Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T12:52:39.909Z Has data issue: false hasContentIssue false

The value of cows in reference populations for genomic selection of new functional traits

Published online by Cambridge University Press:  17 November 2011

L. H. Buch*
Affiliation:
Knowledge Centre for Agriculture, Cattle, Agro Food Park 15, DK-8200 Aarhus N, Denmark Department of Molecular Biology and Genetics, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus C, Denmark
M. Kargo
Affiliation:
Knowledge Centre for Agriculture, Cattle, Agro Food Park 15, DK-8200 Aarhus N, Denmark Department of Molecular Biology and Genetics, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus C, Denmark
P. Berg
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus C, Denmark
J. Lassen
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus C, Denmark
A. C. Sørensen
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus C, Denmark
*
E-mail: LHB@vfl.dk
Get access

Abstract

Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic prediction model to test the hypothesis that the value of cows in reference populations depends on the availability of phenotypic records. To test the hypothesis, we investigated different strategies of building a reference population for a new functional trait over a 10-year period. The trait was either recorded on a large scale (30 000 cows per year) or on a small scale (2000 cows per year). For large-scale recording, we compared four scenarios where the reference population consisted of 30 sires; 30 sires and 170 test bulls; 30 sires and 2000 cows; or 30 sires, 2000 cows and 170 test bulls in the first year with measurements of the new functional trait. In addition to varying the make-up of the reference population, we also varied the heritability of the trait (h2 = 0.05 v. 0.15). The results showed that a reference population of test bulls, cows and sires results in the highest accuracy of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new functional trait. The results showed that a reference population of cows results in the highest accuracy of the DGV whether the heritability is 0.05 or 0.15, because variation is lost when phenotypic data on cows are summarized in EBV of their sires. The main conclusions from this study are: (i) the fewer phenotypic records, the larger effect of including cows in the reference population; (ii) for small-scale recording, the accuracy of the DGV will continue to increase for several years, whereas the increases in the accuracy of the DGV quickly decrease with large-scale recording; (iii) it is possible to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls.

Type
Full Paper
Copyright
Copyright © The Animal Consortium 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Buch, LH, Sørensen, AC, Lassen, J, Berg, P, Eriksson, J-Å, Jakobsen, JH, Sørensen, MK 2011a. Hygiene-related and feed-related hoof diseases show different patterns of genetic correlations to clinical mastitis and female fertility. Journal of Dairy Science 94, 15401551.Google Scholar
Buch, LH, Sørensen, MK, Berg, P, Pedersen, LD, Sørensen, AC 2011b. Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of young bulls. Journal of Animal Breeding and Genetics, doi:10.1111/j.1439-0388.2011.00947.x.Google ScholarPubMed
Dassonneville, R, Brøndum, RF, Druet, T, Fritz, S, Guillaume, F, Guldbrandtsen, B, Lund, MS, Ducrocq, V, Su, G 2011. Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations. Journal of Dairy Science 94, 36793686.Google Scholar
de Roos, APW, Hayes, BJ, Spelman, RJ, Goddard, ME 2008. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179, 15031512.Google Scholar
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245257.Google Scholar
Habier, D, Fernando, RL, Dekkers, JCM 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 23892397.Google Scholar
Hayes, BJ, Visscher, PM, McPartlan, HC, Goddard, ME 2003. Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Research 13, 635643.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, 433443.Google Scholar
Kim, E-S, Kirkpatrick, BW 2009. Linkage disequilibrium in the North American Holstein population. Animal Genetics 40, 279288.Google Scholar
Lillehammer, M, Meuwissen, THE, Sonesson, AK 2011. A comparison of dairy cattle breeding designs that use genomic selection. Journal of Dairy Science 94, 493500.CrossRefGoogle ScholarPubMed
Lund, MS, de Roos, APW, de Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, M, Su, G 2010. Improving genomic prediction by EuroGenomics collaboration. 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany.Google Scholar
Meuwissen, T, Goddard, M 2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185, 623631.Google Scholar
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.Google Scholar
Petersson, K-J, Berglund, B, Strandberg, E, Gustavsson, H, Flint, APF, Woolliams, JA, Royal, MD 2007. Genetic analysis of postpartum measures of luteal activity in dairy cows. Journal of Dairy Science 90, 427434.Google Scholar
Schaeffer, LR 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218223.Google Scholar
Sved, JA 1971. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theoretical Population Biology 2, 125141.Google Scholar
Weigel, KA, de los Campos, G, Vazquez, AI, Van Tassel, CP, Rosa, GJM, Gianola, D, O'Connell, JR, VanRaden, PM, Wiggans, GR 2010. Genomic selection and its effect on dairy cattle breeding programs. 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany.Google Scholar