Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-18T13:49:23.089Z Has data issue: false hasContentIssue false
  • English
  • Français

Multiple-breed genomic evaluation by principal component analysis in small size populations

Published online by Cambridge University Press:  08 December 2014

G. Gaspa ,
H. Jorjani ,
C. Dimauro ,
M. Cellesi ,
P. Ajmone-Marsan ,
A. Stella  and
N. P. P. Macciotta
G. Gaspa*
Affiliation:
Dipartimento di Agraria, Viale Italia 39, 07100 Sassari, Italy
H. Jorjani
Affiliation:
Interbull Centre, Box 7023, S-75007 Uppsala, Sweden
C. Dimauro
Affiliation:
Dipartimento di Agraria, Viale Italia 39, 07100 Sassari, Italy
M. Cellesi
Affiliation:
Dipartimento di Agraria, Viale Italia 39, 07100 Sassari, Italy
P. Ajmone-Marsan
Affiliation:
Istituto di Zootecnica, Università Cattolica del Sacro Cuore, Piacenza 29100, Italy
A. Stella
Affiliation:
Parco Tecnologico Padano, 26900 Lodi, Italy
N. P. P. Macciotta
Affiliation:
Dipartimento di Agraria, Viale Italia 39, 07100 Sassari, Italy
*
†E-mail: [email protected]
Get access

Abstract

In this study, the effects of breed composition and predictor dimensionality on the accuracy of direct genomic values (DGV) in a multiple breed (MB) cattle population were investigated. A total of 3559 bulls of three breeds were genotyped at 54 001 single nucleotide polymorphisms: 2093 Holstein (H), 749 Brown Swiss (B) and 717 Simmental (S). DGV were calculated using a principal component (PC) approach for either single (SB) or MB scenarios. Moreover, DGV were computed using all SNP genotypes simultaneously with SNPBLUP model as comparison. A total of seven data sets were used: three with a SB each, three with different pairs of breeds (HB, HS and BS), and one with all the three breeds together (HBS), respectively. Editing was performed separately for each scenario. Reference populations differed in breed composition, whereas the validation bulls were the same for all scenarios. The number of SNPs retained after data editing ranged from 36 521 to 41 360. PCs were extracted from actual genotypes. The total number of retained PCs ranged from 4029 to 7284 in Brown Swiss and HBS respectively, reducing the number of predictors by about 85% (from 82% to 89%). In all, three traits were considered: milk, fat and protein yield. Correlations between deregressed proofs and DGV were used to assess prediction accuracy in validation animals. In the SB scenarios, average DGV accuracy did not substantially change when either SNPBLUP or PC were used. Improvement of DGV accuracy were observed for some traits in Brown Swiss, only when MB reference populations and PC approach were used instead of SB-SNPBLUP (+10% HBS, +16%HB for milk yield and +3% HBS and +7% HB for protein yield, respectively). With the exclusion of the abovementioned cases, similar accuracies were observed using MB reference population, under the PC or SNPBLUP models. Random variation owing to sampling effect or size and composition of the reference population may explain the difficulty in finding a defined pattern in the results.

Keywords

genomic selectionreference populationmultiple breeddairy cattlesmall population
Type
Research Article
Information
animal , Volume 9 , Issue 5 , May 2015 , pp. 738 - 749
Copyright
© The Animal Consortium 2014 

