Volume 8, Issue 17 (1-2018)                   rap 2018, 8(17): 184-193 | Back to browse issues page

XML Persian Abstract Print

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

karimi K. Investigation of the Population Structure in Iranian Native Cattle using Discriminant Analysis of Principal Components . rap. 2018; 8 (17) :184-193
URL: http://rap.sanru.ac.ir/article-1-854-en.html
Abstract:   (2461 Views)
Effective management of genetic resources in the domestic animals is based on characterization of genetic structure and diversity among populations. Strategies reducing complexity and dimensions of data are required to analyze the genetic relationships between populations based on dense genomic data. The objective of this study was to use the discriminant analysis of principal components (DAPC) for investigating the genetic structure of the Iranian native cattle based on a high-density single nucleotide polymorphism data. Samples were genotyped using Illumina BovineHD SNP chip containing 777962 SNPs. Genetic distances among different breeds were determined based on allele frequencies of populations. Discriminant analysis of principal components was performed using "adegenet" package in the R software. Pars and Kermani breeds had the lowest genetic distances among studied breeds and the highest genetic distances were observed between Kurdi and Sistani breeds. Three numbers of genetic clusters were selected as the best number of inferred groups to perform discriminant analysis of principal components. Results from this study confirmed that there were three main genetic groups among Iranian native cattle. In accordance with geographical distribution, Mazandarani and Taleshi breeds were classified as the same cluster. In addition, other two distinct genetic groups included breeds distributed on the North West mountainous area of the country (Sarabi and Kurdi) and those can be found in the Southern area (Sistani, Kermani, Najdi and Pars). Discriminant analysis of principal components could well distinguish individuals from different genetic groups. Genetic structure identified in the current study can be applied to design the genetic conservation programs for Iranian native cattle.
Full-Text [PDF 2334 kb]   (1771 Downloads)    
Type of Study: Research | Subject: Special
Received: 2018/01/10 | Revised: 2018/01/28 | Accepted: 2018/01/10 | Published: 2018/01/10

