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1 Journal of Animal Science and Technology 55(1) 13~18, 한우의유전체육종가의정확도추정 이승수 1 이승환 1 최태정 1 최연호 1 조광현 1 최유림 1 조용민 1 김내수 2 이중재 2 * 1, 2 Estimation of the Accuracy of Genomic Breeding Value in Hanwoo (Korean Cattle) Seung Soo Lee 1, Seung Hwan Lee 1, Tae Jeong Choi 1, Yun Ho Choy 1, Kwang Hyun Cho 1, You Lim Choi 1, Yong Min Cho 1, Nae Soo Kim 2 and Jung Jae Lee 2 * 1 National Institute of Animal Science, RDA, 2 Department of Animal Science, Chungbuk national University Cheongju, Chungbuk, , Korea ABSTRACT This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~ In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls. (Key words : GBLUP, GEBV, SNP, Cross-validation, Genomic selection) 서론 (Genomic Selection, GS),,, (Meuwissen 2003).,, (Quantitative Trait Loci, QTL), QTL. Meuwissen (2001),.,. SNP (Single Nucleotide Polymorphisms),, EU (Rolf, 2010; Su, 2012; VanRaden, 2009; Hayes, 2009a).,, GBULP Bayesian * Corresponding author : Jung Jae Lee, Department of Animal Science, Chungbuk national University Cheongju, Chungbuk, , Korea. Tel: , Fax: , ljj791201@chungbuk.ac.kr - 13-

2 (Lee, 2011; Cho and Lee, 2011), Lee(2012) K SNP chip GBLUP GEBV. () SNP (777K) (Genetic Relationship Matrix, GRM) GBLUP (Genomic Best Linear Unbiased Prediction) GEBV (Genomic Estimated Breeding Value), (Cross-validation). 1. Table 1. Number of heads by batch-number and age of month at slaughtering in progeny-tested steers Progeny-Tested Steers Batch-Number No. Age (Month) No 재료및방법 , 46, Table ,674, (Carcass weight, CW), (Eye Muscle Area, EMA), (Backfat Thickness, BT) (Marbling Score, MS). Genome Nucleic Acid Purification Kit (MagExtractor TM, Toyobo CO., LTD. Osaka, Japan) DNA. SNP Illumina BovineHD BeadChip (777K SNPs)., (Hardy-Weinberg equilibrium, HWE), (Missing proportion, MSP) 20% Minor Allele frequency (MAF) ,489 SNP. Total (1) 육종가추정. Y = Xb + Zu + e Y, X (, ), Z, b, u, e, E (y) = Xb, Var(u) = G = A, Var(e) = R = I, Cov(μ, e) = 0 Var(y) = V = ZGZ' R. A,,. (2) 유전체육종가추정 578,489 SNP 4 SNP, (Likelihood Ratio Test) 5% 3.84 SNP. (over-estimation) SNP SNP BLUP (Ridge Regression Best Linear Unbiased Prediction, RR-BLUP) (Meuwissen et al., 2001; Whittaker ar al., 2000). (Infinitesimal Model) (Hayes et al., 2009a; Visscher et al., 2006), SNP (Genomic estimated breeding value, GEBV).. b(), i, SNP,,.. GEBV, (Gauss

3 Seidel iteration), 1, E-8. (3) 유전체혈연행렬추정 Allele Frequency Method (GOF) (Combined Pedigree and Genomic Relationship Method : H). GOF 0(VanRaden, 2008) n, SNP m, M. M n m., M (11) 0, (12) 1 (22) 2. MM' n n P ( Minor Allele Frequency) 2p j. (Misztal, 2009)., 1 2 SNP, G. (4) 유전체육종가정확도추정 RR-BLUP (Reference Set) SNP (Validation Set) (Cross-validation). SNP (90%) RR-BLUP SNP SNP. 50(10%) GEBV 10. GEBV. (Exact Predicted Error Variance, PEV exact). PC SAS Package (Version 9.1). Intel Visual Fortran Compiler (Window Ver. 11.1). 결과및고찰 552,, Table 2.,, ±37.83 kg, 82.35±8.30 cm 2, 7.99±3.08 mm 3.05±1.47. Hwang (2008) , ±41.89 kg, 75.72±8.19 cm 2, 8.27±3.69 mm 2.91±1.63, Kim (2010) ±38.2 kg, 77.7±8.2 cm 2, 10.3±4.0 mm 3.3±1.7. SNP SNP ,489 SNP Table 2. Descriptive statistics of traits for 552 progeny-tested streers Traits Mean S.D. Min. Max. CW (kg) EMA (cm 2 ) BF (mm) MS CW: Carcass weight; EMA: Eye Muscle Area; BT: Backfat Thickness; MS: Marbling score - 15-

