Professorship for Population Genetics, Department of Life Science Systems, School of Life Sciences, Technical University of Munich, Freising, Germany.
Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
Mol Ecol. 2021 Aug;30(15):3660-3676. doi: 10.1111/mec.16001. Epub 2021 Jun 22.
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.
宿主-寄生虫协同进化无处不在，它影响着相互作用的物种的遗传和表型多样性以及进化轨迹。随着高通量测序技术在模型和非模型生物中的应用不断发展，现在可以更详细地研究（a）协同进化的遗传基础，（b）协同进化位点的动态速度和类型，以及（c）协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，这些方法利用宿主和寄生虫全基因组数据的信息来精确定位协同进化的基因座，并推断协同进化的历史。首先，共全基因组关联研究（co-GWAS）方法可以确定宿主-寄生虫相互作用的基因座。这些方法专注于检测遗传变异与实验感染测试结果之间的关联，或检测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，对种群基因组学方法的扩展可以检测协同进化的基因，并推断协同进化的历史，例如适应性成本。第三，宿主和寄生虫在时间上的种群大小之间的相关性表明存在协同进化，并且独立空间分布的宿主和寄生虫种群之间的多态性水平可以揭示协同进化的基因座并推断协同进化的历史。我们描述了这三种方法的原理，并根据协同进化理论讨论了它们的优点和局限性。我们提出了针对各种宿主（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）物种应用这些方法的建议。最后，我们指出了要从全基因组数据中提取最大信息并阐明协同进化的分子基础，需要填补的方法学和理论空白。
