Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding

G3: Genes, Genomes, Genetics

Volumn 5, Issue 2, 2015, Pages 291-300

  Montesinos López, Osval A ^a   Montesinos López, Abelardo ^b   Pérez Rodríguez, Paulino ^c   de los Campos, Gustavo ^d   Eskridge, Kent ^e   Crossa, José ^f  

^a UNIVERSIDAD DE COLIMA   (Mexico)

^b CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS   (Mexico)

^c Department of Biostatistics University of Alabama at Birmingham ^*  (Mexico)

^d UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^e UNIVERSITY OF NEBRASKA LINCOLN   (United States)

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

Author keywords

Disease resistance; GBLUP; Genotype environment interaction; GenPred; Prediction accuracy; Shared data resource; Threshold model

Indexed keywords

ARTICLE; COLOMBIA; DISEASE RESISTANCE; ENVIRONMENTAL STRESS; GENETIC MODEL; GENETIC SELECTION; GENOTYPE ENVIRONMENT INTERACTION; LEAF SPOT; MAIZE; MEXICO; NONHUMAN; PHENOTYPE; PHYSICAL RESISTANCE; PLANT BREEDING; PLANT DISEASE; PLANT GENOME; PREDICTION; SCORING SYSTEM; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL; ZIMBABWE; ASCOMYCETES; BIOLOGICAL MODEL; BREEDING; GENETICS; GENOTYPE;

ZEA MAYS;

ASCOMYCOTA; BREEDING; DISEASE RESISTANCE; GENOME, PLANT; GENOTYPE; MODELS, GENETIC; PHENOTYPE; PLANT DISEASES; POLYMORPHISM, SINGLE NUCLEOTIDE; ZEA MAYS;

EID: 84922334893     PISSN: None     EISSN: 21601836     Source Type: Journal    
DOI: 10.1534/g3.114.016188     Document Type: Article

References (32)

1. Atkinson, L., 1988 The measurement-statistics controversy: factor analysis and subinterval data. Bull. Psychon. Soc. 26: 361-364.
2. Brier, G. W., 1950 Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78: 1-3.
3. Burgueño, J., G. de Los Campos, K. Weigel, and J. Crossa, 2012 Genomic prediction of breeding values when modeling genotype ×environment interaction using pedigree and dense molecular markers. Crop Sci. 52: 707.
4. Crossa, J., G. de Los Campos, P. Pérez-Rodríguez, D. Gianola, J. Burgueño et al., 2010 Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186: 713-724.
5. Crossa, J., P. Pérez-Rodríguez, G. de Los Campos, G. Mahuku, S. Dreisigacker et al., 2011 Genomic selection and prediction in plant breeding. J. Crop Improv. 25: 239-261.
6. Crossa, J., Y. Beyene, S. Kassa, P. Pérez-Rodríguez, J. M. Hickey et al., 2013a Genomic prediction in maize breeding populations with genotyping- by-sequencing. G3 (Bethesda) 3: 1903-1926.
7. Crossa, J., P. Pérez-Rodríguez, J. Hickey, J. Burgueño, L. Ornella et al., 2013b Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112: 48-60.
8. de Los Campos, G., and P. Pérez-Rodríguez, 2013 BGLR: Bayesian Generalized Linear Regression. R package version 1.0.3. Availabe at: http://CRAN.R-project.org/package=BGLR. Accessed December 30, 2014.
9. de Los Campos, G., H. Naya, D. Gianola, J. Crossa, A. Legarra et al., Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182: 375-385.
10. de Los Campos, G., D. Gianola, G. J. M. Rosa, K. Weigel, and J. Crossa, Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92: 295-308.
11. Garthwaite, P. H., J. B. Kadane, and A. O'Hagan, 2005 Statistical methods for eliciting probability distributions. J. Am. Stat. Assoc. 100: 680-701.
12. Gianola, D., 1980 A method of sire evaluation for dichotomies. J. Anim. Sci. 51: 1266-1271.
13. Gianola, D., 1982 Theory and analysis of threshold characters. J. Anim. Sci. 54: 1079-1096.
14. Gianola, D., and J. L. Foulley, 1983 Sire evaluation for ordered categorical data with a threshold model. Genet. Sel. Evol. 15: 1-23.
15. González-Camacho, J. M., G. de Los Campos, P. Pérez-Rodríguez, D. Gianola, J. E. Cairns et al., 2012 Genome-enabled prediction of genetic values using radial basis function neural networks. Theor. Appl. Genet. 125: 759-771.
16. González-Recio, O., and S. Forni, 2011 Genome-wide prediction of discrete traits using Bayesian regressions and machine learning. Genet. Sel. Evol. 43: 7.
17. Heffner, E. L., M. E. Sorrells, and J.-L. Jannink, 2009 Genomic selection for crop improvement. Crop Sci. 49: 1-12.
18. 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.
19. Heslot, N., H.-P. Yang, M. E. Sorrells, and J.-L. Jannink, 2012 Genomic selection in plant breeding: a comparison of models. Crop Sci. 52: 146.
20. Jarquín, D., J. Crossa, X. Lacaze, P. Cheyron, J. Daucourt et al., 2014 A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor. Appl. Genet. 127: 595-607.
21. Kizilkaya, K., R. G. Tait, D. J. Garrick, R. L. Fernando, and J. M. Reecy, Whole genome analysis of infectious bovine kerato conjunctivitis in Angus cattle using Bayesian threshold models. BMC Proc. 5: S22.
22. Kneib, T., B. Baumgartner, and W. J. Steiner, 2007 Semiparametric multinomial logit models for analysing consumer choice behaviour. AStA Adv. Stat. Anal. 91: 225-244.
23. Littell, R. C., W. Stroup, and R. J. Freund, 2002 SAS for Linear Models. ed. 4. SAS Institute, Cary, NC.
24. Meuwissen, T. H. E., B. J. Hayes, and M. E. Goddard, 2001 Prediction of total genetic values using genome-wide dense marker maps. Genetics 157: 1819-1829.
25. Pérez-Rodríguez, P., and G. de Los Campos, 2014 Genome-wide regression & prediction with the BGLR statistical package. Genetics 198: 483-495.
26. Pérez-Rodríguez, P., G. de Los Campos, J. Crossa, and D. Gianola, Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian Linear Regression Package in R. Plant Genome 3: 106-116.
27. Pérez-Rodríguez, P., D. Gianola, J. M. González-Camacho, J. Crossa, Y. Manes, and S. Dreisigacker, 2012 Comparison between linear and non-parametric models for genome-enabled prediction in wheat. G3 (Bethesda) 2: 1595-1605.
28. R Core Team, 2014 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www. R-project.org/. Accessed December 30, 2014.
29. Sorensen, D. A., S. Andersen, D. Gianola, and I. Korsgaard, 1995 Bayesian inference in threshold models using Gibbs sampling. Genet. Sel. Evol. 27: 229-249.
30. Stroup, W., 2012 Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press, Boca Raton, FL.
31. Villanueva, B., J. Fernandez, L. A. Garcia-Cortes, L. Varona, H. D. Daetwyler et al., 2011 Accuracy of genome-wide evaluation for disease resistance in aquaculture breeding programs. J. Anim. Sci. 89: 3433-3442.
32. Wang, C. L., X. D. Ding, J. Y. Wang, J. F. Liu, W. X. Fu et al., 2012 Bayesian methods for estimating GEBVs of threshold traits. Heredity 110: 213-219.