Status and Perspectives of Genomic Selection in Forest Tree Breeding



Trees have long life cycles and become reproductively active only after several years. The progress of tree breeding programs is therefore strongly dependent on the time needed to complete a breeding generation. Additionally, the uncertainties associated with conducting decade-long breeding programs can be high. The convergence of genomics and quantitative genetics has now established the paradigm of genomic selection as a way to accelerate breeding of complex traits. With the progressive accumulation of GS data for thousands of individuals across several unrelated populations, GS should also provide a potentially powerful framework to investigate the molecular underpinnings of complex traits. Genomic selection can increase the rate of genetic gain per unit time of a tree breeding program by radically reducing the generation interval and by increasing the selection intensity because many more young seedlings can be genotyped and their phenotypes predicted than the number of adult trees measured in field trials. Genomic selection has therefore become a hot topic in the tree genetics and breeding community worldwide in the last few years since the first perspectives based on simulations and experimental results were reported. In this chapter, a comprehensive discussion is presented, covering the main factors, both theoretical and practical, relevant to the application of GS to tree breeding, including those that have emerged from the recent flow of experimental studies in different forest tree species. Following a review of the basic insights and perspectives of GS, a detailed compilation is presented of all published experimental GS studies in forest trees to date, highlighting their main contributions to our current understanding of this new breeding approach. The conclusion summarizes the main lessons learned so far, condensed in a nine-point tentative roadmap for implementing GS in a tree breeding program.


Genome-wide selection Genomic prediction Tree breeding Forest trees Eucalyptus Pinus Picea 



This work was supported by CNPq grant 577047/2008-6, PRONEX-FAP-DF grant “NEXTREE” 2009/00106-8, EMBRAPA Macroprogram 2 grant, and a CNPq research fellowship to DG. Special thanks to all my students, collaborators, and colleagues worldwide working in genomic prediction and forest tree breeding with whom I have had the privilege to share and discuss several of the ideas presented in this chapter.


  1. Araujo JA, Borralho NMG, Dehon G (2012) The importance and type of non-additive genetic effects for growth in Eucalyptus globulus. Tree Genet Genomes 8:327–337CrossRefGoogle Scholar
  2. Arus P, Verde I, Sosinski B, Zhebentyayeva T, Abbott AG (2012) The peach genome. Tree Genet Genomes 8:531–547CrossRefGoogle Scholar
  3. Assis TF, de Resende MDV (2011) Genetic improvement of forest tree species. Crop Breed Appl Biotechnol 11:44–49CrossRefGoogle Scholar
  4. Bartholome 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 17:604PubMedPubMedCentralCrossRefGoogle Scholar
  5. Bastiaansen JWM, Coster A, Calus MPL, van Arendonk JAM, Bovenhuis H (2012) Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures. Genet Sel Evol 44:3PubMedPubMedCentralCrossRefGoogle Scholar
  6. Beaulieu J, Doerksen T, Clement S, Mackay J, Bousquet J (2014a) Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity 113:343–352PubMedPubMedCentralCrossRefGoogle Scholar
  7. Beaulieu J, Doerksen TK, MacKay J, Rainville A, Bousquet J (2014b) Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics 15:1048PubMedPubMedCentralCrossRefGoogle Scholar
  8. Beissinger TM, Hirsch CN, Sekhon RS, Foerster JM, Johnson JM, Muttoni G, Vaillancourt B, Buell CR, Kaeppler SM, de Leon N (2013) Marker density and read depth for genotyping populations using genotyping-by-sequencing. Genetics 193:1073–1081PubMedPubMedCentralCrossRefGoogle Scholar
