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Best linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations

Published online by Cambridge University Press:  23 May 2013

J. M. S. VIANA*
Affiliation:
General Biology Department, Federal University of Viçosa, 36570-000 Viçosa, MG, Brazil
G. B. MUNDIM
Affiliation:
General Biology Department, Federal University of Viçosa, 36570-000 Viçosa, MG, Brazil
R. O. DELIMA
Affiliation:
General Biology Department, Federal University of Viçosa, 36570-000 Viçosa, MG, Brazil
F. F. E SILVA
Affiliation:
Statistics Department, Federal University of Viçosa, 36570-000 Viçosa, MG, Brazil
M. D. V. DE RESENDE
Affiliation:
Forestry Science Department, Embrapa Forestry/Federal University of Viçosa, 36570-000 Viçosa, MG, Brazil
*
*To whom all correspondence should be addressed. Email: jmsviana@ufv.br

Summary

The objective of the present study was to present the theory and application of best linear unbiased prediction (BLUP) in reciprocal recurrent selection (RRS). Seven progeny tests from two RRS programmes with popcorn (Zea mays L. ssp. mays [syn. Zea mays L. ssp. everta (Sturtev.) Zhuk.]) populations were conducted and analysed for expansion volume and grain yield. The interpopulation half- and full-sib family models were fitted using ASReml software. Half-sib selection is equivalent to selection for the general combining ability (GCA) of the common parents. With inbred full-sib progeny and BLUP analysis, it is possible to predict the general and specific combining ability effects. The standard error of prediction of the progeny effect was lower than the standard deviation of the best linear unbiased estimation (BLUE) estimate. For half- and full-sib RRS, the BLUE and BLUP provided highly correlated estimates of progeny genotypic values. The coincidence between selected parents ranged from 64 to 95%. With inbred full-sib progeny, the correlations between the BLUE of progeny genotypic values and the BLUP of GCA effects were lower. Consequently, the coincidence between selected parents was lower, ranging from 0 to 57%. The percentage of common selected inbred progeny based on the BLUE and BLUP of the progeny genotypic value ranged from 57 to 100%.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Atkin, F. C., Dieters, M. J. & Stringer, J. K. (2009). Impact of depth of pedigree and inclusion of historical data on the estimation of additive variance and breeding values in a sugarcane breeding program. Theoretical and Applied Genetics 119, 555565.CrossRefGoogle Scholar
Bauer, A. M., Reetz, T. C. & Léon, J. (2006). Estimation of breeding values of inbred lines using best linear unbiased prediction (BLUP) and genetic similarities. Crop Science 46, 26852691.CrossRefGoogle Scholar
Bauer, A. M., Reetz, T. C., Hoti, F., Schuh, W. D., Léon, J. & Sillanpää, M. J. (2009). Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops. Genetic Research, Cambridge 91, 193207.Google Scholar
Bernardo, R. (1994). Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Science 34, 2025.CrossRefGoogle Scholar
Bernardo, R. (1996). Best linear unbiased prediction of maize single-cross performance. Crop Science 36, 5056.Google Scholar
Blasco, A. (2001). The Bayesian controversy in animal breeding. Journal of Animal Science 79, 20232046.Google Scholar
Comstock, R. E., Robinson, H. F. & Harvey, P. H. (1949). A breeding procedure designed to make maximum use of both general and specific combining ability. Agronomy Journal 41, 360367.Google Scholar
dos Reis, M. C., Guedes, F. L., Abreu, G. B. & Souza, J. C. (2012). Reciprocal recurrent selection in maize enhances heterosis and ears yield. Euphytica DOI: 10.1007/s10681-012-0762-5Google Scholar
Forkman, J. & Piepho, H. P. (2013). Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments. Journal of Agricultural Science, Cambridge 151, 383397.Google Scholar
Gilmour, A. R., Gogel, B. J., Cullis, B. R. & Thompson, R. (2009). ASReml User Guide Release 3.0. Hemel Hempstead, UK: VSN International Ltd.Google Scholar
Hallauer, A. R. (1967). Development of single-cross hybrids from two-eared maize populations. Crop Science 7, 192195.Google Scholar
Hallauer, A. R. & Eberhart, S. A. (1970). Reciprocal full-sib selection. Crop Science 10, 315316.CrossRefGoogle Scholar
Harville, D. A. & Carriquiry, A. L. (1992). Classical and Bayesian prediction as applied to an unbalanced mixed linear model. Biometrics 48, 9871003.Google Scholar
