Genomic Selection

  • Elisabeth JonasEmail author
  • Freddy Fikse
  • Lars Rönnegård
  • Elena Flavia Mouresan
Part of the Population Genomics book series (POGE)


Prediction of phenotypes is not only used for selection and breeding in animal and plant populations but also for the assessment of specific phenotypes, especially predisposition to diseases and disorders in human populations. The use of genetic markers has been shown to be useful for prediction and selection for phenotypic traits. The concept of using genetic markers for prediction of breeding values or phenotypes was suggested many decades ago, but applications of marker-assisted selection were limited due to the low number of markers that could be genotyped and the low number of confirmed quantitative trait loci (QTL) that could be selected upon. Genomic selection, in contrast, utilizes dense genetic markers across the whole genome for the prediction of phenotypes as all QTL can be assumed to be in linkage disequilibrium with at least one marker. Genomic selection allows thereby choosing the genetically best individuals without the need to confirm QTL. The concept of genomic selection, proposed in 2001, has since been further developed and applied. Nowadays, genomic selection is widely applied in breeding populations of plants and animals for the selection of future breeding individuals. The chapter introduces the general concept of genomic selection. It further discusses relevant prerequisites for the application of genomic selection, including genotyping platforms and reference populations. Some of the methods applied today as well as suggested advancements of methods are introduced. The final part of the chapter describes briefly applications in animal, plant, and human populations (status when writing this chapter), before concluding with some general notes on genomic selection.


Breeding Genotyping Parametric methods Prediction Reference population Selection candidates Semiparametric methods 


  1. Interbull. 2013. Accessed 31.01.2013.Google Scholar
  2. Albrecht T, et al. Genome-based prediction of testcross values in maize. Theor Appl Genet. 2011;123:339–50.PubMedGoogle Scholar
  3. Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink JL. Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome. 2011;4:132–44.Google Scholar
  4. Avendaño S, Watson KA, Kranis A. Genomics in poultry breeding—from utopias to deliverables. In: 9th world congress on genetics applied to livestock production (WCGALP). Germany: Leipzig; 2010Google Scholar
  5. Baloche G, et al. Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. J Dairy Sci. 2014;97:1107–16.PubMedGoogle Scholar
  6. Barrell PJ, Meiyalaghan S, Jacobs JME, Conner AJ. Applications of biotechnology and genomics in potato improvement. Plant Biotechnol J. 2013;11:907–20.PubMedGoogle Scholar
  7. Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J. Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci. 2016;242:23–36.PubMedGoogle Scholar
  8. Battenfield SD, Guzmán C, Gaynor RC, Singh RP, Peña RJ, Dreisigacker S, Fritz AK, Poland JA. Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program. Plant Genome. 2016;9(2). Scholar
  9. Bauer E, et al. Towards a whole-genome sequence for rye (Secale cereale L.). Plant J. 2017;89:853–69.PubMedGoogle Scholar
  10. Beaulieu J, Doerksen T, Clement S, MacKay J, Bousquet J. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity. 2014a;113:343–52.PubMedPubMedCentralGoogle Scholar
  11. Beaulieu J, Doerksen TK, MacKay J, Rainville A, Bousquet J. Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics. 2014b;15:1048.PubMedPubMedCentralGoogle Scholar
  12. Bernardo R. Genomewide selection with minimal crossing in self-pollinated crops. Crop Sci. 2010;50:624–7.Google Scholar
  13. Bernardo R, Yu J. Marker-assisted selection without QTL mapping: prospects for genome-wide selection for quantitative traits in maize. Maize Genet Cooperat Newslett 2007:26.Google Scholar
  14. Berry DP, Garcia JF, Garrick DJ. Development and implementation of genomic predictions in beef cattle. Anim Front. 2016;6:32–8.Google Scholar
  15. Bertin N, Martre P, Génard M, Quilot B, Salon C. Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits. J Exp Bot. 2010;61:955–67.PubMedGoogle Scholar
  16. Birol I, et al. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics. 2013;29:1492–7.PubMedPubMedCentralGoogle Scholar
