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Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

Published online by Cambridge University Press:  06 August 2013

M. Pszczola ,
R. F. Veerkamp ,
Y. de Haas ,
E. Wall ,
T. Strabel  and
M. P. L. Calus
M. Pszczola*
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, 60-637 Poznan, Poland
R. F. Veerkamp
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands
Y. de Haas
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands
E. Wall
Affiliation:
Animal & Veterinary Sciences, SRUC, Roslin Institute Building, Easter Bush, Midlothian EH25 9RG, UK
T. Strabel
Affiliation:
Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, 60-637 Poznan, Poland
M. P. L. Calus
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands
*
†E-mail: [email protected]
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Abstract

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat–protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

Keywords

accuracybiasgenomic selectionmulti-trait analysesdairy cow
Type
Breeding and genetics
Information
animal , Volume 7 , Issue 11 , November 2013 , pp. 1759 - 1768
Copyright
Copyright © The Animal Consortium 2013 

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References

Aguilar, I, Misztal, I, Tsuruta, S, Wiggans, GR, Lawlor, TJ 2011. Multiple trait genomic evaluation of conception rate in Holsteins. Journal of Dairy Science 94, 2621–2624.Google Scholar
Banos, G, Coffey, MP, Veerkamp, RF, Berry, DP, Wall, E 2012. Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries. Animal 6, 1040–1048.Google Scholar
Bell, MJ, Wall, E, Russell, G, Simm, G, Stott, AW 2011. The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systems. Journal of Dairy Science 94, 3662–3678.Google Scholar
Calus, MPL 2010. Genomic breeding value prediction: methods and procedures. Animal 4, 157–164.Google Scholar
Calus, MPL, Veerkamp, RF 2011. Accuracy of multi-trait genomic selection using different methods. Genetics Selection Evolution 43, 26.Google Scholar
Calus, MPL, Mulder, HA, Bastiaansen, JWM 2011. Identification of Mendelian inconsistencies between SNP and pedigree information of sibs. Genetics Selection Evolution 43, 34.Google Scholar
Calus, MPL, de Haas, Y, Pszczola, M, Veerkamp, RF 2013. Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal 7, 183–191.Google Scholar
Coffey, MP, Simm, G, Oldham, JD, Hill, WG, Brotherstone, S 2004. Genotype and diet effects on energy balance in the first three lactations of dairy cows. Journal of Dairy Science 87, 4318–4326.Google Scholar
Daetwyler, HD, Villanueva, B, Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395.CrossRefGoogle ScholarPubMed
de Haas, Y, Calus, MPL, Veerkamp, RF, Wall, E, Coffey, MP, Daetwyler, HD, Hayes, BJ, Pryce, JE 2012. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. Journal of Dairy Science 95, 6103–6112.Google Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Thompson, R 2009. ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead, UK.Google Scholar
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.CrossRefGoogle ScholarPubMed
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.CrossRefGoogle ScholarPubMed
Horan, B, Dillon, P, Berry, DP, O'Connor, P, Rath, M 2005. The effect of strain of Holstein Friesian, feeding system and parity on lactation curves characteristics of spring-calving dairy cows. Livestock Production Science 95, 231–241.Google Scholar
Jia, Y, Jannink, J-L 2012. Multiple trait genomic selection methods increase genetic value prediction accuracy. Genetics 192, 1513–1522.Google Scholar
Jiménez-Montero, JA, González-Recio, O, Alenda, R 2012. Genotyping strategies for genomic selection in small dairy cattle populations. Animal 6, 1216–1224.Google Scholar
Johanson, JM, Berger, PJ 2003. Birth weight as a predictor of calving ease and perinatal mortality in Holstein cattle. Journal of Dairy Science 86, 3745–3755.Google Scholar
Lee, SH, Goddard, ME, Visscher, PM, van der Werf, JHJ 2010. Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits. Genetics Selection Evolution 42, 22.CrossRefGoogle ScholarPubMed
Lund, M, de Roos, A, de Vries, A, Druet, T, Ducroq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, F, Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics Selection Evolution 43, 43.CrossRefGoogle ScholarPubMed
Meuwissen, T 2009. Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genetics Selection Evolution 41, 35.Google Scholar
Meuwissen, T, Hayes, B, Goddard, M 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
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.Google Scholar
Philipsson, J, Ral, G, Berglund, B 1995. Somatic cell count as a selection criterion for mastitis resistance in dairy cattle. Livestock Production Science 41, 195–200.Google Scholar
Pryce, JE, Nielsen, BL, Veerkamp, RF, Simm, G 1999. Genotype and feeding system effects and interactions for health and fertility traits in dairy cattle. Livestock Production Science 57, 193–201.Google Scholar
Pszczola, M, Strabel, T, Mulder, HA, Calus, MPL 2012a. Reliability of genomic selection for animals with different relationships within and to the reference population. Journal of Dairy Science 95, 389–400.Google Scholar
Pszczola, M, Strabel, T, van Arendonk, JAM, Calus, MPL 2012b. The impact of genotyping different groups of animals on accuracy when moving from traditional to genomic selection. Journal of Dairy Science 95, 5412–5421.Google Scholar
Thompson, R, Meyer, K 1986. A review of theoretical aspects in the estimation of breeding values for multi-trait selection. Livestock Production Science 15, 299–313.Google Scholar
Tsuruta, S, Misztal, I, Aguilar, I, Lawlor, TJ 2011. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science 94, 4198–4204.Google Scholar
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
Veerkamp, RF 1998. Selection for economic efficiency of dairy cattle using information on live weight and feed intake: a review. Journal of Dairy Science 81, 1109–1119.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Simm, G, Oldham, JD 1994. Effects of interaction between genotype and feeding system on milk-production, feed-intake, efficiency and body tissue mobilization in dairy cows. Livestock Production Science 39, 229–241.CrossRefGoogle Scholar
Veerkamp, RF, Emmans, GC, Cromie, AR, Simm, G 1995. Variance components for residual feed intake in dairy cows. Livestock Production Science 41, 111–120.Google Scholar
Veerkamp, RF, Oldenbroek, JK, Van Der Gaast, HJ, Werf, JHJVD 2000. Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science 83, 577–583.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Mulder, HA, Thompson, R, Calus, MPL 2011. Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. Journal of Dairy Science 94, 4189–4197.Google Scholar
Veerkamp, RF, Coffey, MP, Berry, DP, de Haas, Y, Strandberg, E, Bovenhuis, H, Calus, MPL, Wall, E 2012. Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy 6 cows from experimental research herds in four European countries. Animal 6, 1738–1749.CrossRefGoogle ScholarPubMed
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