Section of Population Genetics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
PLoS Comput Biol. 2020 Mar 23;16(3):e1007668. doi: 10.1371/journal.pcbi.1007668. eCollection 2020 Mar.
There is a long-standing interest in understanding host-parasite coevolutionary dynamics and associated fitness effects. Increasing amounts of genomic data for both interacting species offer a promising source to identify candidate loci and to infer the main parameters of the past coevolutionary history. However, so far no method exists to perform the latter. By coupling a gene-for-gene model with coalescent simulations, we first show that three types of biological costs, namely, resistance, infectivity and infection, define the allele frequencies at the internal equilibrium point of the coevolution model. These in return determine the strength of selective signatures at the coevolving host and parasite loci. We apply an Approximate Bayesian Computation (ABC) approach on simulated datasets to infer these costs by jointly integrating host and parasite polymorphism data at the coevolving loci. To control for the effect of genetic drift on coevolutionary dynamics, we assume that 10 or 30 repetitions are available from controlled experiments or several natural populations. We study two scenarios: 1) the cost of infection and population sizes (host and parasite) are unknown while costs of infectivity and resistance are known, and 2) all three costs are unknown while populations sizes are known. Using the ABC model choice procedure, we show that for both scenarios, we can distinguish with high accuracy pairs of coevolving host and parasite loci from pairs of neutrally evolving loci, though the statistical power decreases with higher cost of infection. The accuracy of parameter inference is high under both scenarios especially when using both host and parasite data because parasite polymorphism data do inform on costs applying to the host and vice-versa. As the false positive rate to detect pairs of genes under coevolution is small, we suggest that our method complements recently developed methods to identify host and parasite candidate loci for functional studies.
人们一直以来都对理解宿主-寄生虫协同进化动态及其相关适应度效应感兴趣。越来越多的交互物种基因组数据为识别候选基因座和推断过去协同进化历史的主要参数提供了有希望的来源。然而，到目前为止，还没有方法可以执行后者。通过将基因对基因模型与合并模拟相结合，我们首先表明，三种生物成本，即抗性、传染性和感染性，定义了协同进化模型内部平衡点的等位基因频率。这些反过来又决定了在协同进化的宿主和寄生虫基因座上选择特征的强度。我们应用近似贝叶斯计算 (ABC) 方法在模拟数据集上推断这些成本，方法是通过在协同进化基因座上共同整合宿主和寄生虫多态性数据。为了控制遗传漂变对协同进化动态的影响，我们假设从受控实验或几个自然种群中可以获得 10 或 30 次重复。我们研究了两种情况：1）感染成本和种群大小（宿主和寄生虫）未知，而传染性和抗性成本已知，2）所有三个成本都未知，而种群大小已知。使用 ABC 模型选择过程，我们表明，对于这两种情况，我们都可以以高精度区分协同进化的宿主和寄生虫基因座对与中性进化的基因座对，尽管随着感染成本的增加，统计能力会降低。在两种情况下，参数推断的准确性都很高，尤其是在同时使用宿主和寄生虫数据时，因为寄生虫多态性数据可以提供适用于宿主的成本信息，反之亦然。由于检测协同进化下基因对的假阳性率较小，我们建议我们的方法补充了最近开发的用于识别候选宿主和寄生虫基因座进行功能研究的方法。
