Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Uncovering genetic information from commercial forest plantations—making up for lost time using “Breeding without Breeding”

Abstract

An application of “Breeding without Breeding” (BwB) is proposed to uncover or extract genetic information from existing plantations, using pedigree reconstruction and BLUP to predict breeding values and identify genetically superior individuals. The focus is on the use of the methodology at the initiation of an operational breeding program to circumvent the first cycle of breeding and testing, but it could also have application in more advanced tree improvement programs. A simulation study was done to examine different sizes of three conceptual populations used in the BwB approach, and to compare the genetic gains achieved using that approach with those that would have been achieved with a full-sib breeding and testing strategy if it had been started years before. The BwB approach is based on pedigree reconstruction with a relatively small number of trees (from 1,200 to 3,600), comprised of a randomly selected sub-population of size N R = 600 to 3,000, and a top-phenotype sub-population of size N T = 600 (pre-selected out of 5,940 to 23,760 trees on the basis of phenotype alone). With the reconstructed pedigree, a combined REML/BLUP analysis of phenotypic data is done to predict breeding values, and a linear optimization is done to make the final selections to maximize gain while constraining relatedness to a given effective population size N e = 5, 10, or 20. Results indicate that the BwB strategy can achieve substantial levels of genetic gain, equivalent to 80 to 98 % of the gain that could have been achieved using a full-sib strategy.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. Adams WT, Hipkins VD, Burczyk J, Randall WK (1997) Pollen contamination trends in a maturing Douglas-fir seed orchard. Can J Forest Res 27:131–134

  2. Blonk RJ, Komen H, Kamstra A, van Arendonk JA (2010) Estimating breeding values with molecular relatedness and reconstructed pedigrees in natural mating populations of common sole, Solea solea. Genetics 184:213–219

  3. Brown CL, Goddard RE (1961) Silvicultural considerations in the selection of plus phenotypes. J Forest 59:420–426

  4. Butler D, Cullis BR, Gilmour AR, Goge BJ (2007) ASReml-R reference manual. Guide Release 2.00. VSN International Ltd, Hemel Hempstead, UK. URL http://www.vsni.co.uk/

  5. R Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/

  6. Cros D, Sánchez L, Cochard B, Samper P, Denis M, Bouvet JM, Fernández J (2014) Estimation of genealogical coancestry in plant species using a pedigree reconstruction algorithm and application to an oil palm breeding population. Theor Appl Genet 127:981–994

  7. Dutkowski GW, Costa e Silva J, Gilmour AR, Lopez GA (2002) Spatial analysis methods for forest genetic trials. Can J Forest Res 32:2201–2214

  8. El-Kassaby YA, Cook C (1994) Female reproductive energy and reproductive success in a Douglas-fir seed orchard and its impact on genetic diversity. Silvae Genet 43:243–246

  9. El-Kassaby YA, Lstibůrek M (2009) Breeding without breeding. Genet Res 91:111–120

  10. El-Kassaby YA, Rudin D, Yazdani R (1989) Levels of outcrossing and contamination in two Pinus sylvestris L. seed orchards in northern Sweden. Scand J Forest Res 4:41–49

  11. El-Kassaby YA, Lstibůrek M, Liewlaksaneeyanawin C, Slavov GT, and Howe GT (2007) Breeding without breeding: approach, example, and proof of concept. In: Proc. IUFRO, Low input breeding and genetic conservation of forest tree species. Antalya, Turkey, pp. 43-54

  12. Falconer DS (1981) Introduction to Quantitative Genetics, Second Edition. Longman Group Limited, 340 p

  13. Funda T, Liewlaksaneeyanawin C, Fundová I, Lai BSK, Walsh C, Niejenhuis AV, Cook C, Graham H, Woods J, El-Kassaby YA (2011) Congruence between parental reproductive investment and success determined by DNA-based pedigree reconstruction in conifer seed orchards. Can J Forest Res 41:380–389

  14. Gezan SA, Huber DA, White TL (2006) Post hoc blocking to improve heritability and precision of best linear unbiased genetic predictions. Can J Forest Res 36:2141–2147

  15. Grattapaglia D, do Amaral Diener PS, dos Santos GA (2014) Performance of microsatellites for parentage assignment following mass controlled pollination in a clonal seed orchard of loblolly pine (Pinus taeda L.). Tree Genet Genomes 10:1631–1643

  16. Gurobi Development Team (2014) Gurobi Optimizer Reference Manual. Gurobi Optimization, Inc. Houston TX, USA. URL http://www.gurobi.com/

  17. Hansen OK, McKinney LV (2010) Establishment of a quasi-field trial in Abies nordmanniana—test of a new approach to forest tree breeding. Tree Genet Genomes 6:345–355

