Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data

Crop Science

Volumn 53, Issue 3, 2013, Pages 921-933

  Heslot, Nicolas ^a,b   Jannink, Jean Luc ^a   Sorrells, Mark E ^a  

^a Department of Obstetrics Gynecology   (United States)

^b Limagrain Europe ^*  (France)

Author keywords

[No Author keywords available]

Indexed keywords

HORDEUM; HORDEUM VULGARE SUBSP. VULGARE;

EID: 84876436922     PISSN: 0011183X     EISSN: 14350653     Source Type: Journal    
DOI: 10.2135/cropsci2012.07.0420     Document Type: Article

References (38)

1. Beeck, C.P., W.A. Cowling, A.B. Smith, and B.R. Cullis. 2010. Analysis of yield and oil from a series of canola breeding trials. Part I. Fitting factor analytic mixed models with pedigree information. Genome 53:992-1001. doi:10.1139/G10-051
2. Burgueño, J., J. Crossa, P.L. Cornelius, and R.-C. Yang. 2008. Using factor analytic models for joining environments and genotypes without crossover genotype × environment interaction. Crop Sci. 48:1291-1305. doi:10.2135/cropsci2007.11.0632
3. Burgueño, J., J. Crossa, J.M. Cotes, F.S. Vicente, and B. Das. 2011. Prediction assessment of linear mixed models for multienvironment trials. Crop Sci. 51:944-954. doi:10.2135/cropsci2010.07.0403
4. Burgueño, J., G. de los Campos, K.A. Weigel, and J. Crossa. 2012. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. 52:707-719. doi:10.2135/cropsci2011.06.0299
5. Chapman, S.C., M. Cooper, D. Butler, and R. Henzell. 2000a. Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Aust. J. Agric. Res. 51:197-207. doi:10.1071/AR99020
6. Chapman, S.C., M. Cooper, G.L. Hammer, and D. Butler. 2000b. Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Aust. J. Agric. Res. 51:209-221. doi:10.1071/AR99021
7. Chapman, S.C., G.L. Hammer, D. Butler, and M. Cooper. 2000c. Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Aust. J. Agric. Res. 51:223-233. doi:10.1071/AR99022
8. Comstock, R.E. 1977. Quantitative genetics and the design of breeding programs. In: E. Pollak, O. Kempthorne, and T.B. Bailey, editors, Proceedings of the International Conference on Quantitative Genetics. Iowa State Univ. Press, Ames. p. 705-718.
9. Cooper, M., and I.H. DeLacy. 1994. Relationships among analytical methods used to study genotypic variation and genotype-byenvironment interaction in plant breeding multi-environment experiments. Theor. Appl. Genet. 88:561-572. doi:10.1007/ BF01240919
10. Cooper, M., and P.N. Fox. 1996. Environmental characterization based on probe and reference genotypes. In: M. Cooper and G.L. Hammer, editors, Plant adaptation and crop improvement. CAB Int., Wallingford, UK. p. 529-547.
11. Cooper, M., and G.L. Hammer, editors. 1996. Plant adaptation and crop improvement. CAB Int., Wallingford, UK.
12. Cullis, B.R., A.B. Smith, C.P. Beeck, and W.A. Cowling. 2010. Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis. Genome 53:1002-1016. doi:10.1139/G10-080
13. Dekkers, J.C.M. 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. J. Anim. Breed. Genet. 124:331-341. doi:10.1111/j.1439-0388.2007.00701.x
14. de los Campos, G., H. Naya, D. Gianola, J. Crossa, A. Legarra, E. Manfredi, K.A. Weigel, and J.M. Cotes. 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375-385. doi:10.1534/ genetics.109.101501
15. Elshire, R.J., J.C. Glaubitz, Q. Sun, J.A. Poland, K. Kawamoto, E.S. Buckler, and S.E. Mitchell. 2011. A robust, simple genotypingby-sequencing (GBS) approach for high diversity species. PLoS ONE 6:E19379. doi:10.1371/journal.pone.0019379
16. Gilmour, A.R., B. Gogel, B.R. Cullis, and R. Thompson. 2009. ASREML user guide release 3.0. VSN International Ltd., Hemel Hempstead, UK.
17. Hallauer, A.R., M.J. Carena, and J.B. Miranda Filho. 2010. Quantitative genetics in maize breeding. Iowa State Univ. Press, Ames.
