Genomic scan for positive selection signatures associated with economically important traits in Iranian (Zandi) and Egyptian (Barki and Rahmani) sheep breeds using hapFLK method

Introduction: Artificial and natural selection not only increases the frequency of new-useful mutations but also remains some signals throughout the genome. Since these regions often control economically important traits, identifying and tracking these regions is the most important subject in animal genetics. Also, natural and artificial selection related to adaptation and economic traits, such as litter size, results in changes at the genomic level which leads to the appearance of selection signatures. Several tests including the linkage disequilibrium-based approach, site frequency spectrum, and population differentiation-based approach have been developed to explore the footprints of selection in the genome. Domestication and selection have significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans left detectable signatures on the genome of modern sheep. The identification of these signals can help us to improve the genetic characteristics of economically important traits in sheep. Over the last decade, interest in the detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. One of the best ways to understand physiological processes is to analyze gene regulation networks. Identification of genes involved in economic traits as molecular markers in breeding is of special importance. Gene regulation networks enable the researcher to study all of the genes together. This study aimed to identify selection signature regions and candidate genes related to adaptation and the number of lambs born. Materials and methods: To identify the signatures of selection in Iranian native sheep and Egyptian breeds, genomic information of 96 native sheep (including 96 Zandi) and 107 Egyptian sheep (including 59 Barki and 48 Rahmani) were used. The genomic information of foreign breeds was extracted from the Dryad database (https://dryad.com/articles/dataset). To determine the genotype of the samples, Illumina Bead Chip 50K was used. Quality control was conducted using the Plink software. The markers or individuals were excluded from the further study based on the following criteria: unknown chromosomal or physical location, call rate <0.95, missing genotype frequency >0.05, minor allele frequency (MAF) < 0.05, and a P-value for Hardy–Weinberg equilibrium test less than 10-6. After the quality control of the data, the hapFLK statistical method, with hapFLK v1.4 software, was used to identify selection signatures. The genomic version of the Oar_v4.0 database in NCBI was used for detecting the genomic position of single nucleotide polymorphisms (SNPs) in the sheep genome. Candidate genes were identified by SNPs located at 0.1% upper range of hapFLK using BioMart software in ensemble 109. Then, using the PANTHER database, the general biological function of the genes was checked. At this stage, it is assumed that genes that belong to a functional class can be considered as a group of genes that have some specific and common characteristics, and the QTLs in the selected region were extracted using the Animal Genome database, and the genes were compared with other research. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes. Results and discussion: Based on the results of hapFLK, by comparing the Zandi population with Egyptian breeds (Barki and Rahmani), seven genomic regions on chromosomes 1, 2 (three regions), 10, 25, and 26 were identified. Candidate genes of DNAJB4, FNDC3B, GULP1, ACVR1, and FGF9 were in these regions. Further investigation using bioinformatics tools showed these genomic regions overlapped with the immune system, adaptation, litter size, and lipid and muscle metabolism. Conclusions: The results of this study may provide an important source to facilitate the identification of genomic regions and then, the genes affecting economically important traits in the sheep industry. However, it will be necessary to carry out more association and functional studies to demonstrate the implications of these genes. Therefore, in subsequent studies with more samples and more breeds of domestic and wild sheep in Iran, a better understanding of candidate genes for important economic traits in domestic and wild species would be achieved.