Skip to main content

Advertisement

SpringerLink
Log in

Cross-amplification of ungulate microsatellite markers in the endemic Indian antelope or blackbuck (Antilope cervicapra) for population monitoring and conservation genetics studies in south Asia

  • Original Article
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

The Indian antelope or blackbuck (Antilope cervicapra) is endemic to the Indian subcontinent, inhabiting scrublands and dry grasslands. Most of the blackbuck populations are small, isolated, and threatened by habitat fragmentation and degradation. Management of such disjunct populations requires genetic characterization, which is critical for assessing hazards of stochastic events and inbreeding. Addressing the scarcity of such information on the blackbuck, we describe a novel panel of microsatellite markers that could be used to monitor blackbuck demography and population genetic parameters using non-invasive faecal sampling. We screened microsatellites (n = 40) that had been reported to amplify in bovid and cervid species using faecal samples of the blackbuck collected from Kaimoor Wildlife Sanctuary, Uttar Pradesh, India and its vicinities. We selected 12 markers for amplification using faecal DNA extracts (n = 140) in three multiplex reactions. We observed a mean amplification success rate of 72.4% across loci (92.1–25.7%) with high allele diversity (mean number of alleles/locus = 8.67 ± 1.03). Mean genotyping error rates across the markers were low to moderate (allelic drop-out rate = 0.09; false allele rate = 0.11). The proportions of first- and second-order relatives in the study population were 0.69% and 6.21%, respectively. Based on amplification success, genotyping error rates and the probability of identity (PID), we suggest (i) a panel of five microsatellite markers (cumulative PID = 1.24 × 10–5) for individual identification and population monitoring and (ii) seven additional markers for conservation genetics studies. This study provides essential tools capable of augmenting blackbuck conservation strategies at the landscape level, integral to protecting the scrubland-grassland ecosystem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

Raw genotypes used in this study will be made available upon reasonable request. All other data have been included in the form of tables in the manuscript and supplementary materials.

References

  1. Balkenhol N, Cushman SA, Storfer AT, Waits LP (2016) Landscape genetics: concepts, methods, applications. Wiley-Blackwell, Oxford, UK

  2. Barton N, Clark A (1990) Population structure and processes in evolution. In: Wöhrmann K, Jain SK (eds) Population biology. Springer-Verlag, Berlin, Germany, pp 115–173

    Chapter  Google Scholar 

  3. Rahmani AR (1991) Present distribution of the blackbuck Antilope cervicapra Linn in India, with special emphasis on the lesser known populations. J Bombay Nat Hist Soc 88:35–46

    Google Scholar 

  4. Khanal L, Chalise MK (2010) Population status of blackbuck (Antilope cervicapra) at Khairarpur, Bardiya. Nepal J Nat Hist Museum 25:266–275

    Google Scholar 

  5. Mirza ZB, Waiz A (1973) Food availability for blackbuck (Antilope cervicapra) at Lal Suhanra Sanctuary, Pakistan. Biol Conserv 5:119–122. https://doi.org/10.1016/0006-3207(73)90091-8

    Article  Google Scholar 

  6. Meena R, Saran RP (2018) Distribution, ecology and conservation status of blackbuck (Antilope cervicapra): an update. Int J Biol Res 3:79–86

    Google Scholar 

  7. MoEFCC (2018) Report from India. Ministry of environment, forest and climate change, government of India - United Nations Convention to Combat Desertification. New Delhi, India

  8. Lacy RC (2000) Considering threats to the viability of small populations using individual-based models. Ecol Bull 39-51

  9. Lamb CT, Ford AT, Proctor MF, Royle JA, Mowat G, Boutin S (2019) Genetic tagging in the anthropocene: scaling ecology from alleles to ecosystems. Ecol Appl 29:e01876. https://doi.org/10.1002/eap.1876

    Article  PubMed  Google Scholar 

  10. Primack RB (2010) Essentials of conservation biology. Sunderland, MA, USA

  11. Selkoe KA, Toonen RJ (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett 9:615–629. https://doi.org/10.1111/j.1461-0248.2006.00889.x

