Selection signatures in livestock genome: A review of concepts, approaches and applications

• Published in: Livestock science Vol. 241; p. 104257
• Main Authors: Saravanan, K.A., Panigrahi, Manjit, Kumar, Harshit, Bhushan, Bharat, Dutt, Triveni, Mishra, B.P.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: bioinformatics; domestication; genomics; linkage disequilibrium; livestock; positive selection; selection signatures; genomics; positive selection; selection signatures; bioinformatics; livestock
• ISSN: 1871-1413; 1878-0490
• DOI: 10.1016/j.livsci.2020.104257

•Owing to developments in high-throughput technologies, the genome-wide detection of selection signatures has become more common in recent years•Various statistical methods have been developed for the identification of selection signatures based on site frequency spectrum, linkage disequilibrium, reduced local variability, and population differentiation.•In future, the advanced Machine Learning enabled neural networks can also be applied in livestock populations to handle large &complex data sets efficiently and to improve the accuracy of detection of selection signals.•In this review, we have discussed the results of significant studies done in the recent years. 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.