Volume 14, Issue 39 (5-2023)                   rap 2023, 14(39): 154-162 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Nasirpour M, moradi shahr babak M, Moradi Shahr Babak H, mehrabani yeganeh H, doosti Y. (2023). Analysis of SNP Chip 70k Genomic Data to Identify Loci Related to Differentiation of Caspian and Kurdish Horses using Selection Sweep. rap. 14(39), 154-162. doi:10.61186/rap.14.39.154
URL: http://rap.sanru.ac.ir/article-1-1341-en.html
College of Agriculture & Natural Resources, University of Tehran
Abstract:   (1286 Views)
Extended Abstract
Introduction and Objective:
 Selection in animal populations to increase the frequency of ideal alleles causes of remaining some signs in the animal genome, which are usually associated with important traits. Today, with developments in next-generation sequencing and easier access to animal genomic information, some models have been proposed to identify selective signatures based on allelic frequency and haplotype blocks. This research aimed to identify the selective signatures in two horse breeds of Caspian and Kurdish using a k70 SNP chip.

Material and Methods: In this research blood samples from 35 Caspian and 31 Kurdish horses were collected. After DNA extraction and sequencing (by Illumina company), Quality control of the data was performed for minimum allele frequency (PMAF<0.05), genotyping rate (PGENO < 0.5), and deviation from Hardy-Weinberg equilibrium (PH-W<1 ͯ 10-6). Then the theta (θ) test and ensemble site were used for identifying selective signatures and the positions of snaps respectively, and finally, the genes related to these positions were identified.
Results: After quality control of data and integration of genomic data of two populations based on R package protocols, 61 horses with 52,650 SNPs remained for the rest analysis. Finally, Fst statistical test based on unbiased estimator theta method 31 differentiating markers of the two studied horse breed were identified
Conclusion: The results showed that Caspian and Kurdish horse breeds belong to two separate groups. The Kurdish horse breed has more diversity and variety than the Caspian. The most important genes associated with significant SNPs were INPP5F, HPSE2, R3HCC1, DOCK3, ITGB6, GM6B, PHEX, WDR13, LRCH2, GRIA3 and THOC2. Most of the identified genes were associated with intercellular exchanges, muscle contractions, immunity, membrane support, DNA stability, and the nervous system.
Full-Text [PDF 1618 kb]   (406 Downloads)    
Type of Study: Research | Subject: ژنتیک و اصلاح نژاد دام
Received: 2022/12/4 | Revised: 2023/05/30 | Accepted: 2023/01/25 | Published: 2023/05/30

1. Amirinia, C., H. Seyedabadi, M.H. Banabazi and M.A. Kamali. 2007. Bottleneck study and genetic structure of Iranian Caspian horse population using microsatellites. Pakistan Journal of Biological Sciences, 10(9): 1540-1543. [DOI:10.3923/pjbs.2007.1540.1543]
2. Andolfatto, P. 2001. Adaptive hitchhiking effects on genome variability. Current opinion in genetics development, 11(6): 635-641. [DOI:10.1016/S0959-437X(00)00246-X]
3. Babaei, N., A. Raft, A. Moradi and F. Derakhshi. 2021. Comparison of principal component analysis (PCA) and diagnostic analysis of principal components (DAPC) methods in investigating the population structure of Akhal-Teke, Arabian and Caspian horse breeds using genomic information. Iran Animal Science Research, 13(3): 453-462(In Persian).
4. Behrouzinia, S., S.Z. Mirhosseini, F. Afraz, A. Sohrabi, S.A. Mohammadi, S. Shahbazi and S.B. Delirasfat. 2013. Genetic description of two populations of Iranian Turkmen horses in Turkmen Sahara and Turkmen Jorglan regions using microsatellite markers. Iranian Animal Science Research 1: 0-63 (In Persian).
