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Expected benefits of genomic selection for growth and wood quality traits in Eucalyptus grandis

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Abstract

Genomic selection (GS) can substantially reduce breeding cycle times in forest trees compared to traditional breeding cycles. Practical implementation of GS in tree breeding requires an assessment of significant drivers of genetic gains over time, which may differ among species and breeding objectives. We present results of a GS study of growth and wood quality traits in an operational Eucalyptus grandis breeding program in South Africa. The training population consisted of 1575 full and half-sib individuals, genotyped with the Eucalyptus (EUChip60K) SNP chip resulting in 15,040 informative SNP markers. The accuracy of the GS models ranged from 0.47 (diameter) to 0.67 (fibre width). We compared a 4-year GS breeding cycle equivalent to half of a traditional 8-year E. grandis breeding cycle and obtained GS efficiencies ranging from 1.20 (wood density) to 1.62 (fibre length). Simulated over 17 years, the ratio of the accumulated genetic gains between three GS cycles and two traditional breeding cycles ranged from 1.53 (diameter) to 3.35 (wood density). To realise these genetic gains per unit time in E. grandis breeding, we show that significant adjustments have to be made to integrate GS into operational breeding steps.

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References

  • Agarwal M, Shrivastava N, Padh H (2008) Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep 27(4):617–631

    Article  CAS  PubMed  Google Scholar 

  • Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J (2016) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci 242:23–36

    Article  CAS  PubMed  Google Scholar 

  • Batholome J, Van Heerwaarden J, Isik F, Boury C, Vidal M, Plomion C, Bouffier L (2016) Performance of genomic prediction within and across generations in maritime pine. BCM Genomics 17(604)

  • Beaulieu J, Doerksen T, Clement S, MacKay J, Bousquet J (2014) Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity 113:343–352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Booth TH (2013) Eucalypt plantations and climate change. For Ecol Manag 301:28–34

    Article  Google Scholar 

  • Bouquet A, Juga J (2013) Integrating genomic selection into dairy cattle breeding programmes: a review. Animal 7(5):705–713

    Article  CAS  PubMed  Google Scholar 

  • Brondani RPV, Brondani C, Tarchini R, Grattapaglia D (1998) Development, characterization and mapping of microsatellite markers in Eucalyptus grandis and E. urophylla. Theor Appl Genet 97:816–827

    Article  CAS  Google Scholar 

  • Butler, D. G., Cullis, B. R., Gilmour, A. R., and Goqel, B. J. (2009). ASReml-R reference manual.

  • Cappa EP, de Lima BM, da Silva-Junior OB, Carcia CC, Mansfield SD, Grattapaglia D (2019) Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP. Plant Sci 284:9–15

    Article  CAS  PubMed  Google Scholar 

  • Carle, J., and Holmgren, P. (2008). Wood from planted forests a global outlook 2005 - 2030. Forest Product Journal 58(12): 6-18.

  • Christensen OF, Lund MS (2010) Genomic prediction when some animals are not genotyped. Genet Sel Evol 42:2

    Article  PubMed  PubMed Central  Google Scholar 

  • Crain J, Mondal S, Rutkoski J, Singh RP, Poland J (2018) Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. The Plant Genome 11(1):1–14

    Article  Google Scholar 

  • Cros D, Mbo-Nkoulou L, Bell JM, Oum J, Masson A, Soumahoro M, Tran DM, Achour Z, Guen VL, Clement-Demanage A (2019) Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production. Ind Crop Prod 138:111464

    Article  Google Scholar 

  • Crossa J, de los Campos G, Maccaferri M, Tuberosa R, Burgueno J, Perez-Todriguez P (2017) Extending the marker x environment interaction model for genomic-enabled prediction and genome-wide association analysis in Durum Wheat. Crop Sci 56:2193–2209

    Article  Google Scholar 

  • Duran R, Isik F, Zapara-Valenzuela J, Balocchi C, Valanzuela A (2017) Genomic predictions of breeding values in a cloned Eucalyptus globulus population in Chile. Tree Genetics & Genome 13:74

    Article  Google Scholar 

  • Garcia-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-Lopez FJ, Van Tassell CP (2016) Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. PNAS 113(33):E4928

    Google Scholar 

  • Gilmour, A. R., Gogel, B.J., Cullis, B.R., Welham, S.J., and Thompson, R. (2015). ASReml guide release 4.1 functional specification. Hemel Hempstead, HP1 1ES, UK, VSN International Ltd (www.vsni.co.uk).