  17. Boichard D, Ducrocq V, Croiseau P, Fritz S. Genomic selection in domestic animals: principles, applications and perspectives. C R Biol. 2016;339:274–7.PubMedGoogle Scholar
  18. Bouquet A, Juga J. Integrating genomic selection into dairy cattle breeding programmes: a review. Animal. 2013;7:705–13.PubMedGoogle Scholar
  19. Brenchley R, et al. Analysis of the bread wheat genome using whole genome shotgun sequencing. Nature. 2012;491:705–10.PubMedPubMedCentralGoogle Scholar
  20. Breseghello F. Traditional and modern plant breeding methods with examples in rice (Oryza sativa L.). J Agric Food Chem. 2013;61:8277–86.PubMedGoogle Scholar
  21. Burgueno J, de los Campos G, Weigel K, Crossa J. Genomic prediction of breeding values when modeling genotype x environment interaction using pedigree and dense molecular markers. Crop Sci. 2012;52:707–19.Google Scholar
  22. Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Araus JL. High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. J Integr Plant Biol. 2012;54(5):312–20.PubMedGoogle Scholar
  23. Calus MPL. Editorial: genomic selection with numerically small reference populations. Animal. 2016;10:1016–7.PubMedGoogle Scholar
  24. de los Campos G, et al. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics. 2009;182:375–85.PubMedPubMedCentralGoogle Scholar
  25. de los Campos G, Gianola D, Allison DB. Predicting genetic predisposition in humans: the promise of whole-genome markers. Nat Rev Genet. 2010;11:880–6.PubMedGoogle Scholar
  26. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics. 2013a;193:327–45.PubMedCentralGoogle Scholar
  27. de los Campos G, Vazquez AI, Fernando R, Klimentidis YC, Sorensen D. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet. 2013b;9:e1003608.PubMedCentralGoogle Scholar
  28. Caspi R, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014;42:D459–71.PubMedGoogle Scholar
  29. Cericola F, et al. Optimizing training population size and genotyping strategy for genomic prediction using association study results and pedigree information. A case of study in advanced wheat breeding lines. PLoS One. 2017;12:e0169606.PubMedPubMedCentralGoogle Scholar
  30. Chagné D, et al. The draft genome sequence of European Pear (Pyrus communis L. “Bartlett”). PLoS One. 2014;9:e92644.PubMedPubMedCentralGoogle Scholar
  31. Chao S, Zhang W, Dubcovsky J, Sorrells M. Evaluation of genetic diversity and genome-wide linkage disequilibrium among US wheat (Triticum aestivum L.) germplasm representing different market classes. Crop Sci. 2007;47:1018–30.Google Scholar
  32. Chao SM, et al. Population- and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genomics. 2010;11:727.PubMedPubMedCentralGoogle Scholar
  33. Chawade A, Alexandersson E, Bengtsson T, Andreasson E, Levander F. Targeted proteomics approach for precision plant breeding. J Proteome Res. 2016;15:638–46.PubMedGoogle Scholar
  34. Chen HD, He H, Zhou FS, Yu HH, Deng XW. Development of genomics-based genotyping platforms and their applications in rice breeding. Curr Opin Plant Biol. 2013;16:247–54.PubMedGoogle Scholar
  35. Cowling WA, Balazs E. Prospects and challenges for genome-wide association and genomic selection in oilseed Brassica species. Genome. 2010;53:1024–8.PubMedGoogle Scholar
  36. Cowling WA, Buirchell BJ, Falk DE. A model for incorporating novel alleles from the primary gene pool into elite crop breeding programs while reselecting major genes for domestication or adaptation. Crop Pasture Sci. 2009;60:1009–15.Google Scholar
  37. Croft D, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39:D691–7.PubMedGoogle Scholar
  38. Crossa J, et al. Genomic selection and prediction in plant breeding. J Crop Improv. 2011;25:239–61.Google Scholar
  39. Crossa J, et al. Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity. 2013;112:48–60.PubMedPubMedCentralGoogle Scholar
  40. Cullis BR, Smith AB, Beeck CP, Cowling WA. Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis. Genome. 2010;53:1002–16.PubMedGoogle Scholar
  41. Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One. 2008;3:e3395.PubMedPubMedCentralGoogle Scholar
  42. Daetwyler HD, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet. 2014;46:858–65.PubMedGoogle Scholar
  43. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011;12(7):499–510. Scholar
