Journal of Animal Science and Technology

Korean Society of Animal Sciences and Technology (한국축산학회)
권호 표지
DOI QR코드

DOI QR Code

A Comparison of Discriminating Powers Between 14 Microsatellite markers and 60 SNP Markers Applicable to the Cattle Identification Test

소 동일성 검사에 적용 가능한 14 Microsatellite marker와 60 Single Nucleotide Polymorphism marker 간의 판별 효율성 비교

  • Lim, Hyun-Tae (Division of Applied Life Science (BK21 program), Graduate School of Gyeongsang National University) ;
  • Seo, Bo-Yeong (Division of Applied Life Science (BK21 program), Graduate School of Gyeongsang National University) ;
  • Jung, Eun-Ji (Division of Applied Life Science (BK21 program), Graduate School of Gyeongsang National University) ;
  • Yoo, Chae-Kyoung (Division of Applied Life Science (BK21 program), Graduate School of Gyeongsang National University) ;
  • Yoon, Du-Hak (National Institute of Animal Science, R.D.A.) ;
  • Jeon, Jin-Tae (Division of Applied Life Science (BK21 program), Graduate School of Gyeongsang National University)
  • 임현태 (경상대학교 응용생명과학부(BK21)) ;
  • 서보영 (경상대학교 응용생명과학부(BK21)) ;
  • 정은지 (경상대학교 응용생명과학부(BK21)) ;
  • 유채경 (경상대학교 응용생명과학부(BK21)) ;
  • 윤두학 (농촌진흥청 국립축산과학원) ;
  • 전진태 (경상대학교 응용생명과학부(BK21))
  • Received : 2009.08.04
  • Accepted : 2009.10.15
  • Published : 2009.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

When 14 microsatellite (MS) markers were applied in the identifying test for 480 Hanwoo, the discriminating power was estimated as $3.43{\times}10^{-27}$ based on the assumption of a random mating group (PI). This rate is 1,000 times higher than that of 60 single nucleotide polymorphism (SNP) markers. On the other hand, the power of the 60 SNP markers was estimated as $4.69{\times}10^{-20}$ and $8.02{\times}10^{-12}$ on the assumption of a half-sib mating group ($PI_{half-sibs}$) and a full-sib mating group ($PI_{sibs}$), respectively. These powers were 10 times and 10,000 times higher than those of the 14 MS markers. The results indicated that the total number of alleles (MS vs SNP = 146 vs 120) acted as a key factor for the discriminating power in a random mating population, and the total number of markers (MS vs SNP = 14 vs 60) was a dominant influence on the power in half-sib and full-sib populations. In the Hanwoo population, in which it was assumed that the entire population is the enormous half-sib group formed by the absolute genetic contribution of a few nuclear bulls, there will be only a 10 times difference in the discriminating power between the 14 MS markers and the 60 SNP makers. However, the probability of not excluding a candidate parent pair from the parentage of an arbitrary offspring, given that only the genotype of the offspring ($PNE_{pp}$) was 1,000 times higher as shown by the 14 MS markers than that by the 60 SNP markers. The strong points of SNP makers are the stability of the variation (low mutation rate) and automation of high-throughput genotyping. In order to apply these merits for the practical and constant Hanwoo identity test, research and development are required to set a cost-effective platform and produce a homemade apparatus for SNP genotyping.

