Detecting and Quantifying Natural Selection at Two Linked Loci from Time Series Data of Allele Frequencies with Forward-in-Time Simulations
Detecting and Quantifying Natural Selection at Two Linked Loci from Time Series Data of Allele Frequencies with Forward-in-Time Simulations
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DOI：
10.1534/genetics.120.303463
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发表时间：
2020-10-01
期刊：
影响因子：
3.3
通讯作者：
Yu, Feng
中科院分区：
文献类型：
--
作者：
He, Zhangyi;Dai, Xiaoyang;Yu, Feng
DNA测序技术的最新进展使随着时间的推移对基因组进行更详细的监测成为可能。这一改进为我们研究基于基因组时间序列样本的自然选择提供了机会，同时考虑了遗传重组效应和局部连锁信息。这样的时间序列基因组数据可以更准确地估计种群遗传参数，并对自然选择最近的行动进行假设检验。在这项工作中，我们开发了一个新的贝叶斯统计框架，通过利用DNA数据的时间方面以及对包含未知等位基因的样本染色体建模的额外灵活性，来推断一对连锁基因的自然选择。我们的方法建立在隐马尔可夫模型的基础上，其中潜在的过程是带有选择的双轨迹Wright-Fisher扩散，这使得我们能够显式地模拟遗传重组和局部连锁。应用粒子边际Metropolis-Hastings算法计算选择系数的后验概率分布，使我们能够有效地计算似然。我们通过大量的模拟对我们的贝叶斯推理过程的性能进行了评估，结果表明，我们的方法可以给出准确的选择系数估计，并且遗传重组和局部连锁的加入使自然选择的推理得到了显著的改善。我们还通过对与马的白色斑点图案相关的古代DNA数据的应用来说明我们的方法在真实数据上的实用性。
Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modeling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses.