In order to identify the signatures of selection in three Iranian native cattle and Holstein breeds, genomic information of 153 native cattle (including 63 Sarabi, 44 Najdi and 46 Taleshi) and 60 Holstein cattle and 46 Brahma cattle (as an outgroup breed) were used. In order to determine the genotype of the samples, Illumina Bead Chip 40K (for native breeds) and Illumina Bead Chip 770K (for Holstein and Brahman breeds) were used. The genomic information of foreign breeds was extracted from the WIDDE database. After the quality control of the data, hapFLK statistical method with hapFLK v1.4 software was used to identify selection signatures. Considering the high hapFLK value of 0.1%, selection signatures were identified using the Ensmble Biomart tool, which included 57 genes on chromosome 25. Then, using the PANTHER database, the general biological function of the genes was checked, and the QTLs in the selected region were extracted using the Animalgenome database, and the genes were compared with other researches.The results showed that these genes were associated with different biological pathways such as ATP-dependent activity, binding, catalytic activity, molecular adapter activity, molecular function regulator, molecular transducer activity, transcription regulator activity and transporter activity.The QTLs reported in these areas were also related to the traits of stature and withers hight, milk yield and contents, muscle iron content, body weight and calving ease traits.

Introduction:  The principal aim of the sheep industry worldwide is to produce high-quality meat. In addition to the meat, milk, and wool production are the economic traits in sheep breeding programs. Wool production is one of the most important economic characteristics of sheep with a complex physiological and biochemical process that is influenced by genetics, environment, and nutrition. Almost all Iranian sheep breeds are double-coated and produce carpet wool. Therefore, considering the role of wool on the country's economy, it is necessary to conduct a study to identify the genetic factors affecting this trait. Identifying the genomic regions under selection is effective in understanding the processes involved in the evolution of the genome and also in identifying the genomic regions involved in the emergence of economic traits. Selective signatures in the whole genome can help us to understand the mechanisms of selection and to identify the genomic regions that have been under natural or artificial selection for many years. Since Selective signatures are usually associated with major effect genes and important economic traits, they can provide suitable information sources to improve the performance of selection programs in the future. The objective of this study was to identify the genomic regions that have been under selection in skin and wool sheep breeds.Materials and methods: In the present study, Illumina ovine SNP600K BeadChip genomic arrays of 80 sheep from six breeds were used, three breeds were bred for their skin (Karakul, SiahKabud, and Gray Shiraz) and three breeds were bred for their wool (Sanjabi, Kermani and Baluchi). Unbiased methods of Weir and Cockerham’s FST (Theta) and hapFLK were used to detect the selection signatures. Also, to check the genes and QTLs in the selected regions, the Biomart database, OAR 3.1 version of the sheep genome, was used, and the function of the identified genes was analyzed through a wide search in different databases such as Genecards and OMIM. Finally, the list of genes related to the selected regions was reported. For this purpose, the chromosomal position of SNPs with high numerical values of theta and hapFLK, as well as the 250 kbp region around these markers, were further investigated. Then, the DAVID database online search was used to investigate the biological and functional processes of genes and to study the ontology. Finally, Cytoscape software was used to determine gene networks.Then, DAVID database online search was used to investigate the biological and functional processes of genes and to study the ontology.سپس از جستجوی آنلاین پایگاه داده DAVID برای بررسی فرآیندهای بیولوژیکی و عملکردی ژن ها و مطالعه هستی شناسی استفاده شد.Then, to investigate the biological and functional processes of genes and to study the ontology, DAVID database was used to search online.سپس برای بررسی فرآیندهای بیولوژیکی و عملکردی ژن ها و مطالعه هستی شناسی از پایگاه داده DAVID برای جستجوی آنلاین استفاده شد.Can't load full resultsTry againRetrying...Results and discussion: The results of the Theta analysis revealed 26 genomic regions on 1, 2, 6, 19, and 24 chromosomes, and the results of hapFLK revealed seven genomic regions on 6 and 19 chromosomes. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with known genes related to pigment traits and characteristics of wool (KIT on chr 6, MITF on chr 19, IGSF10 on chr 1, PDGFRA on chr 6), muscles (MICALL2 on chr 24), vasodilation and immune response (P2RY on chr 1) and cancer (MIR339 on chr 24, ELFN1 on chr 24, MAD1L1 on chr 24, GPR87 on chr 1). The investigation of reported QTLs showed that these regions are related to QTLs of important economic traits, including traits related to meat, carcass, milk, body weight, bone density, and the total number of lambs born. Also, the analysis of Gene Ontology and Enriched pathway terms in regions under positive selection were related to the pathways involved in the differentiation of melanocytes and pigments, differentiation of stem cells, cellular processes, development of the immune system, blood system, reproductive and cellular processes. The results of the gene networks with the information obtained from Theta and hapFLK statistics showed that the genes identified were significantly active in the development and morphogenesis networks of the embryonic digestive tract, the networks related to pigment and melanocyte differentiation, and the networks related to Purine and G protein. However, to identify the exact function of the identified genes and QTLs, it is recommended to carry out more investigations.نتایج شبکه های ژنی با اطلاعات به دست آمده از آمار تتا و hapFLK نشان داد که ژن های شناسایی شده به طور معنی داری در شبکه های رشد و مورفوژنز دستگاه گوارش جنینی، شبکه های مربوط به تمایز رنگدانه و ملانوسیت و شبکه های مربوط بهThe results of the gene networks with the information obtained from theta and hapFLK statistics showed that the genes identified were significantly in the development and morphogenesis networks of the embryonic digestive system, the networks related to the differentiation of pigment and melanocytes and the networks related toنتایج شبکه های ژنی با اطلاعات به دست آمده از آمار تتا و hapFLK نشان داد که ژن های شناسایی شده به طور معنی داری در شبکه های رشد و مورفوژنز دستگاه گوارش جنینی، شبکه های مربوط به تمایز رنگدانه ها و ملانوسیت ها و شبکه های مربوط بهCan't load full resultsTry againRetrying...Conclusions: The results of the present study and the identified genomic regions can play an important role in the study of the effect of the selection on population differentiation in two sheep breeds that bred for skin and sheep production. Subsequently, this would direct us to identify the genomic regions associated with traits that differentiate these groups. However, these areas need to be confirmed in other independent studies with more samples. In general, the data of this research can be used in research related to genomic selection, design of mating systems, and additional reviews and evaluations to improve skin and wool production in sheep.

Introduction: The principal aim of the dairy sheep industry worldwide is to produce high-quality milk. Mastitis is an inflammatory disease in dairy animals that occurs in response to infectious factors and has substantial impacts on animal health, and economic profitability. Somatic cell count has been used as an indirect method to control mastitis. Genetic resistance to mastitis involves linked biological mechanisms resulting from differences in mastitis response, which activate and regulate different levels of the immune response. Over the last decade, interest in identifying genes or genomic regions targeted by selection has grown. Identifying selection signatures can provide valuable insights into 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. This study aimed to identify effective genes and genomic regions under positive selection in two sheep breeds using selection signature methods. For this purpose, FST and hapFLK analyses were performed using the genome-wide single nucleotide polymorphisms (SNPs). Materials and methods: In this study, data from 585 sheep of different breeds were used to identify genomic regions under selection related to the somatic cell counts in milk. Illumina ovine Bead Chip 50K was used to determine the genotype of the samples. The genomic information of sheep breeds was extracted from the Zenodo database. Quality control on genotyped samples was performed using PLINK v1.9. SNPs with a minor allele frequency (MAF) of less than 0.02 and those with a call rate of less than 0.97 were excluded. In addition, individuals with more than 10% missing genotype data were removed. SNPs that did not conform to Hardy–Weinberg equilibrium (P<10-6) were also eliminated. Following these quality control measures, 41,673 SNPs from 585 sheep were retained for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under the software packages FST and hapFLK, respectively. The candidate genes were identified using PLINK v1.9 software and the Illumina gene list in R by SNPs located in the 0.01 percentile of FST and hapFLK values. In addition, the latest published version of the animal genome database was used to define QTLs associated with economically important traits at identified loci. The 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: Using the FST approach, nine genomic regions on chromosomes 2, 5, 7, 10, 11, 13 (two regions), 17, and 22 were identified. The identified candidate genes associated with somatic cell count in these genomic regions included IL11RA, CDC16, CARD14, BTRC, OTUD4, COL23A1, LACTB, and PRELID3B. Some of the genes located in the identified selection regions were associated with the immune system, innate immune response, inflammation response, cancer disease, and milk production. Some of these genes in the selected regions were consistent with previous studies. The investigation of reported QTLs showed that these regions are related to QTLs of important economic traits, including milk somatic cell count, udder height and depth, clinical mastitis, bovine tuberculosis susceptibility, and heat tolerance. Moreover, the results of hapFLK statistics in this research led to the identification of six genomic regions on chromosomes 3, 4, 5, 7, 10, and 13. The identified candidate genes associated with the somatic cell count in these genomic regions included FAM49A, CDK6, and DLGAP5. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with known genes related to innate immune and various cancers. Conclusions: Different genes that emerged in the aforementioned regions can be considered candidates for selection based on their function. It was found that most of the selected genes were consistent with some previous studies and were involved in production traits. However, further investigation is recommended to determine the exact function of the identified genes and QTLs. These areas should also be confirmed by other independent studies using larger samples. In general, the data from this study may be used in research on genomic selection and genomic regions associated with mastitis in dairy sheep and in further reviews and evaluations for the improvement of dairy sheep production.

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.

Objective Over the last decade, interest in 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. The aim of this study was to identify genomic region with a positive signature of selection related to wool quality in sheep breeds using selection signature FST and hapFLK methods. Materials and methods In this study, data from 146 Zandi and Merino animals genotyped using 50 K BeadChip were used to identify genomic regions under selection associated with wool quality traits. After quality control of the initial data using plink software, 46,793 SNP markers in 144 animals of sheep were finally entered for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under FST and hapFLK software packages, respectively. Candidate genes were identified by SNPs located at 0.1% upper range of FST and hapFLK values. GeneCards and UniProtKB databases were also used to interpret the function of the obtained genes. Results Using FST approach, we identified eight genomic regions on chromosomes 1 (two region), 1, 3 (two region), 10, 13, and 19 chromosomes, which were in the 99.9 percentile of all FST values. The identified candidate genes associated with wool trait in these genomic regions included POU1F1, FGF12, GNAS, LHX2, TMTC3, NBEA, MITF. Genes located in identified regions under selection were associated with the crimp of wool, growth and development of skin, fiber diameter, hair follicle development, growth and development of fiber, stimulation of collagen, regulation of epithelial cell and skin pigments, which can be directly and indirectly related to the trait of the wool quality. Also, the results of hapFLK statistics in this research led to the identification of four genomic regions on chromosomes 7, 10, 14, and 19. The identified candidate genes associated with the wool trait in these genomic regions included DUOX1, RHPN2 and LOC106991379. It was determined that they had different functions in collagen differentiation and gene expression of keratinocytes. Conclusions Various genes that were founded within these regions can be considered as candidates for selection based on function. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. The results of our research can be used to understand the genetic mechanism controlling wool traits and using these findings could potentially be useful for genetic selection in sheep for better clean fleece yield and mean fiber diameters.

