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Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

Published online by Cambridge University Press:  12 August 2016

Y. Uemoto*
Affiliation:
Department of Animal Breeding, National Livestock Breeding Center, Nishigo, Fukushima 961-8511, Japan
T. Osawa
Affiliation:
Department of Animal Breeding, National Livestock Breeding Center, Nishigo, Fukushima 961-8511, Japan
J. Saburi
Affiliation:
Department of Animal Breeding, National Livestock Breeding Center, Nishigo, Fukushima 961-8511, Japan
*
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Abstract

This study evaluated the dependence of reliability and prediction bias on the prediction method, the contribution of including animals (bulls or cows), and the genetic relatedness, when including genotyped cows in the progeny-tested bull reference population. We performed genomic evaluation using a Japanese Holstein population, and assessed the accuracy of genomic enhanced breeding value (GEBV) for three production traits and 13 linear conformation traits. A total of 4564 animals for production traits and 4172 animals for conformation traits were genotyped using Illumina BovineSNP50 array. Single- and multi-step methods were compared for predicting GEBV in genotyped bull-only and genotyped bull-cow reference populations. No large differences in realized reliability and regression coefficient were found between the two reference populations; however, a slight difference was found between the two methods for production traits. The accuracy of GEBV determined by single-step method increased slightly when genotyped cows were included in the bull reference population, but decreased slightly by multi-step method. A validation study was used to evaluate the accuracy of GEBV when 800 additional genotyped bulls (POPbull) or cows (POPcow) were included in the base reference population composed of 2000 genotyped bulls. The realized reliabilities of POPbull were higher than those of POPcow for all traits. For the gain of realized reliability over the base reference population, the average ratios of POPbull gain to POPcow gain for production traits and conformation traits were 2.6 and 7.2, respectively, and the ratios depended on heritabilities of the traits. For regression coefficient, no large differences were found between the results for POPbull and POPcow. Another validation study was performed to investigate the effect of genetic relatedness between cows and bulls in the reference and test populations. The effect of genetic relationship among bulls in the reference population was also assessed. The results showed that it is important to account for relatedness among bulls in the reference population. Our studies indicate that the prediction method, the contribution ratio of including animals, and genetic relatedness could affect the prediction accuracy in genomic evaluation of Holstein cattle, when including genotyped cows in the reference population.

