Skip to main content Accessibility help

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

  • X. Li (a1) (a2), Z. Zhang (a3), X. Liu (a1) and Y. Chen (a1)


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.


Corresponding author


Hide All

Equal contributors



Hide All
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.
Christensen, OF and Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics, Selection, Evolution: GSE 42, 2.
Christensen, OF, Madsen, P, Nielsen, B, Ostersen, T and Su, G 2012. Single-step methods for genomic evaluation in pigs. Animal 6, 15651571.
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.
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, 503512.
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.
Jia, Y and Jannink, JL 2012. Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192, 15131522.
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.
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.
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.
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.
Meuwissen, T, Hayes, B and Goddard, M 2016. Genomic selection: A paradigm shift in animal breeding. Animal Frontiers 6, 6.
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, 342355.
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, 909917.
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 44144423.
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, 1624.
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.
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.
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.
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, 6167.
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.
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.


Type Description Title
Supplementary materials

Li et al. supplementary material
Tables S1-S3

 Word (25 KB)
25 KB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed