Identification of positive selection signatures associated with milk and composition traits in Murciano-Granadina and Barki

Introduction Understanding the genomic features of livestock is essential for successful breeding programs and conservation. Artificial selection is one of the major forces modifying the genetic composition of livestock populations. 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 left detectable signatures on the genome of modern goat. Understanding the genomic features of livestock is essential for successful breeding programs and conservation. Artificial selection is one of the major forces modifying the genetic composition of livestock populations. Identifying genes under selection could be useful to elucidate their impact on phenotypic variation. 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. This study aimed to identify effective genes and genomic regions on positive signature of selection in Murciano-Granadina and Barki goats using selection signature method.Material and Methods: In this study, data from 923 Murciano-Granadina and 68 Barki goats genotyped using Caprine 50 K Bead Chip were used to identify genomic regions under selection associated with important traits. 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 position. After quality control of the initial data using Plink software (v1.90; http://pngu.mgh.harvard.edu/purcell/plink), 43,170 SNP markers were finally entered for further analysis. To identify the signatures of selection, the statistical method FST was used under R software packages. Candidate genes were identified by SNPs located at 1% upper range of FST 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. 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 FST approach, we identified seven genomic regions on chromosomes 4, 6, 14, 15 (two regions per chromosome), 17, and 23 chromosomes. Candidate genes were detected. Some of the genes located in identified regions under selection were associated with milk yield, fat yield and fat metabolism, milk protein percentage and cholesterol milk content. Some of the genes under selection were found to be consistent with some previous studies|. Results of gene ontology analysis identified two biological pathways namely skeletal system development and calcium channel complex with two important KEGG pathways including glucagon signaling pathway and AMPK signaling pathway which play an important role in glucose metabolism and homeostasis and skeletal system development. Conclusion: By the way, various genes that were found within these regions can be considered as 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 milk yield and metabolic pathways. Also, a survey on extracted QTLs showed that these QTLs involved in some economical important traits in goat such as milk yield and milk composition traits. 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. The results of our research can be used to understand the genetic mechanism controlling milk and composition traits and considering, this study supported previous results from genome scan of production traits, also revealed additional regions, using these findings could potentially be useful for genetic selection in goat for better milk yield.