Introduction: Molecular markers that reveal polymorphisms at the DNA level now play a key role in animal genetics. However, the selection of molecular markers is crucial depending on the purpose, viz. this depends on different molecular biology techniques and their effects. Over the last decade, interest in identifying genes or genomic regions targeted by selection has grown. Identifying selection signatures can provide valuable insights into 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. Type characteristics are important for breed identification and classification and are also positively correlated with body weight. This study aimed to identify effective genes and genomic regions under positive selection signatures in different goat breeds using selection signature methods. For this purpose, FST and hapFLK analyses were performed using the genome-wide single nucleotide polymorphisms (SNPs). Materials and methods: In this research, the information from 728 goats of four different breeds was used to identify genomic regions associated with type traits. To determine the genotype of the samples, Illumina caprine Bead Chip 50K was used. The genomic information of goat breeds was extracted from the Figshare database. Quality control was performed using the Plink software. The markers or individuals were excluded from 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 10-3. After quality control, 36861 SNPs from goat SNP chip 50K from 691 goats remained for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under the software packages FST and hapFLK, respectively. Candidate genes were identified using the Plink v1.9 software and the Illumina gene list in R by SNPs located in the highest  FST and hapFLK values. In addition, the latest published version of the animal genome database was used to define QTLs associated with economically important traits at identified loci. The 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: The FST and hapFLK statistics were used to identify genomic regions subjected to positive selection associated with type traits in four goat breeds. Using the FST approach, we identified eight genomic regions on chromosomes 3, 4, 7, 13, 15, 18, 20, and 29. The identified candidate genes associated with type traits in these genomic regions included TGFBR3, CALCR, ACAD8, BCAR1, and ADAMTS6. Some of the genes located in the identified selection regions were directly and indirectly related to cell differentiation and proliferation, skeletal muscle growth and development, body length, calcium channel regulation, muscle fiber homeostasis, protein synthesis, and muscle cell size. Some of these genes in the selected regions were consistent with previous studies. The results of the reported QTLs in the selected regions and the bovine orthologous regions were QTLs located in the identified regions that were related to average daily gain, body weight, trunk width, and metabolic body weight. Furthermore, the results of the hapFLK statistics in this research led to the identification of five genomic regions on chromosomes 1, 5, 6, 13, and 30, and they were in the 99.9th percentile of all hapFLK values. The identified candidate genes associated with the type trait in these genomic regions included FNDC3B, STAB2, and CCNY. They were found to have different functions in fibroblast proliferation and bone cell differentiation. Conclusions: Various/different genes that emerged in studied regions can be considered candidates for selection based on their function. By the way, various genes found in these regions can be considered candidates for selection based on their function. Most of the selected genes were found to be consistent with some previous studies and to be involved in production traits. A survey of extracted QTLs also found that these QTLs are involved in some economically important traits in goats, such as average daily gain and body weight in yearlings. However, further association and functional studies are required to demonstrate the importance of the genes obtained from association analyses. Leveraging these findings can accelerate genetic progress in breeding programs and help understand the genetic mechanism that controls these traits. Material and Methods: In this research, to identify genomic regions under selection associated with type traits were used the information obtained from 728 goats of different breeds including Beetal, Daira Deen Panah, Barbari, Teddi, In order to determine the genotype of the samples, Illumina caprine Bead Chip 50K were used. The genomic information of goat breeds was extracted from the figshare database. 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 10-3. After quality control, 36,861 SNPs from Goat SNP chip 50K on 691 goats were remained for the future analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under FST and hapFLK software packages, respectively. Candidate genes were identified by SNPs located at 1% upper range of FST and hapFLK using Plink v1.9 software and the gene list of Illumina in R. Additionally, the latest published version of Animal genome database was used for defining QTLs associated with economic important traits in identified locations. 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: We used the FST and hapFLK statistics to identify genomic regions that have been under positive selection associated with type traits in four goat breeds. Using FST approach, we identified eight genomic regions on chromosomes 3, 4, 7, 13, 15, 18, 20, and 29 chromosome. The identified candidate genes associated with type trait in these genomic regions included TGFBR3, CALCR, ACAD8, BCAR1, ADAMTS6. Some of the genes located in identified regions under selection were associated with the cell differentiation and proliferation, skeletal muscle growth and development, body length, calcium channel regulation, muscle fiber homeostasis, protein synthesis and muscle cell size which can be directly and indirectly related to the trait of the type traits. Some of these genes in the selected regions were consistent with previous studies. Result of the reported QTLs in the selected regions and the orthologous regions of cattle were located in the identified regions, QTLs related to average daily gain, body weight, rump width and body metabolic weight. Also, the results of hapFLK statistics in this research led to the identification of five genomic regions on chromosomes 1, 5, 6, 13, and 30, and they were in the 99.9 percentile of all hapFLK values. The identified candidate genes associated with the type trait in these genomic regions included FNDC3B, STAB2 and CCNY. It was determined that they had different functions in proliferation of fibroblasts and differentiation of bone cells. Result of the reported QTLs in the selected regions and orthologous cattle in the identified regions, QTLs related to metabolic body weight were located.Conclusion: various genes that were founded within these regions can be considered as candidates under selection based on function. Most of the genes under selection were found are consistent with some previous studies and to be involved in production traits. Also, survey on extracted QTLs was shown that these QTLs involved in some economical important traits in goat such as average daily gain and body weight in yearling. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes and survey on QTLs related to selected regions. However, will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. Using these findings can accelerate the genetic progress in the breeding programs and can be used to understand the genetic mechanism controlling this trait.