14개의 microsatellite (MS) marker를 사용 할 경우 무작위 교배 집단(PI) 가정 하에 $3.43{\times}10^{-27}$의 판별율을 보여 60 개의 single nucleotide polymorphism (SNP) marker에 비해 약 1,000배의 높은 판별 효과를 나타내는 것으로 파악되었다. 그러나, 60개의 SNP marker의 경우 반형매 교배 집단($PI_{half-sibs}$)으로 가정할 경우 $4.69{\times}10^{-20}$과 전형매 교배 집단($PI_{sibs}$)으로 가정 할 경우 $8.02{\times}10^{-12}$으로 14개의 MS marker에 비해 약 10배와 10,000배의 높은 판별 효과를 나타내는 것으로 추정되었다. 이러한 결과는 무작위 교배집단에서는 사용된 marker의 전체 대립유전자수(MS : SNP = 146 : 120)에 의하여 판별효율이 결정되는 반면, 혈연관계가 높은 반형매와 전형매 집단에서는 비슷한 총 대립유전자수일 경우 marker의 수(MS : SNP = 14 : 60)가 많은 경우가 더 높은 판별율을 보이는 것으로 나타났다. 한육우의 경우 소수의 보증 종모우를 이용해 인공수정을 통해 형성 된 거대한 반형매 집단으로 가정하였을 경우 MS와 SNP marker의 판별율은 10배 정도의 차이로 큰 차이를 보이지 않을 것으로 예견되나, likelihood rato를 이용 하는 inclusion 방법에 의하여 부모를 동시에 찾을 확률은 MS marker가 1,000 배 정도 더 효율적인 것으로 나타났다. SNP marker의 장점인 변이의 안정성, 유전자형 분석의 자동화 및 대용량화 등을 한육우의 동일성 검사에 활용하기 위해서는 분석비용 절감 방안과 분석방법 및 장비의 국산화 등 실용 및 상용화적 측면에서의 연구개발이 필요하다고 사료된다.

Keywords

References

  1. Ajmone-Marsan, P., Valentini, A., Cassandro, M., Vecchiotti-Antaldi, G. and Bertoni, G. 1997. AFLP markers for DNA fingerprinting in cattle. Anim. Genet. 28:418-426. https://doi.org/10.1111/j.1365-2052.1997.00204.x
  2. Ayres, K. L. and Overall, A. D. J. 2004. API-CALC 1.0: a computer program for calculating the average probability of identity allowing for substructure, inbreeding and the presence of close relatives. Molecular Ecology Notes 4:315-318. https://doi.org/10.1111/j.1471-8286.2004.00616.x
  3. Chamberlain, J. S., Gibbs, R. A., Ranier, J. E., Nguyen, P. N. and Caskey, C. T. 1988. Deletion screening of the Duchenne muscular dystrophy locus via multiplex DNA amplification. Nucleic Acids Res. 16:11141-11156. https://doi.org/10.1093/nar/16.23.11141
  4. Crisan, D. 1994. Molecular diagnostic testing for determination of myeloid lineage in acute leukemias. Ann. Clin. Lab. Sci. 24:355-363.
  5. Glowatzki-Mullis, M. L., Gaillard, C., Wigger, G. and Fries, R. 1995. Microsatellite-based parentage control in cattle. Anim. Genet. 26:7-12. https://doi.org/10.1111/j.1365-2052.1995.tb02612.x
  6. Goudet, J. 2001. FSTAT, a program to Estimate and Test Gene Diversities and Fixation Indices (version 2.9.3). Available from http://www.unil.ch/izea/software/fstat.html
  7. Heyen, D. W., Beever, J. E., Da, Y., Evert, R. E. and Green, C. 1997. Exclusion probabilities of 22 bovine microsatellite markers in fluorescent multiplexes for semiautomated parentage testing. Anim. Genet. 28:21-27. https://doi.org/10.1111/j.1365-2052.1997.t01-1-00057.x
  8. Keyue, D., Kaixin, Z., Fuchu, H. and Yan S. 2003. LDA - A java-based linkage disequilibrium analyzer. Bioinformatics 19: 2147-2148. https://doi.org/10.1093/bioinformatics/btg276
  9. Mansfield, E. S., Robertson, J. M., Lebo, R. V., Lucero, M. Y., Mayrand, P. E., Rappaport, E., Parrella, T, Sartore, M., Surrey, S. and Fortina, P. 1993. Duchenne Becker muscular dystrophy carrier detection using quantitative PCR and fluorescence-based strategies. Am. J. Med. Genet. 48:200-208. https://doi.org/10.1002/ajmg.1320480406
  10. Marshall, T. C., Slate, J., Kruuk, L. E. B. and Pemberton, J. M. 1998. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 7:639-655. https://doi.org/10.1046/j.1365-294x.1998.00374.x
  11. Michael P. Heaton, Gregory P. Harhay, Gary L. Bennett, Roger T. Stone, W. Michael Grosse, Eduardo Casas, John W. Keele, Timothy P.L. Smith, Carol G. Chitko-McKown and Will W. Laegreid 2002. Selection and use of SNP markers for animal identification and paternity analysis in U.S. beef cattle. Mammalian Genome 13:272-281. https://doi.org/10.1007/s00335-001-2146-3