  9. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664CrossRefGoogle Scholar
  10. Bernardo R, Yu JM (2007) Prospects for genome wide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  11. Berry DP, Kearney JF (2011) Imputation of genotypes from low- to high-density genotyping platforms and implications for genomic selection. Animal 5:1162–1169PubMedCrossRefGoogle Scholar
  12. Blondel M, Onogi A, Iwata H, Ueda N (2015) A ranking approach to genomic selection. PLoS One 10:e0128570PubMedPubMedCentralCrossRefGoogle Scholar
  13. Boichard D, Chung H, Dassonneville R, David X, Eggen A, Fritz S, Gietzen KJ, Hayes BJ, Lawley CT, Sonstegard TS, Van Tassell CP, PM VR, Viaud-Martinez KA, Wiggans GR, Consortium BL (2012) Design of a bovine low-density SNP array optimized for imputation. PLoS One 7:e34130PubMedPubMedCentralCrossRefGoogle Scholar
  14. Bouvet JM, Makouanzi G, Cros D, Vigneron P (2016) Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications. Heredity 116:146–157PubMedCrossRefGoogle Scholar
  15. Bouvet JM, Saya A, Vigneron P (2009) Trends in additive, dominance and environmental effects with age for growth traits in Eucalyptus hybrid populations. Euphytica 165:35–54CrossRefGoogle Scholar
  16. Brondani RP, Williams ER, Brondani C, Grattapaglia D (2006) A microsatellite-based consensus linkage map for species of Eucalyptus and a novel set of 230 microsatellite markers for the genus. BMC Plant Biol 6:20PubMedPubMedCentralCrossRefGoogle Scholar
  17. Burgueno J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype x environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719CrossRefGoogle Scholar
  18. Chancerel E, Lepoittevin C, Le Provost G, Lin YC, Jaramillo-Correa JP, Eckert AJ, Wegrzyn JL, Zelenika D, Boland A, Frigerio JM, Chaumeil P, Garnier-Gere P, Boury C, Grivet D, Gonzalez-Martinez SC, Rouze P, Van de Peer Y, Neale DB, Cervera MT, Kremer A, Plomion C (2011) Development and implementation of a highly-multiplexed SNP array for genetic mapping in maritime pine and comparative mapping with loblolly pine. BMC Genomics 12:368PubMedPubMedCentralCrossRefGoogle Scholar
  19. Charlier C, Coppieters W, Rollin F, Desmecht D, Agerholm JS, Cambisano N, Carta E, Dardano S, Dive M, Fasquelle C, Frennet JC, Hanset R, Hubin X, Jorgensen C, Karim L, Kent M, Harvey K, Pearce BR, Simon P, Tama N, Nie H, Vandeputte S, Lien S, Longeri M, Fredholm M, Harvey RJ, Georges M (2008) Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat Genet 40:449–454PubMedCrossRefGoogle Scholar
  20. Chen C, Mitchell SE, Elshire RJ, Buckler ES, El-Kassaby YA (2013) Mining conifers’ mega-genome using rapid and efficient multiplexed high-throughput genotyping-by-sequencing (GBS) SNP discovery platform. Tree Genet Genomes 9:1537–1544CrossRefGoogle Scholar
  21. Coster A, Bastiaansen JWM, Calus MPL, van Arendonk JAM, Bovenhuis H (2010) Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance. Genet Sel Evol 42:9PubMedPubMedCentralCrossRefGoogle Scholar
  22. Cronn R, Knaus BJ, Liston A, Maughan PJ, Parks M, Syring JV, Udall J (2012) Targeted enrichment strategies for next-generation plant biology. Am J Bot 99:291–311PubMedCrossRefGoogle Scholar
  23. Crossa J, de los Campos G, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan JB, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724PubMedPubMedCentralCrossRefGoogle Scholar
  24. Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM (2013) Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193:347–365PubMedPubMedCentralCrossRefGoogle Scholar
  25. Daetwyler HD, Villanueva B, Bijma P, Woolliams JA (2007) Inbreeding in genome-wide selection. J Anim Breed Genet 124:369–376PubMedCrossRefGoogle Scholar
  26. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3:e3395PubMedPubMedCentralCrossRefGoogle Scholar
  27. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510PubMedCrossRefGoogle Scholar