18. Hardy, O.J., and X. Vekemans. 2002. SPAGeDi: A versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2:618-620. doi:10.1046/j.1471-8286.2002.00305.x
19. Heffner, E.L., M.E. Sorrells, and J.-L. Jannink. 2009. Genomic selection for crop improvement. Crop Sci. 49:1-12. doi:10.2135/ cropsci2008.08.0512
20. Heslot, N., H.-P. Yang, M.E. Sorrells, and J.-L. Jannink. 2012. Genomic selection in plant breeding: A comparison of models. Crop Sci. 52:146-160.
21. Holland, J.B., W.E. Nyquist, and C.T. Cervantes Martinez. 2002. Estimating and interpreting heritability for plant breeding: An update. In: J. Janick, editor, Plant breeding reviews. Vol. 22. John Wiley & Sons, Oxford, UK. p. 9-112.
22. Kelly, A.M., B.R. Cullis, A.R. Gilmour, J.A. Eccleston, and R. Thompson. 2009. Estimation in a multiplicative mixed model involving a genetic relationship matrix. Genet. Sel. Evol. 41:33. doi:10.1186/1297-9686-41-33
23. Löffler, C.M., J. Wei, T. Fast, J. Gogerty, S. Langton, M. Bergman, B. Merrill, and M. Cooper. 2005. Classification of maize environments using crop simulation and geographic information systems. Crop Sci. 45:1708-1716. doi:10.2135/cropsci2004.0370
24. Lorenz, A.J., S. Chao, F.G. Asoro, E.L. Heffner, T. Hayashi, H. Iwata, K.P. Smith, M.E. Sorrells, and J.-L. Jannink. 2011. Genomic selection in plant breeding: Knowledge and prospects. Adv. Agron. 110:77-123. doi:10.1016/B978-0-12-385531-2.00002-5
25. Lorenz, A.J., K.P. Smith, and J.-L. Jannink. 2012. Potential and optimization of genomic selection for fusarium head blight resistance in six-row barley. Crop Sci. 52:1609-1621. doi:10.2135/ cropsci2011.09.0503
26. Lorenzana, R.E., and R. Bernardo. 2009. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor. Appl. Genet. 120:151-161. doi:10.1007/ s00122-009-1166-3
27. Malysheva-Otto, L.V., M.W. Ganal, and M.S. Röder. 2006. Analysis of molecular diversity, population structure and linkage disequilibrium in a worldwide survey of cultivated barley germplasm (Hordeum vulgare L.). BMC Genet. 7:6. doi:10.1186/1471-2156-7-6
28. Meuwissen, T.H.E., B.J. Hayes, and M.E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829.
29. Nei, M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583-590.
30. Park, T., and G. Casella. 2008. The Bayesian Lasso. J. Am. Stat. Assoc. 103:681-686. doi:10.1198/016214508000000337
31. Pérez, P., G. de los Campos, J. Crossa, and D. Gianola. 2010. Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Gen. 3:106-116. doi:10.3835/plantgenome2010.04.0005
32. Piepho, H.P. 1998. Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures. Theor. Appl. Genet. 97:195-201. doi:10.1007/s001220050885
33. Piepho, H.P., J.O. Ogutu, T. Schulz-Streeck, B. Estaghvirou, A. Gordillo, and F. Technow. 2012. Efficient computation of ridgeregression best linear unbiased prediction in genomic selection in plant breeding. Crop Sci. 52:1093-1104. doi:10.2135/cropsci2011.11.0592
34. Podlich, D.W., M. Cooper, and K.E. Basford. 1999. Computer simulation of a selection strategy to accommodate genotype-environment interactions in a wheat recurrent selection programme. Plant Breed. 118:17-28. doi:10.1046/j.1439-0523.1999.118001017.x
35. R Development Core Team. 2010. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
36. van Eeuwijk, F.A., M. Malosetti, X. Yin, P.C. Struik, and P. Stam. 2005. Statistical models for genotype by environment data: From conventional ANOVA models to eco-physiological QTL models. Aust. J. Agric. Res. 56:883-894. doi:10.1071/AR05153
37. Warnes, G.R. 2001. gplots: Various R programming tools for plotting data. http://www.r-project.org (accessed 10 July 2012).
38. Yi, N., and S. Xu. 2008. Bayesian LASSO for quantitative trait loci mapping. Genetics 179:1045-1055. doi:10.1534/genetics. 107.085589