1. Bartenhagen, C., H.U. Klein, C. Ruckert, X. Jiang and M. Dugas. 2010. Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data. BMC Bioinformatics, 11: 1-11. [DOI:10.1186/1471-2105-11-567]
2. Ben Jemaa, S., M. Boussaha, M. Ben Mehdi, J.H. Lee and S.H. Lee. 2015. Genome-wide insights into population structure and genetic history of tunisian local cattle using the illumina bovinesnp50 beadchip. BMC Genomics, 16: 677. [DOI:10.1186/s12864-015-1638-6]
3. Boettcher, P.J., M. Tixier-Boichard, M.A. Toro, H. Simianer, H. Eding, G. Gandini, S. Joost, D. Garcia, L. Colli, P. Ajmone-Marsan and G. Consortium. 2010. Objectives, criteria and methods for using molecular genetic data in priority setting for conservation of animal genetic resources. Animal Genetics, 41: 64-77. [DOI:10.1111/j.1365-2052.2010.02050.x]
4. Burgos-Paz, W., C.A. Souza, A. Castello, A. Mercade, N. Okumura, I.N. Sheremet'eva and M. Perez-Enciso. 2013. Worldwide genetic relationships of pigs as inferred from X chromosome SNPs. Animal Genetics, 44(2): 130-138. [DOI:10.1111/j.1365-2052.2012.02374.x]
5. Filippi, C.V., A. Natalia, J.G. Rivas, J. Zubrzycki, A. Puebla, D. Cordes, M.V. Moreno, C.M. Fusari, D. Alvarez, R.A. Heinz, H.E. Hopp, N.B. Paniego and V.V. Lia. 2015. Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biology, 15: 52. [DOI:10.1186/s12870-014-0360-x]
6. Decker, J.E., S.D. McKay, M.M. Rolf, J. Kim, A. Molina Alcala, T.S. Sonstegard, O. Hanotte, A. Gotherstrom, C.M. Seabury, L. Praharani, M. Saif-Ur-Rehman, R.D. Schnabel and J.F. Taylor. 2014. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genetics, 10: e1004254. [DOI:10.1371/journal.pgen.1004254]
7. Degner, J.F., J.C. Marioni, A.A. Pai, J.K. Pickrell, E. Nkadori, Y. Gilad and J.K. Pritchard. 2009. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics, 25(24): 3207-3212. [DOI:10.1093/bioinformatics/btp579]
8. Dell'Acqua, M., D.M. Gatti, G. Pea, F. Cattonaro, F. Coppens, G. Magris and M.E. Pè. 2015. Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biology, 16(1): 1-23. [DOI:10.1186/s13059-015-0716-z]
9. Heitlinger, E., H. Taraschewski, U. Weclawski, K. Gharbi and M. Blaxter. 2014. Transcriptome analyses of Anguillicola crassus from native and novel hosts. PeerJ, 2: e684. [DOI:10.7717/peerj.684]
10. Ji, H., X. Li, Q.f. Wang and Y. Ning. 2013. Differential principal component analysis of ChIP-seq. Proceedings of the National Academy of Sciences, 110(17): 6789-6794. [DOI:10.1073/pnas.1204398110]
11. Jombart, T. 2008. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics, 24: 1403-1405. [DOI:10.1093/bioinformatics/btn129]
12. Jombart, T., S. Devillard and F. Balloux. 2010. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics, 11: 1-15. [DOI:10.1186/1471-2156-11-94]
13. Jombart, T. and I. Ahmed. 2011. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics, 27: 3070-3071. [DOI:10.1093/bioinformatics/btr521]
14. Karimi, K., A. Esmailizadeh Koshkoiyeh, M. Asadi Fuzi, L.R. Porto-Neto and C. Gondro. 2015. Prioritization for conservation of Iranian native cattle breeds based on genome-wide SNP data. Conservation Genetics, 17: 77-89. [DOI:10.1007/s10592-015-0762-9]
15. Karimi, K., A. Esmailizadeh Koshkoiyeh and M. AsadiFuzi. 2015. Analysis of genetic structure of Iranian indigenous cattle populations using dense single nucleotide polymorphism markers. Animal Production Research, 4(3): 93-104.
16. Kim, E-S. and M.F. Rothschild. 2014. Genomic adaptation of admixed dairy cattle in East Africa. Frontiers in Genetics, 5 [DOI:10.3389/fgene.2014.00443]
17. Kim, E-S., R. Ros-Freixedes, R.N. Pena, T.J. Baas, J. Estany and M.F. Rothschild. 2015. Identification of signatures of selection for intramuscular fat and backfat thickness in two Duroc populations. Journal of Animal Science, 93: 3292-3302. [DOI:10.2527/jas.2015-8879]
18. Lawson, D.J. and D. Falush. 2012. Population identification using genetic data. Annual Review of Genomics and Human Genetics, 13: 337-361. [DOI:10.1146/annurev-genom-082410-101510]
19. Lin, B.Z., S. Sasazaki and H. Mannen. 2010. Genetic diversity and structure in Bos taurus and Bos indicus populations analyzed by SNP markers. Animal Science Journal, 81: 281-289. [DOI:10.1111/j.1740-0929.2010.00744.x]
20. McVean, G. 2009. A genealogical interpretation of principal components analysis. PLoS Genetics, 5:e1000686. [DOI:10.1371/journal.pgen.1000686]
21. Nei, M. 1972. Genetic distance between populations. American Naturalist, 106: 283-292. [DOI:10.1086/282771]
22. Paradis, E., J. Claude and K. Strimmer. 2004. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics, 20: 289-290. [DOI:10.1093/bioinformatics/btg412]
23. Patterson, N., A.L. Price and D. Reich. 2006. Population Structure and Eigenanalysis. PLoS Genetics, 2:e190. [DOI:10.1371/journal.pgen.0020190]
24. Pembleton, L.W., N.O. Cogan and J.W. Forster. 2013. StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Molecular Ecology Resources, 13: 946-952. [DOI:10.1111/1755-0998.12129]
25. Pometti, C.L., C.F. Bessega, B.O. Saidman and J.C. Vilardi. 2014. Analysis of genetic population structure in Acacia caven (Leguminosae, Mimosoideae), comparing one exploratory and two Bayesian-model-based methods. Genetics and Molecular Biology, 37: 64-72. [DOI:10.1590/S1415-47572014000100012]
26. Purcell, S., B. Neale, K. Todd-Brown, L. Thomas, M.A. Ferreira, D. Bender, J. Maller, P. Sklar, P.I. de Bakker, M.J. Daly and P.C. Sham. 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81: 559-575. [DOI:10.1086/519795]
27. Reynolds, J., B.S. Weir and C.C. Cockerham. 1983. Estimation of the coancestry coefficient: Basis for a short term genetic distance. Genetics, 105: 767-779.
28. Ringner, M. 2008. What is principal component analysis?. Nature Biotechnology, 26: 303-304. [DOI:10.1038/nbt0308-303]
29. Taberlet, P., E. Coissac, J. Pansu and F. Pompanon. 2011. Conservation genetics of cattle, sheep, and goats. Comptes Rendus Biologies, 334: 247-254. [DOI:10.1016/j.crvi.2010.12.007]
30. 30. Scheu, A., A. Powell, R. Bollongino, J.D. Vigne, A. Tresset, C. Çakırlar and J. Burger. 2015. The genetic prehistory of domesticated cattle from their origin to the spread across Europe. BMC Genetics, 16(1): 1-11. [DOI:10.1186/s12863-015-0203-2]
31. Smith, O. and J. Wang. 2014. When can noninvasive samples provide sufficient information in conservation genetics studies?. Molecular Ecology Resources, 14: 1011-1023. [DOI:10.1111/1755-0998.12250]
32. Tavakolian, j. 1999. Introduction on genetic resources of indigenous animals and poultry in Iran. Iranian Animal Research Institute, Karaj, Iran, 3-45 pp.
33. Wang, M.D., K. Dzama, C.A. Hefer and F.C. Muchadeyi. 2015. Genomic population structure and prevalence of copy number variations in South African Nguni cattle. BMC Genomics, 16(1): 1-16. doi: 10.1186/s12864-015-2122-z. [DOI:10.1186/s12864-015-2122-z]
34. Willing, E., C. Dreyer and C. Van Oosterhout. 2012. Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many snp markers. PLoS One, 7(8): e42649. [DOI:10.1371/journal.pone.0042649]
35. Xie, J., R. Li, S. Li, X. Ran, J. Wang, J. Jiang, and P. Zhao. 2016. Identification of Copy Number Variations in Xiang and Kele Pigs. PLoS One, 11(2): e0148565. [DOI:10.1371/journal.pone.0148565]
36. Xu, H-M., X-W. Sun, T. Qi, W.Y. Lin, N. Liu and X.Y. Lou. 2014. Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits. PLoS One, 9: e108103. [DOI:10.1371/journal.pone.0108103]

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

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

© 2022 CC BY-NC 4.0 | Research On Animal Production(Scientific and Research)

Designed & Developed by : Yektaweb