  28. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345PubMedCentralCrossRefGoogle Scholar
  29. de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183:1545–1553PubMedPubMedCentralCrossRefGoogle Scholar
  30. Denis M, Bouvet JM (2013) Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding. Tree Genet Genomes 9:37–51CrossRefGoogle Scholar
  31. Dillen S, Storme V, Marron N, Bastien C, Neyrinck S, Steenackers M, Ceulemans R, Boerjan W (2008) Genomic regions involved in productivity of two interspecific poplar families in Europe. 1. Stem height, circumference and volume. Tree Genet Genomes 5:147–164CrossRefGoogle Scholar
  32. Echt CS, Saha S, Krutovsky KV, Wimalanathan K, Erpelding JE, Liang C, Nelson CD (2011) An annotated genetic map of loblolly pine based on microsatellite and cDNA markers. BMC Genet 12:17PubMedPubMedCentralCrossRefGoogle Scholar
  33. Eckert AJ, Pande B, Ersoz ES, Wright MH, Rashbrook VK, Nicolet CM, Neale DB (2009) High-throughput genotyping and mapping of single nucleotide polymorphisms in loblolly pine (Pinus taeda L.) Tree Genet Genomes 5:225–234CrossRefGoogle Scholar
  34. Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD, Ross-Ibarra J, Gonzalez-Martinez SC, Neale DB (2010) Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda l., Pinaceae). Genetics 185:969–982PubMedPubMedCentralCrossRefGoogle Scholar
  35. El-Dien OG, Ratcliffe B, Klapste J, Chen C, Porth I, El-Kassaby YA (2015) Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 16:370CrossRefGoogle Scholar
  36. El-Kassaby YA, Lstiburek M (2009) Breeding without breeding. Genet Res 91:111–120CrossRefGoogle Scholar
  37. Esfandyari H, Sorensen AC, Bijma P (2015) Maximizing crossbred performance through purebred genomic selection. Genet Sel Evol 47:16PubMedPubMedCentralCrossRefGoogle Scholar
  38. Freeman JS, Potts BM, Downes GM, Pilbeam D, Thavamanikumar S, Vaillancourt RE (2013) Stability of quantitative trait loci for growth and wood properties across multiple pedigrees and environments in Eucalyptus globulus. New Phytol 198:1121–1134PubMedCrossRefGoogle Scholar
  39. Geraldes A, Difazio SP, Slavov GT, Ranjan P, Muchero W, Hannemann J, Gunter LE, Wymore AM, Grassa CJ, Farzaneh N, Porth I, Mckown AD, Skyba O, Li E, Fujita M, Klapste J, Martin J, Schackwitz W, Pennacchio C, Rokhsar D, Friedmann MC, Wasteneys GO, Guy RD, El-Kassaby YA, Mansfield SD, Cronk QCB, Ehlting J, Douglas CJ, Tuskan GA (2013) A 34K SNP genotyping array for Populus trichocarpa: design, application to the study of natural populations and transferability to other Populus species. Mol Ecol Resour 13:306–323PubMedCrossRefGoogle Scholar
  40. Geraldes A, Pang J, Thiessen N, Cezard T, Moore R, Zhao YJ, Tam A, Wang SC, Friedmann M, Birol I, Jones SJM, Cronk QCB, Douglas CJ (2011) SNP discovery in black cottonwood (Populus trichocarpa) by population transcriptome resequencing. Mol Ecol Resour 11:81–92PubMedCrossRefGoogle Scholar
  41. Gion JM, Carouche A, Deweer S, Bedon F, Pichavant F, Charpentier JP, Bailleres H, Rozenberg P, Carocha V, Ognouabi N, Verhaegen D, Grima-Pettenati J, Vigneron P, Plomion C (2011) Comprehensive genetic dissection of wood properties in a widely-grown tropical tree: Eucalyptus. BMC Genomics 12:301PubMedPubMedCentralCrossRefGoogle Scholar
  42. Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136:245–257PubMedCrossRefGoogle Scholar
  43. Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 10:381–391PubMedCrossRefGoogle Scholar
  44. Grattapaglia D (2014) Breeding forest trees by genomic selection: current progress and the way forward. Chapter 26. In: Tuberosa R, Graner A, Frison E (eds) Advances in genomics of plant genetic resources. Springer, New York, pp 652–682Google Scholar
