Volume 15, Issue 4 (12-2024)
Res Anim Prod 2024, 15(4): 83-92 |
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Mohammadi H, Najafi A. (2024). Identification of Positive Selection Signatures Associated with Important Economic Traits in Beetal Goats. Res Anim Prod. 15(4), 83-92. doi:10.61186/rap.15.4.83
URL: http://rap.sanru.ac.ir/article-1-1450-en.html
URL: http://rap.sanru.ac.ir/article-1-1450-en.html
1- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran
2- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Pakdasht, Iran
2- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Pakdasht, Iran
Abstract: (720 Views)
Extended Abstract
Background: The identification of selection targeted genomic regions is one of the main aims of biological research. Domestication and selection have significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans has left detectable signatures on the genome of modern cattle. The identification of these signals can help us to improve the genetic characteristics of economically important traits in cattle. The interest in the detection of genes or genomic regions that are targeted by selection has been growing over the last decade. Identifying the 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. This study aimed to identify effective genes and genomic regions on positive signatures of selection in Beetal goats using selection signature and gene ontology methods.
Methods: In this study, data from 631 Beetal goats genotyped using the Caprine 50 K Bead Chip were used to identify genomic regions under selection associated with important traits in Beetal goats. Quality control measures were performed in Plink by setting an animal call rate of 0.90, SNP call rate of 0.95, and SNPs with minor allele frequencies (MAF) lower than 0.05 or that do not conform to the Hardy–Weinberg expectation (p-value ≤ 0.000001) and unknown positions. After quality control of the initial data using Plink software (v1.90; http://pngu.mgh.harvard.edu/purcell/plink), 36,861 SNP markers in 594 animals of cattle were finally entered for further analysis. To identify the signatures of selection, the statistical method iHS was used under REHH software packages. Candidate genes were identified by SNPs located at 1% upper the range of iHS 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 economically important traits in identified locations. In addition, the DAVID database (http://david.abcc.ncifcrf.gov) was used to determine biological routes. At this stage, it is assumed that genes belonging to a functional class can be considered a group of genes that have some specific and common characteristics. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes. Moreover, gene ontology analysis for identified genes was performed using the DAVID online database.
Results: After quality control filters, 36,861 SNPs were left while 9761 SNPs were removed due to Hardy-Weinberg Equilibrium, 1342 SNPs were removed due to unknown positions, 3963 were removed due to a minor allele threshold, and 1342 were removed due to missing genotypes. Besides 37 individuals were removed after quality control measures. Using the iHS approach, 10 genomic regions were identified on chromosomes 4, 6, 7, 11 (two regions per chromosome), 13, 14, 15, 17, and 18. Some of the genes located in identified regions under selection were associated with body size (SPP1, SCN9A, and TNPO2), fat metabolism (SDCBP), skeletal development (IBSP and MEPE), and energy metabolism (UCP2, TRPC3, and FBP1). Some of the genes under selection were found to be consistent with some previous studies. Results of the gene ontology analysis identified two biological pathways, namely skeletal system development and a calcium channel complex, with two important KEGG pathways, including the glucagon signaling pathway and the AMPK signaling pathway, which play an important role in glucose metabolism, homeostasis, and skeletal system development.
Conclusion: Overall, various genes found within these regions can be considered the candidates under selection based on function. Most of the genes under selection were found to be consistent with some previous studies and to be involved in several processes, such as growth, body weight, metabolic pathways, and domestication-related changes, including system development and immune systems. Furthermore, a survey on extracted QTLs showed that these QTLs were involved in some important economic traits in goats, such as feed conversion ratio, subcutaneous fat, Marbling score, meat tenderness, body weight, average daily gain, conformation traits, the length of productive life, carcass traits, and composition carcass traits. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes and surveys 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 genetic progress in breeding programs and can be used to understand the genetic mechanism controlling this trait. The results of our research can be used to understand the genetic mechanism controlling growth traits. Since this study supported previous results from the genome scan of production traits and revealed additional regions, using these findings could potentially be useful for genetic selection in goats for a better body weight.
Background: The identification of selection targeted genomic regions is one of the main aims of biological research. Domestication and selection have significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans has left detectable signatures on the genome of modern cattle. The identification of these signals can help us to improve the genetic characteristics of economically important traits in cattle. The interest in the detection of genes or genomic regions that are targeted by selection has been growing over the last decade. Identifying the 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. This study aimed to identify effective genes and genomic regions on positive signatures of selection in Beetal goats using selection signature and gene ontology methods.
Methods: In this study, data from 631 Beetal goats genotyped using the Caprine 50 K Bead Chip were used to identify genomic regions under selection associated with important traits in Beetal goats. Quality control measures were performed in Plink by setting an animal call rate of 0.90, SNP call rate of 0.95, and SNPs with minor allele frequencies (MAF) lower than 0.05 or that do not conform to the Hardy–Weinberg expectation (p-value ≤ 0.000001) and unknown positions. After quality control of the initial data using Plink software (v1.90; http://pngu.mgh.harvard.edu/purcell/plink), 36,861 SNP markers in 594 animals of cattle were finally entered for further analysis. To identify the signatures of selection, the statistical method iHS was used under REHH software packages. Candidate genes were identified by SNPs located at 1% upper the range of iHS 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 economically important traits in identified locations. In addition, the DAVID database (http://david.abcc.ncifcrf.gov) was used to determine biological routes. At this stage, it is assumed that genes belonging to a functional class can be considered a group of genes that have some specific and common characteristics. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes. Moreover, gene ontology analysis for identified genes was performed using the DAVID online database.
Conclusion: Overall, various genes found within these regions can be considered the candidates under selection based on function. Most of the genes under selection were found to be consistent
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