Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.
Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.
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DOI：
10.1371/journal.pbio.3001633
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发表时间：
2022-05
期刊：
影响因子：
9.8
通讯作者：
Ram, Yoav
中科院分区：
文献类型：
--
作者：
Avecilla, Grace;Chuong, Julie N.;Li, Fangfei;Sherlock, Gavin;Gresham, David;Ram, Yoav
适应性进化的速率取决于将有益突变引入种群的速率以及这些突变的适应度效应。有益突变的发生率及其预期的适应效果通常很难凭经验量化。由于这两个参数决定了种群进化变化的速度，适应性进化的动态可以推断出它们的值。拷贝数变异 (CNV) 是可遗传变异的普遍来源，可以促进快速适应性进化。此前，我们开发了一种位点特异性荧光 CNV 报告基因，用于使用恒化器量化在营养限制条件下维持的进化群体中的 CNV 动态。在这里，我们使用 CNV 适应动力学来估计通过从头突变引入有益 CNV 的速率及其使用基于模拟的无似然推理方法的适应度效果。我们测试了 2 种进化模型的适用性：标准 Wright-Fisher 模型和恒化器模型。我们评估了 2 种无似然推理算法：完善的顺序蒙特卡罗近似贝叶斯计算 (ABC-SMC) 算法，以及最近开发的神经后验估计 (NPE) 算法，该算法应用人工神经网络直接估计后验分布。通过系统地评估不同推理方法和模型的适用性，我们表明 NPE 比 ABC-SMC 具有多种优势，并且 Wright-Fisher 进化模型在大多数情况下就足够了。使用我们经过验证的推理框架，我们估计酿酒酵母中 GAP1 位点的 CNV 形成率为每次细胞分裂 10−4.7 至 10−4 个 CNV，并且谷氨酰胺限制恒化器中 GAP1 CNV 每代的适应系数为 0.04 至 0.1。我们使用两种不同的实验方法（条形码谱系跟踪和成对适应度测定）对基于推理的估计进行了实验验证，这为我们的方法的准确性提供了独立的确认。我们的结果与有益的 CNV 供应率一致，该供应率比有益的单核苷酸突变的估计率高 10 倍，这解释了 CNV 在快速适应性进化中的巨大重要性。更一般地说，我们的研究证明了基于新型神经网络的无似然推理方法的实用性，可以从经验数据推断进化过程的速率和影响，其可能的应用范围从肿瘤到病毒进化。这项研究表明，使用神经网络进行基于模拟的进化动力学推断可以产生难以通过实验确定的适应度和突变率参数值，包括酵母实验适应性进化过程中的拷贝数变异（CNV）的参数值。
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10−4.7 to 10−4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution. This study shows that simulation-based inference of evolutionary dynamics using neural networks can yield parameter values for fitness and mutation rate that are difficult to determine experimentally, including those of copy number variants (CNVs) during experimental adaptive evolution of yeast.
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影响因子：
3.9
作者：
Dulmage KA;Darnell CL;Vreugdenhil A;Schmid AK
通讯作者：
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影响因子：
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作者：
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