  45. Grattapaglia D, Chaparro J, Wilcox P, Mccord S, Werner D, Amerson H, Mckeand S, Bridgwater F, Whetten R, O'malley D, Sederoff RR (1992) Mapping in woody plants with RAPD markers: applications to breeding in forestry and horticulture. Proceedings of the symposium “applications of RAPD technology to plant breeding”. Crop Science Society of America, American Society of Horticultural Science, American Genetic Association, pp 37–40Google Scholar
  46. Grattapaglia D, de Alencar S, Pappas G (2011a) Genome-wide genotyping and SNP discovery by ultra-deep Restriction-Associated DNA (RAD) tag sequencing of pooled samples of E. grandis and E. globulus. BMC Proc 5:P45PubMedCentralCrossRefGoogle Scholar
  47. Grattapaglia D, Plomion C, Kirst M, Sederoff RR (2009) Genomics of growth traits in forest trees. Curr Opin Plant Biol 12:148–156PubMedCrossRefGoogle Scholar
  48. Grattapaglia D, Resende MDV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7:241–255CrossRefGoogle Scholar
  49. Grattapaglia D, Resende MDV, Resende M, Sansaloni C, Petroli C, Missiaggia A, Takahashi E, Zamprogno K, Kilian A (2011b) Genomic selection for growth traits in Eucalyptus: accuracy within and across breeding populations. BMC Proc 5:O16PubMedCentralCrossRefGoogle Scholar
  50. Grattapaglia D, Ribeiro VJ, Rezende GD (2004) Retrospective selection of elite parent trees using paternity testing with microsatellite markers: an alternative short term breeding tactic for Eucalyptus. Theor Appl Genet 109:192–199PubMedCrossRefGoogle Scholar
  51. Grattapaglia D, Sederoff R (1994) Genetic-linkage maps of Eucalyptus-grandis and Eucalyptus-urophylla using a pseudo-testcross – mapping strategy and RAPD markers. Genetics 137:1121–1137PubMedPubMedCentralGoogle Scholar
  52. Grattapaglia D, Silva OB, Kirst M, de Lima BM, Faria DA, Pappas GJ (2011c) High-throughput SNP genotyping in the highly heterozygous genome of Eucalyptus: assay success, polymorphism and transferability across species. BMC Plant Biol 11:65PubMedPubMedCentralCrossRefGoogle Scholar
  53. Grattapaglia D, Vaillancourt R, Shepherd M, Thumma B, Foley W, Külheim C, Potts B, Myburg A (2012) Progress in Myrtaceae genetics and genomics: Eucalyptus as the pivotal genus. Tree Genet Genomes 3:463–508CrossRefGoogle Scholar
  54. Greenwood MS, Adams GW, Gillespie M (1991) Stimulation of flowering by grafted black spruce and white spruce – a comparative-study of the effects of gibberellin A4/7, cultural treatments, and environment. Can J For Res 21:395–400CrossRefGoogle Scholar
  55. Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397PubMedPubMedCentralGoogle Scholar
  56. Habier D, Fernando RL, Dekkers JCM (2009) Genomic selection using low-density marker panels. Genetics 182(1):343–353Google Scholar
  57. Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194:597–607PubMedPubMedCentralCrossRefGoogle Scholar
  58. Haley CS, Visscher PM (1998) Strategies to utilize marker-quantitative trait loci associations. J Dairy Sci 81:85–97PubMedCrossRefGoogle Scholar
  59. Harfouche A, Meilan R, Kirst M, Morgante M, Boerjan W, Sabatti M, Mugnozza GS (2012) Accelerating the domestication of forest trees in a changing world. Trends Plant Sci 17:64–72PubMedCrossRefGoogle Scholar
  60. Hasan O, Reid JB (1995) Reduction of generation time in Eucalyptus-globulus. Plant Growth Regul 17:53–60Google Scholar
  61. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009a) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443PubMedCrossRefGoogle Scholar
  62. Hayes BJ, Visscher PM, Goddard ME (2009b) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 91:47–60CrossRefGoogle Scholar
  63. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  64. Heslot N, Jannink JL, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55:1–12CrossRefGoogle Scholar
  65. Heslot N, Rutkoski J, Poland J, Jannink JL, Sorrells ME (2013) Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity. PLoS One 8:e74612PubMedPubMedCentralCrossRefGoogle Scholar
  66. Heslot N, Yang HP, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160CrossRefGoogle Scholar
