Abstract
Genomic selection is an attractive option to complement the existing investments of the lodgepole pine (Pinus contorta Douglas) breeding program in British Columbia, Canada. There were two cycles of progeny testing established from 1984 to 2006 connected by full- and half-sib family structure that span a diverse range of ecosystems and climates. The relationship structure across the program is ideal for genomic selection, but it is unclear how genomic selection models will perform using a fixed content array with 51,213 single-nucleotide polymorphism (SNP) markers and different amounts of relatedness between the training and selection populations, across testing cycles of different ages, and across environments for growth and wood quality traits. Through cross-validation, we compared the sensitivity of genomic selection using two Bayesian models (Bayes B and C) with best linear unbiased prediction (BLUP) using a realized relationship matrix (GBLUP) and a pedigree (ABLUP). GEBVs from GBLUP were used to approximate the true breeding value for comparing models. Prediction accuracy was very high when there was relatedness between the training and validation populations (0.81 for Bayes C and 0.83 for GBLUP), but dropped considerably when relatedness was removed (0.27 for Bayes C and 0.29 for GBLUP). Prediction accuracy was high when predicting between test cycles and was highest when older first-cycle progeny tests were used to predict for younger trees in second-cycle tests (0.68 for Bayes C). Marker-based (Bayesian) and relationship-based (GBLUP) genomic selection models performed well and should be considered as a tool to complement the lodgepole pine breeding program in British Columbia.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baltunis BS, Gapare WJ, Wu HX (2010) Genetic parameters and genotype by environment interaction in radiata pine for growth and wood quality traits in Australia. Silvae Genet 59(2–3):113–124
Bartholomé 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. BMC Genomics 17(1):604. BioMed Central. https://doi.org/10.1186/s12864-016-2879-8
Beaulieu J, Doerksen T, Clément S, Mackay J, Bousquet J (2014) Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity (Edinb) 113(4):343–352. https://doi.org/10.1038/hdy.2014.36
Beaulieu J, Doerksen TK, MacKay J, Rainville A, Bousquet J (2014) Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics 15(1):1–16. https://doi.org/10.1186/1471-2164-15-1048
Combs E, Bernardo R (2013) Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers. Plant Genome 6(1):1–7. https://doi.org/10.3835/plantgenome2012.11.0030
de Almeida Filho JE, Guimarães JFR, e Silva FF, de Resende MDV, Muñoz P, Kirst M, Resende MFR (2016) The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity (Edinb) 117(1):33–41. https://doi.org/10.1038/hdy.2016.23
Denis M, Bouvet J-M (2013) Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding. Tree Genet Genomes 9(1):37–51. Springer-Verlag. https://doi.org/10.1007/s11295-012-0528-1
Doerksen TK, Herbinger CM (2010) Impact of reconstructed pedigrees on progeny-test breeding values in red spruce. Tree Genet Genomes 6(4):591–600. Springer-Verlag. https://doi.org/10.1007/s11295-010-0274-1
Durán R, Isik F, Zapata-Valenzuela J, Balocchi C, Valenzuela S (2017) Genomic predictions of breeding values in a cloned Eucalyptus globulus population in Chile. Tree Genet Genomes 13(4):74. Springer Berlin Heidelberg. https://doi.org/10.1007/s11295-017-1158-4
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics (fourth edition). In: In Trends in Genetics, 4th edn. Burnt Mill, Harlow, Essex, England