4 . SNP 578,489 SNP 50,632, 50,435, 67,213 46,592. SNP, (random subsampling replication) 10 (10-fold cross validation) (90%) SNP SNP 50 (10%) GEBV GEBV Table 3., 0.915~ SNP RR-BLUP. reference validation data set, data set (Clack et al., 2012). (Hayes et al., 2009b) (Habier et al., 2007; Habier et al., 2010). reference ( : , : ). 534 BLUP GBLUP Table 4. (GOF) (H) ( ),,, 0.50, 0.41, Table 5 SNP 534 () 3,674 GBLUP BLUP. GBLUP BLUP % ~1.28%, 3, %~1.29%. 534,, 9.56%, 5.78%, 5.78% 4.18%, 3,674 Table 3. Mean accuracy of GEBV in the validation sets using 10-fold cross-validation method Traits 10-Fold Cross validation No. of SNP marker Mean accuracy S.D. CW 51, EMA 47, BF 65, MS 45, CW: Carcass weight; EMA: Eye Muscle Area; BT: Backfat Thickness; MS: Marbling score Table 4. Average accuracies of breeding value for traits using BLUP and GBLUP Population Analysis CW EMA BF MS Steers (534) Total (3,674) A (BLUP) GOF (GBLUP) A (BLUP) H-matrix (GBLUP)

5 Table 5. Comparison with mean difference and maximum difference of accuracies between BLUP and GBLUP from combined relationship matrix for traits Accuracy Mean Difference (%) Maximum Difference (%) Traits CW EMA BF MS Steers 1) Total 2) Steers Total ) Steers are 534 heads; 2) Total is 3,674 heads %, 6.50%, 6.50% 4.31%. Forni (2011) SNP, 6%~27%, 1% ~4%. Lee (2012)., Forni (2011) BLUP, GBLUP 534,, 9.56%, 5.78%, 5.78% 4.18%, 3, %, 6.50%, 6.50% 4.31%.. genomic breeding value, BLUP.,,.. ( 주제어 : GBLUP, GEBV, SNP,, ) 요 약 사 사 552,, SNP (777K)(Genetic Relationship Matrix, GRM) GBLUP (Genomic Best Linear Unbiased Prediction) GEBV (Genomic Estimated Breeding Value) (Cross-validation) ~ 21 (: PJ008188). 인용문헌 Cho, C. I. and Lee, D. H Study on Genetic Evaluation using Genomic Information in Animal Breeding Simulation Study for Estimation of Marker Effects. J. Anim. Sci. Tech. (Kor.) 53(1):

6 Forni, S., Aguilar, I. and Misztal, I Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution. 43:1. Habier, D, Tetens, J., Seefried, F. R., Lichtner, P. and Thaller, G The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution. 42:5. Habier, D., Fernando, R. L. and Dekkers, J. C. M The impact of genetic relationship information on genome-assisted breeding values. Genetics. 177: Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K. and Goddard, M. E. 2009a. Accuracy of genomic breeding values in multi-breed dairy cattle popilations. Genetics Selection Evolution. 41:51. Hayes, B. J., Visscher P. M. and Goddard, M. E. 2009b. Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res. 91: Hwang, J. M., Kim, S. D., Choy, Y. H., Yoon, H. B. and Park, C. J Genetic Parameter Estimation of Carcass Traits of Hanwoo Steers. J. Anim. Sci. Tech. (Kor.) 50(5): Kim, H. S., Hwang, J. M., Choi, T. J., Park, B. H., Cho, K. H., Park, C. J, and Kim S. D Research on the Reformation of the Selection Index for Hanwoo Proven Bull. J. Anim. Sci. Tech. (Kor.) 52(2): Lee, J. H Study on Methodology for Estimating Breeding Values using Genomic Information. Ph. D. thesis, Hankyoung National University. Lee, J. J Genetic Evaluation for Carcass Traits of Hanwoo using Pedigree and SNP Marker-Derived Relationship Matrix. Ph. D. thesis, Chungbuk National University. Lee, S. H., Lim D. J., Jang, G. W., Cho, Y. M., Choi, B. H., Kim S. D., Oh S. J., Lee J. H., Yoon D. H., Park E. W., Lee, H. K., Hong, S. K. and Yang B. S Genome Wide Association Study to Identity QTL for Growth Traits in Hanwoo. J. Anim. Sci. Tech. (Kor.) 54(5):1-10. Meuwissen, T Genomic Selection : the future of marker assisted selection and animal breeding. FAO. Meuwissen, T. H. E., Hayes, B. J. and Goddard, M. E Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps. Genetics. 157: Misztal, I., Legarra, A. and Aguilar, I Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci. 92: Rolf, M. M., Taylor, J. F., Schnabel, R. D., Mckay, S. D., Mcclure, M. C., Northcutt, S. L., Kerley, M. S. and Weaber, R. L Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle. BMC. Genetics 11:24. SAS Institute Inc SAS OnlineDoc Cary, NC: SAS Institute Inc. Su, G., Brondum, R. F., Ma, P., Guldbrandtsen, B., Aamand, G. P. and Lund, M. S Comparison of genomic predcitions using medium-density (~54,000) and high-density (~777,000) single uncleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations. J. Dairy Sci. 95: VanRaden, P. M Efficient methods to compute genomic predictions. J. dairy sci. 91: VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F. and Schenkel, F. S Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92: Visscher, P. M., Medland, S. E., Ferreira, M. A., Morley, K. I., Zhu, G., Cornes, B. K., Montgomery, G. W. and Martin, N. G Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLOS Genetic 2(3):e41. Whittaker, J. C., Thompson, R. and Denham, M. C Marker-assisted selection using ridge regression. Genetical Research. 75: (Received Jan. 28, 2013; Revised Feb. 25, 2013; Accepted Feb. 26, 2013) - 18-

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