Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
07 08 2020
07 08 2020
Historique:
received:
06
08
2019
accepted:
24
07
2020
entrez:
10
8
2020
pubmed:
10
8
2020
medline:
10
2
2021
Statut:
epublish
Résumé
Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages), this implementation might not be the most convenient. GS might deliver high-rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with a significant impact on yield potential. The objective of this research consisted in develop a novel method that accurately predicts time-related traits such as DTH in unobserved environments. For this, we propose an implementation that incorporates day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned a root-mean-square error (RMSE) of 3.9 days, a Pearson correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between -4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between -6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between -31.5 and 28.7 days.
Identifiants
pubmed: 32770083
doi: 10.1038/s41598-020-70267-9
pii: 10.1038/s41598-020-70267-9
pmc: PMC7415153
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
13382Références
FAO, IFAD, UNICEF, WFP and WHO. The State of Food Security and Nutrition in the World. Building climate resilience for food security and nutrition. Rome, FAO (2018).
Bernardo, R. Breeding for Quantitative Traits in Plants (Stemma Press, Woodbury, 2002).
Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics157, 1819–1829 (2001).
pubmed: 11290733
pmcid: 1461589
Heffner, H. L., Sorrells, M. E. & Jannink, J. L. Genomic selection for crop improvement. Crop Sci.49, 1–2 (2009).
doi: 10.2135/cropsci2008.08.0512
Jannink, J. L., Lorenz, A. J. & Iwata, H. Genomic selection in plant breeding: From theory to practice. Brief. Funct. Genom.9, 166–177 (2010).
doi: 10.1093/bfgp/elq001
Crossa, J. et al. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics186, 713–724 (2010).
doi: 10.1534/genetics.110.118521
Perez-de-Castro, A. M. et al. Application of genomic tools in plant breeding. Curr. Genom.13, 179–195. https://doi.org/10.2174/138920212800543084 (2012).
doi: 10.2174/138920212800543084
de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D. & Calus, M. P. L. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics193, 327–345 (2013).
doi: 10.1534/genetics.112.143313
Voss-Fels, K. P., Cooper, M. & Hayes, B. J. Accelerating crop genetic gains with genomic selection. Theor. Appl. Genet.132, 669–686 (2019).
doi: 10.1007/s00122-018-3270-8
Onogi, A. et al. Toward integration of genomic selection with crop modelling: The development of an integrated approach to prediction rice heading dates. Theor. Appl. Genet.129, 805–817 (2016).
doi: 10.1007/s00122-016-2667-5
Yin, X., Kropff, M. J., Aggarwal, P. K., Peng, S. & Horie, T. Optimal preflowering phenology of irrigated rice for high yield potential in three Asian environments: A simulation study. Field Crop Res.51, 19–27 (1997).
doi: 10.1016/S0378-4290(96)01043-X
Hori, K., Matsubara, K. & Yano, M. Genetic control of flowering time in rice: Integration of Mendelian genetics and genomics. Theor. Appl. Genet.129, 2241–2252 (2016).
doi: 10.1007/s00122-016-2773-4
Onogi, A. et al. Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.). Theor. Appl. Genet.128, 41–53 (2015).
doi: 10.1007/s00122-014-2411-y
Spindel, J. et al. Genomic selection and association mapping in rice (Oryza sativa): Effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet.11, e1004982 (2015).
doi: 10.1371/journal.pgen.1004982
Li, X., Guo, T., Mu, Q., Li, X. & Yu, J. Genomic and environmental determinants and their interplay underlying phenotypic plasticity. Proc. Natl. Acad. Sci. U. S. A.115, 6679–6684 (2018).
doi: 10.1073/pnas.1718326115
Yabe, S. et al. Description of grain weight distribution leading to genomic selection for grain-filling characteristics in rice. PLoS ONE13(11), e0207627. https://doi.org/10.1371/journal.pone.0207627 (2018).
doi: 10.1371/journal.pone.0207627
pubmed: 30458025
pmcid: 6245794
Murray, M. G. & Thompson, W. F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res.8, 4321–4325 (1980).
doi: 10.1093/nar/8.19.4321
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics30, 2114–2120 (2014).
doi: 10.1093/bioinformatics/btu170
Kawahara, Y. et al. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice6, 1–10 (2013).
doi: 10.1186/1939-8433-6-4
Li, H. Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly. Bioinformatics28, 1838–1844 (2012).
doi: 10.1093/bioinformatics/bts280
McKenna, A. et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.20, 1297–1303 (2010).
doi: 10.1101/gr.107524.110
Smithsonian Institution. Smithsonian Meteorological Tables. Lxxvi–lxxviii, 211–224 (Lord Baltimore Press, Baltimore, 1939).
Forsythe, W. C. et al. A model comparison for daylength as a function of latitude and day of the year. Ecol. Model.80, 87–95 (1995).
doi: 10.1016/0304-3800(94)00034-F
Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5-5. https://CRAN.R-project.org/package=geosphere (2017).
VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci.91, 4414–4423 (2008).
doi: 10.3168/jds.2007-0980
Crossa, J. et al. Genomic prediction of gene bank wheat landraces. G3 (Bethesda)6, 1819–1834 (2016).
doi: 10.1534/g3.116.029637
Jarquín, D. et al. Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in Kansas Wheat. Plant Genome https://doi.org/10.3835/plantgenome2016.12.0130 (2017).
doi: 10.3835/plantgenome2016.12.0130
pubmed: 28724068
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2018).
de los Campos, G., & Pérez-Rodríguez, P. BGLR: Bayesian generalized linear regression. R package version 1(3) (2013).
Pérez-Rodríguez, P. et al. A pedigree-based reaction norm model for prediction of cotton yield in multienvironment trials. Crop Sci.55, 1143–1151. https://doi.org/10.2135/cropsci2014.08.0577 (2015).
doi: 10.2135/cropsci2014.08.0577