5. Bovo, S., A. Ribani, M. Munoz, E. Alves, J.P. Araujo, R. Bozzi and L. Fontanesi. 2020. Whole-genome sequencing of European autochthonous and commercial pig breeds allows the detection of signatures of selection for adaptation of genetic resources to different breeding and production systems. Genetics Selection Evolution, 52(1): 1-19. [DOI:10.1186/s12711-020-00553-7]
6. Daly, S.B., J.E. Urquhart, E. Hilton, E.A. McKenzie, R.A. Kammerer, M. Lewis and W.G. Newman. 2010. Mutations in HPSE2 cause urofacial syndrome. The American Journal of Human Genetics, 86(6): 963-969.‌ [DOI:10.1016/j.ajhg.2010.05.006]
7. Drabek, K., J. van-de-Peppel, M. Eijken and J.P. van-Leeuwen. 2011. GPM6B regulates osteoblast function and induction of mineralization by controlling cytoskeleton and matrix vesicle release. Journal of Bone and Mineral Research, 26(9): 2045-2051.‌ [DOI:10.1002/jbmr.435]
8. Hedayat-Evrigh, N., E. Azadmard, R. Seyed Sharifi, S. Nikbin, M.D. Shakouri and R. Khalkhali-Evrigh. 2019. Investigation of genetic diversity of Iran northwest horses using microsatellite markers. Agricultural Biotechnology Journal, 11(4): 35-50.
9. Moon, S., J.W. Lee, D. Shin, K.Y. Shin, J. Kim, I.Y. Choi and H. Kim. 2015. A genome-wide scan for selective sweeps in racing horses. Asian-Australasian journal of animal sciences, 28(11): 1525-1531.‌ [DOI:10.5713/ajas.14.0696]
10. Khalkhali-Evrigh, R., N. Hedayat-Evrigh, H. Hafezian, A. Farhadi and M.R. Bakhtiarizadeh. 2020. Identification the copy number variation and its impacts on the genes of Iranian dromedary camels using whole genome sequencing data. Iranian Journal of animal Science, 51(2): 113-119. [DOI:10.1155/2020/2430846]
11. Kim, H.S., A. Li, S. Ahn, H. Song and W. Zhang. 2014. Inositol Polyphosphate-5-Phosphatase F (INPP5F) inhibits STAT3 activity and suppresses gliomas tumorigenicity. Scientific reports, 4(1): 1-10.‌ [DOI:10.1038/srep07330]
12. Kim, Y. and W. Stephan. 2002. Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics, 160(2): 765-777. [DOI:10.1093/genetics/160.2.765]
13. Kumar, R., M.A. Corbett, B.W. Van-Bon, J.A. Woenig, L. Weir, E. Douglas and J. Gecz. 2015. THOC2 mutations implicate mRNA-export pathway in X-linked intellectual disability. The American Journal of Human Genetics, 97(2): 302-310.‌ [DOI:10.1016/j.ajhg.2015.05.021]
14. Liu, N. and H. Zhao. 2006. A non-parametric approach to population structure inference using multilocus genotypes. Human genomics, 2(6): 1-12.‌ [DOI:10.1186/1479-7364-2-6-353]
15. Mohammad-Maghsoudi, S., H. Mehrabani-Yeganeh and A. Nejati Javarami. 2017. Identifying regions under positive selection in the genes of Kurdish and Iranian Arabian horses using the method based on genetic linkage disequilibrium. Animal Science of Iran, 321-333 (In Persian).
16. Moshkelani, S., S. Rabiee and M. Javaheri-Koupaei. 2011. DNA fingerprinting of Iranian Arab horse using fourteen microsatellites marker. Research Journal of Biological Sciences, 6(8): 402-5.
