1. bookVolume 67 (2017): Issue 1 (March 2017)
Journal Details
License
Format
Journal
eISSN
1820-7448
First Published
25 Mar 2014
Publication timeframe
4 times per year
Languages
English
Open Access

Genomic variation across cervid species in respect to the estimation of red deer diversity

Published Online: 30 Mar 2017
Volume & Issue: Volume 67 (2017) - Issue 1 (March 2017)
Page range: 43 - 56
Received: 29 Mar 2016
Accepted: 15 Nov 2016
Journal Details
License
Format
Journal
eISSN
1820-7448
First Published
25 Mar 2014
Publication timeframe
4 times per year
Languages
English
Abstract

The aim of this study was to assess the genetic variations and relationships across evolutionary related cervid species in order to estimate the genetic diversity of the Red deer population that inhabits the forest area in the south-western part of Slovakia. The study was based on the application of cross-species SNP genotyping. The genomic data were obtained from a total of 86 individuals representing six genera (Axis, Dama, Cervus, Alces, Rangifer, and Odocoileus) using Illumina BovineSNP50 BeadChip. From 38.85% of successfully genotyped loci up to 1,532 SNPs showed polymorphism and were informative for subsequent analyses of the diversity and interspecific genetic relationships. Generally, a good level of observed heterozygosity was found across all species. The value of FIS (0.23±0.13) signalised the increase of a homozygous proportion within them. The application of molecular variance analysis to the hierarchical population structure showed that most of the variation was conserved within separate species (96%). The performed diversity analysis of Slovak Red deer population and comparative analysis of their phylogenic relationships among subspecies from genus Cervus did not identify a remarkable loss of genetic variability. Also, were not identified any degree of admixture that could be due to the historical background of deer farming in Slovakia or reintroduction and hybridisation by other species from genus Cervus (C. canadensis, and C. nippon) which are the major risk of loss of autochthonous Red deer populations in many areas of Central Europe. The analysis of individual’s ancestry showed consistent results with patterns of evaluated group differentiations which means low migration rates among all species.

Keywords

1. Gilbert C, Ropiquet A, Hassanin A: Mitochondrial and nuclear phylogenies of Cervidae (Mammalia, Ruminantia). Systematics, morphology, and biogeography. Mol Phylogenet Evol 2006, 40:101–117.10.1016/j.ympev.2006.02.01716584894Search in Google Scholar

2. Bališ M: Jelenia zver. Bratislava, Slovakia: Príroda; 1980.Search in Google Scholar

3. Apollonio M, Andersen R, Putman R: European Ungulates and Their Management in the 21st Century. Cambridge, United Kingdom: Cambridge University Press; 2010.Search in Google Scholar

4. Alcala N, Streit D, Goudet J, Vuilleumier S: Peak and persistent excess of genetic diversity following an abrupt migration increase. Genetics 2013, 193:953–971.10.1534/genetics.112.147785358400923307901Search in Google Scholar

5. Smith SL, Carden RF, Coad B, Birkitt T, Pemberton JM: A survey of the hybridisation status of Cervus deer species on the island of Ireland. Conserv Genet 2014, 15:823–835.10.1007/s10592-014-0582-3Search in Google Scholar

6. Olano-Marin J, Plis K, Sönnichsen L, Borowik T, Niedziałkowska M, Jędrzejewska B: Weak population structure in European roe deer (Capreolus capreolus) and evidence of introgressive hybridization with Siberian roe deer (C. pygargus) in northeastern Poland. PLoS One 2014, 9:e109147.10.1371/journal.pone.0109147418280825271423Search in Google Scholar

7. Hoffmann GS, Johannesen J, Griebeler EM: Species cross-amplification, identification and genetic variation of 17 species of deer (Cervidae) with microsatellite and mitochondrial DNA from antlers. Mol Biol Rep 2015, 42:1059–1067.10.1007/s11033-014-3845-725424838Search in Google Scholar

8. Świsłocka M, Czajkowska M, Duda N, Ratkiewicz M: Admixture promotes genetic variation in bottlenecked moose populations in eastern Poland. Mamm Res 2015, 60:169–179.10.1007/s13364-015-0221-5Search in Google Scholar

