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The detection of selection signatures assists in understanding domestication, evolution, and the identification of genomic regions related to adaptation and production traits in buffaloes. The emergence of high-throughput technologies like Next Generation Sequencing and SNP genotyping had expanded our ability to detect these signatures of selection. In this study, we sought to identify signatures of selection in five buffalo populations (Brazilian Murrah, Bulgarian Murrah, Indian Murrah, Nili-Ravi, and Kundi) using Axiom Buffalo 90 K Genotyping Array data. Using seven different methodologies (Tajima's D, CLR, ROH, iHS, FST, FLK and hapFLK), we identified selection signatures in 374 genomic regions, spanning a total of 381 genes and 350 quantitative trait loci (QTLs). Among these, several candidate genes were associated with QTLs for milk production, reproduction, growth and carcass traits. The genes and QTLs reported in this study provide insight into selection signals shaping the genome of buffalo breeds. Our findings can aid in further genomic association studies, genomic prediction, and the implementation of breeding programmes in Indian buffaloes. [ABSTRACT FROM AUTHOR]

Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next‐generation sequencing, high‐density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large‐scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data. [ABSTRACT FROM AUTHOR]

Domestication and selection are the major driving forces responsible for the determinative genetic variability in livestock. These selection patterns create unique genetic signatures within the genome. BovineSNP50 chip data from 236 animals (seven indicine and five taurine cattle breeds) were analyzed in the present study. We implemented three complementary approaches viz. iHS (Integrated haplotype score), ROH (Runs of homozygosity), and F ST , to detect selection signatures. A total of 179, 56, and 231 regions revealed 518, 277, and 267 candidate genes identified by iHS, ROH, and F ST methods, respectively. We found several candidate genes (e.g., NCR3 , ARID5A , HIST1H2BN , DEFB4 , DEFB7 , HSPA1L , HSPA1B, and DNAJB4) related to production traits and the adaptation of indigenous breeds to local environmental constraints such as heat stress and disease susceptibility. However, further studies are warranted to refine the findings using a larger sample size, whole-genome sequencing, and/or high density genotyping. • The combination of various statistical approaches could facilitate the wider spectrum of detection of both recent and ancient selection signatures. • The identified candidate genes provide insights into the adaptation and production performance of various cattle breeds. • Our observations can provide valuable knowledge for GWAS, genomic selection, implementing breeding schemes and conservation programmes. [ABSTRACT FROM AUTHOR]

• Indian pig breeds were found to be evolutionarily closer to Chinese than European pig breeds. • · Genetic diversity in Indian and Chinese breeds was found to be lower than European breeds. • In Indian pigs genes for adaptation to tropical climate and low input scavenging system were under positive selection. In the present study, the genetic diversity measures among four Indian domestic breeds of pig namely Agonda Goan, Ghurrah, Ghungroo, and Nicobari, of different agro-climatic regions of country were explored and compared with European commercial breeds, European wild boar and Chinese domestic breeds. The double digest restriction site-associated DNA sequencing (ddRADseq) data of Indian pigs (102) and Landrace (10 animals) were generated and whole genome sequencing data of exotic pigs (60 animals) from public data repository were used in the study. The principal component analysis (PCA), admixture analysis and phylogenetic analysis revealed that Indian breeds were closer in ancestry to Chinese breeds than European breeds. European breeds exhibited highest genetic diversity measures among all the considered breeds. Among Indian breeds, Agonda Goan and Ghurrah were found to be more genetically diverse than Nicobari and Ghungroo. The selection signature regions in Indian pigs were explored using iHS and XP-EHH, and during iHS analysis, it was observed that genes related to growth, reproduction, health, meat quality, sensory perception and behavior were found to be under selection pressure in Indian pig breeds. Strong selection signatures were recorded in 24.25–25.25 Mb region of SSC18, 123.25–124 Mb region of SSC15 and 118.75–119.5 Mb region of SSC2 in most of the Indian breeds upon pairwise comparison with European commercial breeds using XP-EHH. These regions were harboring some important genes such as EPHA4 for thermotolerance, TAS2R16 , FEZF1, CADPS2 and PTPRZ1 for adaptability to scavenging system of rearing, TRIM36 and PGGT1B for disease resistance and CCDC112, PIAS1, FEM1B and ITGA11 for reproduction. [ABSTRACT FROM AUTHOR]

• The natural and artificial selection are expected to impose pressure on specific genomic regions and leave footprints called as "selection signatures" • We identified selective sweep regions based on Runs of Homozygosity (ROH) and Integrated Haplotype Score (iHS) • Highlighted candidate genes related to adaptation and production traits Knowledge about genetic diversity and selection signatures are very essential for the management and sustainable utilization of sheep genetic resources in India. In this study, we performed a comprehensive genome-wide analysis of genetic diversity and selection signatures using SNP data. A total of 79 animals from three Indian sheep breeds: Changthangi (n = 29), Deccani (n = 24), and Garole (n = 26) genotyped with Illumina Ovine SNP50 BeadChip were included in this study. The average minor allele frequency was 0.284 with a standard deviation of 0.129. The overall mean observed (H O) and expected heterozygosity (H E) were 0.369±0.141 and 0.373±0.118, respectively. The proportion of polymorphic SNPs (P N) ranged from 0.84 (Garole) to 0.93 (Changthangi). The average pairwise genetic distance (D) ranged from 0.301 (Garole) to 0.313 (Changthangi). We identified a total of 9244 ROH segments and the genomic inbreeding coefficients ranged from 0.0673 (Deccani) to 0.1779 (Garole). Two different statistical approaches (ROH and iHS) revealed selection signatures in 37 genomic regions mapping to 188 genes across 16 of the 26 autosomes in three sheep breeds. The selective sweeps provide new insights into the local adaptation and production potential of three Indian sheep breeds. Some notable candidate genes include the TRPM8 gene involved in cold adaptation, JADE2, PLEKHB2, SPP2, TSHR, and UBE2B genes associated with meat quality traits, and the PPP3CA gene associated with fecundity. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]