Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
REVIEW

Genomic selection in crops, trees and forages: a review

Z. Lin A B C D , B. J. Hayes A B C and H. D. Daetwyler A B C
+ Author Affiliations
- Author Affiliations

A School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.

B Department of Environment and Primary Industries, Biosciences Research Division, AgriBio, 5 Ring Road, Bundoora, Vic. 3083, Australia.

C Dairy Futures Cooperative Research Centre, AgriBio, 5 Ring Road, Bundoora, Vic. 3083, Australia.

D Corresponding author. Email: zibei.lin@depi.vic.gov.au

Crop and Pasture Science 65(11) 1177-1191 https://doi.org/10.1071/CP13363
Submitted: 31 October 2013  Accepted: 7 April 2014   Published: 22 May 2014

Abstract

Genomic selection is now being used at an accelerating pace in many plant species. This review first discusses the factors affecting the accuracy of genomic selection, and then interprets results of existing plant genomic selection studies in light of these factors. Differences between genomic breeding strategies for self-pollinated and open-pollinated species, and between-population level v. within-family design, are highlighted. As expected, more training individuals, higher trait heritability and higher marker density generally lead to better accuracy of genomic breeding values in both self-pollinated and open-pollinated plants. Most published studies to date have artificially limited effective population size by using designs of bi-parental or within-family structure to increase accuracies. The capacity of genomic selection to reduce generation intervals by accurately evaluating traits at an early age makes it an effective tool to deliver more genetic gain from plant breeding in many cases.

Additional keywords: accuracy, genetic markers genomic selection, plants.


References

Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schon CC (2011) Genome-based prediction of testcross values in maize. Theoretical and Applied Genetics 123, 339–350.
Genome-based prediction of testcross values in maize.Crossref | GoogleScholarGoogle Scholar | 21505832PubMed |

Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink JL (2011) Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome 4, 132–144.
Accuracy and training population design for genomic selection on quantitative traits in elite North American oats.Crossref | GoogleScholarGoogle Scholar |

Beavis WD (1998) ‘QTL analyses: Power, precision, and accuracy.’ (CRC Press, Inc.: Boca Raton, FL, USA/CRC Press: London)

Bernardo R (2008) Molecular markers and selection for complex traits in plants: Learning from the last 20 years. Crop Science 48, 1649–1664.
Molecular markers and selection for complex traits in plants: Learning from the last 20 years.Crossref | GoogleScholarGoogle Scholar |

Bernardo R, Yu JM (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Science 47, 1082–1090.
Prospects for genomewide selection for quantitative traits in maize.Crossref | GoogleScholarGoogle Scholar |

Bolormaa S, Pryce JE, Kemper K, Savin K, Hayes BJ, Barendse W, Zhang Y, Reich CM, Mason BA, Bunch RJ, Harrison BE, Reverter A, Herd RM, Tier B, Graser HU, Goddard ME (2013) Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. Journal of Animal Science 91, 3088–3104.
Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtFChsLrL&md5=e5ad59174a15988d774c904502092c16CAS | 23658330PubMed |

Breiman L (2001) Random forests. Machine Learning 45, 5–32.
Random forests.Crossref | GoogleScholarGoogle Scholar |

Burgueño J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Science 52, 707–719.
Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers.Crossref | GoogleScholarGoogle Scholar |

Calus MPL, Meuwissen THE, de Roos APW, Veerkamp RF (2008) Accuracy of genomic selection using different methods to define haplotypes. Genetics 178, 553–561.
Accuracy of genomic selection using different methods to define haplotypes.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1c%2FkvFehtA%3D%3D&md5=6e06d10384ff86a2509f308bcbac5dfbCAS |

Cavanagh CR, Chao SM, Wang SC, Huang BE, Stephen S, Kiani S, Forrest K, Saintenac C, Brown-Guedira GL, Akhunova A, See D, Bai GH, Pumphrey M, Tomar L, Wong DB, Kong S, Reynolds M, da Silva ML, Bockelman H, Talbert L, Anderson JA, Dreisigacker S, Baenziger S, Carter A, Korzun V, Morrell PL, Dubcovsky J, Morell MK, Sorrells ME, Hayden MJ, Akhunov E (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proceedings of the National Academy of Sciences of the United States of America 110, 8057–8062.
Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtV2ksL%2FJ&md5=6f03db85b2427b298923b1c18151267aCAS | 23630259PubMed |