9. Flueck WT, Smith-Flueck JAM: Blood proteins of red deer introduced to Patagonia: genetic origins and variability. Anim Prod Sci 2011, 51:359–364.10.1071/AN10186Search in Google Scholar

10. Macháček Z, Dvořák S, Ježek M, Zahradník D: Impact of interspecific relations between native red deer (Cervus elaphus) and introduced sika deer (Cervus nippon) on their rutting season in the Doupovské hory Mts. J Forest Sci 2014, 60:272–280.10.17221/47/2014-JFSSearch in Google Scholar

11. Herzog S, Gehle T: Hybridization in the Genus Cervus: Evidence for Hybridization between Red and Sika Deer in Germany. Academic Journal of Applied Sciences Research 2016, 1:7–9.Search in Google Scholar

12. Emst M, Putnova L, Pokoradi J, Matouskova J, Zidek R, Marsalkova L, Lamka J: Microsatellite Analysis DNA for Controlled Breeding of Cervidae and Genetic Breeding of White Colored Cervus elaphus in Czech Republic. IJAS 2012, 2:233–237.Search in Google Scholar

13. Maršálková L, Židek R, Pokoradi J, Golian J, Belej Ľ: Genetic diversity and relatedness among seven red deer (Cervus elaphus) populations. Potravinárstvo 2014, 8:15–19.10.5219/320Search in Google Scholar

14. Wada K, Okumura K, Nishibori M, Kikkawa Y, Yokohama M: The Complete Mitochondrial Genome of the Domestic Red Deer (Cervus elaphus) of New Zealand and Its Phylogenic Position within the Family Cervidae. J Anim Sci 2010, 81:551–557.10.1111/j.1740-0929.2010.00799.x20887306Search in Google Scholar

15. Lorenzini R, Garofalo L: Insights into the evolutionary history of Cervus (Cervidae, tribe Cervini) based on Bayesian analysis of mitochondrial marker sequences, with first indications for a new species. J Zool Sys Evol Res 2015, 53:340–349.10.1111/jzs.12104Search in Google Scholar

16. Toonen RJ, Puritz JB, Forsman ZH, Whitney JL, Fernandez-Silva I, Andrews KR, Bird CE: ezRAD: a simplified method for genomic genotyping in non-model organisms. Peer J 2013, 1:e203.10.7717/peerj.203384041324282669Search in Google Scholar

17. Albrechtsen A, Nielsen FC, Nielsen R: Ascertainment biases in SNP chips affect measures of population divergence. Mol Biol Evol 2010, 24:1–20.Search in Google Scholar

18. Kumar S, Banks TW, Cloutier S: SNP Discovery through Next-Generation Sequencing and Its Applications. Int J Plant Genomics 2012, ID 831460.10.1155/2012/831460351228723227038Search in Google Scholar

19. Haynes GD, Latch EK: Identification of novel single nucleotide polymorphisms (SNPs) in deer (Odocoileus spp.) using the BovineSNP50 BeadChip. PLoS One 2012, 7:e36536.10.1371/journal.pone.0036536334815022590559Search in Google Scholar

20. Kharzinova VR, Sermyagin AA, Gladyr EA, Okhlopkov IM, Brem G, Zinovieva NA: A Study of Applicability of SNP Chips Developed for Bovine and Ovine Species to Whole-Genome Analysis of Reindeer Rangifer tarandus. J Hered 2015, 106:758–761.10.1093/jhered/esv08126447215Search in Google Scholar

21. Decker JE, Pires JC, Conant GC, McKay SD, Heaton MP, Chen K, Cooper A, Vilkki J, Seabury CM, Caetano AR, Johnson GS, Brenneman RA, Hanotte O, Eggert LS, Wiener P, Kim JJ, Kim KS, Sonstegard TS, Van Tassell CP, Neibergs HL, McEwan JC, Brauning R, Coutinho LL, Babar ME, Wilson GA, McClure MC, Rolf MM, Kim J, Schnabel RD, Taylor JF: Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc Natl Acad Sci U S A 2009, 106:18644–18649.10.1073/pnas.0904691106276545419846765Search in Google Scholar

22. Rowe SJ, Clarke SM, Van Stijn TC, Hyndman DL, Ward JF, McEwan KM, Dodds KG, McEwan JC, Newman SAN, Asher GW: BRIEF COMMUNICATION: Developing genomic tools in the New Zealand Deer Industry. Proc N Z Soc Anim Prod 2015, 75:91–93.Search in Google Scholar

23. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Skalp P, De Bakker PL, Daly MJ, Sham PC: PLINK: a tool set for whole genome association and population-based linkage analysis. Am J Hum Genet 2007, 81:559–575.10.1086/519795195083817701901Search in Google Scholar

24. Excoffier L, Laval G, Schneider S: Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online 2005, 1:47–50.10.1177/117693430500100003Search in Google Scholar

25. Pembleton LW, Cogan NOI, Forster JW: StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol Ecol Resour 2013:13, 946–952.10.1111/1755-0998.1212923738873Search in Google Scholar

26. Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics portions from molecular data. Mol Biol Evol 2000, 15:1298–1311.Search in Google Scholar

27. Evanno G, Regnaut S, Goudet J: Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 2005, 14:2611–2620.10.1111/j.1365-294X.2005.02553.x15969739Search in Google Scholar

28. Jombart T, Ahmed I: Adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 2011, 1:3070–3071.10.1093/bioinformatics/btr521319858121926124Search in Google Scholar

29. Wilson GA, Rannala B: Bayesian inference of recent migration rates using multilocus genotypes. Genetics 2003, 163:1177–1191.10.1093/genetics/163.3.1177146250212663554Search in Google Scholar

30. Pickrell JK, Pritchard JK: Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet 2012, 8:e1002967.10.1371/journal.pgen.1002967349926023166502Search in Google Scholar

31. R Core Team, 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.Search in Google Scholar

32. Ekblom R, Galindo R: Applications of next generation sequencing in molecular ecology of non-model organisms. J Hered 2011, 107:1–15.10.1038/hdy.2010.152318612121139633Search in Google Scholar

33. Miller JM, Kijas JW, Heaton MP, McEwan JC, Coltman DW: Consistent divergence times and allele sharing measured from cross-species application of SNP chips developed for three domestic species. Mol Ecol Resour 2012, 12:1145–1150.10.1111/1755-0998.1201722994965Search in Google Scholar

34. Bibi F: A multi-calibrated mitochondrial phylogeny of extant Bovidae (Artiodactyla, Ruminantia) and the importance of the fossil record to systematics. BMC Evol Biol 2013, 13:166.10.1186/1471-2148-13-166375101723927069Search in Google Scholar

35. Lachance J, Tishkoff SA: SNP ascertainment bias in population genetic analyses: why it is important, and how to correct it. BioEssays 2013, 35:780–786.10.1002/bies.201300014384938523836388Search in Google Scholar

36. Tabangin ME, Woo GJ, Martin LJ: The effect of minor allele frequency on the likelihood of obtaining false positives. BMC Proc 2009, 3:S41.10.1186/1753-6561-3-S7-S41279594020018033Search in Google Scholar

37. Pertoldi C, Tokarska M, Wójcik JM, Kawałko A, Randi E, Kristensen TN, Loeschcke V, Coltman D, Wilson GA, Gregersen VR, Bendixen Ch: Phylogenetic relationships among the European and American bison and seven cattle breeds reconstructed using the BovineSNP50 Illumina Genotyping BeadChip. Acta Theriologica 2010, 55:97–108.10.4098/j.at.0001-7051.002.2010Search in Google Scholar

38. Wu JJ, Song LJ, Wu FJ, Liang XW, Yang BZ, Wathes DC, Pollott GE, Cheng Z, Shide S, Liu QY, Yang LG, Zhang SJ: Investigation of transferability of BovineSNP50 BeadChip from cattle to water buffalo for genome-wide association study. Mol Biol Rep 2013, 40:743–750.10.1007/s11033-012-1932-123232712Search in Google Scholar

39. Ludt CJ, Schroeder W, Rottmann O, Kuehn R: Mitochondrial DNA phylogeography of red deer (Cervus elaphus). Mol Phylogenet Evol 2004, 31:1064–1083.10.1016/j.ympev.2003.10.00315120401Search in Google Scholar

40. Zhang WQ, Zhang MH: Phylogeny and evolution of Cervidae based on complete mitochondrial genomes. Genet Mol Res 2012, 11:628–635.10.4238/2012.March.14.622535398Search in Google Scholar

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