Assessment of Microsatellite Markers for Parentage Testing in Jersey Crossbred Cattle in Tamil Nadu
Life Sciences-Molecular Genetics
DOI:
https://doi.org/10.22376/ijpbs/lpr.2022.12.6.L86-94Keywords:
Microsatellite Markers, Jersey Crossbred Cattle, Parentage Testing, Probability of ExclusionAbstract
A study was aimed to evaluate a set of polymorphic microsatellite markers for their efficiency in testing parentage in Jersey crossbred cattle available in Tamil Nadu. The objective of validation was based on the criteria such as polymorphism information content, various genetic diversity parameters and the ability of markers to exclude the wrong parent. A total of 21 microsatellite markers were included to verify the parentage in 24 Jersey crossbred trios (comprising 24 dams, 24 progenies and 2 sires). The microsatellite loci were amplified using fluorescently labelled primers by multiplex PCR and fragment analysis was done through capillary electrophoresis using automated DNA analyzer. Statistical analyses were done using Pop gene version 1.31, CERVUS 3.0.7 and GENALEX 6.503 software programs. The number of alleles for the markers utilized in the study ranged from 4 (ILSTS11) to 12 (INRA23 and TGLA122) with an overall mean of 8.0952 ± 0.47 alleles per locus. The markers also exhibited high expected heterozygosity (He) and polymorphism information content (PIC) ranging from 0.6362 (ETH3) to 0.8629 (SPS115) with a mean of 0.7681 ± 0.013 and 0.592(ETH3) to 0.83(CSSM66) with a mean of 0.7256±0.014 respectively, signifying them to be highly informative. Low overall probability of identity (PI) of 0.0943 ± 0.008 and high overall probability of exclusion (PE) of 0.9619±0.02 with the increasing locus combinations were observed. The probability of exclusion was cent per cent (PE=1.0000) when a combination of 8 markers were used with one known parent and a combination of 12 markers when excluding a putative pair, suggesting the efficacy and suitability of the markers used in the study for parentage testing on Jersey crossbreds of Tamil Nadu.
References
Khade AS, Sawane MP, Pawar VD, Maurya BK, Nair RR. Validation of Microsatellite Markers for Parentage Verification in Indian HF Cattle. Int. J. Curr. Microbiol. App. Sci. 2019;8(3):1246-52.
Parentage Verification | National Dairy Development Board (nddb.coop) [Internet]. Available from: https://www.nddb.coop/calf/parentage-verification#:~:text=Parentage%20testing%20relies%20on% 20the,excluded%20from%20the%20calf's%20pedigree.
Animal Husbandry Policy Note 2020-2021[Internet] by Animal Husbandry, Dairying and Fisheries Department, Government of Tamil Nadu (2020). Available from: https://cms.tn.gov.in/ sites/default/files/documents/ah_e_pn_2020_21.pdf
Brenig B, Schütz E. Recent development of allele frequencies and exclusion probabilities of microsatellites used for parentage control in the German Holstein Friesian cattle population. BMC genetics. 2016 Dec;17(1):1-9.
García-Ruiz A, Wiggans GR, Ruiz-López FJ. Pedigree verification and parentage assignment using genomic information in the Mexican Holstein population. Journal of dairy science. 2019 Feb 1;102(2):1806-10.
Řehout V, Hradecká E, Čítek J. Evaluation of parentage testing in the Czech population of Holstein cattle. Czech Journal of Animal Science. 2006;51(12):503-9.
Zhang Y, Wang Y, Sun D, Yu Y, Zhang Y. Validation of 17 microsatellite markers for parentage verification and identity test in Chinese Holstein cattle. Asian-Australasian Journal of Animal Sciences. 2010 Feb 22;23(4):425-9.
Pei J, Bao P, Chu M, Liang C, Ding X, Wang H, Wu X, Guo X, Yan P. Evaluation of 17 microsatellite markers for parentage testing and individual identification of domestic yak (Bos grunniens). PeerJ. 2018 Nov 12;6:e5946.
Stormont C. Contribution of blood typing to dairy science progress. Journal of dairy science. 1967 Feb 1;50(2):253-60.
Heaton MP, Harhay GP, Bennett GL, Stone RT, Grosse WM, Casas E, Keele JW, Smith TP, Chitko-McKown CG, Laegreid WW. Selection and use of SNP markers for animal identification and paternity analysis in US beef cattle. Mammalian genome. 2002 May;13(5):272-81.
Estoup A, Jarne P, Cornuet JM. Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular ecology. 2002 Sep;11(9):1591-604.
Schlötterer C. Evolutionary dynamics of microsatellite DNA. Chromosoma. 2000 Sep;109(6):365-71.
FAO. Molecular genetic characterization of animal genetic resources. FAO Animal Production and Health Guidelines. No. 9. Rome; 2011.
Silva EC, McManus CM, Guimarães MP, Gouveia AM, Facó O, Pimentel DM, Caetano AR, Paiva SR. Validation of a microsatellite panel for parentage testing of locally adapted and commercial goats in Brazil. Genetics and Molecular Biology. 2014;37:54-60.
Sánchez-Velásquez JJ, Reyes-Flores LE, Yzásiga-Barrera C, Zelada-Mázmela E. Performance comparison of gel and capillary electrophoresis-based microsatellite genotyping strategies in a population research and kinship testing framework. BMC research notes. 2021 Dec;14(1):1-8.
Mitchelson KR, Cheng J. Capillary Electrophoresis of Nucleic Acids: Volume II: Practical Applications of Capillary Electrophoresis. Humana Press; 2001.