Henderson, C. R. (1974). General flexibility of linear model techniques for sire evaluation. Journal of Dairy Science 57, 963972.CrossRefGoogle Scholar
Henderson, C. R. (1986). Recent developments in variance and covariance estimation. Journal of Animal Science 63, 208216.Google Scholar
Keeratinijakal, V. & Lamkey, K. R. (1993). Responses to reciprocal recurrent selection in BSSS and BSCB1 maize populations. Crop Science 33, 7377.Google Scholar
Littell, R. C. (2002). Analysis of unbalanced mixed model data: a case study comparison of ANOVA versus REML/GLS. Journal of Agricultural, Biological and Environmental Statistics 4, 472490.Google Scholar
Mathew, B., Bauer, A. M., Koistinen, P., Reetz, T. C., Léon, J. & Sillanpää, M. J. (2012). Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. Heredity 109, 235245.Google Scholar
Mrode, R. A. (2005). Linear Models for the Prediction of Animal Breeding Values, 2nd edn, Wallingford, UK: CABI Publishing.Google Scholar
Oakey, H., Verbyla, A. P., Cullis, B. R., Wei, X. & Pitchford, W. S. (2007). Joint modeling of additive and non-additive (genetic line) effects in multi-environment trials. Theoretical and Applied Genetics 114, 13191332.Google Scholar
Ordás, B., Butrón, A., Alvarez, A., Revilla, P. & Malvar, R. A. (2012). Comparison of two methods of reciprocal recurrent selection in maize (Zea mays L). Theoretical and Applied Genetics 124, 11831191.Google Scholar
Patterson, H. D. & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika 58, 545554.Google Scholar
Peiris, B. L. & Hallauer, A. R. (2005). Comparison of half-sib and full-sib reciprocal recurrent selection and their modifications in simulated populations. Maydica 50, 2537.Google Scholar
Penny, L. H. & Eberhart, S. A. (1971). Twenty years of reciprocal recurrent selection with two synthetic varieties of maize (Zea mays L). Crop Science 11, 900903.Google Scholar
Piepho, H. P. & Möhring, J. (2006). Selection in cultivar trials – is it ignorable? Crop Science 46, 192201.Google Scholar
Piepho, H. P. & Williams, E. R. (2006). A comparison of experimental designs for selection in breeding trials with nested treatment structure. Theoretical and Applied Genetics 113, 15051513.Google Scholar
Piepho, H. P., Möhring, J., Melchinger, A. E. & Büchse, A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161, 209228.CrossRefGoogle Scholar
Plummer, M. (2012). JAGS: Just Another Gibbs Sampler v.3.3.0. Available from: http://mcmc-jags.sourceforge.net/ (verified 12 November 2012).Google Scholar
REAL Software (2004). REALbasic 5.5. Version 5.5.4. Austin, TX: REAL Software Inc.Google Scholar
Romay, M. C., Ordás, B., Revilla, P. & Ordás, A. (2011). Three cycles of full-sib reciprocal recurrent selection in two Spanish maize populations. Crop Science 51, 10161022.CrossRefGoogle Scholar
SAS Institute (2007). The SAS System for Windows, Version 9.2. Cary, NC: SAS Institute Inc.Google Scholar
Schenkel, F. S., Schaeffer, L. R. & Boettcher, P. J. (2002). Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information. Genetics Selection Evolution 34, 4159.CrossRefGoogle ScholarPubMed
Solomon, K. F., Martin, I. & Zeppa, A. (2010). Temporal genetic structure patterns in tropical maize populations under reciprocal recurrent selection. Euphytica 176, 239249.Google Scholar
Souza, C. L. Jr. (1987). Reciprocal recurrent selection with half-sib progenies obtained alternately from noninbred (S 0) and inbred (S 1) plants in maize. Maydica 32, 1931.Google Scholar
Van Tassell, C. P., Casella, G. & Pollak, E. J. (1995). Effects of selection on estimates of variance components using Gibbs sampling and restricted maximum likelihood. Journal of Dairy Science 78, 678692.Google Scholar
Van Tassell, C. P. & Van Vleck, L. D. (1996). Multiple-trait Gibbs sampler for animal models: flexible programs for Bayesian and likelihood-based (co)variance component inference. Journal of Animal Science 74, 25862597.Google Scholar
Viana, J. M. S., Faria, V. R., Silva, F. F. & Resende, M. D. V. (2011). Best linear unbiased prediction and family selection in crop species. Crop Science 51, 23712381.Google Scholar
Viana, J. M. S., Delima, R. O., Mundim, G. B., Condé, A. B. T. & Vilarinho, A. A. (2013). Relative efficiency of the genotypic value and combining ability effects on reciprocal recurrent selection. Theoretical and Applied Genetics 126, 889899.Google Scholar
Waldmann, P., Hallander, J., Hoti, F. & Sillanpää, M. J. (2008). Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees. Genetics 179, 11011112.Google Scholar