  • Grattapaglia D, Macos D, Resende DV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7(2):241–255

    Article  Google Scholar 

  • Grattapaglia D, Silva-Junior OB, Resende RT, Cappa EP, Muller BSF, Tan B, Isik F, Ratcliffe D, El-Kassaby YA (2018) Quantitative genetics and genomics converge to accelerate forest tree breeding. Front Plant Sci 9:1693

    Article  PubMed  PubMed Central  Google Scholar 

  • Greaves BL, Borralhoo NMG, Raymond CA, Evans R, Whitehead PH (1997) Age-age correlation in, and relationships between basic density desity and growth in Eucalyptus nitens. Silvae Genetica 45(5):264–270

    Google Scholar 

  • Griffin AR, Whiteman P, Rudge T, Burgess IP, Moncur M (1993) Effect of paclobutrazol on flower-bud production and vegetative growth in two species of Eucalyptus. Can J For Res 23(4):640–647

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haile JK, N’Diaye A, Clarke F, Clarke J, Knox R, Rutkoski J, Bassi FM, Pozniak CJ (2018) Genomic selection for grain yield and quality traits in durum wheat. Mol Breed 38:78

    Article  CAS  Google Scholar 

  • Harrand L, Hernandez JJV, Upton JL, Valverde GR (2009) Genetic parameters of growth traits and wood density in Eucalyptus grandis progenies planted in Argentina. Silvae Genetica 58:1–2

    Article  Google Scholar 

  • Hasan O, Reid JB (1995) Reduction of generation time in Eucalyptus globulus. Plant Growth Regul 17(1):53–60

    CAS  Google Scholar 

  • Hayes BJ, Bowman PJ, Camberlain AJ, Goddard ME (2008) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447

    Article  CAS  PubMed  Google Scholar 

  • Hill WG, Weir BS (2010) Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genet Res 93:47–64

    Article  Google Scholar 

  • Irland LC, Adams D, Alig R, Betz CJ, Chen C-C, Hutchins M, McCarl BA, Skog K, Sohngen BL (2001) Assessing Socioeconomic Impacts of Climate Change on US Forests, Wood-Product Markets, and Forest Recreation: the effects of climate change on forests will trigger market adaptations in forest management and in wood-products industries and may well have significant effects on forest-based outdoor recreation. BioScience 51(9):753–764

    Article  Google Scholar 

  • Isik F (2014) Genomic selection in forest tree breeding: the concept and an outlook to the future. New For 45:379–401

    Article  Google Scholar 

  • Isik F, Bartholome J, Farjat A, Chancerel E, Raffin A, Sanchez L, Plomion C, Bouffier L (2015) Genomic selection in maratime pine. Plant Sci 242:108–119

    Article  PubMed  CAS  Google Scholar 

  • Isik, F., Holland, J., and Maltecca, C. (2017). Genetic data analysis for plant and animal breeding. USA, Springer International Publishing.