  44. Dawson JC, et al. The use of unbalanced historical data for genomic selection in an international wheat breeding program. Field Crop Res. 2013;154:12–22.Google Scholar
  45. Dekkers JCM. Marker-assisted selection for commercial crossbred performance. J Anim Sci. 2007;85:2104–14.PubMedGoogle Scholar
  46. Dekkers JCM, Hospital F. The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet. 2002;3:22–32.PubMedGoogle Scholar
  47. Druet T, Macleod IM, Hayes BJ. Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity. 2014;112:39–47.PubMedGoogle Scholar
  48. Du Z, Zhou X, Ling Y, Zhang Z, Su Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010. Scholar
  49. Dürr J, Philipsson J. International cooperation: the pathway for cattle genomics. Anim Front. 2012;2:16–21.Google Scholar
  50. Duvick DN. Heterosis: feeding. People and protecting natural resources. In: Coors JG, Pandey S, editors. The genetics and exploitation of heterosis in crops. Madison, WI: American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc.; 1999. p. 19–29.Google Scholar
  51. Elsik CG, Tellam RL, Worley KC. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science. 2009;324:522–8.PubMedPubMedCentralGoogle Scholar
  52. Erbe M, et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J Dairy Sci. 2012;95:4114–29.PubMedGoogle Scholar
  53. Falconer D, Mackay T. Quantitative genetics. London, UK: Longman, Harrow; 1996.Google Scholar
  54. Fernie AR, Schauer N. Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 2009;25:39–48.PubMedGoogle Scholar
  55. Forabosco F, Lohmus M, Rydhmer L, Sundstrom LF. Genetically modified farm animals and fish in agriculture: a review. Livest Sci. 2013;153:1–9.Google Scholar
  56. Funk DA. Major advances in globalization and consolidation of the artificial insemination industry. J Dairy Sci. 2006;89:1362–8.PubMedGoogle Scholar
  57. Gaj T, Gersbach CA, Barbas CF. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 2013;31(7):397–405.PubMedPubMedCentralGoogle Scholar
  58. Ganal MW, Röder MS. Microsatellite and SNP markers in wheat breeding. In: Varshney RK, Tuberosa R, editors. Genomic assisted crop improvement: genomics applications in crops, vol. 2. Dordrecht: Springer; 2007. p. 1–24.Google Scholar
  59. García-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci U S A. 2016;113(28):E3995–4004. Scholar
  60. Garrick DJ. The nature, scope and impact of genomic prediction in beef cattle in the United States. Genet Sel Evol. 2011;43:–17.Google Scholar
  61. Garrick DJ, Taylor JF, Fernando RL. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet Sel Evol. 2009;41:55.PubMedPubMedCentralGoogle Scholar
  62. Gerrits RJ, et al. Perspectives for artificial insemination and genomics to improve global swine populations. Theriogenology. 2005;63:283–99.PubMedGoogle Scholar
  63. Gianola D. Priors in whole-genome regression: the Bayesian alphabet returns. Genetics. 2013;194:573–96.PubMedPubMedCentralGoogle Scholar
  64. Gianola D, van Kaam JBCHM. Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics. 2008;178:2289–303.PubMedPubMedCentralGoogle Scholar
  65. Goddard ME. Uses of genomics in livestock agriculture. Animal Production Science. 2012;52:73–7.Google Scholar
  66. Goddard ME, Hayes BJ. Genomic selection. J Anim Breed Genet. 2007;124:323–30.PubMedGoogle Scholar
  67. Goddard ME, Hayes BJ, Meuwissen THE. Using the genomic relationship matrix to predict the accuracy of genomic selection. J Anim Breed Genet. 2011;128:409–21.PubMedGoogle Scholar
  68. Goff SA, et al. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science. 2002;296:92–100.PubMedGoogle Scholar
  69. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet. 2016;17:333–51.Google Scholar
  70. Gouy M, et al. Experimental assessment of the accuracy of genomic selection in sugarcane. Theor Appl Genet. 2013;126:2575–86.PubMedGoogle Scholar
  71. Grattapaglia D, Resende MDV. Genomic selection in forest tree breeding. Tree Genet Genomes. 2011;7:241–55.Google Scholar
  72. van Grevenhof I. Breeding against osteochondrosis. Wageningen: Wageningen University; 2011.Google Scholar
  73. Groenen MAM, et al. Analyses of pig genomes provide insight into porcine demography and evolution. Nature. 2012;491:393–8.PubMedPubMedCentralGoogle Scholar
  74. Gupta PK, Langridge P, Mir RR. Marker-assisted wheat breeding: present status and future possibilities. Mol Breed. 2010;26:145–61.Google Scholar