  67. Ibanz-Escriche N, Fernando RL, Toosi A, Dekkers JCM (2009) Genomic selection of purebreds for crossbred performance. Genet Sel Evol 41:12CrossRefGoogle Scholar
  68. Ingvarsson PK, Garcia MV, Luquez V, Hall D, Jansson S (2008) Nucleotide polymorphism and phenotypic associations within and around the phytochrome B2 Locus in European aspen (Populus tremula, Salicaceae). Genetics 178:2217–2226PubMedPubMedCentralCrossRefGoogle Scholar
  69. Isik F, Bartholome J, Farjat A, Chancerel E, Raffin A, Sanchez L, Plomion C, Bouffier L (2016) Genomic selection in maritime pine. Plant Sci 242:108–119PubMedCrossRefGoogle Scholar
  70. Iwata H, Hayashi T, Tsumura Y (2011) Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica. Tree Genet Genomes 7:747–758CrossRefGoogle Scholar
  71. Jannink JL (2010) Dynamics of long-term genomic selection. Genet Sel Evol 42:35PubMedPubMedCentralCrossRefGoogle Scholar
  72. Jannink JL, Zhong SQ, Dekkers JCM, Fernando RL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364PubMedPubMedCentralCrossRefGoogle Scholar
  73. Jarquin D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Perez P, Calus M, Burgueno J, de los Campos G (2014) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127:595–607PubMedCrossRefGoogle Scholar
  74. Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192:1513–1522PubMedPubMedCentralCrossRefGoogle Scholar
  75. Jonas E, de Koning DJ (2013) Does genomic selection have a future in plant breeding? Trends Biotechnol 31:497–504PubMedCrossRefGoogle Scholar
  76. Jonas E, de Koning DJ (2015) Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs. Front Genet 6:49PubMedPubMedCentralCrossRefGoogle Scholar
  77. Junghans DT, Alfenas AC, Brommonschenkel SH, Oda S, Mello EJ, Grattapaglia D (2003) Resistance to rust (Puccinia psidii Winter) in eucalyptus: mode of inheritance and mapping of a major gene with RAPD markers. Theor Appl Genet 108:175–180PubMedCrossRefGoogle Scholar
  78. Kerr RJ, Dieters MJ, Tier B (2004) Simulation of the comparative gains from four different hybrid tree breeding strategies. Can J For Res 34:209–220CrossRefGoogle Scholar
  79. Kilian A, Wenzl P, Huttner E, Carling J, Xia L, Blois H, Caig V, Heller-Uszynska K, Jaccoud D, Hopper C, Aschenbrenner-Kilian M, Evers M, Peng K, Cayla C, Hok P, Uszynski G (2012) Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol Biol 888:67–89PubMedCrossRefGoogle Scholar
  80. Kizilkaya K, Fernando RL, Garrick DJ (2010) Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J Anim Sci 88:544–551PubMedCrossRefGoogle Scholar
  81. Lambeth C, Lee BC, O'Malley D, Wheeler NC (2001) Polymix breeding with parental analysis of progeny: an alternative to full-sib breeding and testing. Theor Appl Genet 103:930–943CrossRefGoogle Scholar
  82. Legarra A, Robert-Granie C, Manfredi E, Elsen JM (2008) Performance of genomic selection in mice. Genetics 180:611–618PubMedPubMedCentralCrossRefGoogle Scholar
  83. Lepoittevin C, Frigerio JM, Garnier-Gere P, Salin F, Cervera MT, Vornam B, Harvengt L, Plomion C (2010) In vitro vs in silico detected SNPs for the development of a genotyping array: what can we learn from a non-model species? PLoS One 5:e11034PubMedPubMedCentralCrossRefGoogle Scholar
  84. Lima BM (2014) Bridging genomics and quantitative genetics of Eucalyptus: genome-wide prediction and genetic parameter estimation for growth and wood properties using high-density SNP data. Genetics Dep. University of São Paulo, Piracicaba, SP, Brazil, pp 93. Available in English at
  85. Lin Z, Hayes BJ, Daetwyler HD (2014) Genomic selection in crops, trees and forages: a review. Crop Pasture Sci 65:1177–1191CrossRefGoogle Scholar
  86. Liu HM, Sorensen AC, Meuwissen THE, Berg P (2014) Allele frequency changes due to hitch-hiking in genomic selection programs. Genet Sel Evol 46:8PubMedPubMedCentralCrossRefGoogle Scholar
  87. Long N, Gianola D, Rosa GJM, Weigel KA (2011) Long-term impacts of genome-enabled selection. J Appl Genet 52:467–480PubMedCrossRefGoogle Scholar
