Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

G3: Genes, Genomes, Genetics

Volumn 6, Issue 9, 2016, Pages 2799-2808

  Rutkoski, Jessica ^a,b,c   Poland, Jesse ^d   Mondal, Suchismita ^c   Autrique, Enrique ^c   Pérez, Lorena González ^c   Crossa, José ^c   Reynolds, Matthew ^c   Singh, Ravi ^c  

^a Plant Breeding and Genetics Section in the School of Integrative Plant Science   (United States)

^b Department of Obstetrics Gynecology   (United States)

^c INTERNATIONAL MAIZE AND WHEAT IMPROVEMENT CENTER CIMMYT   (Mexico)

^d KANSAS STATE UNIVERSITY   (United States)

Author keywords

GenPred; Multivariate analysis; Secondary traits in genomic selection; Selection index; Shared data resource

Indexed keywords

BREEDING; CANOPY; GRAIN YIELD; MULTIVARIATE ANALYSIS; NONHUMAN; PEDIGREE; PHENOTYPE; PREDICTION; STATISTICAL MODEL; TRITICUM AESTIVUM; VALIDATION PROCESS; VEGETATION; ENERGY METABOLISM; FOOD GRAIN; GENETIC SELECTION; GENETICS; GENOMICS; GROWTH, DEVELOPMENT AND AGING; PLANT GENOME; QUANTITATIVE TRAIT LOCUS; SINGLE NUCLEOTIDE POLYMORPHISM; TEMPERATURE; WHEAT;

BREEDING; EDIBLE GRAIN; ENERGY METABOLISM; GENOME, PLANT; GENOMICS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; SELECTION, GENETIC; TEMPERATURE; TRITICUM;

EID: 84994235616     PISSN: None     EISSN: 21601836     Source Type: Journal    
DOI: 10.1534/g3.116.032888     Document Type: Article

References (23)

1. Babar, M. A., M. P. Reynolds, M. Van Ginkel, A. R. Klatt, W. R. Raun et al., 2006 Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 46: 1046-1057.
2. Bernardo, R., and J. Yu, 2007 Prospects for genome-wide selection for quantitative traits in maize. Crop Sci. 47: 1082-1090.
3. Calus, M. P. L., and R. F. Veerkamp, 2011 Accuracy ofmulti-trait genomic selection using different methods. Genet. Sel. Evol. 43: 26.
4. Elshire, R. J., J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto et al., 2011 A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6: e19379.
5. Endelman, J. B., 2011 Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4: 250-255.
6. Endelman, J. B., and J.-L. Jannink, 2012 Shrinkage estimation of the realized relationship matrix. G3 Genes Genome. Genet. 2: 1405-1413.
7. Gilmour A. R., B. J. Gogel, B. R. Cullis, and R. Thompson, 2009 ASReml user guide release 3.0. VSN International, Hemel Hempstead, UK
8. Gitelson, A. A., and M. N. Merzlyak, 1997 Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 18: 2691-2697.
9. Glaubitz, J. C., T. M. Casstevens, F. Lu, J. Harriman, R. J. Elshire et al., 2014 TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline. PLoS One 9: e90346.
10. Heffner, E. L., A. J. Lorenz, J.-L. Jannink, and M. E. Sorrells, 2010 Plant breeding with genomic selection: gain per unit time and cost. Crop Sci. 50: 1681-1690.
11. International Wheat Genome Sequencing Consortium (IWGSC), 2014 A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345: 1251788.
12. Jia, Y., and J.-L. Jannink, 2012 Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192: 1513-1522.
13. Lorenz, A. J., S. Chao, F. G. Asoro, E. L. Heffner, T. Hayashi et al., 2011 Genomic selection in plant breeding: knowledge and prospects. Adv. Agron. 110: 77-123.
14. Mondal, S., R. P. Singh, J. Huerta-Espino, Z. Kehel, and E. Autrique, 2015 Characterization of heat-and drought-stress tolerance in high-yielding spring wheat. Crop Sci. 55: 1552-1562.
15. Pask, A., A. K. Joshi, Y. Manès, I. Sharma, R. Chatrath et al., 2014 A wheat phenotyping network to incorporate physiological traits for climate change in south asia. F. Crop. Res. 168: 156-167.
16. Pinto, R. S., M. P. Reynolds, K. L. Mathews, C. L. McIntyre, J. J. Olivares-Villegas et al., 2010 Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor. Appl. Genet. 121: 1001-1021.
17. Pryce, J. E., and H. D. Daetwyler, 2012 Designing dairy cattle breeding schemes under genomic selection : a review of international research. Anim. Prod. Sci. 52: 107-114.
18. Pszczola, M., R. F. Veerkamp, Y. de Haas, E. Wall, T. Strabel et al., 2013 Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population. Animal 7: 1759-1768.
19. Reynolds, M., and P. Langridge, 2016 Physiological breeding. Curr. Opin. Plant Biol. 31: 162-171.
20. Schaeffer, L. R., 1984 Sire and cow evaluation under multiple trait models. J. Dairy Sci. 67: 1567-1580.
21. Wiegand, C. L., and A. J. Richardson, 1990a Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: I. Rationale. Agron. J. 82: 623-629.
22. Wiegand, C. L., and A. J. Richardson, 1990b Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: II. Results. Agron. J. 82: 630-636.
23. Wong, C. K., and R. Bernardo, 2008 Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor. Appl. Genet. 116: 815-824.