Journal of Animal Science and Technology

  • 제55권3호
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  • Pages.165-171
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  • 2013
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  • 2672-0191(pISSN)
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  • 2055-0391(eISSN)
한국축산학회 (Korean Society of Animal Sciences and Technology)
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유전체 관계행렬 구성에 따른 Landrace 순종돈의 육종가 비교

Comparison of Breeding Value by Establishment of Genomic Relationship Matrix in Pure Landrace Population

  • 이준호 (국립한경대학교 동물생명환경과학과) ;
  • 조광현 (농촌진흥청 국립축산과학원) ;
  • 조충일 (농촌진흥청 국립축산과학원) ;
  • 박경도 (국립한경대학교 동물생명환경과학과) ;
  • 이득환 (국립한경대학교 동물생명환경과학과)
  • Lee, Joon-Ho (Department of Animal Life Resources, Hankyong National University) ;
  • Cho, Kwang-Hyun (National Institute of Animal Science, R.D.A.) ;
  • Cho, Chung-Il (National Institute of Animal Science, R.D.A.) ;
  • Park, Kyung-Do (Department of Animal Life Resources, Hankyong National University) ;
  • Lee, Deuk Hwan (Department of Animal Life Resources, Hankyong National University)
  • 투고 : 2013.04.05
  • 심사 : 2013.06.25
  • 발행 : 2013.06.30
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초록

돼지 유전체 전장의 고밀도 단일염기다형 유전자형을 이용하여 혈연관계행렬을 구성하고 이를 이용하여 유전체 육종가를 추정하였다. 이상치를 제거한 랜드레이스 순종돈 448두의 40,706개 단일염기다형 유전자형 정보를 이용하였으며, G05, GMF, GOF, $GOF^*$ 및 GN의 5가지 방법을 이용하여 유전체 관계행렬을 구성하고 이를 이용하여 유전체 육종가를 추정하였다. GOF 방법에 의하여 계산된 혈연계수가 기존의 혈통정보를 이용한 혈연계수와 가장 작은 편차를 나타내고 평균소수대립유전자빈도를 이용하는 GMF 방법에서는 큰 차이가 나타나 대립유전자빈도 기준이 혈연계수의 평균이동을 유발함을 확인하였으며, $GOF^*$를 제외한 모든 방법에서 정규 분포형태의 멘델리안샘플링이 나타나는 것을 확인하였다. 등지방두께 평균과 90 kg 도달일령에 대한 육종가 추정 모형을 설정하고 유전체 관계행렬을 이용하여 유전모수와 육종가를 추정한 결과 혈통정보를 이용한 육종가와의 상관은 GOF 방법에서 가장 높게 나타났으며, 유전체 관계행렬의 척도(scale)에 베타함수를 이용한 $GOF^*$의 경우 모든 형질에서 유전분산이 크게 추정되어 분모부분을 구성하는 척도는 유전모수 추정치 영향하는 것을 확인하였다. 동일한 표현형 정보량을 이용할 경우 유전체관계행렬을 이용한 육종가 추정의 정확도가 혈통정보를 이용한 육종가보다 높게 나타났으며, 90 kg 도달일령보다는 등지방두께 평균에서 그 차이가 더 크게 나타났다. 집단 내 누적 표현형자료가 부족한 경우, 외래 유전자원이 도입되어 집단 내 혈연관계가 부족할 경우 또는 멘델리안 분포가 전혀 고려되지 않는 어린 동복자손의 육종가를 예측해야 하는 경우에 유전체 정보를 활용하면 유전능력 평가의 정확성을 크게 향상시킬 수 있을 것으로 사료된다.