  88. Lorenz AJ, Chao SM, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink JL (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123CrossRefGoogle Scholar
  89. MacLeod IM, Hayes BJ, Goddard ME (2014) The Effects of demography and long-term selection on the accuracy of genomic prediction with sequence data. Genetics 198 (4):1671–1684Google Scholar
  90. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O'Connell J, Moore SS, Smith TPL, Sonstegard TS, Van Tassell CP (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4:e5350PubMedPubMedCentralCrossRefGoogle Scholar
  91. McKeand SE, Bridgwater FE (1998) A strategy for the third breeding cycle of loblolly pine in the Southeastern US. Silvae Genet 47:223–234Google Scholar
  92. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  93. Moser G, Tier B, Crump RE, Khatkar MS, Raadsma HW (2009) A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet Sel Evol 41:56PubMedPubMedCentralCrossRefGoogle Scholar
  94. 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 124:342–355PubMedCrossRefGoogle Scholar
  95. Munoz PR, Resende MFR, Gezan SA, Resende MDV, de los Campos G, Kirst M, Huber D, Peter GF (2014) Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics 198:1759–1768PubMedPubMedCentralCrossRefGoogle Scholar
  96. Murray C, Huerta-Sanchez E, Casey F, Bradley DG (2010) Cattle demographic history modelled from autosomal sequence variation. Philos T R Soc B 365:2531–2539CrossRefGoogle Scholar
  97. Namkoong G, Kang HC, Brouard JS (1988) Tree breeding: principles and strategies. Springer Verlag, New YorkCrossRefGoogle Scholar
  98. Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nat Rev Genet 12:111–122PubMedCrossRefGoogle Scholar
  99. Neale DB, Williams CG (1991) Restriction-fragment-length-polymorphism mapping in conifers and applications to forest genetics and tree improvement. Can J For Res 21:545–554CrossRefGoogle Scholar
  100. Nejati-Javaremi A, Smith C, Gibson JP (1997) Effect of total allelic relationship on accuracy of evaluation and response to selection. J Anim Sci 75:1738–1745PubMedCrossRefGoogle Scholar
  101. Neves LG, Davis JM, Barbazuk WB, Kirst M (2014) A high-density gene map of loblolly pine (Pinus taeda L.) based on exome sequence capture genotyping. G3 Genes Genom Genet 4:29–37Google Scholar
  102. Nielsen HM, Sonesson AK, Yazdi H, Meuwissen THE (2009) Comparison of accuracy of genome-wide and BLUP breeding value estimates in sib based aquaculture breeding schemes. Aquaculture 289:259–264CrossRefGoogle Scholar
  103. Nirea KG, Sonesson AK, Woolliams JA, Meuwissen THE (2012) Effect of non-random mating on genomic and BLUP selection schemes. Genet Sel Evol 44:11PubMedPubMedCentralCrossRefGoogle Scholar
  104. Novaes E, Drost DR, Farmerie WG, Pappas GJ Jr, Grattapaglia D, Sederoff RR, Kirst M (2008) High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomics 9:312PubMedPubMedCentralCrossRefGoogle Scholar
  105. Novaes E, Osorio L, Drost DR, Miles BL, Boaventura-Novaes CRD, Benedict C, Dervinis C, Yu Q, Sykes R, Davis M, Martin TA, Peter GF, Kirst M (2009) Quantitative genetic analysis of biomass and wood chemistry of Populus under different nitrogen levels. New Phytol 182:878–890PubMedCrossRefGoogle Scholar
  106. Pan J, Wang BS, Pei ZY, Zhao W, Gao J, Mao JF, Wang XR (2015) Optimization of the genotyping-by-sequencing strategy for population genomic analysis in conifers. Mol Ecol Resour 15:711–722PubMedCrossRefGoogle Scholar
  107. Parchman TL, Gompert Z, Mudge J, Schilkey FD, Benkman CW, Buerkle CA (2012) Genome-wide association genetics of an adaptive trait in lodgepole pine. Mol Ecol 21:2991–3005PubMedCrossRefGoogle Scholar
  108. Pavy N, Gagnon F, Rigault P, Blais S, Deschenes A, Boyle B, Pelgas B, Deslauriers M, Clement S, Lavigne P, Lamothe M, Cooke JEK, Jaramillo-Correa JP, Beaulieu J, Isabel N, Mackay J, Bousquet J (2013) Development of high-density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners. Mol Ecol Resour 13:324–336PubMedCrossRefGoogle Scholar