  • Klapste J, Lstiburek M, El-Kassaby YA (2014) Estimates of genetic parameters and breeding values from western larch open-pollinated families using marker-based relationship. Tree Genet Genomes 10:241–249

    Article  Google Scholar 

  • Klapste J, Suontanna M, Dungey HS, Telfer EJ, Graham NJ, Low CB, Stovold GT (2018) Effect of hidden relatedness on single-step genetic evaluation in an advanced open-pollinated breeding program. J Hered 109(7):802–810

    PubMed  PubMed Central  Google Scholar 

  • Legarra A, Aguilar I, Misztal I (2009) A relationship matrix including full pedigree and genomic information. J Dairy Sci 92:4656–4663

    Article  CAS  PubMed  Google Scholar 

  • Leksono B, Kurinobu S, Ide Y (2008) Realized genetic gains observed in second generation seedling seed orchards of Eucalyptus pellita in Indonesia. J For Res 13(2):110–116

    Article  Google Scholar 

  • Li Y, Dungey HS (2018) Expected benefit of genomic selection over forward selection in conifer breeding and deployment. PLoS One 13(12):e0208232

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  • Luo J, Zhou G, Wu B, Chen D, Cao J, Lu W, Pegg RE, Arnold RJ (2010) Genetic variation and age-age correlations of Eucalyptus grandis at Dongmen Forest Farm in southern China. Aust For 73(2):67–80

    Article  Google Scholar 

  • Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP markers and their impact on plant breeding. International Journal of Plant Genomics 2012:11

    Article  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Misztal I, Aggrey SE, Muir WM (2013) Experiences with a single-step genome evaluation. Poult Sci 92:2530–2534

    Article  PubMed  Google Scholar 

  • Müller BSF, Neves LG, de Almeida Filho JE, Resende MFR, Muñoz PR, dos Santos PET, Filho EP, Kirst M, Grattapaglia D (2017) Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics 18(1):524

    Article  PubMed  PubMed Central  Google Scholar 

  • Myburg AA, Grattapaglia D, Tuskan GA, Hellsten U, Hayes RD, Grimwood J, Jenkins J, Lindquist E, Tice H, Bauer D, Goodstein DM, Dubchak I, Poliakov A, Mizrachi E, Kullan ARK, Hussey SG, Pinard D, van der Merwe K, Signh P, van Jaarsveld I, Silva-Junior OB, Togawa RC, Pappas MR, Faria DA, Sansaloni CP, Petroli CD, Yang X, Ranjan P, Tschaplinski TJ, Ye C-Y, Li T, Sterck L, Vanneste K, Murat F, Soler M, San Clemente H, Saidi N, Cassan-Wanh H, Dunand C, Hefer CA, Bornberg-Bauer E, Kersting AR, Vining K, Amarasinghe V, Ranik M, Naithani S, Elser J, Boyd AE, Liston A, Spatafora JW, Dharmwardhana P, Rija R, Sullivan C, Romanel E, Alves-Ferreira M, Kulheim C, Foley W, Carocha V, Paiva J, Kudrna D, Brommonschenkel SH, Pasquali G, Byrne M, Rigault P, Tibbits J, Spokeviciuos A, Jones RC, Steane DA, Vaillancourt RE, Potts BM, Joubert F, Barry K, Pappas GJ Jr, Strauss SH, Jaiswal P, Grima-Pettenati J, Salse J, Van de Peer Y, Rokhsar DS, Schmutz J (2014) The genome of Eucalyptus grandis. Nature 510:356–375

    Article  CAS  PubMed  Google Scholar 

  • Namkoong G, Barnes RD, Burley J (1980) Screening for yield in forest tree breeding. Commonwealth Forestry Review 59(1):61–68

    Google Scholar 

  • Osorio LF, White TL, Huber DA (2003) Age-age and trait-trait correlations for Eucalyptus grandis Hill ex Maiden and their implications for optimal selection age and design of clonal trials (Article). Theor Appl Genet 106(4):735–743

    Article  CAS  PubMed  Google Scholar 

  • Piepho H, Mohring P, Melchinger J, Buchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228

    Article  Google Scholar 

  • R_Core (2016). R: A language and environment for statistical computing. Vienna, Austria, https://www.R-project.org/, R Foundation for Statistical Computing.

