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Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding

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Abstract

We developed a simulation study to test the efficiency of genomic selection (GS) in the case of Eucalyptus breeding. We simulated a recurrent selection scheme for clone production over four breeding cycles. Scenarios crossing broad sense heritabilities (H 2 = 0.6 and 0.1) and dominance to additive variance ratios (R = 0.1; 0.5; and 1) were compared. GS was performed with 1,000 SNPs and 22 QTLs per Morgan and tested against phenotypic selection (PS) based on best linear unbiased prediction of parents and clones. When the training population was made up of the first cycle progeny tests and the candidate populations were the progeny tests of three successive cycles, GS accuracy decreased with breeding cycles (e.g., from 0.9 to 0.4 with H 2 = 0.6 and R = 0.1), whereas PS presented constant performances (accuracy of 0.8 with H 2 = 0.6 and R = 0.1). When the training population set was updated by associating data of previous cycles, GS accuracy was improved from 25 % to 418 %, especially with H 2 = 0.1. The GS model including dominance effects performed better in clone selection (genotypic value) when dominance effects were preponderant (R = 1), heritability was high (H 2 = 0.6 and with an updated training set), but no improvement was detected for parent selection (breeding value). The genetic gains over cycles were lower with the GS method without updating the data set but, with an updated training set, were similar to PS. However, the genetic gain per unit time with GS was 1.5 to 3 times higher than with PS for breeding and clone populations. These results highlight the value of GS in Eucalyptus breeding.

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References

  • Arumugasundaram S, Ghosh M, Veerasamy S, Ramasamy Y (2011) Species discrimination, population structure and linkage disequilibrium in Eucalyptus camaldulensis and Eucalyptus tereticornis using SSR markers. PLoS One 6:e28252

    Article  PubMed  CAS  Google Scholar 

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  • Bernardo R, Yu J (2007) Prospects for genome wide selection for quantitative traits in maize. Crop Sci 47:1082–1090

    Article  Google Scholar 

  • Bouvet J-M, Vigneron P, Saya AR, Gouma R (2004) Early selection of Eucalyptus clones in retrospective nursery test using growth, morphological and dry matter criteria, in Republic of Congo. South Hemisphere For J 200:5–17

    Google Scholar 

  • Bouvet J-M, Vigneron P, Villar E, Saya A (2009a) Determining the optimal age for selection by modelling the age-related trends in genetic parameters in Eucalyptus hybrid populations. Silvae Genet 58:102–112

    Google Scholar 

  • Bouvet J-M, Saya A, Vigneron P (2009b) Trends in additive, dominance and environmental effects with age for growth traits in Eucalyptus hybrid populations. Euphytica 165:35–54

    Article  Google Scholar 

  • Bush D (2011) Plantation eucalypt species: recent trends in testing and planting. Proceedings of the IUFRO meeting: “Joining silvicultural and genetic strategies to minimize eucalyptus environmental stresses: from research to practices” 14–18th of November 2011. Porto-Seguro Brazil pp 4–6

  • Calus MPL, Veerkamp RF (2007) Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 124:362–368

    Article  PubMed  CAS  Google Scholar 

  • Calus MPL, Meuwissen THE, de Roos APW, Veerkamp RF (2008) Accuracy of genomic selection using different methods to define haplotypes. Genetics 178:553–561

    Article  PubMed  CAS  Google Scholar 

  • Cleveland MA, Forni S, Deeb N, Maltecca C (2010) Genomic breeding value prediction using three Bayesian methods and application to reduced density marker panels. BMC Proc 4(Suppl 1):S6

    Article  PubMed  Google Scholar 

  • Coster A, Bastiaansen J (2009) Haplosim: Haplosim R package version 1.8

  • Coster A, Bastiaansen JWM, Calus MPL, Maliepaard C, Bink MCAM (2010) Qtlmas 2009: simulated dataset. BMC Proc 4(Suppl 1):S3

    Article  PubMed  Google Scholar 

  • Crossa J, De los Campos G, Pérez P, Gianola D, Atlin G, Burgueño J, Araus JL, Makumbi D, Yan J, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724

    Article  PubMed  CAS  Google Scholar 

  • Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021–1031

    Article  PubMed  CAS  Google Scholar 

  • Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing nature reviews. Genetics 12:499–510