Chagné D, Crowhurst RN, Troggio M, Davey MW, Gilmore B, Lawley C, Vanderzande S, Hellens RP, Kumar S, Cestaro A, Velasco R, Main D, Rees JD, Iezzoni A, Mockler T, Wilhelm L, Van de Weg E, Gardiner SE, Bassil N, Peace C (2012) Genome-wide SNP detection, validation, and development of an 8K SNP array for apple. PLoS ONE 7, e31745
Genome-wide SNP detection, validation, and development of an 8K SNP array for apple.Crossref | GoogleScholarGoogle Scholar | 22363718PubMed |

Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen HM, Frazer KA, Huson DH, Schoelkopf B, Nordborg M, Raetsch G, Ecker JR, Weigel D (2007) Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317, 338–342.
Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXnslGrs7k%3D&md5=f484d9b7b63df9cdde18c33a79680857CAS | 17641193PubMed |

Clark SA, Hickey JM, van der Werf JHJ (2011) Different models of genetic variation and their effect on genomic evaluation. Genetics, Selection, Evolution 43, 18–27.
Different models of genetic variation and their effect on genomic evaluation.Crossref | GoogleScholarGoogle Scholar | 21575265PubMed |

Conaghan P, Casler MD (2011) A theoretical and practical analysis of the optimum breeding system for perennial ryegrass. Irish Journal of Agricultural and Food Research 50, 47–63.

Crossa J, de los Campos G, Perez P, Gianola D, Burgueño 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, 713–724.
Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFOnt7jM&md5=f5e391f4e3c9a3e5aea40f308ff20204CAS | 20813882PubMed |

Crossa J, Perez P, Campos GDL, Mahuku G, Dreisigacker S, Magorokosho C (2011) Genomic selection and prediction in plant breeding. Journal of Crop Improvement 25, 239–261.
Genomic selection and prediction in plant breeding.Crossref | GoogleScholarGoogle Scholar |

Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3, e3395
Accuracy of predicting the genetic risk of disease using a genome-wide approach.Crossref | GoogleScholarGoogle Scholar | 18852893PubMed |

Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.
The impact of genetic architecture on genome-wide evaluation methods.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFOnu7jN&md5=39fe30251981a3cd000b8d106ead3879CAS | 20407128PubMed |

Daetwyler H, Hayden M, Bansal U, Bariana H, Hayes B (2013) Genomic selection for disease and morphological traits in diverse wheat landraces. In ‘Plant and Animal Genomes XXI’. 11–16 January, San Diego, CA. Available at: https://pag.confex.com/pag/xxi/webprogram/Paper6832.html

Dawson JC, Endelman JB, Heslot N, Crossa J, Poland J, Dreisigacker S, Manes Y, Sorrells ME, Jannink JL (2013) The use of unbalanced historical data for genomic selection in an international wheat breeding program. Field Crops Research 154, 12–22.
The use of unbalanced historical data for genomic selection in an international wheat breeding program.Crossref | GoogleScholarGoogle Scholar |

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, 327–345.
Whole-genome regression and prediction methods applied to plant and animal breeding.Crossref | GoogleScholarGoogle Scholar | 22745228PubMed |

de Oliveira EJ, de Resende MDV, Santos VD, Ferreira CF, Oliveira GAF, da Silva MS, de Oliveira LA, Aguilar-Vildoso CI (2012) Genome-wide selection in cassava. Euphytica 187, 263–276.
Genome-wide selection in cassava.Crossref | GoogleScholarGoogle Scholar |

de Roos APW, Hayes BJ, Spelman RJ, Goddard ME (2008) Linkage disequilibrium and persistence of phase in Holstein–Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512.
Linkage disequilibrium and persistence of phase in Holstein–Friesian, Jersey and Angus cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1cvmsFeltw%3D%3D&md5=f704b10e28b81d69b417271daf457337CAS |

de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183, 1545–1553.
Reliability of genomic predictions across multiple populations.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1MfhtFSqsg%3D%3D&md5=f9a94d042fed66e58746e3a27e859ee1CAS |