  75. Guzman C, et al. Wheat quality improvement at CIMMYT and the use of genomic selection on it. Appl Transl Genom. 2016;11:3–8.PubMedPubMedCentralGoogle Scholar
  76. Haberland AM, König von Borstel U, Simianer H, König S. Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection. Animal. 2012;6:1369–76.PubMedGoogle Scholar
  77. Habier D, Fernando RL, Dekkers JCM. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 2007;177:2389–97.PubMedPubMedCentralGoogle Scholar
  78. Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol. 2010;42:–5.Google Scholar
  79. Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12:186.PubMedPubMedCentralGoogle Scholar
  80. Habier D, Fernando RL, Garrick DJ. Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics. 2013;194:597–607.PubMedPubMedCentralGoogle Scholar
  81. Hayes B, Bowman P, Chamberlain A, Goddard M. Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci. 2009a;92:433–43.PubMedGoogle Scholar
  82. Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Sel Evol. 2009b;41:–51.Google Scholar
  83. Hayes BJ, et al. Prospects for genomic selection in forage plant species. Plant Breeding. 2013;132:133–43.Google Scholar
  84. Heffner EL, Sorrells ME, Jannink J-L. Genomic selection for crop improvement. Crop Sci. 2009;49:1–12.Google Scholar
  85. Heffner EL, Jannink JL, Sorrells ME. Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome. 2011;4:65–75.Google Scholar
  86. Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics. 1975a;31:423–47.PubMedGoogle Scholar
  87. Henderson CR. Use of all relstives in intraherd prediction of breeding values and producing abilities. J Dairy Sci. 1975b;58:1910–6.Google Scholar
  88. Heslot N, Yang H-P, Sorrells ME, Jannink J-L. Genomic selection in plant breeding: a comparison of models. Crop Sci. 2012;52:146–60.Google Scholar
  89. Hickey JM. Sequencing millions of animals for genomic selection 2.0. J Anim Breed Genet. 2013;130:331–2.PubMedGoogle Scholar
  90. Hickey JM, et al. Sequencing millions of animals for genomic selection 2.0. In: Proceedings, 10th world congress of genetics applied to livestock production. Vancouver; 2014.Google Scholar
  91. de Roos APW, Hayes BJ, Goddard ME. Reliability of genomic predictions across multiple populations. Genetics. 2009;183:1545–53.PubMedPubMedCentralGoogle Scholar
  92. Hofheinz N, Borchardt D, Weissleder K, Frisch M. Genome-based prediction of test cross performance in two subsequent breeding cycles. Theor Appl Genet. 2012;125:1639–45.PubMedGoogle Scholar
  93. Hutchison JL, Cole JB, Bickhart DM. Short communication: use of young bulls in the United States. J Dairy Sci. 2014;97:3213–120. Scholar
  94. International Barley Genome Sequencing Consortium et al. A physical, genetic and functional sequence assembly of the barley genome. Nature. 2012;491:711–6.Google Scholar
  95. Isik F, et al. Genomic selection in maritime pine. Plant Sci. 2016;242:108–19.PubMedGoogle Scholar
  96. Iwata H, et al. Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia. Breed Sci. 2013;63:125–40.PubMedPubMedCentralGoogle Scholar
  97. Iwata H, Hayashi T, Tsumura Y. Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica. Tree Genet Genomes. 2011;7:747–58.Google Scholar
  98. Iwata H, Jannink J-L. Accuracy of genomic selection prediction in barley breeding programs: a simulation study based on the real single nucleotide polymorphism data of barley breeding lines. Crop Sci. 2011;51:1915–27.Google Scholar
  99. Jannink J-L, Lorenz AJ, Iwata H. Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics. 2010;9:166–77.PubMedGoogle Scholar
  100. Jannink JL. Dynamics of long-term genomic selection. Genet Sel Evol. 2010;42:–35.Google Scholar
  101. Jenko J, et al. Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Genet Sel Evol. 2015;47:55.PubMedPubMedCentralGoogle Scholar
  102. Jiang Y, et al. The sheep genome illuminates biology of the rumen and lipid metabolism. Science. 2014;344:1168–73.PubMedPubMedCentralGoogle Scholar
  103. Johnston DJ, Tier B, Graser HU. Beef cattle breeding in Australia with genomics: opportunities and needs. Animal Production Science. 2012;52:100–6.Google Scholar
  104. Kanehisa M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36:D480–4.PubMedGoogle Scholar
