Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.
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Araus JL (2002) Plant breeding and drought in C3 cereals: what should we breed for? Ann Bot 89(7):925–940. https://doi.org/10.1093/aob/mcf049
Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61. https://doi.org/10.1016/j.tplants.2013.09.008
Arruda MP, Lipka AE, Brown PJ, Krill AM, Thurber C, Brown-Guedira G, Dong Y, Foresman BJ, Kolb FL (2016) Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol Breed 36(7):84. https://doi.org/10.1007/s11032-016-0508-5
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 J 4(2):132. https://doi.org/10.3835/plantgenome2011.02.0007
Auinger HJ, Schönleben M, Lehermeier C, Schmidt M, Korzun V, Geiger HH, Piepho HP, Gordillo A, Wilde P, Bauer E, Schön CC (2016) Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). Theor App Genet 129(11):2043–2053. https://doi.org/10.1007/s00122-016-2756-5
Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput Electron Agric 75(2):304–312. https://doi.org/10.1016/j.compag.2010.12.006
Butler D, Cullis B, Gilmour A, Gogel B (2009) Mixed models for S language environments: ASReml-R reference manual. Queensland Department of Primary Industries, Queensland, Australia. https://www.vsni.co.uk/downloads/asreml/release3/asreml-R.pdf. Accessed 17 Aug 2015
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho J, Pérez-Elizalde S, Beyene Y, Dreisigacker S, ingh R, Zhang X, Gowda M, Roorkiwal M, Rukoski J, Varshney RK (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975. https://doi.org/10.1016/j.tplants.2017.08.011
Cuevas J, Crossa J, Montesinos-López OA, Burgueño J, Pérez-Rodríguez P, de los Campos G (2017) Bayesian genomic prediction with genotype × environment interaction kernel models. G3 Genes Genomes Genet 7(1):41–53
DeGroot BJ, Keown JF, Van Vleck LD, Kachman SD (2007) Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model. Fac Pap Publ Anim Sci 240. http://digitalcommons.unl.edu/animalscifacpub/240. Accessed 28 Feb 2018
Devadas R, Lamb DW, Backhouse D, Simpfendorfer S (2015) Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precis Agric 16(5):477–491. https://doi.org/10.1007/s11119-015-9390-0
Endelman JB (2011) Ridge regression and other kernels for genomic selec- tion with R package rrBLUP. Plant Genome 4:250–255. https://doi.org/10.3835/plantgenome2011.08.0024
Endelman JB, Jannink JL (2012) Shrinkage estimation of the realized relationship matrix. G3 Genes Genomes Genet 2:1405–1413. https://doi.org/10.1534/g3.112.004259
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Pearson Prentice Hall, Harlow
Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One. https://doi.org/10.1371/journal.pone.0090346
Haghighattalab A, González Pérez L, Mondal S, Singh D, Schinstock D, Rutkoski J, Oritiz-Monasterio I, Singh R, Goodin D, Poland J (2016) Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12(1):35. https://doi.org/10.1186/s13007-016-0134-6
Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92(2):433–443. https://doi.org/10.3168/jds.2008-1646
Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50(5):1681–1690. https://doi.org/10.2135/cropsci2009.11.0662
Heffner EL, Jannink JL, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4(1):65. https://doi.org/10.3835/plantgenome2010.12.0029
Heslot N, Akdemir D, Sorrells ME, Jannink JL (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127(2):463–480. https://doi.org/10.1007/s00122-013-2231-5
Hoffstetter A, Cabrera A, Huang M, Sneller C (2016) Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3 Genes Genomes Genet 6(9):2919–2928. https://doi.org/10.1534/g3.116.032532
Holman FH, Riche AB, Michalski A, Castle M, Wooster MJ, Hawkesford MJ (2016) High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. https://doi.org/10.3390/rs8121031
International Wheat Genome Sequencing Consortium (IWGSC) (2014) A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345(6194):1251788. https://doi.org/10.1126/science.1251788
Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M, Burgueño J, de los Campos G (2014) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127(3):595–607. https://doi.org/10.1007/s00122-013-2243-1
Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4):1513–1522. https://doi.org/10.1534/genetics.112.144246
Juliana P, Singh RP, Singh PK, Crossa J, Huerta-Espino J, Lan C, Bhavani S, Rutkoski J, Poland J, Bergstrom G, Sorrells ME (2017) Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat. Theor Appl Genet 130(7):1415–1430. https://doi.org/10.1007/s00122-017-2897-1
Lorenz AJ, Smith KP, Jannink JL (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52:1609–1621. https://doi.org/10.2135/cropsci2011.09.0503
Manickavelu A, Hattori T, Yamaoka S, Yoshimura K, Kondou Y, Onogi A, Matsui M, Iwata H, Ban T (2017) Genetic nature of elemental contents in wheat grains and its genomic prediction: toward the effective use of wheat landraces from Afghanistan. PLoS One 12(1):e0169416. https://doi.org/10.1371/journal.pone.0169416
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829. http://www.genetics.org/content/157/4/1819.abstract
Meyer K (2005) Random regression analyses using B-splines to model growth of Australian Angus cattle. Genet Sel Evol 37:473–500. https://doi.org/10.1186/1297-9686-37-6-473
