[en] ABSTRACT: BACKGROUND: Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to the reference population, the reliability of their phenotypes, and the relatedness of the populations that are combined. METHODS: This paper assesses the increase in reliability achieved when combining four Holstein reference populations of 4000 bulls each, from European breeding organizations, i.e. UNCEIA (France), VikingGenetics (Denmark, Sweden, Finland), DHV-VIT (Germany) and CRV (The Netherlands, Flanders). Each partner validated its own bulls using their national reference data and the combined data, respectively. RESULTS: Combining the data significantly increased the reliability of genomic predictions for bulls in all four populations. Reliabilities increased by 10%, compared to reliabilities obtained with national reference populations alone, when they were averaged over countries and the traits evaluated. For different traits and countries, the increase in reliability ranged from 2% to 19%. CONCLUSIONS: Genomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population.
Invited review: Genomic selection in dairy cattle: Progress and challenges. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME, J Dairy Sci 2009 92 433 443 10.3168/jds.2008-1646 19164653
Invited review: Reliability of genomic predictions for North American Holstein bulls. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS, J Dairy Sci 2009 92 16 24 10.3168/jds.2008-1514 19109259
Reliability of genomic evaluation of Holstein cattle in Canada. Schenkel FS, Sargolzaei M, Kistemaker G, Jansen GB, Sullivan P, Van Doormaal BJ, Van Raden PM, Wiggans GR, Interbull Bull 2009 39 51 58
International genomic co-operation; Who, what, when, where, why and how? Cromie AR, Berry DP, Wickham B, Kearney JF, Pena J, van Kaam JBCH, Gengler N, Szyda J, Schnyder U, Coffey M, Moster B, Hagiya K, Weller JI, Abernethy D, Spelman R, Interbull Bull 2010 42 72 78
Development and characterization of a high density SNP genotyping assay for cattle. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O'Connell J, Moore SS, Smith TPL, Sonstegard TS, Van Tassell CP, PLoS ONE 2009 4 5350 10.1371/journal.pone.0005350 19390634
A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Druet T, Georges M, Genetics 2010 184 789 798 10.1534/genetics.109. 108431 20008575
Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Browning SR, Browning BL, Am J Hum Genet 2007 81 1084 1097 10.1086/521987 17924348 (Pubitemid 47580259)
In silico genotyping of thousands of SNP in dairy cattle for the eurogenomics project. Druet T, Schrooten C, de Roos S, Proceedings of the 9th World Congress on Genetics Applied to livestock: 1-6 August 2010; Leipzig Gesellschaft für Tierzuchtwissenschaften e.V 2010 137
Genetic evaluation for herd life in Canada. Jairath L, Dekkers JCM, Schaeffer LR, Liu Z, Burnside EB, Kolstad B, J Dairy Sci 1998 81 550 562 10.3168/jds.S0022-0302(98)75607-3 9532510 (Pubitemid 128444854)
Weighting factors of sire daughter information in international genetic evaluations. Fikse WF, Banos G, J Dairy Sci 2001 84 1759 1767 10.3168/jds.S0022-0302(01)74611-5 11467826 (Pubitemid 33609739)
Relation between accuracies of genomic predictions and ancestral links to the training data. Lund MS, Su G, Nielsen US, Aamand GE, Interbull Bull 2009 40 162 166
INTERBULL, International bull evaluation service. http://www.interbull. org
Accuracy of genomic selection using different methods to define haplotypes. Calus MPL, Meuwissen THE, de Roos APW, Veerkamp RF, Genetics 2008 178 553 561 10.1534/genetics.107.080838 18202394 (Pubitemid 351398768)
French report on the use of genomic evaluation. Ducrocq V, Fritz S, Guillaume F, Boichard D, Interbull Bull 2009 39 17 21
Fine mapping of quantitative trait loci affecting female fertility in dairy cattle on BTA03 using a dense single-nucleotide polymorphism map. Druet T, Fritz S, Boussaha M, Ben-Jemaa S, Guillaume F, Derbala D, Zelenika D, Lechner D, Charon C, Boichard D, Gut IG, Eggen A, Gautier M, Genetics 2008 178 2227 2235 10.1534/genetics.107.085035 18430945
Combining genomic and classical information in national BLUP evaluations. Ducrocq V, Liu Z, Interbull Bull 2009 40 172 177
Genomic selection: prediction of accuracy and maximisation of long term response. Goddard ME, Genetica 2009 136 245 257 10.1007/s10709-008-9308-0 18704696
Mapping genes for complex traits and their use in breeding programs. Goddard ME, Hayes BJ, Nat Rev Genet 2009 10 381 391 10.1038/nrg2575 19448663
Prediction of total genetic value using genome wide dense marker maps. Meuwissen THE, Hayes BJ, Goddard ME, Genetics 2001 157 1819 1829 11290733 (Pubitemid 32298846)
Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection. Lund MS, Sahana G, de Koning DJ, Su G, Carlborg Ö BMC Proc 2009 3 1 20017962
Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. Guo G, Lund MS, Zhang Y, Su G, J Anim Breed Genet 2010 127 423 432 10.1111/j.1439-0388.2010. 00878.x 21077966
Distribution and location of genetic effects for dairy traits. Cole JB, VanRaden PM, O'Connell JR, Van Tassel CP, Sonstegard TS, Schnabel RD, Taylor JF, Wiggans GR, J Dairy Sci 2009 92 2931 2946 10.3168/jds.2008-1762 19448026
Su G, Guldbrandtsen B, Gregersen VR, Lund MS: Preliminary investigation on reliability of genomic estimated breeding values in the Danish and Swedish Holstein Population. J Dairy Sci 2010, 93:1175-1183.