Denis M, Bouvet JM (2013) Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding. Tree Genetics & Genomes 9, 37–51.
Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding.Crossref | GoogleScholarGoogle Scholar |

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 Genetics & Genomes 5, 225–234.
High-throughput genotyping and mapping of single nucleotide polymorphisms in loblolly pine (Pinus taeda L.).Crossref | GoogleScholarGoogle Scholar |

Erbe M, Hayes BJ, Matukumalli LK, Goswami S, Bowman PJ, Reich CM, Mason BA, Goddard ME (2012) Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science 95, 4114–4129.
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XoslGqt74%3D&md5=2e58fec03bdf37ecca71f98d8f89cd10CAS | 22720968PubMed |

Falconer DS, Mackay TFC (1996) ‘Introduction to quantitative genetics.’ 4th edn (Longmans, Green & Co.: Harlow, UK)

Ganal MW, Durstewitz G, Polley A, Berard A, Buckler ES, Charcosset A, Clarke JD, Graner EM, Hansen M, Joets J, Le Paslier MC, McMullen MD, Montalent P, Rose M, Schon CC, Sun Q, Walter H, Martin OC, Falque M (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS ONE 6, e28334
A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhs1OqurzJ&md5=ceb3f9c02c3af3ef983d1667a85e06b6CAS | 22174790PubMed |

Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173, 1761–1776.
Genomic-assisted prediction of genetic value with semiparametric procedures.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XptVeiu74%3D&md5=3b1a705db79f975f579ed25070503cc2CAS | 16648593PubMed |

Gislum R, Micklander E, Nielsen JP (2004) Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics. Field Crops Research 88, 269–277.
Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics.Crossref | GoogleScholarGoogle Scholar |

Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.
Genomic selection: prediction of accuracy and maximisation of long term response.Crossref | GoogleScholarGoogle Scholar | 18704696PubMed |

Goddard ME, Hayes BJ (2007) Genomic selection. Journal of Animal Breeding and Genetics 124, 323–330.
Genomic selection.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD2sjjvFyiug%3D%3D&md5=da045c5187a44faf0e23986fd866aeb1CAS | 18076469PubMed |

Goddard ME, Hayes BJ, Meuwissen THE (2011) Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of Animal Breeding and Genetics 128, 409–421.
Using the genomic relationship matrix to predict the accuracy of genomic selection.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3Mbnt12itg%3D%3D&md5=c20207fedb30e8fbd4ab8653e20235abCAS | 22059574PubMed |

González-Camacho JM, de los Campos G, Perez P, Gianola D, Cairns JE, Mahuku G, Babu R, Crossa J (2012) Genome-enabled prediction of genetic values using radial basis function neural networks. Theoretical and Applied Genetics 125, 759–771.
Genome-enabled prediction of genetic values using radial basis function neural networks.Crossref | GoogleScholarGoogle Scholar | 22566067PubMed |

Guo ZG, Tucker DM, Lu JW, Kishore V, Gay G (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theoretical and Applied Genetics 124, 261–275.
Evaluation of genome-wide selection efficiency in maize nested association mapping populations.Crossref | GoogleScholarGoogle Scholar |

Guo Z, Tucker D, Basten C, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theoretical & Applied Genetics
The impact of population structure on genomic prediction in stratified populations.Crossref | GoogleScholarGoogle Scholar |

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

Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186–197.
Extension of the bayesian alphabet for genomic selection.Crossref | GoogleScholarGoogle Scholar | 21605355PubMed |

Han YH, Kang Y, Torres-Jerez I, Cheung F, Town CD, Zhao PX, Udvardi MK, Monteros MJ (2011) Genome-wide SNP discovery in tetraploid alfalfa using 454 sequencing and high resolution melting analysis. BMC Genomics 12, 350–360.
Genome-wide SNP discovery in tetraploid alfalfa using 454 sequencing and high resolution melting analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXpsFChsbs%3D&md5=f4777419b89670ebae29cccb2175ce62CAS |

Hayes B, Goddard ME (2001) The distribution of the effects of genes affecting quantitative traits in livestock. Genetics, Selection, Evolution 33, 209–229.
The distribution of the effects of genes affecting quantitative traits in livestock.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXltFWksL4%3D&md5=1460d4d12c156449902a09d17acdb1deCAS | 11403745PubMed |

Hayes B, Goddard M (2010) Genome-wide association and genomic selection in animal breeding. Genome 53, 876–883.

Hayes BJ, Visscher PM, Goddard ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genetical Research 91, 47–60.
Increased accuracy of artificial selection by using the realized relationship matrix.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXit1aisbc%3D&md5=7d0e422472dfb2f45c8f69ada6cb6f85CAS |

Hayes BJ, Cogan NOI, Pembleton LW, Goddard ME, Wang JP, Spangenberg GC, Forster JW (2013) Prospects for genomic selection in forage plant species. Plant Breeding 132, 133–143.
Prospects for genomic selection in forage plant species.Crossref | GoogleScholarGoogle Scholar |

Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Science 49, 1–12.
Genomic selection for crop improvement.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjsF2it78%3D&md5=aecf6a77c7c8c592fde59ef52e072856CAS |

Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant breeding with genomic selection: Gain per unit time and cost. Crop Science 50, 1681–1690.
Plant breeding with genomic selection: Gain per unit time and cost.Crossref | GoogleScholarGoogle Scholar |

Heffner EL, Jannink JL, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4, 65–75.
Genomic selection accuracy using multifamily prediction models in a wheat breeding program.Crossref | GoogleScholarGoogle Scholar |

Henderson CR (1984) ‘Applications of linear models in animal breeding.’ (University of Guelph: Guelph, ON, Canada)

Heslot N, Yang HP, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: A comparison of models. Crop Science 52, 146–160.
Genomic selection in plant breeding: A comparison of models.Crossref | GoogleScholarGoogle Scholar |

Heslot N, Jannink JL, Sorrells ME (2013) Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data. Crop Science 53, 921–933.
Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data.Crossref | GoogleScholarGoogle Scholar |

Hill WG (1993) Variation in genetic identity within kinships. Heredity 71, 652–653.
Variation in genetic identity within kinships.Crossref | GoogleScholarGoogle Scholar |

Huang XH, Feng Q, Qian Q, Zhao Q, Wang L, Wang AH, Guan JP, Fan DL, Weng QJ, Huang T, Dong GJ, Sang T, Han B (2009) High-throughput genotyping by whole-genome resequencing. Genome Research 19, 1068–1076.
High-throughput genotyping by whole-genome resequencing.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXntFGrsLc%3D&md5=970a288a1e44e69541c405f30df21980CAS |

Hudson CJ, Freeman JS, Kullan ARK, Petroli CD, Sansaloni CP, Kilian A, Detering F, Grattapaglia D, Potts BM, Myburg AA, Vaillancourt RE (2012) A reference linkage map for Eucalyptus. BMC Genomics 13, 240–250.
A reference linkage map for Eucalyptus.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xhslamu7zM&md5=7fc8eed016a25b9fba0f453a3c853dedCAS | 22702473PubMed |

Iwata H, Jannink JL (2011) 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 Science 51, 1915–1927.
Accuracy of genomic selection prediction in barley breeding programs: A simulation study based on the real single nucleotide polymorphism data of barley breeding lines.Crossref | GoogleScholarGoogle Scholar |

Iwata H, Hayashi T, Tsumura Y (2011) Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica. Tree Genetics & Genomes 7, 747–758.
Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica.Crossref | GoogleScholarGoogle Scholar |

Jannink JL (2010) Dynamics of long-term genomic selection. Genetics, Selection, Evolution 42, 35–47.
Dynamics of long-term genomic selection.Crossref | GoogleScholarGoogle Scholar | 20712894PubMed |

Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics 9, 166–177.
Genomic selection in plant breeding: from theory to practice.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXjvF2hu7Y%3D&md5=d8eab064c7ccd2d6a2bbee4ece06270fCAS | 20156985PubMed |

Johnson JA, Bellinger MR, Toepfer JE, Dunn P (2004) Temporal changes in allele frequencies and low effective population size in greater prairie-chickens. Molecular Ecology 13, 2617–2630.
Temporal changes in allele frequencies and low effective population size in greater prairie-chickens.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXotlemtLo%3D&md5=c298c98254c42ea7b835c0c110a515eeCAS | 15315675PubMed |

Jonas E, de Koning DJ (2013) Does genomic selection have a future in plant breeding? Trends in Biotechnology 31, 497–504.
Does genomic selection have a future in plant breeding?Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtFakt7bI&md5=ebcc9b29ced060f6e5ad9b24b535497bCAS | 23870753PubMed |

Kijas JW, Lenstra JA, Hayes B, Boitard S, Neto LRP, San Cristobal M, Servin B, McCulloch R, Whan V, Gietzen K, Paiva S, Barendse W, Ciani E, Raadsma H, McEwan J, Dalrymple B, Int Sheep Genomics C (2012) Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biology 10, e1001258
Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XislamtLo%3D&md5=972959eed26859df275dbba798718d33CAS | 22346734PubMed |

Kumar S, Chagné D, Bink M, Volz RK, Whitworth C, Carlisle C (2012) Genomic selection for fruit quality traits in apple (Malus × domestica Borkh.). PLoS ONE 7, e36674
Genomic selection for fruit quality traits in apple (Malus × domestica Borkh.).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xnt1Ogs70%3D&md5=1b57a1fed934c2a433d25ad56843e2c2CAS | 22574211PubMed |

Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743–756.

Li X, Acharya A, Wei Y, Hansen J, Crawford J, Viands D, Brummer E (2013) Genomic selection in tetraploid alfalfa using genotyping-by-sequencing. In ‘The 4th International Symposium of Forage Breeding’. 23–25 Sept. 2013, Centre of AgriBioscience, Melbourne. Abstract 44. (Organising Committee International Symposium of Forage Breeders)

Liu CC, Lin CC, Li KC, Chen WSE, Chen JC, Yang MT, Yang PC, Chang PC, Chen JJW (2007) Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis. BMC Bioinformatics 8, 164–178.
Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis.Crossref | GoogleScholarGoogle Scholar | 17518996PubMed |

Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: A simulation experiment. G3 – Genes, Genomes, Genetics 3, 481–491.

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. In ‘Advances in agronomy’. Vol. 110. (Ed. DL Sparks) pp. 77–123. (Elsevier Academic Press Inc.: San Diego, CA, USA)

Lorenz AJ, Smith KP, Jannink JL (2012) Potential and optimization of genomic selection for fusarium head blight resistance in six-row barley. Crop Science 52, 1609–1621.
Potential and optimization of genomic selection for fusarium head blight resistance in six-row barley.Crossref | GoogleScholarGoogle Scholar |

Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theoretical and Applied Genetics 120, 151–161.
Accuracy of genotypic value predictions for marker-based selection in biparental plant populations.Crossref | GoogleScholarGoogle Scholar | 19841887PubMed |

Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP markers and their impact on plant breeding. International Journal of Plant Genomics 2012, 728398
SNP markers and their impact on plant breeding.Crossref | GoogleScholarGoogle Scholar | 23316221PubMed |

McNally KL, Childs KL, Bohnert R, Davidson RM, Zhao K, Ulat VJ, Zeller G, Clark RM, Hoen DR, Bureau TE, Stokowski R, Ballinger DG, Frazer KA, Cox DR, Padhukasahasram B, Bustamante CD, Weigel D, Mackill DJ, Bruskiewich RM, Ratsch G, Buell CR, Leung H, Leach JE (2009) Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences of the United States of America 106, 12273–12278.
Genomewide SNP variation reveals relationships among landraces and modern varieties of rice.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXpslCjsrs%3D&md5=ad57266a80a636a1eb4a606c209b7ee2CAS | 19597147PubMed |

Meuwissen THE (2009) Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genetics, Selection, Evolution 41, 35–44.
Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping.Crossref | GoogleScholarGoogle Scholar |

Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.

Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends in Plant Science 12, 433–436.
Novel throughput phenotyping platforms in plant genetic studies.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtFWgsLvM&md5=ff9592d240c237a20a0cbd9e35fc4a65CAS | 17719833PubMed |

Nakaya A, Isobe SN (2012) Will genomic selection be a practical method for plant breeding? Annals of Botany 110, 1303–1316.
Will genomic selection be a practical method for plant breeding?Crossref | GoogleScholarGoogle Scholar | 22645117PubMed |

Nejati Javaremi A, Smith C,, Gibson JP (1997) Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of Animal Science 75, 1738–1745.

Park T, Casella G (2008) The Bayesian Lasso. Journal of the American Statistical Association 103, 681–686.
The Bayesian Lasso.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXptlansL8%3D&md5=40f62cbf2069701e679875d8ea53cea4CAS |

Pembleton L, Cogan N, Wang J, Forster JW (2013) High-throughput Automated low-cost quantification of individual water soluble carbohydrates and proteiningrass herbage. In ‘The 4th International Symposium of Forage Breeding’. 23–25 September 2013, Centre of AgriBioscience, Melbourne. Abstract 71. (Organising Committee International Symposium of Forage Breeders)

Perez-Rodriguez P, Gianola D, González-Camacho JM, Crossa J, Manes Y, Dreisigacker S (2012) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 – Genes, Genomes, Genetics 2, 1595–1605.

Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Science 49, 1165–1176.
Ridge regression and extensions for genomewide selection in maize.Crossref | GoogleScholarGoogle Scholar |

Poland J, Endelman J, Dawson J, Rutkoski J, Wu SY, Manes Y, Dreisigacker S, Crossa J, Sanchez-Villeda H, Sorrells M, Jannink JL (2012a) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5, 103–113.
Genomic selection in wheat breeding using genotyping-by-sequencing.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXlvVaksrw%3D&md5=2fdf8805e6924f5237d31bbff33742bfCAS |

Poland JA, Brown PJ, Sorrells ME, Jannink JL (2012b) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, e32253
Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xjs1ejsb8%3D&md5=2eb3829f6f30aa3a626dc5ef74972761CAS | 22389690PubMed |

Rasmusson DC, Phillips RL (1997) Plant breeding progress and genetic diversity from de novo variation and elevated epistasis. Crop Science 37, 303–310.
Plant breeding progress and genetic diversity from de novo variation and elevated epistasis.Crossref | GoogleScholarGoogle Scholar |

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 Phytologist 194, 116–128.
Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees.Crossref | GoogleScholarGoogle Scholar |

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 Phytologist 193, 617–624.
Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments.Crossref | GoogleScholarGoogle Scholar |

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, 1503–1510.
Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).Crossref | GoogleScholarGoogle Scholar |

Riedelsheimer C, Melchinger AE (2013) Optimizing the allocation of resources for genomic selection in one breeding cycle. Theoretical and Applied Genetics 126, 2835–2848.
Optimizing the allocation of resources for genomic selection in one breeding cycle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhslWhur%2FO&md5=c848b7963ad2c8ca42ccf25e06cffe66CAS | 23982591PubMed |

Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics 44, 217–220.
Genomic and metabolic prediction of complex heterotic traits in hybrid maize.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xnt1Omug%3D%3D&md5=464b21a6749bba54b307330f66205bfcCAS | 22246502PubMed |

Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194, 493–503.
Genomic predictability of interconnected biparental maize populations.Crossref | GoogleScholarGoogle Scholar | 23535384PubMed |

Robertson A (1961) Inbreeding in artificial selection programmes. Genetical Research 2, 189–194.
Inbreeding in artificial selection programmes.Crossref | GoogleScholarGoogle Scholar |

Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink JL, Sorrells M (2012) Evaluation of genomic prediction methods for fusarium head blight resistance in wheat. Plant Genome 5, 51–61.
Evaluation of genomic prediction methods for fusarium head blight resistance in wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xht1Ggu7%2FO&md5=e9974220a048c84b74c3b74b280644c5CAS |

Sato K, Nankaku N, Takeda K (2009) A high-density transcript linkage map of barley derived from a single population. Heredity 103, 110–117.
A high-density transcript linkage map of barley derived from a single population.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXptVagu74%3D&md5=15c714d142208448823f37abd0593f64CAS | 19455180PubMed |

Simeão Resende RM, Casler MD, de Resende MDV (2014) Genomic selection in forage breeding: accuracy and methods. Crop Science 54, 143–156.
Genomic selection in forage breeding: accuracy and methods.Crossref | GoogleScholarGoogle Scholar |

Smith C (1967) Improvement of metric traits through specific genetic loci. Animal Production 9, 349–358.
Improvement of metric traits through specific genetic loci.Crossref | GoogleScholarGoogle Scholar |

Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Statistics and Computing 14, 199–222.
A tutorial on support vector regression.Crossref | GoogleScholarGoogle Scholar |

Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE (2008) Genomic selection using different marker types and densities. Journal of Animal Science 86, 2447–2454.
Genomic selection using different marker types and densities.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXht1ams73E&md5=2eb8b2d4bcbe279a7e8672de6150a1acCAS | 18407980PubMed |

Soller M (1978) Use of loci associated with quantitative effects in dairy-cattle improvement. Animal Production 27, 133–139.
Use of loci associated with quantitative effects in dairy-cattle improvement.Crossref | GoogleScholarGoogle Scholar |

Staller J, Tykot R, Benz B (2006) ‘Histories of maize: multidisciplinary approaches to the prehistory, linguistics, biogeography, domestication, and evolution of maize.’ (Academic Press: Waltham, MA, USA)

Studer B, Byrne S, Nielsen RO, Panitz F, Bendixen C, Islam MS, Pfeifer M, Lubberstedt T, Asp T (2012) A transcriptome map of perennial ryegrass (Lolium perenne L.). BMC Genomics 13, 140–153.
A transcriptome map of perennial ryegrass (Lolium perenne L.).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhslGqu73I&md5=35e9fea2ba0912cc28dc45cb64d4171dCAS | 22513206PubMed |

Technow F, Burger A, Melchinger AE (2013) Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. G3 – Genes, Genomes, Genetics 3, 197–203.

Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM (2007) Recent human effective population size estimated from linkage disequilibrium. Genome Research 17, 520–526.
Recent human effective population size estimated from linkage disequilibrium.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXktFGntrw%3D&md5=8aa72bf2b7d952124246e9a561d7b841CAS | 17351134PubMed |

Thuillet AC, Bataillon T, Poirier S, Santoni S, David JL (2005) Estimation of long-term effective population sizes through the history of durum wheat using microsatellite data. Genetics 169, 1589–1599.
Estimation of long-term effective population sizes through the history of durum wheat using microsatellite data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXktFGjtrs%3D&md5=7c7878538fd40a8a1e6535afd881eb28CAS | 15545658PubMed |

Uimari P, Tapio M (2011) Extent of linkage disequilibrium and effective population size in Finnish Landrace and Finnish Yorkshire pig breeds. Journal of Animal Science 89, 609–614.
Extent of linkage disequilibrium and effective population size in Finnish Landrace and Finnish Yorkshire pig breeds.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXjtlSmtr4%3D&md5=75e0b9c3220373dbbb85afce314b6357CAS | 21036932PubMed |

van der Werf JHJ, Kinghorn BP, Banks RG (2010) Design and role of an information nucleus in sheep breeding programs. Animal Production Science 50, 998–1003.
Design and role of an information nucleus in sheep breeding programs.Crossref | GoogleScholarGoogle Scholar |

VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtlajtLzO&md5=23399e2f64679bd04f67b1f3d089d8ebCAS | 18946147PubMed |

VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.
Invited review: Reliability of genomic predictions for North American Holstein bulls.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXlsVOrsw%3D%3D&md5=d996a11a1c1ca902d95d50be11aa351cCAS | 19109259PubMed |

Villa-Angulo R, Matukumalli LK, Gill CA, Choi J, Van Tassell CP, Grefenstette JJ (2009) High-resolution haplotype block structure in the cattle genome. BMC Genetics 10, 19–32.
High-resolution haplotype block structure in the cattle genome.Crossref | GoogleScholarGoogle Scholar | 19393054PubMed |

Villumsen TM, Janss L, Lund MS (2009) The importance of haplotype length and heritability using genomic selection in dairy cattle. Journal of Animal Breeding and Genetics 126, 3–13.
The importance of haplotype length and heritability using genomic selection in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1M7jt1Cksw%3D%3D&md5=9ed7188bd6e76bc91db9345dd53edcd4CAS | 19207924PubMed |

Wientjes YCJ, Veerkamp RF, Calus MPL (2013) The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics 193, 621–631.
The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtVWnu7zL&md5=a8d92c0cff4d93ba384814a8222e1316CAS |

Wimmer V, Lehermeier C, Albrecht T, Auinger HJ, Wang Y, Schon CC (2013) Genome-wide prediction of traits with different genetic architecture through efficient variable selection. Genetics 195, 573–587.
Genome-wide prediction of traits with different genetic architecture through efficient variable selection.Crossref | GoogleScholarGoogle Scholar | 23934883PubMed |

Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 – Genes, Genomes, Genetics 2, 1427–1436.

Wong CK, Bernardo R (2008) Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theoretical and Applied Genetics 116, 815–824.
Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXkvFyjtrg%3D&md5=11dc9e4a179da55f420c869f931e690cCAS | 18219476PubMed |

Würschum T, Reif JC, Kraft T, Janssen G, Zhao YS (2013) Genomic selection in sugar beet breeding populations. BMC Genetics 14, 85–93.
Genomic selection in sugar beet breeding populations.Crossref | GoogleScholarGoogle Scholar | 24047500PubMed |

Xu SZ (2013) Genetic mapping and genomic selection using recombination breakpoint data. Genetics 195, 1103–1115.
Genetic mapping and genomic selection using recombination breakpoint data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXhtlGjurs%3D&md5=c6a7200c93fa9533aaa862fd30e550a0CAS |

Yabe S, Ohsawa R, Iwata H (2013) Potential of genomic selection for mass selection breeding in annual allogamous crops. Crop Science 53, 95–105.
Potential of genomic selection for mass selection breeding in annual allogamous crops.Crossref | GoogleScholarGoogle Scholar |

Yang JA, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42, 565–569.
Common SNPs explain a large proportion of the heritability for human height.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXns1GisL8%3D&md5=9f40d45d2f505d03c993e0740800a3aaCAS |

Zhao YS, Gowda M, Liu WX, Würschum T, Maurer HP, Longin FH, Ranc N, Reif J (2012) Accuracy of genomic selection in European maize elite breeding populations. Theoretical and Applied Genetics 124, 769–776.
Accuracy of genomic selection in European maize elite breeding populations.Crossref | GoogleScholarGoogle Scholar |

Zhao YS, Gowda M, Liu WX, Würschum T, Maurer HP, Longin FH, Ranc N, Piepho HP, Reif JC (2013a) Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations. Plant Breeding 132, 99–106.
Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations.Crossref | GoogleScholarGoogle Scholar |

Zhao YS, Zeng J, Fernando R, Reif JC (2013b) Genomic prediction of hybrid wheat performance. Crop Science 53, 802–810.
Genomic prediction of hybrid wheat performance.Crossref | GoogleScholarGoogle Scholar |

Zhong SQ, Dekkers JCM, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study. Genetics 182, 355–364.
Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXlvFCmtL4%3D&md5=c06f0a44a6210a399178c0c757aa0a0fCAS |