Selective signatures provide information about the stages of evolution of different species, including sheep, which lead to changes in their genome over many years. The aim of this study was to detect signatures of selection in the genome of Iranian sheep in the highlands. A total of 58 sheep from 4 breeds, two breeds scattered in the highlands and two breeds scattered in the lowlands, were genotyped using Illumina ovine SNP600K BeadChip genomic arrays. Two statistical tests of unbiased FST (Theta) and hapFLK were used to identify the selection signatures. The results of Theta revealed 22 genomic regions and the results of hapFLK revealed 15 genomic regions. Bioinformatics analysis demonstrated that many of genes had important effects on adaptation to hypoxia conditions e.g. rheumatoid arthritis (TXNDC5), hematopoiesis (FANCA), immunity and fighting infection (LEF1, NMUR1, PTMA and COPS7B), regulating blood pressure and responding to pain and inflammation (NMUR1) and suppressing cancer (CD82, GAS8, PRMT1, B3GNT7). For example, TXNDC5 and FANCA functional genes, which are located on chromosome 13 and 14, were related to reacts to hypoxic conditions and hematopoiesis. Also, genes such as LEF1, NMUR1, PTMA and COPS7B are effective in immunogenicity and fighting infection. Study of the reported QTL in these regions of the sheep genome showed that they overlapped with QTL of economically important traits such as corpuscular hemoglobin concentration, hematocrit, traits related to meat, carcass, milk, body weight, bone density, and the total number of lambs born. Due to the fact that little research has been done regarding adaptation to altitude in sheep, the results of this research may facilitate the identification of genes affecting adaptation to altitude. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes.

Selection to increase the frequency of new mutations useful only in some subpopulations leaves markers at the genome level. Most of these regions are related to genes and QTLs associated with significant economic traits. The aim of this study was to identify selection traits related to functional traits in the number of world's sheep using modern methods. For this purpose, genotyping files were used using genomic chips related to 1591 sheep from different breeds of the world, (Dryad، Frontiresin، Zenodo، Animal Genom، Hapmap( including 52 breeds from 20 countries on all continents, and 13 studies were stored in various genomic databases. First, to determine the population mixing between different breeds from regions with different climatic conditions, population structure analysis or population stratification was carried out. Fst, hapflk, iHs, Rsb, XpEHH tests were used to identify the selection signatures, and finally, a meta-analysis of the above methods was used. The population was divided into three groups by PCA analysis: AIT: Asian, Turkish and African breeds, AUNZ: Australian and New Zealand breeds, and EU: European breeds. Based on the meta-analysis test, 10 genus regions were identified on chromosomes 2, 3, 4, 5, 6, 7, 9, 12, 20, 22. For example, the MC1R gene is associated with skin color, the DSG2 gene with the quality and quantity of wool, and the AICDA gene with the production of immunoglobulin. Bioinformatics analysis showed that some of these gene regions are directly and indirectly associated with genes affecting environmental adaptation (ADAMTS9), reproduction (GNAQ), melanocyte regulation (KITLG), and immunoglobulin production (BATF, TNFSF13, AICDA, MZB1), Cell differentiation (TNFSF13, TGFB1), Immune process (AGBL5, ATG7, PRDX1, CCL26, IL17RC, TNFSF12, TGFB2, PTGS1), Cellular response to external stimulation, NKK1, PRF3 (PR) Interferon alpha (DDX58), defense response process (CCL24, FAM13B, LDLR, CLDN7), cellular homeostasis (BAMBI, DVL2), regulation of mitogen-activated protein kinase (PLCE1) and regulation of stress response (FXR2, HSPH1). Comparative analysis of meta-analysis of selection cues based on bioinformatics databases can identify selected genomic regions for traits of economic and biological importance in sheep.