Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 04 2020
27 04 2020
Historique:
received:
17
07
2019
accepted:
11
03
2020
entrez:
29
4
2020
pubmed:
29
4
2020
medline:
15
12
2020
Statut:
epublish
Résumé
An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.
Identifiants
pubmed: 32341406
doi: 10.1038/s41598-020-63919-3
pii: 10.1038/s41598-020-63919-3
pmc: PMC7184575
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
7023Références
Meuwissen, T., Hayes, B. & Goddard, M. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).
pubmed: 1461589
pmcid: 1461589
Battenfield, S. D. et al. Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program. The Plant Genome 9 (2016).
Eathington, S. R., Crosbie, T. M., Edwards, M. D., Reiter, R. S. & Bull, J. K. Molecular markers in a commercial breeding program. Crop. Sci. 47, S-154–S-163 (2007).
doi: 10.2135/cropsci2007.04.0015IPBS
Cabrera-Bosquet, L., Crossa, J., von Zitzewitz, J., Serret, M. D. & Luis Araus, J. High‐throughput Phenotyping and Genomic Selection: The Frontiers of Crop Breeding Converge F. J. Integr. plant. Biol. 54, 312–3C0 (2012).
pubmed: 22420640
doi: 10.1111/j.1744-7909.2012.01116.x
Araus, J. L. & Cairns, J. E. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant. Sci. 19, 52–61 (2014).
pubmed: 24139902
doi: 10.1016/j.tplants.2013.09.008
Jannink, J.-L., Lorenz, A. J. & Iwata, H. Genomic selection in plant breeding: from theory to practice. Brief. Funct. genomics 9, 166–177 (2010).
pubmed: 20156985
doi: 10.1093/bfgp/elq001
Lorenz, A. J. et al. In Advances in agronomy Vol. 110 77–123 (Elsevier, 2011).
Buckler, E. S. et al. The genetic architecture of maize flowering time. Science 325, 714–718 (2009).
pubmed: 19661422
doi: 10.1126/science.1174276
Burgueño, J., Crossa, J., Cotes, J. M., Vicente, F. S. & Das, B. Prediction assessment of linear mixed models for multienvironment trials. Crop. Sci. 51, 944–954 (2011).
doi: 10.2135/cropsci2010.07.0403
So, Y.-S. & Edwards, J. Predictive ability assessment of linear mixed models in multienvironment trials in corn. Crop. Sci. 51, 542–552 (2011).
doi: 10.2135/cropsci2010.06.0338
Montesinos-López, O. A. et al. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant. methods 13, 4 (2017).
pubmed: 28053649
pmcid: 5209864
doi: 10.1186/s13007-016-0154-2
Aguate, F. M. et al. Use of hyperspectral image data outperforms vegetation indices in prediction of maize yield. Crop. Sci. 57, 2517–2524 (2017).
doi: 10.2135/cropsci2017.01.0007
Pérez-Rodríguez, P. et al. Single-step genomic and pedigree genotype× environment interaction models for predicting wheat lines in international environments. The plant genome (2017).
Cuevas, J. et al. Bayesian genomic prediction with genotype× environment interaction kernel models. G3: Genes, Genomes, Genet. 7, 41–53 (2017).
doi: 10.1534/g3.116.035584
Crain, J., Mondal, S., Rutkoski, J., Singh, R. P. & Poland, J. Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. The plant genome (2018).
Montesinos-López, A. et al. Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data. Plant. Methods 13, 62 (2017).
pubmed: 28769997
pmcid: 5530534
doi: 10.1186/s13007-017-0212-4
Krause, M. R. et al. Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat. G3: Genes, Genomes, Genet. g3, 200856.202018 (2019).
