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Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins

Published online by Cambridge University Press:  16 October 2017

F. G. Colinet ,
J. Vandenplas ,
S. Vanderick ,
H. Hammami ,
R. R. Mota ,
A. Gillon ,
X. Hubin ,
C. Bertozzi  and
N. Gengler
F. G. Colinet
Affiliation:
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
J. Vandenplas
Affiliation:
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium National Fund for Scientific Research, 1000 Brussels, Belgium
S. Vanderick
Affiliation:
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
H. Hammami
Affiliation:
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
R. R. Mota
Affiliation:
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
A. Gillon
Affiliation:
Research and Development Department, Walloon Breeding Association, 5590 Ciney, Belgium
X. Hubin
Affiliation:
Research and Development Department, Walloon Breeding Association, 5590 Ciney, Belgium
C. Bertozzi
Affiliation:
Research and Development Department, Walloon Breeding Association, 5590 Ciney, Belgium
N. Gengler*
Affiliation:
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
*
†E-mail: nicolas.gengler@ulg.ac.be
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Abstract

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.

Keywords

dairy cattleBayesian integrationgenomic evaluationreliabilities
Type
Research Article
Information
animal , Volume 12 , Issue 5 , May 2018 , pp. 898 - 905
Copyright
© The Animal Consortium 2017 

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Footnotes

b

Present address: Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH Wageningen, The Netherlands.

a

These authors contributed equally to this work.