Gamal El-Dien O, Ratcliffe B, Klápště J, Chen C, Porth I, El-Kassaby YA (2015) Prediction accuracies for growth and wood attributes of interior spruce in space using genoty**-by-sequencing. BMC Genomics 16(1):370. BioMed Central. https://doi.org/10.1186/s12864-015-1597-y
Gapare WJ, Ivković M, Liepe KJ, Hamann A, Low CB (2015) Drivers of genotype by environment interaction in radiata pine as indicated by multivariate regression trees. For Ecol Manag 353:21–29. https://doi.org/10.1016/j.foreco.2015.05.027
Gianola D (2013) Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194(3):573–596. https://doi.org/10.1534/genetics.113.151753
Goddard, M.E., MacLeod, I.M., Chamberlain, A.J., and Hayes, B.J. 2016. Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture. In proceedings of the Royal Society B: Biological Sciences. p. 283
Grattapaglia D (2014) Breeding forest trees by genomic selection: current progress and the way forward. In Genomics of Plant Genetic Resources. Springer Netherlands, Dordrecht, pp 651–682. https://doi.org/10.1007/978-94-007-7572-5_26
Grattapaglia D (2017) Status and perspectives of genomic selection in forest tree breeding. Genomic Selection for Crop Improvement. Springer International Publishing, Cham, pp 199–249. https://doi.org/10.1007/978-3-319-63170-7_9
Grattapaglia D, Resende MDV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7(2):241–255. https://doi.org/10.1007/s11295-010-0328-4
Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2):423–447
Hunt RS, Ying CC, Ashbee D (1987) Variation in damage among Pinus contorta provenances caused by the needle cast fungus Lophodermella concolor. Can J For Res 17:594–597
Isaac-Renton M, Montwé D, Hamann A, Spiecker H, Cherubini P, Treydte K (2018) Northern forest tree populations are physiologically maladapted to drought. Nat Commun 9:5254. https://doi.org/10.1038/s41467-018-07701-0
Isik F (2014) Genomic selection in forest tree breeding: the concept and an outlook to the future. New For 45(3):379–401. https://doi.org/10.1007/s11056-014-9422-z
Isik F, Bartholomé J, Farjat A, Chancerel E, Raffin A, Sanchez L, Plomion C, Bouffier L (2015) Genomic selection in maritime pine. Plant Sci 242:108–119. Elsevier Ireland Ltd. https://doi.org/10.1016/j.plantsci.2015.08.006
Johnson GR, Burdon RD (1990) Family-site interaction in Pinus radiata: implications for progeny testing strategy and regionalised breeding in New Zealand. Silvae Genet 39(2):55–62
Kärkkäinen HP, Sillanpää MJ (2012) Back to basics for Bayesian model building in genomic selection. Genetics 191(3):969–987. https://doi.org/10.1534/genetics.112.139014
Klápště J, Suontama M, Telfer E, Graham N, Low C, Stovold T, McKinley R, Dungey H (2017) Exploration of genetic architecture through sib-ship reconstruction in advanced breeding population of Eucalyptus nitens. PLoS One 12(9). Public Library of Science). https://doi.org/10.1371/journal.pone.0185137
Lindgren D, Gea L, Jefferson P (1996) Loss of genetic diversity monitored by status number. Silvae Genet 45:52–59
Liu H, Sørensen AC, Meuwissen THE, Berg P (2014) Allele frequency changes due to hitch-hiking in genomic selection programs. Genet Sel Evol 46(8):1–14. https://doi.org/10.1186/1297-9686-46-8
Megraw R, Leaf G, Bremer D (1998) Longitudinal shrinkage and microfibril angle in loblolly pine. In: Butterfield BA (ed) IAWA/IUFRO international workshop on the significance of microfibril angle to wood quality. Univ. of Canterbury Press, Christchurch, pp 27–61
Meuwissen, T.H.E., Hayes, B.J., and Goddard, M.E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819–1829. doi:11290733
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. https://doi.org/10.1186/s12864-017-3920-2
