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Impact of genotyping strategy on the accuracy of genomic prediction in simulated populations of purebred swine

Published online by Cambridge University Press:  08 January 2019

X. Li ,
Z. Zhang ,
X. Liu  and
Y. Chen
X. Li
Affiliation:
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, North Third Road, Guangzhou Higher Education Mega Center, Guangzhou, Guangdong 510006, P. R. China Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong, 510225, P. R. China
Z. Zhang
Affiliation:
Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, P. R. China
X. Liu
Affiliation:
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, North Third Road, Guangzhou Higher Education Mega Center, Guangzhou, Guangdong 510006, P. R. China
Y. Chen*
Affiliation:
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, North Third Road, Guangzhou Higher Education Mega Center, Guangzhou, Guangdong 510006, P. R. China
*
†E-mail: chyaosh@mail.sysu.edu.cn
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Abstract

Single-step genomic BLUP (ssGBLUP) has been widely used in genomic evaluation due to relatively higher prediction accuracy and simplicity of use. The prediction accuracy from ssGBLUP depends on the amount of information available concerning both genotype and phenotype. This study investigated how information on genotype and phenotype that had been acquired from previous generations influences the prediction accuracy of ssGBLUP, and thus we sought an optimal balance about genotypic and phenotypic information to achieve a cost-effective and computationally efficient genomic evaluation. We generated two genetically correlated traits (h2 = 0.35 for trait A, h2 = 0.10 for trait B and genetic correlation 0.20) as well as two distinct populations mimicking purebred swine. Phenotypic and genotypic information in different numbers of previous generations and different genotyping rates for each litter were set to generate different datasets. Prediction accuracy was evaluated by correlating genomic estimated breeding values with true breeding values for genotyped animals in the last generation. The results revealed a negligible impact of previous generations that lacked genotyped animals on the prediction accuracy. Phenotypic and genotypic data, including the most recent three to four generations with a genotyping rate of 40% or 50% for each litter, could lead to asymptotic maximum prediction accuracy for genotyped animals in the last generation. Single-step genomic best linear unbiased prediction yielded an optimal balance about genotypic and phenotypic information to ensure a cost-effective and computationally efficient genomic evaluation of populations of polytocous animals such as purebred pigs.

Keywords

single-step genomic best linear unbiased predictionphenotypic recordsgenotyping ratinggenomic evaluationswine breeding
Type
Research Article
Information
animal , Volume 13 , Issue 9 , September 2019 , pp. 1804 - 1810
Copyright
© The Animal Consortium 2019 

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Footnotes

a

Equal contributors

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, 743–752.CrossRefGoogle ScholarPubMed
Christensen, OF and Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics, Selection, Evolution: GSE 42, 2.CrossRefGoogle Scholar
Christensen, OF, Madsen, P, Nielsen, B, Ostersen, T and Su, G 2012. Single-step methods for genomic evaluation in pigs. Animal 6, 1565–1571.CrossRefGoogle ScholarPubMed
Gao, H, Christensen, OF, Madsen, P, Nielsen, US, Zhang, Y, Lund, MS and Su, G 2012. Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics, Selection, Evolution: GSE 44, 8.CrossRefGoogle ScholarPubMed
Guo, X, Christensen, OF, Ostersen, T, Wang, Y, Lund, MS and Su, G 2015. Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method. Journal of Animal Science 93, 503–512.CrossRefGoogle ScholarPubMed
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, 433–443.CrossRefGoogle ScholarPubMed
Jia, Y and Jannink, JL 2012. Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192, 1513–1522.CrossRefGoogle ScholarPubMed
Karaman, E, Cheng, H, Firat, MZ, Garrick, DJ and Fernando, RL 2016. An upper bound for accuracy of prediction using GBLUP. PLoS ONE 11, e0161054.CrossRefGoogle ScholarPubMed
Li, X, Wang, S, Huang, J, Li, L, Zhang, Q and Ding, X 2014. Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending. Genetics, Selection, Evolution: GSE 46, 66.CrossRefGoogle ScholarPubMed
Madsen, P and Jensen, J 2013. A user’s guide to DMU. Center for Quantitative Genetics and Genomics Dept. of Molecular Biology and Genetics, University of Aarhus Research Centre Foulum Box 50, 8830 Tjele Denmark.Google Scholar
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google ScholarPubMed
Meuwissen, T, Hayes, B and Goddard, M 2016. Genomic selection: A paradigm shift in animal breeding. Animal Frontiers 6, 6.CrossRefGoogle Scholar
Muir, WM 2007. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics 124, 342–355.CrossRefGoogle ScholarPubMed
Su, G, Madsen, P, Nielsen, US, Mäntysaari, E a, 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, 909–917.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Wang, C, Li, X, Qian, R, Su, G, Zhang, Q and Ding, X 2017. Bayesian methods for jointly estimating genomic breeding values of one continuous and one threshold trait. PLoS ONE 12, e0175448.CrossRefGoogle ScholarPubMed
Wolc, A, Arango, J, Settar, P, Fulton, JE, O’Sullivan, NP, Preisinger, R, Habier, D, Fernando, R, Garrick, DJ and Dekkers, JCM 2011. Persistence of accuracy of genomic estimated breeding values over generations in layer chickens. Genetics, Selection, Evolution: GSE 43, 23.CrossRefGoogle ScholarPubMed
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, 366–375.CrossRefGoogle ScholarPubMed
Yang, H and Su, G 2016. Impact of phenotypic information of previous generations and depth of pedigree on estimates of genetic parameters and breeding values. Livestock Science 187, 61–67.CrossRefGoogle Scholar
Zhang, Z, Ding, X, Liu, J, de Koning, D-J and Zhang, Q 2011. Genomic selection for QTL-MAS data using a trait-specific relationship matrix. BMC Proceedings 5 (Suppl 3), S15.CrossRefGoogle ScholarPubMed
Zhang, Z, Li, X, Ding, X, Li, J and Zhang, Q 2015. GPOPSIM: A simulation tool for whole-genome genetic data. BMC Genetics 16, 10.CrossRefGoogle ScholarPubMed
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