Michel S, Ametz C, Gungor H, Epure D, Grausgruber H, Löschenberger F, Buerstmayr H (2016) Genomic selection across multiple breeding cycles in applied bread wheat breeding. Theor Appl Genet 129(6):1179–1189. https://doi.org/10.1007/s00122-016-2694-2
Montesinos-López OA, Montesinos-López A, Crossa J, de los Campos G, Alvarado G, Mondal S, Rutkoski J (2017a) Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant Methods 13(4):1–23. https://doi.org/10.1186/s13007-016-0154-2
Montesinos-López A, Montesinos-López OA, Cuevas J, Mata-López WA, Burgueño J, Mondal S, Huerta J, Singh R, Autrique E, González-Pérez L, Crossa J (2017b) Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data. Plant Methods 13(1):62. https://doi.org/10.1186/s13007-017-0212-4
Montesinos-López A, Montesinos-López OA, de los Caampos G, Crossa J, Burgueno J, Lune Vazquez J (2018) Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture. Plant Methods 14:46. https://doi.org/10.1186/s13007-018-0314-7
Mrode RA (2005) Linear models for the prediction of animal breeding values. CABI Publishing, London. https://doi.org/10.1079/9780851990002.0000
Narjesi V, Mardi M, Hervan EM, Azadi A, Naghavi, Ebrahimi M, Zali AA (2015) Analysis of quantitative trait loci (QTL) for grain yield and agronomic traits in wheat (Triticum aestivum L.) under normal and salt-stress conditions. Plant Mol Biol Rep 33(6):2030–2040. https://doi.org/10.1007/s11105-015-0876-8
Ovenden B, Milgate A, Wade LJ, Rebetzke GJ, Holland JB (2018) Accounting for genotype-by-environment interactions and residual genetic variation in genomic selection for water-soluble carbohydrate concentration in wheat. G3 Genes Genomes Genet 8:g3.200038. https://doi.org/10.1534/g3.118.200038
Poland J, Endelman J, Dawson J, Rutkoski J, Wu SY, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink JL (2012a) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5(3):103–113. https://doi.org/10.3835/Plantgenome2012.06.0006
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 7:2. https://doi.org/10.1371/journal.pone.0032253
R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8:6. https://doi.org/10.1371/journal.pone.0066428
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 J 5(2):51. https://doi.org/10.3835/plantgenome2012.02.0001
Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S, Barbier H, Rouse MN, Jannink JL, Sorrells ME (2014) Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome. https://doi.org/10.3835/plantgenome2014.02.0006
Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J, Jannink JL, Sorrells ME (2015) Genetic gain from phenotypic and genomic selection for quantitative resistance to stem rust of wheat. Plant Genome 8:2. https://doi.org/10.3835/plantgenome2014.10.0074
Rutkoski J, Poland J, Mondal S, Autrique E, Párez LG, Crossa J, Reynolds M, Singh R (2016) Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3 Genes Genomes Genet 6(9):2799–2808. https://doi.org/10.1534/g3.116.032888
Sun J, Rutkoski JE, Poland JA, Crossa J, Jannink JL, Sorrells ME (2017) Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield. Plant Genome. https://doi.org/10.3835/plantgenome2016.11.0111
Velu G, Crossa J, Singh RP, Hao Y, Dreisigacker S, Perez-Rodriguez P, Joshi A, Chatrath R, Gupta V, Balasubramaniam A, Tiwari C, Mishra VK, Sohu VS, Mavi GS (2016) Genomic prediction for grain zinc and iron concentrations in spring wheat. Theor Appl Genet 129(8):1595–1605. https://doi.org/10.1007/s00122-016-2726-y
Wang Y, Mette M, Miedaner T, Gottwald M, Wilde P, Reif JC, Zhao Y (2014) The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years. BMC Genom 15(1):556. https://doi.org/10.1186/1471-2164-15-556
Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N, Iwata H (2017) High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci 8(March):1–11. https://doi.org/10.3389/fpls.2017.00421
White I, Thompson R, Brotherstone S (1999) Genetic and environmental smoothing of lactation curves with cubic splines. J Dairy Sci 82:632–638. https://doi.org/10.3168/jds.S0022-0302(99)75277-X
Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X, Wang G, Luo Q, Zhng Q, Liu Q, Xiong L (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087. https://doi.org/10.1038/ncomms6087
Zhang J, Song Q, Cregan PB, Jiang GL (2016) Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet 129(1):117–130. https://doi.org/10.1007/s00122-015-2614-x
The research was funded by the United States Agency for International Development (USAID) “Feed the Future Initiative” (Cooperative Agreement #AID-OAA-A-13-00051) and by participating US and Host Country institutions. We also thank the Delivering Genetic Gain in Wheat project, supported by aid from the U.K. Government’s Department of International Development (DFID) and the Bill & Melinda Gates Foundation (OPP113319). Partial funding was provided by Hatch project 149-430. This work was also partially supported by the Agriculture and Food Research Initiative Competitive Grants 2011-68002-30029 (Triticeae-CAP) and 2017-67007-25939 (Wheat-CAP) from the USDA National Institute of Food and Agriculture.
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Sun, J., Poland, J.A., Mondal, S. et al. High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. Theor Appl Genet 132, 1705–1720 (2019). https://doi.org/10.1007/s00122-019-03309-0