Skip to main content Accessibility help
×
Home

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

  • G. Gaspa (a1), H. Jorjani (a2), C. Dimauro (a1), M. Cellesi (a1), P. Ajmone-Marsan (a3), A. Stella (a4) and N. P. P. Macciotta (a1)...

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.

Copyright

Corresponding author

E-mail: gigaspa@uniss.it

References

Hide All
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, 47004707.
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, 67036715.
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, 33753384.
de Roos, APW, Hayes, BJ and Goddard, ME 2009. Reliability of genomic predictions across multiple populations. Genetics 183, 15451553.
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, 41144129.
Goddard, ME and Hayes, BJ 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics 10, 381391.
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.
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.
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.
Harris, BL, Creagh, FE, Winkelman, AM and Johnson, DL 2011. Experiences with the illumina high density bovine beadchip. Interbull Bulletin 44, 37.
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.
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, 433443.
Jorjani, H, Jakobsen, J, Hjerpe, E, Palucci, V and Dürr, J 2012. Status of genomic evaluation in the brown swiss populations. Interbull Bulletin 46, 4654.
Kaiser, HF 1960. The application of electronic computers to factor analysis. Educational and Psychological Measurement 20, 141151.
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.
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, 544551.
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.
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
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, 247257.
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.
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, 27652774.
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, 1019.
Mäntysaari, E, Liu, Z and VanRaden, P 2011. Interbull validation test for genomic evaluations. Interbull Bulletin 41, 1721.
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.
Olson, KM, VanRaden, PM and Tooker, ME 2012. Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. Journal of Dairy Science 95, 53785383.
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, 26132620.
Patterson, N, Price, AL and Reich, D 2006. Population structure and eigenanalysis. PLoS Genetics 2, e190.
Patry, C, Jorjani, H, and Ducrocq, V 2013. Effects of a national genomic preselection on the international genetic evaluations. Journal of Dairy Science 96, 32723284.
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, 3240.
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, 33903400.
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, 26252630.
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, 389400.
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, 7982.
Solberg, TR, Sonesson, AK, Woolliams, JA and Meuwissen, THE 2009. Reducing dimensionality for prediction of genome-wide breeding values. Genetics Selection Evolution 41, 29.
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, 1624.
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, 668678.
Vitezica, ZG, Aguilar, I, Misztal, I, and Legarra, A 2011. Bias in genomic predictions for populations under selection. Genetics Research 93, 357366.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed