Inference of coevolutionary dynamics and parameters from host and parasite polymorphism data of repeated experiments

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.

学界长期以来致力于解析宿主-寄生虫协同进化动力学及其相关适合度效应。两类互作物种的基因组数据日益丰富，为鉴定候选基因座（locus）以及推断过去协同进化历史的核心参数提供了极具前景的途径。然而，目前尚无方法可实现后者（即推断协同进化历史的核心参数）。本研究将基因对基因模型（gene-for-gene model）与溯祖模拟（coalescent simulations）相结合，首先证实三类生物学代价——分别为抗性（resistance）、侵染力（infectivity）与感染（infection）——决定了协同进化模型内部平衡点处的等位基因频率，而这些代价反过来决定了协同进化的宿主与寄生虫基因座上的选择信号强度。我们采用近似贝叶斯计算（Approximate Bayesian Computation, ABC）方法处理模拟数据集，通过整合协同进化基因座上的宿主与寄生虫多态性数据，来推断上述三类生物学代价。为控制遗传漂变（genetic drift）对协同进化动力学的影响，本研究假设可从受控实验或多个自然种群中获取10组或30组重复数据。本研究设置了两种研究场景：① 感染代价与种群大小（宿主与寄生虫）为未知参数，而侵染力代价与抗性代价已知；② 三类生物学代价均为未知参数，而种群大小已知。借助近似贝叶斯计算模型选择流程，本研究证实：在两种场景下，我们均可高准确度地区分协同进化的宿主-寄生虫基因座对与中性进化基因座对，不过统计效力会随感染代价升高而下降。在两种场景下，参数推断的准确度均较高，尤其当同时使用宿主与寄生虫数据时效果更佳——这是因为寄生虫多态性数据可反映宿主相关的代价，反之亦然。由于检测协同进化基因对的假阳性率较低，本研究认为我们提出的方法可与近期开发的、用于鉴定宿主与寄生虫候选基因座以开展功能研究的方法形成互补。