  105. Kärkkäinen HP, Sillanpää MJ. Fast genomic predictions via Bayesian G-BLUP and Multilocus models of threshold traits including censored Gaussian data. G3 (Bethesda). 2013;3:1511–23.Google Scholar
  106. Kizilkaya K, Fernando RL, Garrick DJ. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J Anim Sci. 2010;88:544–51.PubMedGoogle Scholar
  107. Kumar S, Bink MCAM, Volz RK, Bus VGM, Chagne D. Towards genomic selection in apple (Malus x domestica Borkh.) breeding programmes: prospects, challenges and strategies. Tree Genet Genomes. 2012a;8:1–14.Google Scholar
  108. Kumar S, et al. Genomic selection for fruit quality traits in apple (Malus x domestica Borkh.). PLoS One. 2012b;7(5):e36674.PubMedPubMedCentralGoogle Scholar
  109. Lenz PRN, et al. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics. 2017;18:335.PubMedPubMedCentralGoogle Scholar
  110. Li Z-K, Zhang F. Rice breeding in the post-genomics era: from concept to practice. Curr Opin Plant Biol. 2013;16:261–9.PubMedGoogle Scholar
  111. Lien S, et al. The Atlantic salmon genome provides insights into rediploidization. Nature. 2016;533:200–5.PubMedGoogle Scholar
  112. Lillehammer M, Meuwissen THE, Sonesson AK. Genomic selection for maternal traits in pigs. J Anim Sci. 2011;89:3908–16.PubMedGoogle Scholar
  113. Lillehammer M, Meuwissen THE, Sonesson AK. A low-marker density implementation of genomic selection in aquaculture using within-family genomic breeding values. Genet Sel Evol. 2013;45Google Scholar
  114. Lin Z, Hayes BJ, Daetwyler HD. Genomic selection in crops, trees and forages: a review. Crop Pasture Sci. 2014;65:1177–91.Google Scholar
  115. Lindblad-Toh K, et al. Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature. 2005;438:803–19.PubMedGoogle Scholar
  116. Lôbo RB, et al. Implementation of DNA markers to produce genomically—enhanced EPDs in Nellore cattle. Acta Sci Vet. 2011;39(Suppl 1):s23–7.Google Scholar
  117. Longin CFH, Mi X, Würschum T. Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding. Theor Appl Genet. 2015;128:1297–306.PubMedGoogle Scholar
  118. Lorenzana RE, Bernardo R. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet. 2009;120:151–61.PubMedGoogle Scholar
  119. Lund MS, et al. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol. 2011;43:43.PubMedPubMedCentralGoogle Scholar
  120. Lush JL. Linebreeding. Iowa Agric Exp Sta Bull 1933:301.Google Scholar
  121. Lush JL. Family merit and individual merit as bases for selection. Am Nat. 1947;81:241–61.Google Scholar
  122. Lynch M, Walsh B. Genetics and analysis of quantitative traits. Sunderland, MA: Sinauer; 1998.Google Scholar
  123. Maccaferri M, Sanguineti MC, Noli E, Tuberosa R. Population structure and long-range linkage disequilibrium in a durum wheat elite collection. Mol Breed. 2005;15:271–89.Google Scholar
  124. MacLeod IM, et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics. 2016;17:144.PubMedPubMedCentralGoogle Scholar
  125. Makowsky R, et al. Beyond missing heritability: prediction of complex traits. PLoS Genet. 2011;7:e1002051.PubMedPubMedCentralGoogle Scholar
  126. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511.PubMedGoogle Scholar
  127. Marulanda JJ, et al. Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale. Theor Appl Genet. 2016;129:1901–13.PubMedGoogle Scholar
  128. Mascher M, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544:427–33.PubMedGoogle Scholar
  129. Mather KA, et al. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics. 2007;177:2223–32.PubMedPubMedCentralGoogle Scholar
  130. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One. 2009;4:e5350. Scholar
  131. McCouch S. Diversifying selection in plant breeding. PLoS Biol. 2004;2:1507–12.Google Scholar
  132. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–29.PubMedPubMedCentralGoogle Scholar
  133. Meuwissen THE, Luan T, Woolliams JA. The unified approach to the use of genomic and pedigree information in genomic evaluations revisited. J Anim Breed Genet. 2011;128:429–39.PubMedGoogle Scholar
  134. Morandini P, Salamini F. Plant biotechnology and breeding: allied for years to come. Trends Plant Sci. 2003;8:70–5.PubMedGoogle Scholar
  135. Morota G, Gianola D. Kernel-based whole-genome prediction of complex traits: a review. Front Genet. 2014;5:363.PubMedPubMedCentralGoogle Scholar
  136. Morrell PL, Buckler ES, Ross-Ibarra J. Crop genomics: advances and applications. Nat Rev Genet. 2012;13:85–96.Google Scholar