    Article  PubMed  Google Scholar 

  12. Carroll EL, Bruford MW, DeWoody JA, Leroy G, Strand A, Waits L, Wang J (2018) Genetic and genomic monitoring with minimally invasive sampling methods. Evol Appl 11:1094–1119. https://doi.org/10.1111/eva.12600

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Estoup A, Jarne P, Cornuet J-M (2002) Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol Ecol 11:1591–1604

    Article  CAS  PubMed  Google Scholar 

  14. Yue G-H, Balazs K, Laszlo O (2010) A new problem with cross-species amplification of microsatellites: generation of non-homologous products. Zool Res 31:131–140. https://doi.org/10.3724/SP.J.1141.2010.02131

    Article  CAS  Google Scholar 

  15. Li B, Kimmel M (2013) Factors influencing ascertainment bias of microsatellite allele sizes: impact on estimates of mutation rates. Genetics 195:563–572. https://doi.org/10.1534/genetics.113.154161

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kim KS, Min MS, An JH, Lee H (2004) Cross-species amplification of bovidae microsatellites and low diversity of the endangered Korean goral. J Hered 95:521–525. https://doi.org/10.1093/jhered/esh082

    Article  CAS  PubMed  Google Scholar 

  17. Maudet C, Luikart G, Taberlet P (2001) Development of microsatellite multiplexes for wild goats using primers designed from domestic bovidae. Genet Sel Evol 33:S193–S203. https://doi.org/10.1186/BF03500880

    Article  CAS  PubMed Central  Google Scholar 

  18. Miller SM, Clarke AB, Bloomer P, Guthrie AJ, Harper CK (2016) Evaluation of microsatellites for common ungulates in the South African wildlife industry. Conserv Genet Resour 8:329–341. https://doi.org/10.1007/s12686-016-0554-7

    Article  Google Scholar 

  19. Osmers B, Petersen BS, Hartl GB, Grobler JP, Kotze A, Van Aswegen E, Zachos FE (2012) Genetic analysis of southern African gemsbok (Oryx gazella) reveals high variability, distinct lineages and strong divergence from the East African Oryx beisa. Mamm Biol 77:60–66. https://doi.org/10.1016/j.mambio.2011.08.003

    Article  Google Scholar 

  20. Røed KH (1998) Microsatellite variation in scandinavian cervidae using primers derived from bovidae. Hereditas 129:19–25. https://doi.org/10.1111/j.1601-5223.1998.00019.x

    Article  PubMed  Google Scholar 

  21. Gaur A, Singh A, Arunabala V, Umapathy G, Shailaja K, Singh L (2003) Development and characterization of 10 novel microsatellite markers from chital deer (Cervus axis) and their cross-amplification in other related species. Mol Ecol Notes 3:607–609. https://doi.org/10.1046/j.1471-8286.2003.00528.x

    Article  CAS  Google Scholar 

  22. Kocher TD, Thomas WK, Meyer A, Edwards SV, Paabo S, Villablanca FX, Wilson AC (1989) Dynamics of mitochondrial DNA evolution in animals: amplification and sequencing with conserved primers. Proc Natl Acad Sci USA 86:6196–6200. https://doi.org/10.1073/pnas.86.16.6196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. FAO (2011) Molecular genetic characterization of animal genetic resources. commission on genetic resources for food and agriculture food and agriculture organization. Rome, Italy

  24. Kappes SM, Keele JW, Stone RT, Mcgraw RA, Sonstegard TS, Smith TPL, Lopez-Corrales NL, Beattie CW (1997) A second-generation linkage map of the bovine genome. Genome Res 7:235–249

    Article  CAS  PubMed  Google Scholar 

  25. Vial L, Maudet C, Luikart G (2003) Thirty-four polymorphic microsatellites for European roe deer. Mol Ecol Notes 3:523–527. https://doi.org/10.1046/j.1471-8286.2003.00499.x

