Summary
Additive genetic components of variance and narrow-sense heritabilities were estimated for flowering time (FT) and cut-flower yield (Y) for six generations of the Davis Population of gerbera using Derivative-Free Restricted Maximum Likelihood (DFRML). Additive genetic variance accounted for 54% of the total variability for FT and 30% of the total variability for Y. The heritability of FT (0.54) agreed with previous ANOVA-based estimates. However, the heritability of Y (0.30) was substantially lower than estimates using ANOVA. The advantages of DFRML and its applications in the estimation of components of genetic variance and heritabilities of plant populations are discussed.
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References
Becker WA (1981) Manual of quantitative genetics, 4th edn. Academic Enterprises, Pullman, Wash.
Comstock RE, Robinson HF (1948) The components of genetic variance in populations of biparental progenies and their use in estimating the average degree of dominance. Biometrics 4:254–266
Falconer DS (1989) Introduction to quantitative genetics. Longman Scientific and Technical and John Wiley and Sons, New York
Gianola D, Fernando RL (1986) Bayesian methods in animal breeding theory. J Anim Sci 63:217
Graser H-U, Smith SP, Tier B (1987) A derivative-free approach for estimating variance components in animal models by restricted maximum likelihood. J Anim Sci 64:1362
Harding J, Byrne TG, Nelson RL (1981) Estimation of heritability and response to selection for cut-flower yield in Gerbera. Euphytica 30:313–322
Harding J, Drennan D, Byrne TG (1985) Components of genetic variation of cut-flower yield in the Davis population of Gerbera. Euphytica 34:759–767
Hartley HO, Rao JNK (1967) Maximum-likelihood estimation for the mixed analysis of variance model. Biometrika 54:93
Hazel LN, Lush JL (1942) The efficiency of three methods of selection. J Hered 33:393
Henderson CR (1984) ANOVA, MIVQUE, RML and ML algorithms for estimation of variances and covariances. In: David HA, David HT (eds) Statistics: an appraisal. Proc 50th Anniversary Conf Iowa State Stat Lab. The Iowa State University Press, Ames, Iowa, pp 257–286
Lange K, Westlake J, Spence MA (1976) Extensions to pedigree analysis. III. Variance components by the scoring method. Ann Hum Genet 39:486–491
Meyer K (1983) Maximum likelihood procedures for estimating genetic parameters for later lactions of dairy cattle. J Dairy Sci 66:1988–1997
Meyer K (1988) DFRML — A set of programs to estimate variance components under an individual animal model. J Dairy Sci 71 [Suppl 2]:33 (Abstr)
Patterson HD, Thompson R (1971) Recovery of interblock information when block sizes are unequal. Biometrika 58:545
SAS Institute (1988) SAS/STAT user's guide, version 6.03 edn. SAS Institute Inc, Cary, N.C.
Smith HF (1936) A discriminant function for plant selection. Ann Eugenics 7:240–250
Smith SP, Graser H-U (1986) Estimating variance components in a class of mixed models by restricted maximum likelihood. J Dairy Sci 69:1156
Sorenson DA, Kennedy BW (1984) Estimation of genetic variances from unselected and selected populations. J Anim Sci 59:1213
Yu Y (1991) Genetics of flowering time in gerbera (Gerbera hybrida, Compositae). PhD thesis, University of California, Davis
Yu Y, Byrne TG, Harding J (1991) Quantitative analysis of flowering time in the Davis population of Gerbera. I. Components of genetic variances and heritability. Euphytica 53:19–23
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Communicated by A.L. Kahler
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Yu, Y., Harding, J., Byrne, T. et al. Estimation of components of genetic variance and heritability for flowering time and yield in gerbera using Derivative-Free Restricted Maximum Likelihood (DFRML). Theoret. Appl. Genetics 86, 234–236 (1993). https://doi.org/10.1007/BF00222084
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DOI: https://doi.org/10.1007/BF00222084