Type
Research Article
Copyright
© The Animal Consortium 2016 

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References

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S and Lawlor, TJ 2010. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743752.Google Scholar
Calus, MPL 2016. Editorial: genomic selection with numerically small reference populations. Animal 10, 10161017.CrossRefGoogle ScholarPubMed
Clark, SA, Hickey, JM, Daetwyler, HD and van der Werf, JH 2012. The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genetics Selection Evolution 44, 4.CrossRefGoogle ScholarPubMed
Cooper, TA, Wiggans, GR and VanRaden, PM 2015. Short communication: analysis of genomic predictor population for Holstein dairy cattle in the United States – Effects of sex and age. Journal of Dairy Science 98, 27852788.Google Scholar
Daetwyler, HD, Pong-Wong, R, Villanueva, B and Woolliams, JA 2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 10211031.Google Scholar
de Roos, APW 2011. Genomic selection in dairy cattle. PhD thesis. Wageningen University, Wageningen, the Netherlands.Google Scholar
Fikse, WF and Banos, G 2001. Weighting factors of sire daughter information in international genetic evaluations. Journal of Dairy Science 84, 17591767.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433443.Google Scholar
Hickey, JM, Veerkamp, RF, Calus, MP, Mulder, HA and Thompson, R 2009. Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance. Genetics Selection Evolution 41, 23.CrossRefGoogle ScholarPubMed
Jairath, L, Dekkers, JCM, Schaeffer, LR, Liu, Z, Burnside, EB and Kolstad, B 1998. Genetic evaluation for herd life in Canada. Journal of Dairy Science 81, 550562.CrossRefGoogle ScholarPubMed
Koivula, M, Strandén, I, Pösö, J, Aamand, GP and Mäntysaari, EA 2015. Single-step genomic evaluation using multitrait random regression model and test-day data. Journal of Dairy Science 98, 27752784.Google Scholar
Legarra, A, Christensen, OF, Aguilar, I and Misztal, I 2014. Single step, a general approach for genomic selection. Livestock Science 166, 5465.Google Scholar
Liu, Z, Reinhardt, F, Bünger, A and Reents, R 2004. Derivation and calculation of approximate reliabilities and daughter yield-deviations of a random regression test-day model for genetic evaluation of dairy cattle. Journal of Dairy Science 87, 18961907.Google Scholar
Lourenco, DAL, Misztal, I, Tsuruta, S, Aguilar, I, Ezra, E, Ron, M, Shirak, A and Weller, JI 2014. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science 97, 17421752.Google Scholar
Lund, MS, De Roos, APW, De Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, F and Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics Selection Evolution 43, 1.CrossRefGoogle ScholarPubMed
Mäntysaari, EA, Liu, Z and VanRaden, P 2010. Interbull validation test for genomic evaluations. Interbull Bulletin 41, 1722.Google Scholar
Misztal, I, Aguilar, I, Legarra, A and Lawlor, TJ 2010. Choice of parameters for single-step genomic evaluation for type. Journal of Dairy Science 93 (suppl. 1), 533.Google Scholar
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, DH 2002. BLUPF90 and related programs (BGF90). Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 19–23 August 2002, Montpelier, France, Communication No. 28-27.Google Scholar
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MA, Bender, D, Maller, J, Sklar, P, de Bakker, PI, Daly, MJ and Sham, PC 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559575.CrossRefGoogle ScholarPubMed
Sargolzaei, M, Chesnais, JP and Schenkel, FS 2014. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478.CrossRefGoogle ScholarPubMed
Schopen, GCB and Schrooten, C 2013. Reliability of genomic evaluations in Holstein-Friesians using haplotypes based on the BovineHD BeadChip. Journal of Dairy Science 96, 79457951.CrossRefGoogle ScholarPubMed
Su, G, Madsen, P, Nielsen, US, Aamand, GP, Wiggans, G, Guldbrandtsen, B and Lund, MS 2016. Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey. Animal 10, 10671075.CrossRefGoogle ScholarPubMed
Su, G, Madsen, P, Nielsen, US, Mäntysaari, EA, Aamand, GP, Christensen, OF and Lund, MS 2012. Genomic prediction for Nordic Red Cattle using one-step and selection index blending. Journal of Dairy Science 95, 909917.Google Scholar
Tsuruta, S, Misztal, I and Lawlor, TJ 2013. Genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows. Journal of Dairy Science 96, 33323335.Google Scholar
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 44144423.Google Scholar
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, FS 2009. Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 1624.Google Scholar
Weigel, KA and Gianola, D 1993. A computationally simple Bayesian method for estimation of heterogeneous within-herd phenotypic variances. Journal of Dairy Science 76, 14551465.Google Scholar
Weller, JI, Kashi, Y and Soller, M 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. Journal of Dairy Science 73, 25252537.Google Scholar
Weller, JI, Stoop, WM, Eding, H, Schrooten, C and Ezra, E 2015. Genomic evaluation of a relatively small dairy cattle population by combination with a larger population. Journal of Dairy Science 98, 49454955.Google Scholar
Wiggans, GR, Cooper, TA, VanRaden, PM and Cole, JB 2011a. Adjustment of traditional cow evaluations to improve accuracy of genomic predictions. Journal of Dairy Science 94, 61886193.CrossRefGoogle ScholarPubMed
Wiggans, GR, VanRaden, PM and Cooper, TA 2011b. The genomic evaluation system in the United States: past, present, future. Journal of Dairy Science 94, 32023211.Google Scholar
Wu, X, Lund, MS, Sun, D, Zhang, Q and Su, G 2015. Impact of relationships between test and training animals and among training animals on reliability of genomic prediction. Journal of Animal Breeding and Genetics 132, 366375.Google Scholar
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