  137. Muir B, Van Doormaal BJ, Kistemaker G. International genomic co-operation—North American perspective. In: Proceedings of the Interbull international workshop, Paris, France; 2010. pp 71–76.Google Scholar
  138. Myburg AA, et al. The genome of Eucalyptus grandis. Nature. 2014;510:356–62.PubMedGoogle Scholar
  139. Myles S. Improving fruit and wine: what does genomics have to offer? Trends Genet. 2013;29:190–6.PubMedGoogle Scholar
  140. Neale DB, et al. Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies. Genome Biol. 2014;15:R59.PubMedPubMedCentralGoogle Scholar
  141. Nielsen HM, Sonesson AK, Meuwissen THE. Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes. J Anim Sci. 2011;89:630–8.PubMedGoogle Scholar
  142. Northcutt SL. Genomic choices. American Angus Association®/AngusGenetics Inc. release. 2011. (posted July, 2011)
  143. Nystedt B, et al. The Norway spruce genome sequence and conifer genome evolution. Nature. 2013;497:579–84.PubMedGoogle Scholar
  144. Ober U, et al. Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data. Genetics. 2011;188:695–708.PubMedPubMedCentralGoogle Scholar
  145. Patry C. Impacts of genomic selection on classical genetic evaluations. Jouy-en-Josas: Institut National de la Recherche Agronomique (INRA); 2011.Google Scholar
  146. Potato Genome Sequencing Consortium, et al. Genome sequence and analysis of the tuber crop potato. Nature. 2011;475:189–95.Google Scholar
  147. Preisinger R. Genome-wide selection in poultry. Animal Production Science. 2012;52:121–5.Google Scholar
  148. Proudfoot C, et al. Genome edited sheep and cattle. Transgenic Res. 2015;24:147–53.PubMedGoogle Scholar
  149. Pryce JE, Daetwyler HD. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Anim Prod Sci. 2012;52:107–14.Google Scholar
  150. Pszczola M, Calus MPL. Updating the reference population to achieve constant genomic prediction reliability across generations. Animal. 2015;10:1018–24.PubMedGoogle Scholar
  151. Pszczola M, Strabel T, van Arendonk JAM, Calus MPL. The impact of genotyping different groups of animals on accuracy when moving from traditional to genomic selection. J Dairy Sci. 2012;95:5412–21.PubMedGoogle Scholar
  152. Ratcliffe B, et al. A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods. Heredity (Edinb). 2015;115(6):547–55.Google Scholar
  153. Remington DL, et al. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Natl Acad Sci U S A. 2001;98:11479–84.PubMedPubMedCentralGoogle Scholar
  154. Resende M, et al. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol. 2012a;193Google Scholar
  155. Resende MDV, et al. Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 2012b;194:116–28.PubMedGoogle Scholar
  156. Resende RMS, Casler MD, Resende MDV. Genomic selection in forage breeding: accuracy and methods. Crop Sci. 2014;54:143–56.Google Scholar
  157. Resende RT, Resende MDV, Silva FF, Azevedo CF, Takahashi EK, Silva-Junior OB, Grattapaglia D. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity (Original Article). 2017. Scholar
  158. Rius-Vilarrasa E, et al. Influence of model specifications on the reliabilities of genomic prediction in a Swedish–Finnish red breed cattle population. J Anim Breed Genet. 2012;129:369–79.PubMedGoogle Scholar
  159. Rosenberg NA, Nordborg M. Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nat Rev Genet. 2002;3:380–90.PubMedGoogle Scholar
  160. Rubin C-J, et al. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature. 2010;464:587–91.PubMedGoogle Scholar
  161. Rudi N, Norton GW, Alwang J, Asumugha G. Economic impact analysis of maker-assisted breeding for resistance to pests and post harvest deterioration of cassava. Afr J Agr Res Econ. 2010;4:110–22.Google Scholar
  162. Rutkoski JE, Heffner EL, Sorrells ME. Genomic selection for durable stem rust resistance in wheat. Euphytica. 2011;179:161–73.Google Scholar
  163. Saatchi M, Schnabel RD, Rolf MM, Taylor JF, Garrick DJ. Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle. Genet Sel Evol. 2012;44:38.PubMedPubMedCentralGoogle Scholar
  164. Sánchez-Molano E, et al. Genomic prediction of traits related to canine hip dysplasia. Front Genet. 2015;6:97.PubMedPubMedCentralGoogle Scholar
  165. Schaeffer L. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet. 2006;123:218–23.PubMedGoogle Scholar
  166. Schnable PS, et al. The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326:1112–5.PubMedGoogle Scholar