  12. Mutirangura, A. F., Greenberg, M. G., Butler, S., Malcolm, R. D., Nicholls, A., Chakravarti and Ledberrer, D. H. 1993. Multiplex PCR of three dinucleotide repeats in the Prader-Willi/Angelman critical resgion: molecular diagnosis and mechanism of uniparental disomy. Hum. Mol. Genet. 48: 200-208.
  13. Raymond M. and Rousset F. 1995. GENEPOP (version 1. 2): population genetics software for exact tests and ecumenicism. J. Heredity 86:248-249. https://doi.org/10.1093/oxfordjournals.jhered.a111573
  14. SEQUENOM$^{{\copyright}}$ Application Note. 2005. iPLEX$^{TM}$ Assay: Increased plexing efficiency and flexibility for MassARRAY$^{{\copyright}}$ system through single base primer extension with Msaa-Modified terminators.
  15. Shuber, A. P., Skoletsky, J., Stern, R. and Handelin, B. L. 1993. Efficient 12-mutation testing in the CFTR gene: a general model for complex mutation analysis. Hum. Mol. Genet. 2:153-158. https://doi.org/10.1093/hmg/2.2.153
  16. Tom, R. G., Santiago, R., Carlos, Z. and Ian, N. M. D. 2006. MIDAS: software for analysis and visualisation of interallelic disequilibrium between multiallelic markers. BMC Bioinformatics 7: 227. https://doi.org/10.1186/1471-2105-7-227
  17. Usha A. P., Simpson S. P. and Williams J. L. 1995. Probability of random sire exclusion using microsatellite markers for parentage verification. Anim. Genet. 26:155-161. https://doi.org/10.1111/j.1365-2052.1995.tb03155.x
  18. Weir, B. S. and Hill, W. G. 2002. Estimating F-statistics. Annu. Rev. Genet. 36:721-50. https://doi.org/10.1146/annurev.genet.36.050802.093940
  19. Werner, F. A. O., Durstewitz, G., Habermann, F. A., Thaller, G., Kraemer, W., Kollers, S., Buitkamp, J., Georges, M., Brem, G., Mosner, J. and Fries, R. 2003. Detection and characterization of SNPs useful for identity control and parentage testing in major European dairy breeds. Anim. Genet. 35:44-49. https://doi.org/10.1046/j.1365-2052.2003.01071.x
  20. Wiliams J. L., Usha A. P., Urquhart B. G. and Kilroy M. 1997. Verification of the identity of bovine semen using DNA microsatellite markers. Vet. Rec. 140:446-449. https://doi.org/10.1136/vr.140.17.446
  21. 임현태, 민희식, 문원곤, 이재봉, 김재환, 조인철, 이학교, 이용욱, 이정규, 전진태. 2005. 한우 생산이력제에 활용 가능한 Microsatellite의 분석과 선발. 동물자원과학회지. 47(4):491-500.

Cited by

  1. Analysis of Genetic Characteristics and Probability of Individual Discrimination in Korean Indigenous Chicken Brands by Microsatellite Marker vol.55, pp.3, 2013, https://doi.org/10.5187/JAST.2013.55.3.185
  2. Comparison of the Microsatellite and Single Nucleotide Polymorphism Methods for Discriminating among Hanwoo (Korean Native Cattle), Imported, and Crossbred Beef in Korea vol.34, pp.6, 2014, https://doi.org/10.5851/kosfa.2014.34.6.763
  3. Discrimination of the commercial Korean native chicken population using microsatellite markers vol.57, pp.1, 2015, https://doi.org/10.1186/s40781-015-0044-6
  4. Paternity Identification Using the Multiplex PCR with Microsatellite Markers in Chicken vol.48, pp.2, 2014, https://doi.org/10.14397/jals.2014.48.2.69
  5. Establishment of a Microsatellite Marker set for Individual Identification in Goat vol.48, pp.3, 2014, https://doi.org/10.14397/jals.2014.48.3.157
  6. Molecular Genetic Considerations of Jeju Black Cattle using Microsatellite Markers vol.49, pp.2, 2015, https://doi.org/10.14397/jals.2015.49.2.57