Host-parasite coevolution is ubiquitous, shaping genetic and phenotypic diversity and the evolutionary trajectory of interacting species. With the advances of high throughput sequencing technologies applicable to model and non-model organisms alike, it is now feasible to study in greater detail (a) the genetic underpinnings of coevolution, (b) the speed and type of dynamics at coevolving loci, and (c) the genomic consequences of coevolution. This review focuses on three recently developed approaches that leverage information from host and parasite full genome data simultaneously to pinpoint coevolving loci and draw inference on the coevolutionary history. First, co-genome-wide association study (co-GWAS) methods allow pinpointing the loci underlying host-parasite interactions. These methods focus on detecting associations between genetic variants and the outcome of experimental infection tests or on correlations between genomes of naturally infected hosts and their infecting parasites. Second, extensions to population genomics methods can detect genes under coevolution and infer the coevolutionary history, such as fitness costs. Third, correlations between host and parasite population size in time are indicative of coevolution, and polymorphism levels across independent spatially distributed populations of hosts and parasites can reveal coevolutionary loci and infer coevolutionary history. We describe the principles of these three approaches and discuss their advantages and limitations based on coevolutionary theory. We present recommendations for their application to various host (prokaryotes, fungi, plants, and animals) and parasite (viruses, bacteria, fungi, and macroparasites) species. We conclude by pointing out methodological and theoretical gaps to be filled to extract maximum information from full genome data and thereby to shed light on the molecular underpinnings of coevolution.

中文翻译：

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研究宿主和寄生虫之间对抗性协同进化的新基因组方法

宿主-寄生虫共同进化无处不在，塑造了遗传和表型多样性以及相互作用物种的进化轨迹。随着适用于模型和非模型生物的高通量测序技术的进步，现在可以更详细地研究 (a) 共同进化的遗传基础，(b) 共同进化位点的动力学速度和类型，以及(c) 共同进化的基因组后果。本综述重点介绍了三种最近开发的方法，它们同时利用来自宿主和寄生虫的全基因组数据的信息来确定共同进化的位点并推断共同进化的历史。首先，全基因组关联研究 (co-GWAS) 方法允许精确定位宿主 - 寄生虫相互作用的潜在位点。这些方法侧重于检测遗传变异与实验感染测试结果之间的关联，或自然感染宿主的基因组与其感染寄生虫之间的相关性。其次，种群基因组学方法的扩展可以检测共同进化下的基因并推断共同进化历史，例如适应度成本。第三，宿主和寄生虫种群大小在时间上的相关性表明了共同进化，宿主和寄生虫独立空间分布种群的多态性水平可以揭示共同进化位点并推断共同进化历史。我们描述了这三种方法的原理，并基于协同进化理论讨论了它们的优点和局限性。我们提出了将它们应用于各种宿主（原核生物、真菌、植物、和动物）和寄生虫（病毒、细菌、真菌和大型寄生虫）物种。最后，我们指出了需要填补的方法学和理论空白，以从全基因组数据中提取最大信息，从而阐明协同进化的分子基础。