  109. Pavy N, Parsons LS, Paule C, MacKay J, Bousquet J (2006) Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs. BMC Genomics 7:174PubMedPubMedCentralCrossRefGoogle Scholar
  110. Pavy N, Pelgas B, Beauseigle S, Blais S, Gagnon F, Gosselin I, Lamothe M, Isabel N, Bousquet J (2008) Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce. BMC Genomics 9:1–17CrossRefGoogle Scholar
  111. Pelgas B, Bousquet J, Beauseigle S, Isabel N (2005) A composite linkage map from two crosses for the species complex Picea mariana x Picea rubens and analysis of synteny with other Pinaceae. Theor Appl Genet 111:1466–1488PubMedCrossRefGoogle Scholar
  112. Perez-Enciso M, Rincon JC, Legarra A (2015) Sequence- vs. chip-assisted genomic selection: accurate biological information is advised. Genet Sel Evol 47:43PubMedPubMedCentralCrossRefGoogle Scholar
  113. Plomion C, Bartholome J, Lesur I, Boury C, Rodriguez-Quilon I, Lagraulet H, Ehrenmann F, Bouffier L, Gion JM, Grivet D, de Miguel M, de Maria N, Cervera MT, Bagnoli F, Isik F, Vendramin GG, Gonzalez-Martinez SC (2016) High-density SNP assay development for genetic analysis in maritime pine (Pinus pinaster). Mol Ecol Resour 16:574–587PubMedCrossRefGoogle Scholar
  114. Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5:92–102CrossRefGoogle Scholar
  115. Pryce JE, Daetwyler HD (2012) Designing dairy cattle breeding schemes under genomic selection: a review of international research. Anim Prod Sci 52:107–114CrossRefGoogle Scholar
  116. Rae A, Pinel M, Bastien C, Sabatti M, Street N, Tucker J, Dixon C, Marron N, Dillen S, Taylor G (2008) QTL for yield in bioenergy Populus: identifying G×E interactions from growth at three contrasting sites. Tree Genet Genomes 4:97–112CrossRefGoogle Scholar
  117. Ratcliffe B, El-Dien OG, Klapste J, Porth I, Chen C, Jaquish B, El-Kassaby YA (2015) A comparison of genomic selection models across time in interior spruce (Picea engelmannii x glauca) using unordered SNP imputation methods. Heredity 115:547–555PubMedPubMedCentralCrossRefGoogle Scholar
  118. Resende MDV, Resende MFR, Sansaloni CP, Petroli CD, Missiaggia AA, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria DA, Pappas GJ, Kilian A, Grattapaglia D (2012a) Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 194:116–128PubMedCrossRefGoogle Scholar
  119. Resende MFR, Munoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2012b) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624PubMedCrossRefGoogle Scholar
  120. Resende MFR, Munoz P, Resende MDV, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012c) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) Genetics 190:1503–1510PubMedPubMedCentralCrossRefGoogle Scholar
  121. Rezende GDSP, Resende MDV, Assis TF (2014) Eucalyptus breeding for clonal forestry. In: Fenning T (ed) Challenges and opportunities for the world’s forests in the 21st century. Springer Science+Business Media, Dordrecht, pp 393–424CrossRefGoogle Scholar
  122. Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194:493–503PubMedPubMedCentralCrossRefGoogle Scholar
  123. Rincent R, Laloe D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodriguez VM, Moreno-Gonzalez J, Melchinger A, Bauer E, Schoen CC, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.) Genetics 192:715–728PubMedPubMedCentralCrossRefGoogle Scholar
  124. Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, Taxis TM, Chapple RH, Ramey HR, Northcutt SL, Bauck S, Woodward B, Dekkers JCM, Fernando RL, Schnabel RD, Garrick DJ, Taylor JF (2011) Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol 43:40PubMedPubMedCentralCrossRefGoogle Scholar
  125. Sansaloni C, Petroli C, Jaccoud D, Carling J, Detering F, Grattapaglia D, Kilian A (2011) Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proc 5:P54PubMedCentralCrossRefGoogle Scholar