Blum, A., Shpiler, L., Golan, G. & Mayer, J. Yield stability and canopy temperature of wheat genotypes under drought-stress. Field Crop. Res 22, 289–296 (1989).
doi: 10.1016/0378-4290(89)90028-2
Amani, I., Fischer, R. & Reynolds, M. Canopy temperature depression association with yield of irrigated spring wheat cultivars in a hot climate. J. Agron. Crop. Sci. 176, 119–129 (1996).
doi: 10.1111/j.1439-037X.1996.tb00454.x
Bavec, F. & Bavec, M. Chlorophyll meter readings of winter wheat cultivars and grain yield prediction. Commun. Soil. Sci. Plant. Anal. 32, 2709–2719 (2001).
doi: 10.1081/CSS-120000956
Blum, A., Klueva, N. & Nguyen, H. Wheat cellular thermotolerance is related to yield under heat stress. Euphytica 117, 117–123 (2001).
doi: 10.1023/A:1004083305905
Raun, W. R. et al. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93, 131–138 (2001).
doi: 10.2134/agronj2001.931131x
Monostori, I. et al. Relationship between SPAD value and grain yield can be affected by cultivar, environment and soil nitrogen content in wheat. Euphytica 211, 103–112 (2016).
doi: 10.1007/s10681-016-1741-z
Weber, V. et al. Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crop. Res. 128, 82–90 (2012).
doi: 10.1016/j.fcr.2011.12.016
De los Campos, G., Gianola, D., Rosa, G. J., Weigel, K. A. & Crossa, J. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92, 295–308, https://doi.org/10.1017/S0016672310000285 (2010).
doi: 10.1017/S0016672310000285
Gianola, D. & van Kaam, J. B. Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178, 2289–2303, https://doi.org/10.1534/genetics.107.084285 (2008).
doi: 10.1534/genetics.107.084285
pubmed: 18430950
pmcid: 2323816
Pérez, P., de los Campos, G., Crossa, J. & Gianola, D. Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. plant. genome 3, 106–116 (2010).
pubmed: 21566722
pmcid: 3091623
doi: 10.3835/plantgenome2010.04.0005
Pérez, P. & de Los Campos, G. Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483–495 (2014).
pubmed: 25009151
pmcid: 4196607
doi: 10.1534/genetics.114.164442
Xu, Y., Xu, C. & Xu, S. Prediction and association mapping of agronomic traits in maize using multiple omic data. Heredity 119, 174 (2017).
pubmed: 28590463
pmcid: 5564377
doi: 10.1038/hdy.2017.27
Rutkoski, J. et al. 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, 2799–2808 (2016).
doi: 10.1534/g3.116.032888
Falconer, D. S. & Mackay, T. F. C. Introduction to quantitative genetics. 4th edn, (Longman, 1996).
Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L. & Royo, C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92, 83–91 (2000).
doi: 10.2134/agronj2000.92183x
Royo, C. et al. Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. Int. J. Remote. Sens. 24, 4403–4419 (2003).
doi: 10.1080/0143116031000150059
Marti, J., Bort, J., Slafer, G. & Araus, J. Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index? Ann. Appl. Biol. 150, 253–257 (2007).
doi: 10.1111/j.1744-7348.2007.00126.x
Babar, M. et al. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop. Sci. 46, 578–588 (2006).
doi: 10.2135/cropsci2005.0059
Tattaris, M., Reynolds, M. P. & Chapman, S. C. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front. Plant. Sci. 7, 1131 (2016).
pubmed: 27536304
pmcid: 4971441
doi: 10.3389/fpls.2016.01131
Khan, Z., Rahimi-Eichi, V., Haefele, S., Garnett, T. & Miklavcic, S. J. Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging. Plant. methods 14, 20 (2018).
pubmed: 29563961
pmcid: 5851000
doi: 10.1186/s13007-018-0287-6
Rischbeck, P. et al. Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. Eur. J. Agron. 78, 44–59 (2016).
doi: 10.1016/j.eja.2016.04.013
Fischer, R. et al. Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop. Sci. 38, 1467–1475 (1998).
doi: 10.2135/cropsci1998.0011183X003800060011x
Araus, J., Slafer, G., Reynolds, M. & Royo, C. Plant breeding and drought in C3 cereals: what should we breed for? Ann. Bot 89, 925–940 (2002).
