Blockwise Site Frequency Spectra for Inferring Complex Population Histories and Recombination
Blockwise Site Frequency Spectra for Inferring Complex Population Histories and Recombination
复制标题
用于推断复杂种群历史和重组的分块位点频谱
DOI：
10.1101/077958
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Beeravolu C
中科院分区：
文献类型：
--
作者：
Beeravolu C
基因组规模数据集中包含的丰富信息极大地鼓励了以前所未有的分辨率推断人口历史的方法的发展。基于位点频谱(SFS)的方法计算效率高，但丢弃了关于连锁不平衡的信息，而利用连锁和重组的方法计算量更大，依赖于近似方法，如顺序马尔可夫合并。克服了这些限制，我们引入了一种新的复合似然(CL)框架，该框架允许对任意复杂的种群历史和来自多个基因组的平均全基因组重组率进行联合推断。我们建立在现有的分析方法的基础上，该方法将基因组分成相等(和任意)大小的区块，并将多态和连锁信息总结为SFS类型的区块计数(BSFS)。这一统计数据比SFS更丰富，因为它保留了整个基因组中包含的短距离连锁区块中包含的家系变异的信息。我们的近似分段似然估计方法(ABLE)通过蒙特卡罗模拟来逼近任意总体历史的CL，形成了重组的结合体，克服了解析似然计算的局限性。首先将其与小样本的预期分析结果进行比较，并且不进行块内重组，从而对其进行评估。通过使用这两种猩猩的全基因组数据，并将我们在涉及发散和各种形式的连续或脉冲混合的一系列模型下的推论与之前基于SFS和SMC的分析进行比较，进一步说明了该方法的有效性。最后，我们探讨了抽样(不同区块长度和个体数量)的效果，并发现通过合理的计算努力可以实现对人口统计和重组的准确推断。我们的方法也特别适用于非阶段性数据和碎片化的组装，使得它特别适合于模式生物和非模式生物。作者摘要在本文中，我们利用区块SFS(BSFS)模式的分布来从多个基因组序列推断任意种群历史。后者可以是完整的基因组，也可以是碎片化的，如RADSeq数据。值得注意的是，我们的方法允许同时推断人口历史和全基因组历史重组率。此外，我们不需要阶段性基因组，因为bSFS方法不区分发生突变的样本谱系。与站点频谱(SFS)一样，我们也可以通过折叠bSFS来忽略外群。我们用C/C++实现的近似顺时针似然估计(ABLE)方法利用了并行计算能力，适合于研究模式物种和非模式物种的种群历史。
The wealth of information contained in genome-scale datasets has substantially encouraged the development of methods inferring population histories with unprecedented resolution. Methods based on the Site Frequency Spectrum (SFS) are computationally efficient but discard information about linkage disequilibrium, while methods making use of linkage and recombination are computationally more intensive and rely on approximations such as the Sequentially Markov Coalescent. Overcoming these limitations, we introduce a novel Composite Likelihood (CL) framework which allows for the joint inference of arbitrarily complex population histories and the average genome-wide recombination rate from multiple genomes. We build upon an existing analytic approach that partitions the genome into blocks of equal (and arbitrary) size and summarizes the polymorphism and linkage information as blockwise counts of SFS types (bSFS). This statistic is a richer summary than the SFS because it retains information on the variation in genealogies contained in short-range linkage blocks across the genome. Our method, ABLE (Approximate Blockwise Likelihood Estimation), approximates the CL of arbitrary population histories via Monte Carlo simulations form the coalescent with recombination and overcomes limitations arising from analytical likelihood calculations. ABLE is first assessed by comparing it to expected analytic results for small samples and no intra-block recombination. The power of this approach is further illustrated by using whole genome data from the two species of orangutan and comparing our inferences under a series of models involving divergence and various forms of continuous or pulsed admixture with previous analyses based on the SFS and the SMC. Finally, we explore the effects of sampling (different block lengths and number of individuals) and find that accurate inference of demography and recombination can be achieved with reasonable computational effort. Our approach is also notably adapted to unphased data and fragmented assemblies making it particularly suitable for model as well as non-model organisms.Author SummaryIn this paper, we make use of the distribution of blockwise SFS (bSFS) patterns for the inference of arbitrary population histories from mutliple genome sequences. The latter can be whole genomes or of a fragmented nature such as RADSeq data. Our method notably allows for the simultaneous inference of demographic history and the genome-wide historical recombination rate. Additionally, we do not require phased genomes as the bSFS approach does not distinguish the sampled lineage in which a mutation occurred. As with the Site Frequency Spectrum (SFS), we can also ignore outgroups by folding the bSFS. Our Approximate Blockwise Likelihood Estimation (ABLE) approach implemented in C/C++ and taking advantage of parallel computing power is tailored for studying the population histories of model as well as non-model species.
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影响因子：
3.3
作者：
R. Hudson
通讯作者：
R. Hudson
影响因子：
9.8
作者：
Johnston, Henry R.;Cutler, David J.
通讯作者：
Cutler, David J.
使用复合似然的总体缩放参数估计量的一致性
影响因子：
1.9
作者：
C. Wiuf
通讯作者：
C. Wiuf
影响因子：
4.9
作者：
Ewing, Gregory B.;Jensen, Jeffrey D.
通讯作者：
Jensen, Jeffrey D.
DOI：
10.1111/1467-9868.00355
发表时间：
2002-01-01
影响因子：
5.8
作者：
Fearnhead, P;Donnelly, P
通讯作者：
Donnelly, P