    Article  CAS  Google Scholar 

  26. Taberlet P, Griffin S, Goossens B, Questiau S, Manceau V, Escaravage N, Waits LP, Bouvet J (1996) Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Res 24:3189–3194. https://doi.org/10.1093/nar/24.16.3189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hauge XY, Litt M (1993) A study of the origin of ‘shadow bands’ seen when typing dinucleotide repeat polymorphisms by the PCR. Hum Mol Genet 2:411–415

    Article  CAS  PubMed  Google Scholar 

  28. Clark JM (1988) Novel non-templated nucleotide addition reactions catalyzed by procaryotic and eucaryotic DNA polymerases. Nucleic Acids Res 16:9677–9686. https://doi.org/10.1093/nar/16.20.9677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Matsumoto T, Yukawa W, Nozaki Y et al (2004) Novel algorithm for automated genotyping of microsatellites. Nucleic Acids Res 32:6069–6077. https://doi.org/10.1093/nar/gkh946

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Salin F (2010) Autobin v0.9. http://www4.bordeaux-aquitaine.inra.fr/biogeco/Resources/Logiciels/Autobin. Accessed 26 April 2016.

  31. Peakall R, Smouse PE (2012) GenALEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research - an update. Bioinformatics 28:2537–2539. https://doi.org/10.1093/bioinformatics/bts460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chapuis MP, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24:621–631. https://doi.org/10.1093/molbev/msl191

    Article  CAS  PubMed  Google Scholar 

  33. Valière N (2002) GIMLET: a computer program for analysing genetic individual identification data. Mol Ecol Notes 2:377–379

    Google Scholar 

  34. Hansen H, Ben-David M, McDonald DB (2008) Effects of genotyping protocols on success and errors in identifying individual river otters (Lontra canadensis) from their faeces. Mol Ecol Resour 8:282–289. https://doi.org/10.1111/j.1471-8286.2007.01992.x

    Article  CAS  PubMed  Google Scholar 

  35. Wang J (2004) Sibship reconstruction from genetic data with typing errors. Genetics 166:1963–1979. https://doi.org/10.1534/genetics.166.4.1963

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jones OR, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10:551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x

    Article  PubMed  Google Scholar 

  37. Cavalli-Sforza LL, Edwards AW (1967) Phylogenetic analysis: models and estimation procedures. Am J Hum Genet 19:233–257. https://doi.org/10.2307/2406616

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Séré M, Thévenon S, Belem AMG, De Meeûs T (2017) Comparison of different genetic distances to test isolation by distance between populations. Heredity 119:55–63. https://doi.org/10.1038/hdy.2017.26

    Article  PubMed  PubMed Central  Google Scholar 

  39. Langella O (2002) POPULATIONS, a free population genetics software. https://bioinformatics.org/populations. Accessed 31 March 2019.

  40. Rambaut A (2014) FigTree v1.4.2: tree figure drawing tool. http://tree.bio.ed.ac.uk/software/figtree. Accessed 17 Jan 2018.

  41. Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233–234. https://doi.org/10.1038/72708

    Article  CAS  PubMed  Google Scholar 

  42. Forgiarini C, Curto M, Stiehl-Alves EM, Bräuchler C, Kollmann J, Meimberg H, de Souza-Chies TT (2017) Fifteen microsatellite markers for Herbertia zebrina (Iridaceae): an endangered species from South American grasslands. Appl Plant Sci 5:1–4. https://doi.org/10.3732/apps.1700035

    Article  Google Scholar 

  43. Rivière-Dobigny T, Doan LP, Le QN, Maillard JC, Michaux J (2009) Species identification, molecular sexing and genotyping using non-invasive approaches in two wild bovids species: Bos gaurus and Bos javanicus. Zoo Biol 28:127–136. https://doi.org/10.1002/zoo.20211

    Article  CAS  PubMed  Google Scholar 

  44. Soto-CalderÓn ID, Ntie S, Mickala P, Maisels F, Wickings EJ, Anthony NM (2009) Effects of storage type and time on DNA amplification success in tropical ungulate faeces: technical advances. Mol Ecol Resour 9:471–479. https://doi.org/10.1111/j.1755-0998.2008.02462.x