References

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, 743–752.CrossRefGoogle Scholar
Calus, MPL, Vandenplas, J, ten Napel, J and Veerkamp, RF 2016. Validation of simultaneous deregression of cow and bull breeding values and derivation of appropriate weights. Journal of Dairy Science 99, 6403–6419.CrossRefGoogle ScholarPubMed
Christensen, OF and Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42, 2.CrossRefGoogle Scholar
Gengler, N, Nieuwhof, G, Konstantinov, K and Goddard, M 2012. Alternative single-step type genomic prediction equations. Presented at the 63rd Annual Meeting European Federation of Animal Science, 27–31 August 2012, Bratislava, Slovakia. Retrieved on 11 January 2017, from http://hdl.handle.net/2268/138366.Google Scholar
Gengler, N, Soyeurt, H, Dehareng, F, Bastin, C, Colinet, F, Hammami, H, Vanrobays, ML, Lainé, A, Vanderick, S, Grelet, C, Vanlierde, A, Froidmont, E and Dardenne, P 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. Journal of Dairy Science 99, 4071–4079.CrossRefGoogle ScholarPubMed
Henderson, CR 1984. Applications of linear models in animal breeding, 2nd edition. University of Guelph, Guelph, ON, Canada.Google Scholar
Harris, BL, Winkelman, AM and Johnson, DL 2012. Large-scale single-step genomic evaluation for milk production traits. Interbull Bulletin 46, 20–24.Google Scholar
Koivula, M, Strandén, I, Pösö, J, Aamand, GP and Mäntysaari, EA 2012. Single step genomic evaluations for the Nordic Red Dairy cattle test day data. Interbull Bulletin 46, 115–120.Google Scholar
Legarra, A, Christensen, OF, Aguilar, I and Misztal, I 2014. Single step, a general approach for genomic selection. Livestock Science 166, 54–65.CrossRefGoogle Scholar
Liu, Z, Goddard, ME, Reinhardt, F and Reents, R 2014. A single-step genomic model with direct estimation of marker effects. Journal of Dairy Science 97, 5833–5850.CrossRefGoogle ScholarPubMed
Liu, Z, Goddard, ME, Hayes, BF, Reinhardt, F and Reents, R 2016. Technical note: equivalent genomic models with a residual polygenic effect. Journal of Dairy Science 99, 2016–2025.CrossRefGoogle ScholarPubMed
Liu, Z, Seefried, FR, Reinhardt, F, Rensing, S, Thaller, G and Reents, R 2011. Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction. Genetics Selection Evolution 43, 19.CrossRefGoogle ScholarPubMed
Masuda, Y, Misztal, I, Tsuruta, S, Legarra, A, Aguilar, I, Lourenco, DAL, Fragomeni, BO and Lawlor, TJ 2016. Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. Journal of Dairy Science 99, 1968–1974.CrossRefGoogle ScholarPubMed
Mäntysaari, E, Liu, Z and VanRaden, P 2010. Interbull validation test for genomic evaluations. Interbull Bulletin 41, 17–21.Google Scholar
Misztal, I 2013. BLUPF90 family of programs. Retrieved on 11 January 2017, from http://nce.ads.uga.edu/wiki/doku.php.Google Scholar
Misztal, I, Tsuruta, S, Aguilar, I, Legarra, A, VanRaden, PM and Lawlor, TJ 2012. Methods to approximate reliabilities in single-step genomic evaluation. Journal of Dairy Science 96, 647–654.CrossRefGoogle ScholarPubMed
Misztal, I, Vitezica, ZG, Legarra, A, Aguilar, I and Swan, AA 2013. Unknown-parent groups in single-step genomic evaluation. Journal of Animal Breeding and Genetics 130, 252–258.CrossRefGoogle ScholarPubMed
Misztal, I and Wiggans, GR 1988. Approximation of prediction error variance in large-scale animal models. Journal of Dairy Science 71 (suppl. 2), 27–32.CrossRefGoogle Scholar
Přibyl, J, Madsen, P, Bauer, J, Přibylová, J, Šimečková, M, Vostrý, L and Zavadilová, L 2013. Contribution of domestic production records, Interbull estimated breeding values, and single nucleotide polymorphism genetic markers to the single-step genomic evaluation of milk production. Journal of Dairy Science 96, 1865–1873.CrossRefGoogle Scholar
Tsuruta, S, Misztal, I, Aguilar, I and Lawlor, TJ 2011. Multiple-traits genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science 94, 4198–4204.CrossRefGoogle Scholar
Schenkel, F, Sargolzaei, M, Kistemaker, G, Jansen, G, Sullivan, P, Van Doormaal, B, VanRaden, P and Wiggans, G 2009. Reliability of genomic evaluation of Holstein cattle in Canada. Interbull Bull 39, 51–58.Google Scholar
Vandenplas, J, Colinet, FG and Gengler, N 2014. Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation. Genetics Selection Evolution 46, 59.CrossRefGoogle ScholarPubMed
Vandenplas, J, Colinet, FG, Glorieux, G, Bertozzi, C and Gengler, N 2015. Integration of external estimated breeding values and associated reliabilities using correlations among traits and effects. Journal of Dairy Science 98, 9044–9050.CrossRefGoogle ScholarPubMed
Vandenplas, J and Gengler, N 2012. Comparison and improvements of different Bayesian procedures to integrate external information into genetic evaluations. Journal of Dairy Science 95, 1513–1526.CrossRefGoogle ScholarPubMed
Vandenplas, J, Spehar, M, Potocnik, K, Gengler, N and Gorjanc, G 2017. National single-step genomic method that integrates multi-national genomic information. Journal of Dairy Science 100, 465–478.CrossRefGoogle ScholarPubMed
Vanlierde, A, Vanrobays, ML, Dehareng, F, Froidmont, E, Soyeurt, H, McParland, SC, Lewis, E, Deighton, MH, Grandl, F, Kreuzer, M, Gredler, B, Dardenne, P and Gengler, N 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. Journal of Dairy Science 98, 5740–5747.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM 2012. Avoiding bias from genomic pre-selection in converting daughter information across countries. Interbull Bulletin 45, 29–33.Google Scholar
VanRaden, PM, Olson, K, Null, D, Sargolzaei, M, Winters, M and Van Kaam, JB 2012. Reliability increases from combining 50,000-and 777,000-marker genotypes from four countries. Interbull Bulletin 46, 75–79.Google Scholar
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, 16–24.CrossRefGoogle ScholarPubMed
Wiggans, GR, VanRaden, PM and Cooper, TA 2011a. The genomic evaluation system in the United States: past, present, future. Journal of Dairy Science 94, 3202–3211.CrossRefGoogle ScholarPubMed
Wiggans, G, VanRaden, P and Cooper, T 2011b. Dairy genomics in application. Paper presented at the Minnesota Dairy Health Conference 2011. May 17th - 19th, Bloomington, Minnesota, USA. Retrieved on 10 August 2017, from http://hdl.handle.net/11299/118925.Google Scholar
Yin, T and König, S 2016. Genomics for phenotype prediction and management purposes. Animal Frontiers 6, 65–72.CrossRefGoogle Scholar