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Accuracy and bias of genomic prediction with different de-regression methods

Published online by Cambridge University Press:  16 November 2017

H. Song ,
L. Li ,
Q. Zhang ,
S. Zhang  and
X. Ding
H. Song
Affiliation:
National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
L. Li
Affiliation:
National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
Q. Zhang
Affiliation:
National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
S. Zhang
Affiliation:
National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
X. Ding*
Affiliation:
National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
*
†E-mail: xding@cau.edu.cn
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Abstract

Genomic selection has become increasingly important in the breeding of animals and plants. The response variable is an important factor, influencing the accuracy of genomic selection. The de-regressed proof (DRP) based on traditional estimated breeding value (EBV) is commonly used as response variable. In the current study, simulated data from 16th QTL-MAS Workshop and real data from Chinese Holstein cattle were used to compare accuracy and bias of genomic prediction with two methods of calculating DRP. Our results with simulated data showed that the correlation between genomic EBV and true breeding value achieved using the Jairath method (DRP_J) was superior to that achieved using the Garrick method (DRP_G) for simulated trait 1 but the reverse was true for simulated trait 3, and these two methods performed comparably for simulated trait 2. For all three simulated traits, DRP_J yielded larger bias of genomic prediction. However, DRP_J outperformed DRP_G in both accuracy and unbiasedness for four milk production traits in Chinese Holstein. In the estimation of genomic breeding value using genomic BLUP model, two methods for weighting diagonal elements of incidence matrix associated with residual error were also compared. With increasing the proportion of genetic variance unexplained by markers, the accuracy of genomic prediction was decreased and the bias was increased. Weighting by the reliability of DRP produced accuracy comparable to the evaluation where the proportion of genetic variance unexplained by markers was considered, but with smaller bias in general.

Keywords

de-regressed proofEBVGEBVgenomic selectionresponse variable
Type
Research Article
Information
animal , Volume 12 , Issue 6 , June 2018 , pp. 1111 - 1117
Copyright
© The Animal Consortium 2017 

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References

Aguilar, I, Misztal, I, Johnson, D, Legarra, A, Tsuruta, S and Lawlor, T 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.Google Scholar
Browning, BL and Browning, SR 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. American Journal of Human Genetics 84, 210–223.CrossRefGoogle ScholarPubMed
Christensen, OF and Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42, 2.Google Scholar
Ding, X, Zhang, Z, Li, X, Wang, S, Wu, X, Sun, D, Yu, Y, Liu, J, Wang, Y, Zhang, Y, Zhang, S, Zhang, Y and Zhang, Q 2013. Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. Journal of Dairy Science 96, 5315–5323.Google Scholar
Gao, H, Lund, MS, Zhang, Y and Su, G 2013. Accuracy of genomic prediction using different models and response variables in the Nordic Red cattle population. Journal of Animal Breeding and Genetics 130, 333–340.CrossRefGoogle ScholarPubMed
Garrick, DJ, Taylor, JF and Fernando, RL 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution 41, 55.Google Scholar
Goddard, ME and Hayes, BJ 2007. Genomic selection. Journal of Animal Breeding and Genetics 124, 323–330.CrossRefGoogle ScholarPubMed
Guo, G, Lund, MS, Zhang, Y and Su, G 2010. Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. Journal of Animal Breeding and Genetics 127, 423–432.Google Scholar
Haile-Mariam, M, Nieuwhof, GJ, Beard, KT, Konstatinov, KV and Hayes, BJ 2013. Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations. Journal of Animal Breeding and Genetics 130, 20–31.Google Scholar
Israel, C and Weller, JI 1998. Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 81, 1653–1662.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, 550–562.CrossRefGoogle ScholarPubMed
Legarra, A, Aguilar, I and Misztal, I 2009. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663.CrossRefGoogle ScholarPubMed
Loberg, A and Dürr, JW 2009. Interbull survey on the use of genomic information. Interbull Bulletin 39, 3–14.Google Scholar
Ma, P, Lund, MS, Ding, X, Zhang, Q and Su, G 2014. Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese-Nordic reference population. Journal of Animal Breeding and Genetics 131, 462–472.Google Scholar
Madsen, P, Jensen, J, Labouriau, R, Christensen, OF and Sahana, G 2014. DMU-a package for analyzing multivariate mixed models in quantitative genetics and genomics. Proceedings of the 10th World Congress of genetics applied to livestock production, August 17-22, 2014, Canada. pp. 17–22.Google Scholar
Meuwissen, THE 2009. Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genetics Selection Evolution 41, 35.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Misztal, I, Legarra, A and Aguilar, I 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science 92, 4648–4655.CrossRefGoogle ScholarPubMed
Steiger, JH 1980. Tests for comparing elements of a correlation matrix. Psychological Bulletin 87, 245–251.CrossRefGoogle Scholar
Su, G, Guldbrandtsen, B, Gregersen, V and Lund, M 2010. Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. Journal of Dairy Science 93, 1175–1183.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.Google Scholar