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

Brondum, RF, Rius-Vilarrasa, E, Stranden, I, Su, G, Guldbrandtsen, B, Fikse, WF and Lund, MS 2011. Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. Journal of Dairy Science 94, 4700–4707.Google Scholar
Calus, MPL, de Haas, Y and Veerkamp, RF 2013. Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies. Journal of Dairy Science 96, 6703–6715.Google Scholar
Daetwyler, HD, Kemper, KE, van der Werf, JHJ and Hayes, BJ 2012. Components of the accuracy of genomic prediction in a multi-breed sheep population. Journal of Animal Science 90, 3375–3384.Google Scholar
de Roos, APW, Hayes, BJ and Goddard, ME 2009. Reliability of genomic predictions across multiple populations. Genetics 183, 1545–1553.CrossRefGoogle ScholarPubMed
Erbe, M, Hayes, BJ, Matukumalli, LK, Goswami, S, Bowman, PJ, Reich, CM, Mason, BA and Goddard, ME 2012. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science 95, 4114–4129.CrossRefGoogle ScholarPubMed
Goddard, ME and Hayes, BJ 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics 10, 381–391.CrossRefGoogle ScholarPubMed
Gredler, B, Nirea, KG, Solberg, TR, Egger-Danner, C, Meuwissen, T and Sölkner, J 2009. A comparison of methods for genomic selection in Austrian dual purpose simmental cattle. Proceeding of the 18th Conference Advancement of Animal Breeding and Genetics, 28 September, Barossa Valley, South Australia pp. 568–571.Google Scholar
Gredler, B, Schwarzenbacher, H, Egger-Danner, C, Fuerst, C, Emmerling, R and Sölkner, J 2010. Accuracy of genomic selection in dual purpose Fleckvieh cattle using three types of methods and phenotypes. In Proceeding of 9th World Congress of Genetics Applied to Livestock Production, 1–6 August, Leipzig, Germany.Google Scholar
Habier, D, Tetens, J, Seefried, FR, Lichtner, P and Thaller, G 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution 42, 5.Google Scholar
Harris, BL, Creagh, FE, Winkelman, AM and Johnson, DL 2011. Experiences with the illumina high density bovine beadchip. Interbull Bulletin 44, 3–7.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AC, Verbyla, K and Goddard, ME 2009a. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41, 51.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009b. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.Google Scholar
Jorjani, H, Jakobsen, J, Hjerpe, E, Palucci, V and Dürr, J 2012. Status of genomic evaluation in the brown swiss populations. Interbull Bulletin 46, 46–54.Google Scholar
Kaiser, HF 1960. The application of electronic computers to factor analysis. Educational and Psychological Measurement 20, 141–151.Google Scholar
Karoui, S, Carabano, MJ, Diaz, C and Legarra, A 2012. Joint genomic evaluation of french dairy cattle breeds using multiple-trait models. Genetics Selection Evolution 44, 39.Google Scholar
Kizilkaya, K, Fernando, RL and Garrick, DJ 2010. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. Journal of Animal Science 88, 544–551.Google Scholar
Ledesma, RD and Valero-Mora, P 2007. Determining the number of factors to retain in EFA: an easy-to-use computer program for carrying out Parallel Analysis. Practical Assessment, Research & Evaluation 12, 1–11.Google Scholar
Legarra, A, Ricard, A and Filangi, O 2012. GS3 Manual User (genomic selection, Gibbs sampling Gauss Seidel). Retrieved December 12, 2013, from http://snp.toulouse.inra.fr/~alegarra/manualgs3_last.pdf Google Scholar
Long, N, Gianola, D, Rosa, GJM and Weigel, KA 2011. Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins. Journal of Animal Breeding and Genetics 128, 247–257.Google Scholar
Lund, MS, Roos, AP, Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, F and Su, G 2011. A common reference population from four european holstein populations increases reliability of genomic predictions. Genetics Selection Evolution 43, 43.Google Scholar
Macciotta, NPP, Gaspa, G, Steri, R, Nicolazzi, EL, Dimauro, C, Pieramati, C and Cappio-Borlino, A 2010. Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis. Journal of Dairy Science 93, 2765–2774.Google Scholar
Makgahlela, ML, Mantysaari, EA, Stranden, I, Koivula, M, Nielsen, US, Sillanpaa, MJ and Juga, J 2013. Across breed multi-trait random regression genomic predictions in the Nordic Red dairy cattle. Journal of Animal Breeding and Genetics 130, 10–19.Google Scholar
Mäntysaari, E, Liu, Z and VanRaden, P 2011. Interbull validation test for genomic evaluations. Interbull Bulletin 41, 17–21.Google Scholar
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Olson, KM, VanRaden, PM and Tooker, ME 2012. Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. Journal of Dairy Science 95, 5378–5383.Google Scholar
Olson, KM, VanRaden, PM, Tooker, ME and Cooper, TA 2011. Differences among methods to validate genomic evaluations for dairy cattle. Journal of Dairy Science 94, 2613–2620.Google Scholar
Patterson, N, Price, AL and Reich, D 2006. Population structure and eigenanalysis. PLoS Genetics 2, e190.Google Scholar
Patry, C, Jorjani, H, and Ducrocq, V 2013. Effects of a national genomic preselection on the international genetic evaluations. Journal of Dairy Science 96, 3272–3284.Google Scholar
Pintus, MA, Nicolazzi, EL, Van Kaam, JBCHM, Biffani, S, Stella, A, Gaspa, G, Dimauro, C and Macciotta, NPP 2013. Use of different statistical models to predict direct genomic values for productive and functional traits in Italian Holsteins. Journal of Animal Breeding and Genetics 130, 32–40.CrossRefGoogle ScholarPubMed
Pintus, MA, Gaspa, G, Nicolazzi, EL, Vicario, D, Rossoni, A, Ajmone-Marsan, P, Nardone, A, Dimauro, C and Macciotta, NP 2012. Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach. Journal of Dairy Science 95, 3390–3400.Google Scholar
Pryce, JE, Gredler, B, Bolormaa, S, Bowman, PJ, Egger-Danner, C, Fuerst, C, Emmerling, R, Solkner, J, Goddard, ME and Hayes, BJ 2011. Short communication: genomic selection using a multi-breed, across-country reference population. Journal of Dairy Science 94, 2625–2630.Google Scholar
Pszczola, M, Strabel, T, Mulder, HA and Calus, MPL 2012. Reliability of direct genomic values for animals with different relationships within and to the reference population. Journal of Dairy Science 95, 389–400.Google Scholar
Scotti, E, Fontanesi, L, Schiavini, F, La Mattina, V, Bagnato, A and Russo, V 2010. DGAT1 p.K232A polymorphism in dairy and dual purpose Italian cattle breeds. Italian Journal of Animal Science 9, 79–82.CrossRefGoogle Scholar
Solberg, TR, Sonesson, AK, Woolliams, JA and Meuwissen, THE 2009. Reducing dimensionality for prediction of genome-wide breeding values. Genetics Selection Evolution 41, 29.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
VanRaden, PM, Null, DJ, Sargolzaei, M, Wiggans, GR, Tooker, ME, Cole, JB, Sonstegard, TS, Connor, EE, Winters, M, van Kaam, JBCHM, Valentini, A, Van Doormaal, BJ, Faust, MA and Doak, GA 2013. Genomic imputation and evaluation using high-density Holstein genotypes. Journal of Dairy Science 96, 668–678.Google Scholar
Vitezica, ZG, Aguilar, I, Misztal, I, and Legarra, A 2011. Bias in genomic predictions for populations under selection. Genetics Research 93, 357–366.Google Scholar