Livestock populations have been consistently improving in terms of performance and productivity by selective breeding over the centuries. These selection strategies are expected to leave footprints in the genome that are identified as selection signatures. Due to advances in high-throughput technologies, the genome-wide detection of selection signatures has become more widespread in recent years. Such studies provide insights into the domestication and evolutionary processes that resulted in a huge number of livestock breeds able to live in diverse environments and production systems. Furthermore, these studies facilitate the identification of candidate genes under selection that are associated with economically important traits in livestock populations. Various statistical methods have been developed for the detection of selection signatures based on site frequency spectrum, linkage disequilibrium, reduced local variability, and population differentiation. The objectives of this review are to give a comprehensive overview of the general concept, various methodologies, and bioinformatics tools currently available for the detection of selective sweeps and to summarize the results of recent selection signature studies carried out in various livestock species.

几个世纪以来，通过选择性育种，牲畜种群的性能和生产力一直在不断提高。这些选择策略有望在基因组中留下被识别为选择标记的足迹。由于高通量技术的进步，近年来，选择标记的全基因组检测变得越来越普遍。这些研究为驯化和进化过程提供了见识，这些过程导致大量的牲畜品种能够生活在不同的环境和生产系统中。此外，这些研究有助于鉴定与牲畜种群中具有重要经济意义的性状相关的候选基因。已经开发了各种统计方法，用于基于站点频谱，连锁不平衡，减少的局部变异性和种群分化来检测选择特征。此次审查的目的是全面概述当前可用于检测选择性扫除的一般概念，各种方法和生物信息学工具，并总结近期在各种牲畜物种中进行的选择标记研究的结果。