Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies.
Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies.
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DOI：
10.1534/genetics.115.177980
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发表时间：
2015-09
期刊：
影响因子：
3.3
通讯作者：
Ramachandran S
中科院分区：
文献类型：
--
作者：
Palacios JA;Wakeley J;Ramachandran S
最近出现了复杂的推理工具，结合模型估计过去的人口规模从基因组数据。最近的方法，模型重组需要小样本量，使人口规模变化的约束性假设，并不报告估计的不确定性措施。在这里，我们开发了一个基于高斯过程的贝叶斯非参数方法，再加上一个顺序马尔可夫合并模型，允许准确的推断人口规模随着时间的推移，从一组家谱。与目前的方法相比，我们的方法考虑了广泛的重组事件，包括那些不改变本地家谱。我们表明，我们的方法优于最近的基于似然的方法，依赖于参数空间的离散化。我们说明了我们的方法应用到多个人口的历史，包括人口瓶颈和指数增长。在模拟中，我们的贝叶斯方法产生的点估计比最大似然估计（基于真实值和估计值之间的绝对差之和）精确四倍。此外，我们的方法作为时间函数的人口规模的可信区间覆盖了多个人口统计情景中90%的真实值，从而能够对人口规模随时间的差异进行正式的假设检验。使用ARGweaver估计的家谱，我们将我们的方法应用于来自1000个基因组项目的欧洲和约鲁班样本，并确认了过去15万年来人口规模历史的关键已知方面。
Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past population sizes from genomic data. Recent methods that model recombination require small sample sizes, make constraining assumptions about population size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model that allows accurate inference of population sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum-likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method’s credible intervals for population size as a function of time cover 90% of true values across multiple demographic scenarios, enabling formal hypothesis testing about population size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of population size history over the past 150,000 years.