Neale DB, Wegrzyn JL, Stevens KA, Zimin AV, Puiu D, Crepeau MW, Cardeno C, Koriabine M, Holtz-Morris AE, Liechty JD, Martínez-García PJ, Vasquez-Gross HA, Lin BY, Zieve JJ, Dougherty WM, Fuentes-Soriano S, Wu LS, Gilbert D, Marçais G, Roberts M, Holt C, Yandell M, Davis JM, Smith KE, Dean JFD, Lorenz WW, Whetten RW, Sederoff R, Wheeler N, McGuire PE, Main D, Loopstra CA, Mockaitis K, DeJong PJ, Yorke JA, Salzberg SL, Langley CH (2014) Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies. Genome Biol 15(3):1–13. https://doi.org/10.1186/gb-2014-15-3-r59
O’Neill GA, Hamann A, Wang T (2008) Accounting for population variation improves estimates of impact of climate change on species’ growth and distribution. J Appl Ecol 45:1040–1049. https://doi.org/10.1111/j.1365-2664.2008.0
Ødegård J, Meuwissen THE (2015) Identity-by-descent genomic selection using selective and sparse genoty** for binary traits. Genet Sel Evol 46(3):1–4. https://doi.org/10.1186/s12711-015-0090-z
Pérez P, De Los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–495. https://doi.org/10.1534/genetics.114.164442
Ratcliffe B, El-Dien OG, Klápště J, Porth I, Chen C, Jaquish B, El-Kassaby YA (2015) A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods. Heredity (Edinb) 115(6):547–555. https://doi.org/10.1038/hdy.2015.57
Ratcliffe B, Gamal El-Dien O, Cappa EP, Porth I, Klapste J, Chen C, El-kassaby YA (2017) Single-step BLUP with varying genoty** effort in open-pollinated Picea glauca. Genes Genomes Genet 7(March):935–942. https://doi.org/10.5061/dryad.6rd6f
Raymond CA (2011) Genotype by environment interactions for Pinus radiata in New South Wales. Australia Tree Genet Genomes 7(4):819–833. https://doi.org/10.1007/s11295-011-0376-4
Resende MFR Jr, Munoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624
Resende MFR, Muñoz P, Resende MDV, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190(4):1503–1510. Genetics Society of America. https://doi.org/10.1534/genetics.111.137026
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 (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 194(1):116–128. https://doi.org/10.1111/j.1469-8137.2011.04038.x
Resende RT, Resende MDV, Silva FF, Azevedo 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 (Edinb) 119(4):245–255. Nature Publishing Group. https://doi.org/10.1038/hdy.2017.37
Rolf MM, Garrick DJ, Fountain T, Ramey HR, Weaber RL, Decker JE, Pollak EJ, Schnabel RD, Taylor JF (2015) Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle. Genet Sel Evol 47(1):1–14. https://doi.org/10.1186/s12711-015-0106-8
Sonesson AK, Woolliams JA, Meuwissen THE (2012) Genomic selection requires genomic control of inbreeding. Genet Sel Evol 44(27). https://doi.org/10.1186/1297-9686-44-27
St Clair JB (1994) Genetic variation in tree structure and its relation to size in Douglas fir.1. Biomass partitioning, foliage efficiency, stem form, and wood density. Can J For Res 24:1226–1235. https://doi.org/10.1139/x94-161
Suren H, Hodgins KA, Yeaman S, Nurkowski KA, Smets P, Rieseberg LH, Aitken SN, Holliday JA (2016) Exome capture from the spruce and pine giga-genomes. Mol Ecol Resour 16(5):1136–1146. https://doi.org/10.1111/1755-0998.12570
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. BMC Plant Biol 17(1):1–15. BMC Plant Biology. https://doi.org/10.1186/s12870-017-1059-6
Thistlethwaite FR, Ratcliffe B, Klápště J, Porth I, Chen C, Stoehr MU, El-Kassaby YA (2017) Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genoty** platform. BMC Genomics 18:930. https://doi.org/10.1186/s12864-017-4258-5
Thistlethwaite FR, Ratcliffe B, Klápště J, Porth I, Chen C, Stoehr MU, El-Kassaby YA (2019) Genomic selection of juvenile height across a single-generational gap in Douglas-fir. Heredity (Edinb) 122:848–863. Nature Publishing group. https://doi.org/10.1038/s41437-018-0172-0