pubmed: 12102518
pmcid: 4233799
doi: 10.1093/aob/mcf049
Pinto, R. S. et al. Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor. Appl. Genet. 121, 1001–1021 (2010).
pubmed: 20523964
pmcid: 2938441
doi: 10.1007/s00122-010-1351-4
Reynolds, M., Balota, M., Delgado, M., Amani, I. & Fischer, R. Physiological and morphological traits associated with spring wheat yield under hot, irrigated conditions. Funct. Plant. Biol. 21, 717–730 (1994).
doi: 10.1071/PP9940717
Gutiérrez-Rodríguez, M., Reynolds, M. P., Escalante-Estrada, J. A. & Rodríguez-González, M. T. Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions. Aust. J. Agric. Res 55, 1139–1147 (2004).
doi: 10.1071/AR04214
Rosyara, U. R., Subedi, S., Duveiller, E. & Sharma, R. C. Photochemical efficiency and SPAD value as indirect selection criteria for combined selection of spot blotch and terminal heat stress in wheat. J. Phytopathol 158, 813–821 (2010).
doi: 10.1111/j.1439-0434.2010.01703.x
Ibrahim, A. M. & Quick, J. S. Genetic control of high temperature tolerance in wheat as measured by membrane thermal stability. Crop. Sci. 41, 1405–1407 (2001).
doi: 10.2135/cropsci2001.4151405x
Harris, K. et al. Sorghum stay-green QTL individually reduce post-flowering drought-induced leaf senescence. J. Exp. Botany 58, 327–338, https://doi.org/10.1093/jxb/erl225 (2006).
doi: 10.1093/jxb/erl225
Lopes, M. S. & Reynolds, M. P. Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. J. Exp. Botany 63, 3789–3798, https://doi.org/10.1093/jxb/ers071 (2012).
doi: 10.1093/jxb/ers071
Doyle, J. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull 19, 11–15 (1987).
Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. (1987).
Poland, J. A., Brown, P. J., Sorrells, M. E. & Jannink, J.-L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS one 7, e32253, https://doi.org/10.1371/journal.pone.0032253 (2012).
doi: 10.1371/journal.pone.0032253
pubmed: 22389690
pmcid: 3289635
Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS one 6, e19379 (2011).
pubmed: 21573248
pmcid: 3087801
doi: 10.1371/journal.pone.0019379
Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635, https://doi.org/10.1093/bioinformatics/btm308 (2007).
doi: 10.1093/bioinformatics/btm308
pubmed: 17586829
Money, D. et al. LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms. G3: Genes|Genomes|Genetics 5, 2383–2390, https://doi.org/10.1534/g3.115.021667 (2015).
doi: 10.1534/g3.115.021667
pubmed: 26377960
pmcid: 4632058
Poland, J. et al. Genomic Selection in Wheat Breeding using Genotyping-by-Sequencing. The Plant. Genome 5, 103–113, https://doi.org/10.3835/plantgenome2012.06.0006 (2012).
doi: 10.3835/plantgenome2012.06.0006
Bansal, V. et al. Accurate detection and genotyping of SNPs utilizing population sequencing data. Genome Res 20, 537–545, https://doi.org/10.1101/gr.100040.109 (2010).
doi: 10.1101/gr.100040.109
pubmed: 20150320
pmcid: 2847757
Jarquín, D. et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor. Appl. Genet. 127, 595–607 (2014).
pubmed: 24337101
doi: 10.1007/s00122-013-2243-1
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94, https://doi.org/10.1186/1471-2156-11-94 (2010).
doi: 10.1186/1471-2156-11-94
pubmed: 20950446
pmcid: 2973851
Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 package. R package version 2, 74 (2007).
de los Campos, G. & Pérez-Rodríguez, P. Bayesian generalized linear regression. R package version 1 (2014).
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
pubmed: 18397895
doi: 10.1093/bioinformatics/btn129
Kuhn, M. Building Predictive Models in R Using the caret Package. 2008 28, 26, https://doi.org/10.18637/jss.v028.i05 (2008).
doi: 10.18637/jss.v028.i05