17. Namekata, K., C. Harada, X. Guo, A. Kimura, D. Kittaka, H. Watanabe and T. Harada. 2012. Dock3 stimulates axonal outgrowth via GSK-3β-mediated microtubule assembly. Journal of Neuroscience, 32(1): 264-274.‌ [DOI:10.1523/JNEUROSCI.4884-11.2012]
18. Nielsen, R. 2005. Molecular signatures of natural selection. Annual Review of Genetics, 39(1): 197-218. [DOI:10.1146/annurev.genet.39.073003.112420]
19. Novembre, J. and M. Stephens. 2008. Interpreting principal component analyses of spatial population genetic variation. Nature genetics, 40(5): 646-649.‌ [DOI:10.1038/ng.139]
20. Paschou, P., E. Ziv, E.G. Burchard, S. Choudhry, W. Rodriguez-Cintron, M.W. Mahoney and P. Drineas. 2007. PCA-correlated SNPs for structure identification in worldwide human populations. PLoS Genetics, 3(9): e160.‌ [DOI:10.1371/journal.pgen.0030160]
21. Quarles, L.D. 2003. FGF23, PHEX and MEPE regulation of phosphate homeostasis and skeletal mineralization. American Journal of Physiology-Endocrinology And Metabolism, 285(1): E1-E9.‌ [DOI:10.1152/ajpendo.00016.2003]
22. Rafeie, F., C. Amirinia, A.N. Javaremi, S.Z. Mirhoseini and N. Amirmozafari. 2011. A study of patrilineal genetic diversity in Iranian indigenous horse breeds. African Journal of Biotechnology, 10(75): 17347-17352. [DOI:10.5897/AJB11.1430]
23. Reich, D., A.L. Price and N. Patterson. 2008. Principal component analysis of genetic data. Nature genetics, 40(5): 491-492.‌ [DOI:10.1038/ng0508-491]
24. Saravanan, K.A., M. Panigrahi, H. Kumar, B. Bhushan, T. Dutt and B.P. Mishra. 2020. Selection signatures in livestock genome: A review of concepts, approaches and applications. Livestock Science, 241: 104257. [DOI:10.1016/j.livsci.2020.104257]
25. Seyedabadi, H.R. and S. Savarsofla. 2017. Microsatellite analysis for parentage verification and genetic characterization of the Turkmen horse population. Kafkas Universitesi Veteriner Fakultesi Dergisi, 23(3): 467-471.
26. Seyedsharifi, R., S. Badbarin, H. khamisabadi, N. hedayat-evrigh and J.S. Davati. 2019. Study of genetic structure and accuracy of assignment of individuals to five horse populations using microsatellite markers. Research on Animal Production (Scientific and Research) 10, no. (24): 120-126 (In Persian). [DOI:10.29252/rap.10.24.120]
27. Shasavarani, H. and G. Rahimi-Mianji. 2010. Analysis of genetic diversity and estimation of inbreeding coefficient within Caspian horse population using microsatellite markers. African Journal of Biotechnology, 9(3): 293-299. [DOI:10.5897/AJB09.1104]
28. Smadja, C.M., E. Loire, P. Caminade, M. Thoma, Y. Latour, C. Roux and P. Boursot. 2015. Seeking signatures of reinforcement at the genetic level: a hitchhiking mapping and candidate gene approach in the house mouse. Molecular Ecology, 24(16): 4222-4237. [DOI:10.1111/mec.13301]
29. Xu, L., D.M. Bickhart, J.B. Cole, S.G. Schroeder, J. Song, C.P.V. Tassell and G.E. Liu. 2015. Genomic signatures reveal new evidences for selection of important traits in domestic cattle. Molecular Biology and Evolution, 32(3): 711-725. [DOI:10.1093/molbev/msu333]
30. Yan, J., D. Ojo, A. Kapoor, X. Lin, J.H. Pinthus, T. Aziz and D. Tang. 2016. Neural cell adhesion protein CNTN1 promotes the metastatic progression of prostate cancer. Cancer research, 76(6): 1603-1614.‌ [DOI:10.1158/0008-5472.CAN-15-1898]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Research On Animal Production

Designed & Developed by : Yektaweb