  18. Hartman HT, Kestler DE (1968) Plant propagation: Principles and Practices. Prentice Hall, Inc., 559 p

  19. Hodge GR, Dvorak WS (2012) Growth potential and genetic parameters of four Mesoamerican pines planted in the Southern Hemisphere. South Forests 74:27–49

  20. Hodge GR, White TL (1992) Selection and gain prediction. In: Fins L, Friedman ST, Brotschol JV (eds) Handbook of Quantitative Forest Genetics. Kluwer Academic Publishers, 403 pp

  21. Huber DA, White TL, Hodge GR (1992) Efficiency of half-sib, half-diallel, and circular mating designs of different sizes in the estimation of genetic parameters. Forest Sci 38:757–776

  22. Korecký J, Lstibůrek M, El-Kassaby YA (2014) Congruence between theory and practice: reduced contamination rate following phenotypic pre-selection within the Breeding without Breeding framework. Scand J Forest Res 29:552–554

  23. Lai BS, Funda T, Liewlaksaneeyanawin C, Klápště J, Niejenhuis AV, Cook C, Stoehr MU, Woods J, El-Kassaby YA (2010) Pollination dynamics in a Douglas-fir seed orchard as revealed by pedigree reconstruction. Ann For Sci 67:808

  24. Lambeth C, Lee B-C, O’Malley D, Wheeler N (2001) Polymix breeding with parental analysis of progeny: an alternative to full-sib breeding and testing. Theor Appl Genet 103:930–943

  25. Lindgren D, El-Kassaby YA (1989) Genetic consequences of combining selective cone harvesting and genetic thinning in clonal seed orchards. Silvae Genet 38:65–70

  26. Lstibůrek M, Ivanková K, Kadlec J, Klápště J, Kobliha J, El-Kassaby YA (2011) Minimum fingerprinting effort with respect to the effective population size. Tree Genet Genomes 7:1069–1078

  27. Lstibůrek M, Klápště J, Kobliha J, El-Kassaby YA (2012) Breeding without breeding: effect of gene flow on fingerprinting effort. Tree Genet Genomes 8:873–877

  28. Massah N, Wang J, Russell J, Van Niejenhuis A, El-Kassaby YA (2010) Genealogical relationship among members of selection and production populations of yellow cedar (Callitropsis nootkatensis [D. Don] Oerst.) in the absence of parental information. J Hered 101:154–163

  29. Meagher TR, Thompson E (1986) The relationship between single parent and parent pair genetic likelihoods in genealogy reconstruction. Theor Popul Biol 29:87–106

  30. Pharis RP, Kuo CG (1977) Physiology of gibberellins in conifers. Can J For Res 7:299–325

  31. Prescher F, Lindgren D, Karlsson B (2008) Genetic thinning of clonal seed orchards using linear deployment may improve both gain and diversity. Forest Ecol Manag 254:188–192

  32. Queller DC, Goodnight KF (1989) Estimating relatedness using genetic markers. Evolution 43:258–275

  33. Rodriguez-Barreto D, Consuegra S, Jerez S, Cejas JR, Martín V, Lorenzo A (2013) Using molecular markers for pedigree reconstruction of the greater amberjack (Seriola dumerili) in the absence of parental information. Anim Genet 44:596–600

  34. Rodríguez-Ramilo ST, Toro MA, Martínez P, Bouza C, Fernández J (2007) Accuracy of pairwise methods in the reconstruction of family relationships, using molecular information from turbot (Scophthalmus maximus). Aquaculture 273:434–442

  35. Slavov GT, Howe GT, Adams WT (2005) Pollen contamination and mating patterns in a Douglas-fir seed orchard as measured by simple sequence repeat markers. Can J Forest Res 35:1592–1603

  36. Wang X-R, Torimaru T, Lindgren D, Fries A (2010) Marker-based parentage analysis facilitates low input ‘breeding without breeding’ strategies for forest trees. Tree Genet Genomes 6:227–235

  37. White TL, Adams WT, Neale DB (2007) Forest Genetics. CAB International, 682 p

  38. Zobel B, Talbert J (1984) Applied forest tree improvement. Wiley, 505 p

Download references

Acknowledgments

We would like to acknowledge the financial support from the Ministry of Education, Youth and Sports, Czech Republic (KONTAKT II; grant LH13021), and members of Camcore, North Carolina State University, USA.

Data archiving statement

In our study, we described a stochastic simulation model and conducted a comparison of hypothetical breeding strategies. No real-world data of any species have been used throughout the study. Output data have been prepared and published in tables and figures and they are included in the manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Correspondence to Milan Lstibůrek.

