Abbas Mirzapour-Abibagloo; Nemat Hedayat; Reza Khalkhali-Evrigh; Reza Seyedsharifi
Volume 26, Issue 1 , March 2024, , Pages 1-13
Abstract
Introduction Iranian sheep breeds, due to the climate diversity of country, show high diversity and have acquired high adaptability. Compromise with poor quality feed, tolerance of adverse weather and manageable body size are among the factors that probably caused sheep to adapt to different climates. ...
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Introduction Iranian sheep breeds, due to the climate diversity of country, show high diversity and have acquired high adaptability. Compromise with poor quality feed, tolerance of adverse weather and manageable body size are among the factors that probably caused sheep to adapt to different climates. Heretofore, several studies have been carried out in the field of identification of selection signatures in the different native breeds based on SNP-chip data. However, the use of whole genome data can provide researchers with more information about the differences between breeds and their genetic capacities. Identifying and evaluating the effects of climate on the genome of native breeds of Iranian sheep can be effective in designing breeding and conservation strategies. The aim of the present study was to identify the signs of selection related to Iranian sheep compared to the Romanov breed at the genomic level.Materials and Methods For present study, we used the whole-genome sequencing data related to 43 Iranian and non-Iranian sheep available in the NCBI database. These reads, after performing quality control, were aligned to the sheep reference genome by BWA program. Here, RealignerTargetCreator and IndelRealigner commands available in the GATK program were used to realign around insertions and deletions. Then, the HaplotypeCaller algorithm was used to identify the variants of all samples in ERC GVCF mode. Further, using GenotypeGVCFs module, the variants of all samples were simultaneously identified and finally a VCF file containing raw variants was created. Using the SelectVariants command of the GATK program, all SNPs were separated from other variants. After applying multiple quality filters, high-quality SNPs were extracted and only bi-allelic SNPs present in autosomal chromosomes were used for downstream analysis. Putative selection signatures were identified by using two methods including Fst and XP-EHH. Genes located in positively selected genomic regions were extracted using BEDtools program and the GTF file related to the sheep genome. Gene ontology (GO) analysis was performed on selected genes by "g:Profiler" web-based tool.Results and Discussion Here, Fst and XP-EHH methods were used to identify the signatures of selection related to Iranian sheep in comparison with Romanov sheep. After converting Fst values to ZFst, 958 genomic windows containing 907 protein-coding genes were detected that had scores above the threshold (ZFst > 3.35). GO analysis on 907 genes identified by the ZFst method led to the identification of 157 significant GO terms in the field of biological processes. In addition, 26 significant terms related to molecular functions and 5 significant terms related to cellular components were also identified. The number of genomic windows identified by the XP-EHH method was 953, which contained a total of 311 protein-coding genes. Among identified genes for each method, 29 genes were detected by both methods as signatures of selection for Iranian sheep. From the GO analysis of 29 common genes, no significant term was obtained. However, these genes were involved on traits related to improving milk fat quality (PCCB), fertility (SPATA5, RAB35 and DICER1), muscle growth and development (NF1, AKAP6 and HDAC9), body weight (FBXL3, GRID2 and ADAMTS17), adaptability to harsh desert and mountain condition (BMPR2 and NF1) and also, milk related traits (EXOC6B).Conclusion The results showed that Iranian sheep were probably selected to adapt to dry desert areas and improve the quality of meat and milk. The gradual accumulation of such information in different populations will improve the understanding and knowledge of researchers and breeders and will help them to implement efficient breeding programs.