Skip to main content Accessibility help
×
Home

Review: How to improve genomic predictions in small dairy cattle populations

  • M. S. Lund (a1), I. van den Berg (a1) (a2) (a3), P. Ma (a1), R. F. Brøndum (a1) and G. Su (a1)...

Abstract

This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Review: How to improve genomic predictions in small dairy cattle populations
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Review: How to improve genomic predictions in small dairy cattle populations
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Review: How to improve genomic predictions in small dairy cattle populations
      Available formats
      ×

Copyright

Corresponding author

E-mail: mogens.lund@mbg.au.dk

References

Hide All
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, 743752.
Brøndum, RF, Rius-Vilarrasa, E, Strandén, 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.
Brøndum, RF, Su, G, Janss, L, Sahana, G, Guldbrandtsen, B, Boichard, D and Lund, MS 2015. Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction. Journal of Dairy Science 98, 41074116.
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.
Carlborg, O, Kerje, S, Schütz, K, Jacobsson, L, Jensen, P and Andersson, L 2003. A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Research 13, 413421.
Chen, L, Li, C, Miller, S and Schenkel, F 2014. Multi-population genomic prediction using a multi-task Bayesian learning model. BMC Genetics 15, 53.
Christensen, OF and Lund, MS 2010. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42, 2.
Cooper, TA, Wiggans, GR and VanRaden, PM 2015. Short communication: analysis of genomic predictor population for Holstein dairy cattle in the United States – effects of sex and age. Journal of Dairy Science 98, 27852788.
Daetwyler, HD, Capitan, A, Pausch, H, Stothard, P, van Binsbergen, R, Brøndum, RF, Liao, X, Djari, A, Rodriguez, SC, Grohs, C, Esquerré, D, Bouchez, O, Rossignol, M-N, Klopp, C, Rocha, D, Fritz, S, Eggen, A, Bowman, PJ, Coote, D, Chamberlain, AJ, Anderson, C, VanTassell, CP, Hulsegge, I, Goddard, ME, Guldbrandtsen, B, Lund, MS, Veerkamp, RF, Boichard, DA, Fries, R and Hayes, BJ 2014. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics 46, 858865.
De Los Campos, G, Vazquez, AI, Fernando, R, Klimentidis, YC and Sorensen, D 2013. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genetics 9, e1003608.
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.
Gao, H, Christensen, OF, Madsen, P, Nielsen, US, Zhang, Y, Lund, MS and Su, G 2012. Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics Selection Evolution 44, 8.
Gaspa, G, Jorjani, H, Dimauro, C, Cellesi, M, Ajmone-Marsan, P, Stella, A and Macciotta, NPP 2015. Multiple-breed genomic evaluation by principal component analysis in small size populations. Animal 9, 738749.
Goddard, M 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245257.
Habier, D, Fernando, RL and Garrick, DJ 2013. Genomic-BLUP decoded: a look into the black box of genomic prediction. Genetics 194, 597607.
Hay, EH and Rekaya, R 2015. A multi-compartment model for genomic selection in multi-breed populations. Livestock Science 177, 17.
Hayashi, T and Iwata, H 2013. A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics 14, 34.
Hayes, BJ, Bowman, PJ, Chamberlain, AC, Verbyla, K and Goddard, ME 2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41, 51.
Heringstad, B, Su, G, Solberg, TR, Guldbrandtsen, B, Svendsen, M and Lund, MS 2011. Genomic predictions based on a joint reference population for Scandinavian red breeds. In Book of abstracts of the 62nd Annual Meeting of the European Federation of Animal Science, Waageningen Academic Publishers, Stavanger, Norway, 29pp.
Huang, W, Richards, S, Carbone, MA, Zhu, D, Anholt, RRH, Ayroles, JF, Duncan, L, Jordan, KW, Lawrence, F, Magwire, MM, Warner, CB, Blankenburg, K, Han, Y, Javaid, M, Jayaseelan, J, Jhangiani, SN, Muzny, D, Ongeri, F, Perales, L, Wu, Y-Q, Zhang, Y, Zou, X, Stone, Ea, Gibbs, Ra and Mackay, TFC 2012. Epistasis dominates the genetic architecture of drosophila quantitative traits. Proceedings of the National Academy of Sciences of the United States of America 109, 1555315559.
Iheshiulor, OOM, Wooliams, JA, Yu, X, Wellmann, R and Meuwissen, THE 2014. Genomic predictions using whole genome sequence data and multi-breed reference populations. In Proceedings, 10th World Congress of Genetics Applied to Livestock Production, 178pp.
Karoui, S, Carabaño, MJ, Díaz, C and Legarra, A 2012. Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genetics Selection Evolution 44, 39.