  167. Sharma HC, Crouch JH, Sharma KK, Seetharama N, Hash CT. Applications of biotechnology for crop improvement: prospects and constraints. Plant Sci. 2002;163:381–95.Google Scholar
  168. Shen X, Alam M, Fikse F, Rönnegård L. A novel generalized ridge regression method for quantitative genetics. Genetics. 2013;193(4):1255–68.PubMedPubMedCentralGoogle Scholar
  169. Shu YJ, et al. Genomic selection of seed weight based on low-density SCAR markers in soybean. Genet Mol Res. 2013;12:2178–88.PubMedGoogle Scholar
  170. Shumbusho F, Raoul J, Astruc JM, Palhiere I, Elsen JM. Potential benefits of genomic selection on genetic gain of small ruminant breeding programs1. J Anim Sci. 2013;91:3644–57.PubMedGoogle Scholar
  171. Snelling WM, et al. Partial-genome evaluation of postweaning feed intake and efficiency of crossbred beef cattle12. J Anim Sci. 2011;89:1731–41.PubMedGoogle Scholar
  172. Somers DJ, Kirkpatrick R, Moniwa M, Walsh A. Mining single-nucleotide polymorphisms from hexaploid wheat ESTs. Genome. 2003;46:431–7.PubMedGoogle Scholar
  173. Sonesson AK, Meuwissen THE. Testing strategies for genomic selection in aquaculture breeding programs. Genet Sel Evol. 2009;41:37.PubMedPubMedCentralGoogle Scholar
  174. Spindel J, et al. Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations. Theor Appl Genet. 2013;126:2699–716.PubMedGoogle Scholar
  175. Stein L. Genome annotation: from sequence to biology. Nat Rev Genet. 2001;2:493–503.PubMedGoogle Scholar
  176. Stock KF, Jönsson L, Ricard A, Mark T. Genomic applications in horse breeding. Anim Front. 2016;6:45–52.Google Scholar
  177. Sun XC, Fernando R, Dekkers J. Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction. Genet Sel Evol. 2016;48(1):77.PubMedPubMedCentralGoogle Scholar
  178. Swan AA, Johnston DJ, Brown DJ, Tier B, Graser H-U. Integration of genomic information into beef cattle and sheep genetic evaluations in Australia. Animal Production Science. 2012;52:126–32.Google Scholar
  179. Sweeney M, McCouch S. The complex history of the domestication of rice. Ann Bot. 2007;100:951–7.PubMedPubMedCentralGoogle Scholar
  180. Takeda S, Matsuoka M. Genetic approaches to crop improvement: responding to environmental and population changes. Nat Rev Genet. 2008;9:444–57.PubMedGoogle Scholar
  181. Tan B, Grattapaglia D, Martins GS, Ferreira KZ, Sundberg B, Ingvarsson PK. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol. 2017;17(1):110. Scholar
  182. Tan W, Proudfoot C, Lillico SG, Whitelaw CBA. Gene targeting, genome editing: from Dolly to editors. Transgenic Res. 2016;25:273–87.PubMedPubMedCentralGoogle Scholar
  183. Tang C, et al. The rubber tree genome reveals new insights into rubber production and species adaptation. Nat Plants. 2016;2:16073.PubMedGoogle Scholar
  184. Tenaillon MI, Austerlitz F, Tenaillon O. Apparent mutational hotspots and long distance linkage disequilibrium resulting from a bottleneck. J Evol Biol. 2008;21:541–50.PubMedGoogle Scholar
  185. Thavamanikumar S, Southerton SG, Bossinger G, Thumma BR. Dissection of complex traits in forest trees—opportunities for marker-assisted selection. Tree Genet Genomes. 2013;9:627–39.Google Scholar
  186. Thornton PK. Livestock production: recent trends, future prospects. Phil Trans Roy Soc B-Biol Sci. 2010;365:2853–67.Google Scholar
  187. Thorwarth P, et al. Genomic prediction ability for yield-related traits in German winter barley elite material. Theor Appl Genet. 2017;130(8):1669–83.PubMedGoogle Scholar
  188. Toro MA, Varona L. A note on mate allocation for dominance handling in genomic selection. Genet Sel Evol. 2010;42:33.PubMedPubMedCentralGoogle Scholar
  189. Trebbi D, et al. High-throughput SNP discovery and genotyping in durum wheat (Triticum durum Desf.). Theor Appl Genet. 2011;123:555–69.PubMedGoogle Scholar
  190. Tribout T, Larzul C, Phocas F. Efficiency of genomic selection in a purebred pig male line. J Anim Sci. 2012;90:4164–76.PubMedGoogle Scholar
  191. Tuskan GA, et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science. 2006;313:1596–604.PubMedGoogle Scholar
  192. van der Werf JHJ. Marker-assisted selection in sheep and goats. In: Guimarães EP, Ruane J, Scherf BD, Sonnino A, Dargie JD, editors. Marker-assisted selection: current status and future perspectives in crops, livestock, forestry and fish. Rome: Food and Agriculture Organization of the United Nations; 2007.Google Scholar
  193. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414–23.PubMedPubMedCentralGoogle Scholar
  194. VanRaden PM, Wiggans GR. Derivation, calculation, and use of national animal model information. J Dairy Sci. 1991;74:2737–46.PubMedGoogle Scholar
  195. VanRaden PM, Wiggans GR, Van Tassell CP, Sonstegard TS, Schenkel F. Benefits from cooperation in genomics. In: Proceedings of the Interbull international workshop. Genomic information in genetic evaluations. Uppsala, Sweden; 2009a pp 67–72.Google Scholar
  196. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS. Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009b;92:16–24. Scholar
  197. Velasco R, et al. The genome of the domesticated apple (Malus [times] domestica Borkh.). Nat Genet. 2010;42:833–9.PubMedGoogle Scholar
  198. Wade CM, et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science. 2009;326:865–7.PubMedPubMedCentralGoogle Scholar
  199. Walsh B. Quantitative genetics. In: eLS. John Wiley & Sons Ltd., Chichester. 2001Google Scholar
  200. Wiggans GR, VanRaden PM, Cooper TA. The genomic evaluation system in the United States: past, present, future. J Dairy Sci. 2011;94:3202–11.PubMedGoogle Scholar
  201. Wilkins PW, Humphreys MO. Progress in breeding perennial forage grasses for temperate agriculture. J Agric Sci. 2003;140:129–50.Google Scholar
  202. Williams AV, Nevill PG, Krauss SL. Next generation restoration genetics: applications and opportunities. Trends Plant Sci. 2014;19:529–37.PubMedGoogle Scholar
  203. Windhausen VS, et al. Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 (Bethesda). 2012;2:1427–36.Google Scholar
  204. Wu J, et al. The genome of the pear (Pyrus bretschneideri Rehd.). Genome Res. 2013;23:396–408.PubMedPubMedCentralGoogle Scholar
  205. Wurschum T, Abel S, Zhao Y. Potential of genomic selection in rapeseed (Brassica napus L.) breeding. Plant Breeding. 2014;133:45–51.Google Scholar
  206. Wurschum T, Reif J, Kraft T, Janssen G, Zhao Y. Genomic selection in sugar beet breeding populations. BMC Genet. 2013;14:85.PubMedPubMedCentralGoogle Scholar
  207. Xu S, Hu Z. Methods of plant breeding in the genome era. Genet Res. 2010;92:423–41.Google Scholar
  208. Yan ZB, Yan WG, Deren CW, McClung A. Hybrid rice breeding. B.R. Wells Rice Research Series—Arkansas Agricultural Experiment Station University of Arkansas, vol 591. 2011. pp 61–63.Google Scholar
  209. Yang J, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–9.PubMedPubMedCentralGoogle Scholar
  210. Young CW, Bonczek RR, Johnson DG. Inbreeding of and relationship among registered Holsteins. J Dairy Sci. 1988;71:1659–66.PubMedGoogle Scholar
  211. Yu J, et al. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science. 2002;296:79–92.PubMedGoogle Scholar
  212. Zamir D. Improving plant breeding with exotic genetic libraries. Nat Rev Genet. 2001;2:983–9.PubMedGoogle Scholar
  213. Zapata-Valenzuela J, et al. SNP markers trace familial linkages in a cloned population of Pinus taeda-prospects for genomic selection. Tree Genet Genomes. 2012;8:1307–18.Google Scholar
  214. Zelener N, Poltri SNM, Bartoloni N, Lopez CR, Hopp HE. Selection strategy for a seedling seed orchard design based on trait selection index and genomic analysis by molecular markers: a case study for Eucalyptus dunnii. Tree Physiol. 2005;25:1457–67.PubMedGoogle Scholar
  215. Zhao F, Xu S. An expectation and maximization algorithm for estimating G x E interaction effects. Theor Appl Genet. 2012;124:1375–87.PubMedGoogle Scholar
  216. Zhao Y, et al. Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet. 2012;124:769–76.PubMedGoogle Scholar
  217. Zhong SQ, Dekkers JCM, Fernando RL, Jannink JL. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics. 2009;182:355–64.PubMedPubMedCentralGoogle Scholar
  218. Zimin A, et al. Sequencing and assembly of the 22-Gb loblolly pine genome. Genetics. 2014;196:875–90.PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Elisabeth Jonas
    • 1
    Email author
  • Freddy Fikse
    • 1
  • Lars Rönnegård
    • 1
    • 2
  • Elena Flavia Mouresan
    • 1
  1. 1.Department of Animal Breeding and GeneticsSwedish University of Agricultural SciencesUppsalaSweden
  2. 2.Statistics Unit, School of Technology and Business StudiesDalarna UniversityFalunSweden

Personalised recommendations