    PubMed  CAS  Google Scholar 

  • de los Campos G, Naya H, Gianola D, Crossa J, Legarra A et al (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182:375–385

    Article  PubMed  Google Scholar 

  • R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org

  • Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GbS) approach for high diversity species. PLoS One 6:e19379

    Article  PubMed  CAS  Google Scholar 

  • Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Prentice Hall, fourthth edn. Addison Wesley Longman, Harlow, Essex, UK

    Google Scholar 

  • Gallais A (1991) Théorie de la sélection en amélioration des plantes. Editions Masson, Paris

    Google Scholar 

  • Gianola D, Fernando RL, Stella A (2006) Genomic­assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776

    Article  PubMed  CAS  Google Scholar 

  • Gratapaglia D, Resende MDV (2011) Twenty years of Eucalyptus molecular breeding: from discrete marker-trait associations to whole-genome prediction of complex traits. Proceedings of the IUFRO meeting: “joining silvicultural and genetic strategies to minimize eucalyptus environmental stresses: from research to practices” 14–18th of November 2011. Porto-Seguro Brazil pp 33–36

  • Grattapaglia D, Resende MDV (2010) Genomic selection in forest tree breeding. Tree Genet Gen 7:241–255

    Article  Google Scholar 

  • Graziano Usai M, Goddard ME, Hayes BJ (2009) LASSO with cross-validation for genomic selection. Genet Res Camb 91:427–436

    Article  PubMed  Google Scholar 

  • Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397

    PubMed  CAS  Google Scholar 

  • Hartl DL, Clark AG (2007) Principles of population genetics, fourthth edn. Sinauer Associates, Inc. Publishers, Sunderland, Massachusetts, USA

    Google Scholar 

  • Hayes B, Goddard M (2010) Genome-wide association and genomic selection in animal breeding. Genome 53:876–883

    Article  PubMed  CAS  Google Scholar 

  • Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009a) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443

    Article  PubMed  CAS  Google Scholar 

  • Hayes BJ, Visscher PM, Goddard ME (2009b) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res Camb 91:47–60

    Article  PubMed  CAS  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

    Article  CAS  Google Scholar 

  • Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

    Article  Google Scholar 

  • Hein PRG, Bouvet J-M, Mandrou E, Clair B, Vigneron P, Chaix (2012) Age trends of microfibril angle inheritance and their genetic and environmental correlations with growth, density and chemical properties in Eucalyptus urophylla S.T. Blake wood. Annals For Sci. doi:10.1007/s13595-012-0186-3

  • Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231

    Article  Google Scholar 

  • Hoer A, Kennard R (1970) Ridge regression: biased estimation for non-orthogonal problems. Technometrics 12:55–67

    Article  Google Scholar 

  • Ibánẽz-Escriche N, Fernando RL, Ali T, Dekkers JCM (2009) Genomic selection of purebreds for crossbred performance. Genet Sel Evol 41:12

    Article  PubMed  Google Scholar 

  • Iwata H, Hayashi T, Tsumura Y (2011) Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica. Tree Genet Gen 7:747–758

    Article  Google Scholar 

  • Jannink JL (2010) Dynamics of long-term genomic selection. Genet Sel Evol: 42-35

  • Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics Proteomics 9:166–177

    Article  CAS  Google Scholar 

  • Kumar S, Bink MCAM, Volz RK, Bus VGM, Chagné D (2012) Towards genomic selection in apple (Malus × domestica Borkh.) breeding programmes: prospects, challenges and strategies. Tree Genet Gen 8:1–14

    Article  Google Scholar 

  • Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink J-L (2011) Genomic selection in plant breeding: knowledge and prospects. In: Sparks D (ed) Advances in Agronomy, vol 110. Elsevier Inc, Newark, pp 78–110

    Google Scholar 

  • Lorenzana R, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161

    Article  PubMed  Google Scholar 

  • Luan T, Woolliams JA, Lien S, Kent M, SvendenM MTHE (2009) The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183:1119–1126

    Article  PubMed  Google Scholar 

  • Meuwissen THE (2009) Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genet Sel Evol 41:35

    Article  PubMed  Google Scholar 

  • Meuwissen THE, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole genome resequencing. Genetics 185:623–631

    Article  PubMed  CAS  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    PubMed  CAS  Google Scholar 

  • Mrode RA, Thompson R (2005) Linear models for the prediction of animal breeding values, 2nd edn. CABI Publishing, Cambridge USA