Kemper, KE, Reich, CM, Bowman, PJ, vander Jagt, CJ, Chamberlain, AJ, Mason, BA, Hayes, BJ and Goddard, ME 2015. Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions. Genetics Selection Evolution 47, 117.
Li, Y, Sidore, C, Kang, HM, Boehnke, M and Abecasis, GR 2011. Low-coverage sequencing: implications for design of complex trait association studies. Genome Research 21, 940951.
Lidauer, M and Strandén, I 1999. Fast and flexible program for genetic evaluation in dairy cattle. Interbull Bulletin 20, 1924.
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, F and Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics Selection Evolution 43, 43.
Ma, P, Lund, MS, Nielsen, US, Aamand, GP and Su, G 2015. Single-step genomic predictions improved prediction reliability and reduced bias of the prediction trend in Danish Jersey. Journal of Dairy Science 98, 90269034.
Makgahlela, ML, Strandén, I, Nielsen, US, Sillanpää, MJ and Mäntysaari, EA 2013. The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations. Journal of Dairy Science 96, 53645375.
Makgahlela, ML, Strandén, I, Nielsen, US, Sillanpää, MJ and Mäntysaari, EA 2014. Using the unified relationship matrix adjusted by breed-wise allele frequencies in genomic evaluation of a multibreed population. Journal of Dairy Science 97, 11171127.
Olson, KM, Vanraden, PM and Tooker, ME 2012. Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. Journal of Dairy Science 95, 53785383.
Pryce, JE, Gredler, B, Bolormaa, S, Bowman, PJ, Egger-Danner, C, Fuerst, C, Emmerling, R, Sölkner, 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.
Schenkel, F, Sargolzaei, M, Kistemaker, G, Jansen, G, Sullivan, P, Van Doormaal, BJ, Vanraden, PM and Wiggans, GR 2009. Reliability of genomic evaluation of Holstein cattle in Canada. Interbull Bulletin 39, 5158.
Strandén, I and Mäntysaari, EA 2010. A recipe for multiple trait deregression. Interbull Bulletin 42, 2124.
Su, G, Ma, P, Nielsen, US, Aamand, GP, Wiggans, G, Guldbrandtsen, B and Lund, MS 2015. Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey. Animal, first published online 2 September 2015, doi:10.1017/S1751731115001792.
Su, G, Madsen, P, Nielsen, US, Mäntysaari, EA, Aamand, GP, Christensen, OF and Lund, MS 2012. Genomic prediction for Nordic Red cattle using one-step and selection index blending. Journal of Dairy Science 95, 909917.
Su, G, Nielsen, US, Wiggans, G, Aamand, GP, Guldbrandtsen, B and Lund, MS 2014. Improving genomic prediction for Danish Jersey using a joint Danish-US reference population. In Proceedings, 10th World Congress of Genetics Applied to Livestock Production, 60pp.
Thomasen, JR, Guldbrandtsen, B, Su, G, Brøndum, RF and Lund, MS 2012. Reliabilities of genomic estimated breeding values in Danish Jersey. Animal 6, 789796.
Thomasen, JR, Willam, A, Guldbrandtsen, B, Lund, MS and Sørensen, AC 2014. Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit. Journal of Dairy Science 97, 458470.
Van den Berg, I, Guldbrandtsen, B, Hoze, C, Brøndum, RF, Boichard, D and Lund, MS 2014. Across breed QTL detection and genomic prediction in French and Danish dairy cattle breeds. In Proceedings, 10th World Congress of Genetics Applied to Livestock Production, 490pp.
Vanraden, PM, Olson, KM, Null, DJ, Sargolzaei, M, Winters, M and van Kaam, JBCHM 2012. Reliability increases from combining 50, 000- and 777, 000- marker genotypes from four countries. Interbull Bulletin 46, 7579.
Weller, JI, Ron, M, Glick, G, Shirak, A, Zeron, Y and Ezra, E 2012. A simple method for genomic selection of moderately sized dairy cattle populations. Animal 6, 193202.
Zhou, L, Ding, X, Zhang, Q, Wang, Y, Lund, MS and Su, G 2013. Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population. Genetics Selection Evolution 45, 7.
Zhou, L, Heringstad, B, Su, G, Guldbrandtsen, B, Meuwissen, THE, Svendsen, M, Grove, H, Nielsen, US and Lund, MS 2014aGenomic predictions based on a joint reference population for the Nordic Red cattle breeds. Journal of Dairy Science 97, 44854496.
Zhou, L, Lund, MS, Wang, Y and Su, G 2014bGenomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices. Journal of Animal Breeding and Genetics 131, 249257.

Keywords

Related content

Powered by UNSILO

Review: How to improve genomic predictions in small dairy cattle populations

  • M. S. Lund (a1), I. van den Berg (a1) (a2) (a3), P. Ma (a1), R. F. Brøndum (a1) and G. Su (a1)...

Metrics

Altmetric attention score

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.