Additional information

This article is part of the Topical Collection on Breeding

Communicated by J. Beaulieu

Appendix: accuracy of prediction (r â,a) for phenotypic, within-family, and mid-parent + within-family selection

Appendix: accuracy of prediction (r â,a) for phenotypic, within-family, and mid-parent + within-family selection

  1. 1.

    Define a = f + w

    where a = overall genetic value, f = midparent genetic value, and w = within-family genetic value, and Var (a) = σ A 2 , Var \( (f)=1/2{\sigma}_A^2 \), and Var \( (w)=1/2{\sigma}_A^2 \)

  2. 2.

    Define â = \( \widehat{f} \) + ŵ

    where â = predicted genetic value, \( \widehat{f} \) = predicted mid-parent genetic value, and ŵ = predicted within-family genetic value.

  3. 3.

    The accuracy of prediction is defined as

    $$ {r}_{\widehat{\mathrm{a}},\mathrm{a}}={\left[Var\left(\widehat{a}\right)/Var(a)\right]}^{1/2} $$
  4. 4.

    For mass selection based on phenotypic value y,

    $$ \widehat{a}={h}^2S, $$

    where h 2 = heritability = Var(a) / Var(y), and S = phenotypic deviation of y, and accuracy of prediction = [Var(â) / Var(a)]1/2 = h (Falconer 1981; Hodge and White 1992). So if h 2 = 0.15, then r â,a = 0.39.

  5. 5.

    Assuming infinite testing (number of tests, number of crosses, number of progeny), then mid-parent genetic values f will be predicted without error, that is, accuracy of prediction = 1.00. \( {r}_{\widehat{f},f}=1.00={\left[Var\left(\widehat{f}\right)/Var(f)\right]}^{1/2}, \)therefore Var(\( \widehat{f} \)) = Var(f) = \( 1/2{\sigma}_A^2 \)

  6. 6,

    For within full-sib family selection based on within full-sib family phenotypic deviation S w ,

    $$ \widehat{w}={h}_w^2{S}_w, $$

    where h w 2  = within-family heritability = \( 1/2{\sigma}_A^2 \) / [Var(y) − \( 1/2{\sigma}_A^2 \)].

    If h 2 = 0.15, h w 2  = 0.075 / [1.00–0.075] = 0.081. As above, accuracy of prediction equals the square root of the heritability, so accuracy of prediction = h w  = [Var(ŵ)/Var(w)]1/2

    $$ \begin{array}{c}\hfill \mathrm{V}\mathrm{a}\mathrm{r}\left(\widehat{w}\right)={h}_w^2\;\mathrm{V}\mathrm{a}\mathrm{r}(w)\hfill \\ {}\hfill =0.081\ 1/2{\sigma}_A^2\hfill \\ {}\hfill =0.0405{\sigma}_A^2\hfill \end{array} $$
  7. 7,

    Now assume offspring selection (equivalent to mid-parent selection + within-family selection), with h 2 = 0.15, and with infinite progeny testing.

    $$ \begin{array}{l}\widehat{a}=\widehat{f}+\widehat{w}\\ {}\begin{array}{c}\hfill \mathrm{V}\mathrm{a}\mathrm{r}\left(\widehat{a}\right)=\mathrm{V}\mathrm{a}\mathrm{r}\left(\widehat{f}\right)+\mathrm{V}\mathrm{a}\mathrm{r}\left(\widehat{w}\right)\hfill \\ {}\hfill =1/2{\sigma}_A^2+0.0405{\sigma}_A^2\hfill \\ {}\hfill =0.5405{\sigma}_A^2\hfill \end{array}\end{array} $$

    The accuracy of selection would then be

    $$ \begin{array}{c}\hfill {r}_{\widehat{\mathrm{a}},\mathrm{a}}=\left[\mathrm{V}\mathrm{a}\mathrm{r}\left(\widehat{a}\right)/\mathrm{V}\mathrm{a}\mathrm{r}{(a)}^{1/2}\right]\hfill \\ {}\hfill =\left[0.5405{\sigma}_A^2/{\sigma}_A^2\right]\hfill \\ {}\hfill =0.7352\hfill \end{array} $$
  8. 8,

    Thus, with h 2 = 0.15, the theoretical maximum accuracy of prediction for offspring selection from full-sib families (mid-parent selection + within-family selection) is r â,a = 0.7352.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lstibůrek, M., Hodge, G.R. & Lachout, P. Uncovering genetic information from commercial forest plantations—making up for lost time using “Breeding without Breeding”. Tree Genetics & Genomes 11, 55 (2015). https://doi.org/10.1007/s11295-015-0881-y

Download citation

Keywords

  • Pedigree reconstruction
  • BLUP
  • Tree breeding
  • Breeding strategy