Genomic relationship matrix (GRM) was constructed using whole genome SNP markers of swine and genomic breeding value was estimated by substitution of the numerator relationship matrix (NRM) based on pedigree information to GRM. Genotypes of 40,706 SNP markers from 448 pure Landrace pigs were used in this study and five kinds of GRM construction methods, G05, GMF, GOF, $GOF^*$ and GN, were compared with each other and with NRM. Coefficients of GOF considering each of observed allele frequencies showed the lowest deviation with coefficients of NRM and as coefficients of GMF considering the average minor allele frequency showed huge deviation from coefficients of NRM, movement of mean was expected by methods of allele frequency consideration. All GRM construction methods, except for $GOF^*$, showed normally distributed Mendelian sampling. As the result of breeding value (BV) estimation for days to 90 kg (D90KG) and average back-fat thickness (ABF) using NRM and GRM, correlation between BV of NRM and GRM was the highest by GOF and as genetic variance was overestimated by $GOF^*$, it was confirmed that scale of GRM is closely related with estimation of genetic variance. With the same amount of phenotype information, accuracy of BV based on genomic information was higher than BV based on pedigree information and these symptoms were more obvious for ABF then D90KG. Genetic evaluation of animal using relationship matrix by genomic information could be useful when there is lack of phenotype or relationship and prediction of BV for young animals without phenotype.

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참고문헌

  1. Aguilar, I., Misztal, I., Johnson, D., Legarra, A., Tsuruta, S. and Lawlor, T. 2010a. A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93:743-752. https://doi.org/10.3168/jds.2009-2730
  2. Aguilar, I., Misztal, I., Legarra, A. and Tsuruta, S. 2010b. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. J. Anim. Breed. Genet. 128(6):422-428.
  3. Forni, S., Aguilar, I. and Misztal, I. 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution 43:1-7. https://doi.org/10.1186/1297-9686-43-1
  4. Gianola, D., de los Campos, G., Hill, W. G., Manfredi E. and Fernando R. 2009. Additive genetic variability and the Bayesian alphabet. Genetics 183:347-363. https://doi.org/10.1534/genetics.109.103952
  5. Habier, D., Fernando, R. L., Kizilkaya, K. and Garrick, D. J. 2011. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics 12:186-197. https://doi.org/10.1186/1471-2105-12-186
  6. Habier, D., Fernando, R. and Dekkers, J. C. M. 2007. The Impact of genetic relationship information on genome-assisted breeding values. genetics 177: 2389-2397.
  7. Hayes, B. J., Bowman, P. J., Chamberlain, A. J. and Goddard, M. E. 2009. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92(2):433-443. https://doi.org/10.3168/jds.2008-1646
  8. Henderson, C. R. 1976. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32:69-83. https://doi.org/10.2307/2529339
  9. Legarra, A. and Misztal, I. 2008. Technical Note: Computing Strategies in Genome-Wide Selection. J. Dairy Sci. 91:360-366. https://doi.org/10.3168/jds.2007-0403
  10. Legarra, A., Aguilar, I. and Misztal, I. 2009. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92:4656-4663. https://doi.org/10.3168/jds.2009-2061
  11. Loberg, A. and Durr, J. W. 2009. Interbull survey on the use of genomic information. Proceedings of the interbull technical workshop 3-13.
  12. Meuwissen, T. H., Hayes, B. J. and Goddard, M. E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-1829.
  13. Misztal, I., Legarra, A. and Aguilar, I. 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci. 92:4648-4655. https://doi.org/10.3168/jds.2009-2064
  14. Stephens, M. and Donnelly, P. 2003. A comparison of Bayesian methods for haplotype reconstruction from population genotype data. American Journal of Human Genetics, 73:1162-1169. https://doi.org/10.1086/379378
  15. Sun, X., Habier, D., Fernando, R. L., Garrick, D. J. and Dekkers, J. C. M. 2010. Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods. BMC Proceedings 5(3):S13.
  16. VanRaden P. M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414-4423. https://doi.org/10.3168/jds.2007-0980
  17. VanRaden P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F. and Schenkel, F. S. 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24. https://doi.org/10.3168/jds.2008-1514
  18. Zhang, Z., Todhunter, R. J. Buckler, E. S. and Van Vleck, L. D. 2007. Technical note: Use of marker-based relationships with multipletrait derivative-free restricted maximal likelihood. J. Anim. Sci. 85:881-885. https://doi.org/10.2527/jas.2006-656
  19. Mrode, R. A. 2005. Linear Models for the Prediction of Animal Breeding Values, 2nd ed. CABI, Scottish Agricultural College, Edinburgh, UK pp10.
  20. Misztal, I. 2011. Computational techniques in animal breeding. University of Georgia, Athens, GA.