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Single-step methods for genomic evaluation in pigs

Published online by Cambridge University Press:  05 April 2012

O. F. Christensen ,
P. Madsen ,
B. Nielsen ,
T. Ostersen  and
G. Su
O. F. Christensen*
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, PO BOX 50, DK-8830 Tjele, Denmark
P. Madsen
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, PO BOX 50, DK-8830 Tjele, Denmark
B. Nielsen
Affiliation:
Breeding and Genetics, Pig Research Centre, The Danish Agriculture & Food Council, Axeltorv 3, DK-1609 Copenhagen V, Denmark
T. Ostersen
Affiliation:
Breeding and Genetics, Pig Research Centre, The Danish Agriculture & Food Council, Axeltorv 3, DK-1609 Copenhagen V, Denmark
G. Su
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, PO BOX 50, DK-8830 Tjele, Denmark
*
†E-mail: [email protected]
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Abstract

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Keywords

breeding valuegenetic parametersgenomic selectionmixed model methodspigs
Type
Full Paper
Information
animal , Volume 6 , Issue 10 , October 2012 , pp. 1565 - 1571
Copyright
Copyright © The Animal Consortium 2012

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References

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S, 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 Scholar
Amer, PR, Banos, G 2010. Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit. Journal of Dairy Science 93, 3320–3330.CrossRefGoogle ScholarPubMed
Brune, T 2011. Stand und perspektiven der genomischen selection beim schwein. Bachelorarbeit, Fakultät Agrarwissenschaften und Landschaftsarchitektur, Hochschule Osnabrück.Google Scholar
Chen, CY, Misztal, I, Aguilar, I, Legarra, A, Muir, WM 2011. Effect of different genomic relationship matrices on accuracy and scale. Journal of Animal Science 89, 2673–2679.CrossRefGoogle ScholarPubMed
Calus, MPL, Veerkamp, RF 2011. Accuracy of multi-trait genomic selection using different methods. Genetics Selection Evolution 43, 26.CrossRefGoogle ScholarPubMed
Christensen, OF, Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42, 2.CrossRefGoogle Scholar
Dunn, OJ, Clark, V 1971. Comparison of tests of the equality of dependent correlation coefficients. Journal of the American Statistical Association 66, 904–908.CrossRefGoogle Scholar
Forni, S, Aguilar, I, Misztal, I 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution 43, 1.CrossRefGoogle ScholarPubMed
Garrick, DJ, Taylor, JF, Fernando, RL 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution 41, 55.CrossRefGoogle ScholarPubMed
Gunsett, FC 1984. Linear index selection to improve traits defined as ratios. Journal of Animal Science 59, 1185–1193.CrossRefGoogle Scholar
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
Hoque, MA, Suzuki, K, Kadowaki, H, Shibata, T, Oikawa, T 2007. Genetic parameters for feed efficiency traits and their relationships with growth and carcass traits in Duroc pigs. Journal of Animal Breeding and Genetics 114, 108–116.CrossRefGoogle Scholar
Legarra, A, Aguilar, I, Misztal, I 2009. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663.CrossRefGoogle ScholarPubMed
Loberg, A, Dürr, JW 2009. Interbull Survey on the Use of Genomic Information. Interbull Bulletin 39, 3–14.Google Scholar
Madsen, P, Jensen, J 2010. A users guide to DMU, version 6, release 5.0. Manual, Faculty of Agricultural Science. University of Aarhus.Google Scholar
Ostersen, T, Christensen, OF, Henryon, M, Nielsen, B, Su, G, Madsen, P 2011. Deregressed EBV as response variable yield reliable genomic predictions for pigs. Genetics Selection Evolution 43, 38.CrossRefGoogle ScholarPubMed
Powell, JE, Visscher, PM, Goddard, ME 2010. Reconciling the analysis of IBD and IBS in complex trait studies. Nature Review Genetics 11, 800–805.CrossRefGoogle ScholarPubMed
Revelle, W 2010. psych: Procedures for psychological, psychometric, and personality research. Northwestern University, Evanston, Illinois 2010, http://personality-project.org/r/psych.manual.pdf. R package version 1.0-90.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.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, Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Vitezica, ZG, Aguilar, I, Misztal, I, Legarra, A 2011. Bias in genomic predictions for populations under selection. Genetics Research 93, 357–366.CrossRefGoogle ScholarPubMed
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