    Article  CAS  PubMed  Google Scholar 

  45. Chybicki IJ, Burczyk J (2009) Simultaneous estimation of null alleles and inbreeding coefficients. J Hered 100:106–113. https://doi.org/10.1093/jhered/esn088

    Article  CAS  PubMed  Google Scholar 

  46. Spong G, Johansson M, Björklund M (2000) High genetic variation in leopards indicates large and long-term stable effective population size. Mol Ecol 9:1773–1782. https://doi.org/10.1046/j.1365-294X.2000.01067.x

    Article  CAS  PubMed  Google Scholar 

  47. Creel S, Spong G, Sands JL, Rotella J, Zeigle J, Joe L, Murphy KM, Smith D (2003) Population size estimation in yellowstone wolves with error-prone noninvasive microsatellite genotypes. Mol Ecol 12:2003–2009. https://doi.org/10.1046/j.1365-294X.2003.01868.x

    Article  PubMed  Google Scholar 

  48. Rahmani AR (2001) India. In: Mallon DP, Kingswood SC (Eds) Antelopes: global survey and regional action plans. Part 4: North Africa, the Middle East and Asia. Gland, Switzerland, pp 178–187

  49. Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors: causes, consequences and solutions. Nat Rev Genet 6:847–859. https://doi.org/10.1038/nrg1707

    Article  CAS  PubMed  Google Scholar 

  50. Shukla MA, Joshi BD, Kumar VP, Mehta AK, Goyal SP (2019) Investigating the genetic diversity and presence of forensically informative nucleotide sequences in Indian antelope (Antilope cervicapra) using multiple genes of the mitochondrial genome. Mol Biol Rep 46:6187–6195. https://doi.org/10.1007/s11033-019-05054-5

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the Director, the Dean, Faculty of Wildlife Science, the Research Coordinator and the Nodal Officer, Wildlife Forensic and Conservation Genetics Cell, Wildlife Institute of India, Dehradun, Uttarakhand, India, for facilitating the study. We appreciate Mr. A. Madhanraj for logistic support. We also thank the Uttar Pradesh State Forest Department for providing permission for the fieldwork. The study was financed by M/s Welspun Energy UP Pvt Ltd.

Funding

The financial support for this study was granted by M/s Welspun Energy UP Pvt Ltd. The funding agency had no role in the design or execution of the experiments.

Author information

Authors and Affiliations

  1. Wildlife Institute of India, Dehradun, Uttarakhand, PIN 248001, India

    Rahul De, Vinay Kumar, Kumar Ankit, Khursid Alam Khan, Himanshu Kumar, Nirmal Kumar, Bilal Habib & Surendra Prakash Goyal

Authors
  1. Rahul De
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinay Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kumar Ankit
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khursid Alam Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Himanshu Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nirmal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bilal Habib
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Surendra Prakash Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Concept: SPG, BH, RD; Fieldwork: RD, KA, KAK, HK; Sample processing: RD, VK, KA, KAK, HK, NK; Performed the experiments: RD, VK, NK; Analysed the data: RD, SPG, BH; Authored the original manuscript: RD; Reviewed and commented on the draft manuscript: SPG, BH; approved the initial and revised drafts: RD, VK, KA, KAK, HK, NK, SPG, BH.

Corresponding authors

Correspondence to Bilal Habib or Surendra Prakash Goyal.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Ethical approval

This study did not involve handling of animals. We did not use any tissue samples. We received permission to collect non-invasive faecal samples from Uttar Pradesh State Forest Department vide letter no. 456 dated 20th August 2018.

Consent for publication

Not applicable as this study does not contain data from any individual person. All authors approved and consented to the submission of the final draft of the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 924 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De, R., Kumar, V., Ankit, K. et al. Cross-amplification of ungulate microsatellite markers in the endemic Indian antelope or blackbuck (Antilope cervicapra) for population monitoring and conservation genetics studies in south Asia. Mol Biol Rep 48, 5151–5160 (2021). https://doi.org/10.1007/s11033-021-06514-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-021-06514-7

Keywords

Search

Navigation