. The inference of several important parameters relying on this derived conditional distribution is demonstrated: quantifying population growth rate and onset time, and estimating the number of ancestral lineages at a specific ancient time. Simulation studies confirm validity of the derivation and statistical efficiency of the methods using the derived intercoalescence time distribution. Two examples of real data are given to show the inference of the population growth rate of a European sample from the NIEHS Environmental Genome Project, and the number of ancient lineages of 31 mitochondrial genomes from Tibetan populations. ? 2013 Blackwell Publishing Ltd/University College London.
机译：追溯到过去的特定时间T，单倍型样本的谱系可能尚未达到其共同祖先，并且可能遗留下m个谱系。对于过去在特定时间T截断的这种不完整族谱，本文以确切的形式推导了确定性时变大小的人口（特别是对于指数增长的人口）的以T为条件的互结时间的分布和期望。 。可以将得出的聚结间时间分布整合到基于聚结的联合等位基因频谱（JAFS）理论中，并且可以用于从大规模基因组数据推断群体遗传，而无需依赖于计算密集型方法，例如重要性抽样和马尔可夫链蒙特卡洛（MCMC）方法。证明了依赖于此导出的条件分布的几个重要参数的推论：量化人口增长率和发病时间，以及估计特定古代的祖传宗谱数量。仿真研究证实了使用推导的共聚发光时间分布的方法的有效性和统计效率。给出了两个实际数据示例，它们可以推断出NIEHS环境基因组计划的欧洲样本的人口增长率，以及藏族人口中31个线粒体基因组的古代谱系数。 ？ 2013布莱克韦尔出版有限公司/伦敦大学学院。

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