Mansor F, Zamri L, Hamzah SS. Electrophoretic techniques for the detection of human microsatellite d19s884. The Malaysian journal of medical sciences: MJMS. 2015 Mar;22(2):18.
Shewale JG, Qi LI, Calandro LM. Principles, practice, and evolution of capillary electrophoresis as a tool for forensic DNA analysis. Forensic Science Review. 2012 Jul 1;24(2):79.
Wang ZF, Dai SP, Lian JY, Chen HF, Ye WH, Cao HL. Allele Size Miscalling due to the Pull-Up Effect Influencing Size Standard Calibration in Capillary Electrophoresis: A Case Study Using HEX Fluorescent Dye in Microsatellites. IntechOpen; 2018.
Sambrook J, Fritsch EF, Maniatis T. Molecular Cloning: A Laboratory Manual, 2nd cdn,('old Spring Harbor Laboratory. New York. 1989.
Wu H, de Gannes MK, Luchetti G, Pilsner JR. Rapid method for the isolation of mammalian sperm DNA. Biotechniques. 2015 Jun;58(6):293-300.
Madilindi MA, Banga CB, Bhebhe E, Sanarana YP, Nxumalo KS, Taela MG, Mapholi NO. Genetic differentiation and population structure of four Mozambican indigenous cattle populations. Livestock Research for Rural Development. 2019;31(4).
De Donato M, Brenneman RA, Stelly DM, Womack JE, Taylor JF. A methodological approach for the construction of a radiation hybrid map of bovine chromosome 5. Genetics and Molecular Biology. 2004;27:22-32.
Sharma R, Pandey AK, Singh Y, Prakash B. Evaluation of genetic variability in Ponwar cattle bymicrosatellite markers. Journal of Applied Animal Research. 2006 Sep 1;30(1):63-7.
Yeh FC. POPGENE (version 1.3. 1). Microsoft Window-Bases Freeware for Population Genetic Analysis. http://www. ualberta. ca/~ fyeh/. 1999.
Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular ecology. 2007 Mar;16(5):1099-106.
Elston RC. Polymorphism information content. Encyclopedia of biostatistics. 2005 Jul 15;6.
Peakall R. PE smouse, 2006. GENALEX 6: genetic analysis in Excel. Population software for teaching and research. Mol. Ecol. Notes.;6:288-95.
Goud TS, Upadhyay RC, Kumar A, Karri S, Choudhary R, Ashraf S, Singh SV, Kumar OS, Kiranmai C. Novel extraction of high quality genomic DNA from frozen bovine blood samples by using detergent method. Open Veterinary Journal. 2018 Nov 12;8(4):415-22.
Hawken RJ, Cavanagh JA, Meadows JR, Khatkar MS, Husaini Y, Zenger KR, McClintock S, McClintock AE, Raadsma HW. Whole-genome amplification of DNA extracted from cattle semen samples. Journal of dairy science. 2006 Jun 1;89(6):2217-21.
Prośniak A, Gloc E, Berent J, Babol-Pokora K, Jacewicz R, Szram S. Estimating the efficiency of DNA isolation methods in semen, blood and saliva stains using the QuantiBlot system. Archiwum Medycyny Sadowej i Kryminologii. 2006 Jan 1;56(1):19-23.
Ghaheri M, Kahrizi D, Yari K, Babaie A, Suthar RS, Kazemi E. A comparative evaluation of four DNA extraction protocols from whole blood sample. Cellular and Molecular Biology. 2016 Mar 31;62(3):120-4.
Luikart G, Biju‐Duval MP, Ertugrul O, Zagdsuren Y, Maudet C, Taberlet P. Power of 22 microsatellite markers in fluorescent multiplexes for parentage testing in goats (Capra hircus). Animal Genetics. 1999 Dec;30(6):431-8.
Hansen C, Shrestha JN, Parker RJ, Crow GH, McAlpine PJ, Derr JN. Genetic diversity among Canadienne, Brown Swiss, Holstein, and Jersey cattle of Canada based on 15 bovine microsatellite markers. Genome. 2002 Oct 1;45(5):897-904.
Chikhi L, Goossens B, Treanor A, Bruford MW. Population genetic structure of and inbreeding in an insular cattle breed, the Jersey, and its implications for genetic resource management. Heredity. 2004 May;92(5):396-401.
Beattie CW. Microsatellite heterozygosity: functional constraints on the development and use of comprehensive genomic maps for livestock. In Genomes of Plants and Animals 1996 (pp. 15-29). Springer, Boston, MA.
Waits LP, Luikart G, Taberlet P. Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Molecular ecology. 2001 Jan;10(1):249-56.
Riojas-Valdes VM, Gomez-de-la-Fuente JC, Garza-Lozano JM, Gallardo-Blanco DC, De Tellitu-Schutz JN, Wong-Gonzalez A, Dávalos-Aranda G, Salinas-Meléndez JA. Exclusion probabilities of 8 DNA microsatellites in 6 cattle breeds from northeast Mexico. Journal of Animal and Veterinary Advances. 2009;8(1):62-6.
Kathiravan P, Kataria RS, Mishra BP. Power of exclusion of 19 microsatellite markers for parentage testing in river buffalo (Bubalus bubalis). Molecular biology reports. 2012 Aug;39(8):8217-23.
Published
How to Cite
Issue
Section
Copyright (c) 2022 Hepsibha P, Karthickeyan S M K, Harriet Sumathy V
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