  • Ratcliffe B, El-Dien OG, Cappa EP, Porth I, Klápste J, Chen C, El-Kassaby YA (2017) Single-step BLUP with varying genotyping effort in open-pollinated Picea glauca. Genes Genomics Genetics 7:935–942

    Google Scholar 

  • Resende MFR Jr, 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 MFR Jr, Munoz P, Resende MDV, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012b) Accuracy of genomic selection methods in standard data set of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510

    Article  PubMed  PubMed Central  Google Scholar 

  • Resende, M. D. V., Resende Jr, M.F.R., Sansaloni, C.P., Petroli, C.D., Missiaggia, A.A., Aguiar, A.M., Abad, J.M., Takahashi, E.K., Rosado, A.M., Faria, D.A., Pappas G.J. Jr, , Kilian, A., and Grattapaglia, D. (2012). Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 193: 617-624.

    Article  PubMed  Google Scholar 

  • Resende RT, Resende MDV, Silva FF, Azevero CF, Takahashi EK, Silva-Junior OB, Grattapaglia D (2017) Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity 119(4):245–255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J, Jannink JL, Sorrells ME (2015) Efficient use of historical data for genomic selection: a case study of stem rust resistance in wheat. The Plant Genome 8(1)

  • Rweyongeza DW (2016) A new approach to prediction of the age-age correlation for use in tree breeding. Annals of Forestry Science 73:1099–1111

    Article  Google Scholar 

  • Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 123(4):218–223

    Article  CAS  PubMed  Google Scholar 

  • Shepherd M, Bartle J, Lee DJ, Brawner J, Bush D, Turnbull P, Macdonel P, Simmons B, Henry R (2011) Eucalypts as a biofuel feedstock. Biofuels 2(6):639–657

    Article  CAS  Google Scholar 

  • Silva-Junior OB, Faria DA, Grattapaglia D (2015) A flexible multi-species genome-wide 60K SNP chip developed from pooled resequencing of 240 Eucalyptus tree genomes across 12 species. New Phytol 206(4):1527–1540

    Article  CAS  PubMed  Google Scholar 

  • Stanturf JA, Vance ED, Fox TR, Kirst M (2013) Eucalyptus beyond its native range: environmental issues in exotic bioenergy plantations. International Journal of Forestry Research 2013:463030

    Article  Google Scholar 

  • Suontama M, Klápště J, Telfer E, Graham N, Stovold T, Low C, McKinley R, Dungey H (2019) Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories. Heredity 122:370–379

    Article  CAS  PubMed  Google Scholar 

  • Tan B, Grattapaglia D, Martins GS, Ferreira KZ, Sundberg B, Ingvarsson PK (2017) Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BCM Plant Biology 17:110

    Article  Google Scholar 

  • VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423

    Article  CAS  PubMed  Google Scholar 

  • Veerkamp RF, Mulder HA, Thompson R, Calus MPL (2011) Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. J Dairy Sci 94:4189–4197

    Article  CAS  PubMed  Google Scholar 

  • Verryn SD, Snedden CL, Eatwell KA (2009) A comparison of deterministically predicted genetic gains with those realised in a South African Eucalyptus grandis breeding program. Southern Forests: a Journal of Forest Science 71(2):141–146

    Article  Google Scholar 

  • Voss-Fels KP, Cooper M, Hayes BJ (2019) Accelerating crop genetic gains with genomic selection. Theor Appl Genet 132(3):669–686

    Article  PubMed  Google Scholar 

  • White TL, Hodge GR (1988) Best linear prediction of breeding values in forest tree improvement programs. Theor Appl Genet 76(5):719–727

    Article  CAS  PubMed  Google Scholar 

  • Wiggans GR, Cole JB, Hubbard SM, Sontegard TS (2017) Genomic selection in dairy cattle: the USDA experience. Annual Review of Animal Bioscieces 5:309–327

    Article  Google Scholar 

  • Williams DR, Potts BM, Smethurst PJ (2003) Promotion of flowering in Eucalyptus nitens by paclobutrazol was enhanced by nitrogen fertilizer. Can J For Res 33:74–81

    Article  CAS  Google Scholar 

  • Wimmer V, Albrecht T, Auinger H-J, Schon C-C (2012) synbreed: a framework for the analysis of genomic predictions data using R. Bioinformatics 28:2086–2087