  126. Schilling MP, Wolf PG, Duffy AM, Rai HS, Rowe CA, Richardson BA, Mock KE (2014) Genotyping-by-sequencing for populus population genomics: an assessment of genome sampling patterns and filtering approaches. PLoS One 9:95292CrossRefGoogle Scholar
  127. Silva-Junior OB, Faria DA, Grattapaglia D (2015) A flexible multi-species genome-wide 60K SNP chip developed from pooled resequencing 240 Eucalyptus tree genomes across 12 species. New Phytol 206:1527–1540PubMedCrossRefGoogle Scholar
  128. Silva-Junior OB, Grattapaglia D (2015) Genome-wide patterns of recombination, linkage disequilibrium and nucleotide diversity from pooled resequencing and single nucleotide polymorphism genotyping unlock the evolutionary history of Eucalyptus grandis. New Phytol 208:830–845PubMedCrossRefGoogle Scholar
  129. Solberg TR, Sonesson AK, Woolliams JA, Odegard J, Meuwissen THE (2009) Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect. Genetics Selection Evolution 41 (1):53Google Scholar
  130. Sonesson AK, Meuwissen THE (2009) Testing strategies for genomic selection in aquaculture breeding programs. Genet Sel Evol 41:37PubMedPubMedCentralCrossRefGoogle Scholar
  131. Stirling B, Newcombe G, Vrebalov J, Bosdet I, Bradshaw HD (2001) Suppressed recombination around the MXC3 locus, a major gene for resistance to poplar leaf rust. Theor Appl Genet 103:1129–1137CrossRefGoogle Scholar
  132. Strauss SH, Lande R, Namkoong G (1992) Limitations of molecular-marker-aided selection in forest tree breeding. Can J For Res 22:1050–1061CrossRefGoogle Scholar
  133. Telfer EJ, Stovold GT, Li YJ, Silva OB, Grattapaglia DG, Dungey HS (2015) Parentage reconstruction in Eucalyptus nitens using SNPs and microsatellite markers: a comparative analysis of marker data power and robustness. PLoS One 10:e0130601PubMedPubMedCentralCrossRefGoogle Scholar
  134. Thumma BR, Southerton SG, Bell JC, Owen JV, Henery ML, Moran GF (2010) Quantitative trait locus (QTL) analysis of wood quality traits in Eucalyptus nitens. Tree Genet Genomes 6:305–317CrossRefGoogle Scholar
  135. Van Eenennaam AL, Weigel KA, Young AE, Cleveland MA, Dekkers JCM (2014) Applied animal genomics: results from the field. Annu Rev Anim Biosci 2:105–139PubMedCrossRefGoogle Scholar
  136. VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423PubMedCrossRefGoogle Scholar
  137. White TL, Adams WT, Neale DB (2007) Forest genetics. CABI Publishing, Cambridge, MA, p 682CrossRefGoogle Scholar
  138. Wilcox PL, Amerson HV, Kuhlman EG, Liu BH, O'Malley DM, Sederoff RR (1996) Detection of a major gene for resistance to fusiform rust disease in loblolly pine by genomic mapping. Proc Natl Acad Sci U S A 93:3859–3864PubMedPubMedCentralCrossRefGoogle Scholar
  139. Williams CG (1988) Accelerated short-term genetic testing for loblolly-pine families. Can J For Res 18:1085–1089CrossRefGoogle Scholar
  140. Williams CG, Neale DB (1992) Conifer wood quality and marker-aided selection: a case-study. Can J For Res 22:1009–1017CrossRefGoogle Scholar
  141. Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159PubMedPubMedCentralGoogle Scholar
  142. 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 6:1307–1318CrossRefGoogle Scholar
  143. 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 Genes Genom Genet 3:909–916Google Scholar
  144. Zeng J, Toosi A, Fernando RL, Dekkers JCM, Garrick DJ (2013) Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action. Genet Sel Evol 45:11PubMedPubMedCentralCrossRefGoogle Scholar
  145. Zhou LC, Holliday JA (2012) Targeted enrichment of the black cottonwood (Populus trichocarpa) gene space using sequence capture. BMC Genomics 13:703PubMedPubMedCentralCrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.EMBRAPA Genetic Resources and Biotechnology – EPqBBrasiliaBrazil
  2. 2.Universidade Católica de Brasília- SGANBrasíliaBrazil

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