Ukrainetz NK, Kang K-Y, Aitken SN, Stoehr M, Mansfield SD (2008) Heritability and phenotypic and genetic correlations of coastal Douglas-fir ( Pseudotsuga menziesii ) wood quality traits. Can J For Res 38(6):1536–1546. https://doi.org/10.1139/x07-234
Ukrainetz NK, Yanchuk AD, Mansfield SD (2018) Climatic drivers of genotype–environment interactions in lodgepole pine based on multi-environment trial data and a factor analytic model of additive covariance. Can J For Res 48(7):835–854. https://doi.org/10.1139/cjfr-2017-0367
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423. Elsevier. https://doi.org/10.3168/jds.2007-0980
Vargas-Hernandez J, Adams WT (1991) Genetic variation of wood density components in young coastal Douglas-fir: implications for tree breeding. Can J For Res 21:1801–1807. https://doi.org/10.1139/x91-248
Verbyla KL, Bowman PJ, Hayes BJ, Goddard ME (2010) Sensitivity of genomic selection to using different prior distributions. BMC Proc 4(Suppl 1):S5. https://doi.org/10.1186/1753-6561-4-s1-s5
Wang T, Hamann A, Yanchuk A, O’neill GAA, Aitken SNN (2006) Use of response functions in selecting lodgepole pine populations for future climates. Glob Chang Biol 12(12):2404–2416. https://doi.org/10.1111/j.1365-2486.2006.01271.x
Wang T, O’Neill GA, Aitken SN (2010) Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecol Appl 20(1):153–163
Woods AJ, Nussbaum A, Golding B (2000) Predicted impacts of hard pine stem rusts on lodgepole pine dominated stands in central British Columbia. Can J For Res. https://doi.org/10.1139/x99-236
Wu HX, Ying CC (1998) Stability of resistance to western gall rust and needle cast in lodgepole pine provenances. Can J For Res 28(3):439–449. https://doi.org/10.1139/x98-009
Yanchuk AD, Kiss G (1993) Genetic variation in growth and wood specific gravity and its utility in the improvement on interior spruce in British Columbia. Silvae Genet 42(2–3):141–148
Yeaman S, Hodgins KA, Lotterhos KE, Suren H, Nadeau S, Degner JC, Nurkowski KA, Smets P, Wang T, Gray LK, Liepe KJ, Hamann A, Holliday JA, Whitlock MC, Rieseberg LH, Aitken SN (2016) Convergent local adaptation to climate in distantly related conifers. Science (80-. ) 353(6306):23–26
Ying CC, Hunt RS (1987) Stability of resistance among Pinus contorta provenances to Lophodermella concolor needle cast. Can J For Res 17:1596–1601
Zapata-Valenzuela J, Isik F, Maltecca C, Wegrzyn J, Neale D, McKeand S, Whetten R (2012) SNP markers trace familial linkages in a cloned population of Pinus taeda-prospects for genomic selection. Tree Genet Genomes 8(6):1307–1318. https://doi.org/10.1007/s11295-012-0516-5
Zapata-Valenzuela J, Whetten RW, Neale D, McKeand S, Isik F (2013) Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine. G3 (Bethesda) 3(5):909–916. G3: Genes, Genomes, Genetics. https://doi.org/10.1534/g3.113.005975
Data archiving statement
All raw SNP data will be submitted to dbSNP on the public website hosted by National Cancer for Biotechnology Information (NCBI) National Cancer for Biotechnology Information (NCBI): http://www.ncbi.nlm.nih.gov/SNP, where submitted SNPs can be downloaded via anonymous FTP at ftp://ncbi.nlm.nih.gov/snp/.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by J. Beaulieu
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 69 kb)
Rights and permissions
About this article
Cite this article
Ukrainetz, N.K., Mansfield, S.D. Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genetics & Genomes 16, 14 (2020). https://doi.org/10.1007/s11295-019-1404-z
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11295-019-1404-z