    Article  CAS  PubMed  Google Scholar 

  • Wingfield MJ, Slippers B, Hurley BP, Coutinho TA, Wingfield BD, Roux J (2008) Eucalypt pest and diseases: growing threats to plantation productivity. Southern Forests 70(2):139–144

    Article  Google Scholar 

  • Wingfield MJ, Brocherhoff EG, Wingfield BD, Slippers B (2015) Planted forest health: the need for a global strategy. Science 349(6250):832–836

    Article  CAS  PubMed  Google Scholar 

  • Wolc A, Kranis A, Arango J, Settar P, Fulton JE, O'Sullivan NP, Avendano A, Watson KA, de los Campos G, Fernando RL, Garrick DJ, Dekkers JCM (2016) Implementation of genomic selection in the poultry industry. Animal Frontiers 6(1):23–31

    Article  Google Scholar 

  • Wu HX, Powell M b, Yang JL, Ivkovic M, McRae TA (2007) Efficiency of early selection for rotation-aged wood quality traits in radiata pine. Annual Forestry Science 64:1–9

    Article  Google Scholar 

  • Zapata-Valenzuela J, Whetten RW, Naele D, McKeand S, Isik F (2013) Genomic estimated breeding values using genomic relationship matrices in a cloned population of Loblolly Pine. Genes Genomics Genetics 3:909–916

    Google Scholar 

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Acknowledgements

We thank Mondi South Africa for the use of their E. grandis breeding population as well as the use of facilities and technical support to generate phenotypic data. The authors acknowledge North Carolina State University for training on R and ASReml analysis.

Data archiving statement

The full SNPs, pedigree, and phenotypic data are provided in the supplementary materials as supporting data.

Funding

SNP genotyping, wet chemistry data generation used to develop the NIR models and pedigree reconstruction was supported through funding from Mondi South Africa for the Forest Molecular Genetics (FMG) Programme at the University of Pretoria. We further thank the financial support from the funding organisation Mondi South Africa (Pty) Ltd: Forests: Research and Development, National Research Foundation (NRF), Department of Science and Technology: Bioinformatics and Functional Genomics Programme (BFG) Grant UID 86936 and 97911 and Department Trade and Industry South Africa: Technology and Human Resource Industry Programme (THRIP) Grant 80118 and 964134.

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Authors and Affiliations

  1. Mondi South Africa, Forests, Research and Development Department, Trahar Technology Centre - TTC, P.O. Box 12, Hilton, 3245, South Africa

    Makobatjatji M. Mphahlele

  2. Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Private Bag X20, Pretoria, 0028, South Africa

    Makobatjatji M. Mphahlele, Marja M. Mostert-O’Neill, S. Melissa Reynolds & Alexander A. Myburg

  3. Department of Forestry & Environmental Resources, North Carolina State University, Raleigh, NC, 27695, USA

    Fikret Isik & Gary R. Hodge

  4. Camcore, North Carolina State University, PO Box 7626, Raleigh, NC, 27695, USA

    Gary R. Hodge

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  1. Makobatjatji M. Mphahlele
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Contributions

M.M. carried out the experimental design, data collection, data analysis, and drafting of this paper as part of his PhD thesis. F.I. assisted with the modelling and scripts of the data analysis tools. F.I., G.H. and A.A.M. supervised the study and helped with data interpretation and the drafting of the manuscript. M.O. assisted with SNP data analysis and M.R. assisted with DNA extraction and pedigree reconstruction. All authors have read and approved the final manuscript.

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Correspondence to Alexander A. Myburg.

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The authors declare that they have no conflict of interest. The author M.M. is an employee of Mondi South Africa (Pty) Ltd.

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Mphahlele, M.M., Isik, F., Mostert-O’Neill, M.M. et al. Expected benefits of genomic selection for growth and wood quality traits in Eucalyptus grandis. Tree Genetics & Genomes 16, 49 (2020). https://doi.org/10.1007/s11295-020-01443-1

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