    Book  Google Scholar 

  • Muir WM (2007) Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet 124:342–355

    Article  PubMed  CAS  Google Scholar 

  • Ødegård J, Yazdi MH, Sonesson AK, Meuwissen THE (2009) Incorporating desirable genetic characteristics from an inferior into a superior population using genomic selection. Genetics 181:737–745

    PubMed  Google Scholar 

  • Park T, Casella G (2008) The bayesian lasso. J Am Stat Assoc 103:681–686

    Article  CAS  Google Scholar 

  • Pérez P, de Los CG, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome 3(2):106–116

    Article  PubMed  Google Scholar 

  • Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228

    Article  Google Scholar 

  • Piyasatian N, Fernando RL, Dekkers JCM (2007) Genomic selection for marker-assisted improvement in line crosses. Theor Appl Genet 115:665–674

    Article  PubMed  CAS  Google Scholar 

  • Resende MFRJ, Munoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2012a) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624

    Article  PubMed  Google Scholar 

  • Resende MDV, Resende MFR Jr, Sansaloni CP, Petroli CD, Missiaggia AA, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria DA, Pappas GJ Jr, Kilian A, Grattapaglia D (2012b) Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 194:116–128

    Article  PubMed  Google Scholar 

  • Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–2454

    Article  PubMed  CAS  Google Scholar 

  • Toosi A, Fernando RL, Dekkers JCM (2010) Genomic selection in admixed and crossbred populations. J Anim Sci 88:32–46

    Article  PubMed  CAS  Google Scholar 

  • Toro M, Varona L (2010) A note on mate allocation for dominance handling in genomic selection. Genet Sel Evol 43:27

    Article  Google Scholar 

  • Verhaegen D, Plomion C (1996) Genetic mapping in Eucalyptus urophylla and Eucalyptus grandis using RAPD markers. Genome 39:1051–1061

    Article  PubMed  CAS  Google Scholar 

  • Vigneron P, Bouvet J-M (2001) Eucalyptus. In: Charrier André (ed.), Jacquot Michel (ed.), Hamon Serge (ed.), Nicolas Dominique (ed.). Tropical plant breeding. (Repères) CIRAD Montpellier: p 223–245

  • White TL, Adams WT, Neale DB (2007) Forest genetics. CABI Publisher: Cambridge, MA, USA

  • Wong CK, Bernardo R (2008) Genome wide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824

    Article  PubMed  CAS  Google Scholar 

  • Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407

    Article  Google Scholar 

  • Zhong S, Dekkers JCM, Fernando RL, Jannink J-L (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

This study was one of the tasks of CIRAD's project SEPANG (genomic selection in tropical plants) funded by the Centre de Coopération Internationale en Recherche Agronomique pour le Développement. We are grateful to the two anonymous reviewers for their valuable comments which greatly improved this manuscript.

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  1. CIRAD—AGAP Research unit 108 “Genetic improvement and adaptation of tropical and Mediterranean plants”, International campus of Baillarguet TA A-108/C, 34398, Montpellier Cedex 5, France

    Marie Denis & Jean-Marc Bouvet

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  1. Marie Denis
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  2. Jean-Marc Bouvet
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Correspondence to Jean-Marc Bouvet.

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Communicated by J. Beaulieu

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Fig. S1

abc Change in selection method accuracy over cycles for breeding population with updating of the training set: influence of heritability (H 2 = 0.6 and H 2 = 0.1) and dominance to additive variance R ratio (R = 0.1, R = 0.5 and R = 1). For GS, the training population is constituted of the individuals of the cycle 1 progeny tests to predict the candidate populations of subsequent cycles. (PPTX 66 kb)

Fig. S1

def (PPTX 66 kb)

Fig. S2

abc Change in selection method accuracy over cycles for breeding population with updating of the training set: influence of heritability (H 2 = 0.6 and H 2 = 0.1) and dominance to additive variance R ratio (R = 0.1, R = 0.5 and R = 1). For GS, the training population is constituted of individuals of the cycle 1 progeny tests to predict the candidate populations of subsequent cycles. (PPTX 66 kb)

Fig. S2

def (PPTX 66 kb)

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Denis, M., Bouvet, JM. Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding. Tree Genetics & Genomes 9, 37–51 (2013). https://doi.org/10.1007/s11295-012-0528-1

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