Abstract
In forest tree species with large natural ranges, there are usually several to many separate breeding populations, each designed to capture elite material suited to a particular geographic region. Separate test series are often dedicated to each population. Because the aim is to optimise gain in the meta-population, it is important to ensure that test series are linked so that individuals can be compared across test series as well as within. Computer simulation was used to determine the most efficient strategy for obtaining linkage. The average accuracy of a genetic value contrast between individuals in the same and in different test series was used as the criterion for assessing the optimal level of linkage. Accuracy is a function of the elements of the inverse coefficient matrix for a mixed linear model within a best linear unbiased prediction framework (BLUP). Material used to link test series was either common test families, common check-lots such as seed orchard bulks, or families generated by inter-crossing parents from different test series. Use of common test families was the most efficient strategy for the scenarios tested, which included having 50 parents crossed to produce 50 test families in each of three populations. For a low-heritability scenario, the amount of linkage material, relative to test material, needed to be 8 and 12 %, for progeny and parents, respectively, in order for a contrast between individuals in different test series to have equivalent accuracy as a contrast between individuals in the same test series. Other strategies were less efficient in terms of the amount of linkage material needed to obtain this equivalency.
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References
Danell, O (1991) Survey of past, current and future Swedish forest tree breeding. Silva Fenn 25:241–247
Eberhart SA, Russell WA (1966) Stability Parameters for Comparing Varieties. Crop Sci 6:36–40. doi:10.2135/cropsci1966.0011183X000600010011x
Fouilloux M-N, Clement V, Laloe D (2008) Measuring connectedness among herds in mixed linear models: from theory to practice in large-sized genetic evaluations. Genet Sel Evol 40:145–159
Foulley JL, Bouix J, Goffinet B, Elsen JM (1990) Connectedness in genetic evaluation. In: Gianola D, Hammond K (eds) Advances in Statistical Methods for Genetic Improvement of Livestock. Springer-Verlag, Heidelberg, pp 277–308
Foulley JL, Hanocq E, Boichard D (1992) A criterion for measuring the degree of connectedness in linear models of genetic evaluation. Genet Sel Evol 24:315–330
Henderson CR (1988) Use of an average numerator relationship matrix for multiple-sire joining. J Anim Sci 66:1614–1621
Jansson G, Danusevicius D, Grotehusman H, Kowalszyk J, Krajmerova D, Skroppa T, Wofl H (2013) Norway Spruce (Picea abies (L.) H. Karst.). In: Paques LE (ed) Forest tree breeding in Europe: current state-of-the-art and perspectives. Managing Forest Ecosystems. Springer Science + Business Media, Dordrecht
Johnson GR (2004) Common families across test series - how many do we need? For Genet 11:103–112
Kennedy BW, Trus D (1993) Considerations on Genetic Connectedness between Management Units under an Animal-Model. J Anim Sci 71:2341–2352
Kuehn LA, Lewis RM, Notter DR (2007) Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application. Genet Sel Evol 39:225–247. doi:10.1051/gse:2007001
Laloe D (1993) Precision and Information in Linear-Models of Genetic Evaluation. Genet Sel Evol 25:557–576
Laloe D, Phocas F, Menissier F (1996) Considerations on measures of precision and connectedness in mixed linear models of genetic evaluation. Genet Sel Evol 28:359–378
Perez-Enciso M, Misztal I, Elzo MA (2004) FSPAK: an interface for public domain sparse matrix subroutines. In: 5th World Congress on Genetics Applied to Livestock Production, Guelph, ON, Canada, p 87–88
Quaas RL, Pollak EJ (1980) Mixed Model Methodology for Farm and Ranch Beef-Cattle Testing Programs. J Anim Sci 51:1277–1287
Raymond C (2011) Genotype by environment interactions for Pinus radiata in New South Wales, Australia. Tree Genet Genomes 7:819–833. doi:10.1007/s11295-011-0376-4
Searle SR (1987) Linear models for unbalanced data. John Wiley and Sons, New York
Westell RA, Quaas RL, Vanvleck LD (1988) Genetic Groups in an Animal-Model. J Dairy Sci 71:1310–1318
Acknowledgments
The authors gratefully acknowledge the financial support received from the Swedish Association for Forest Tree Breeding (grant number 219). They would like to thank the helpful support given by the Southern Tree Breeding Association, particularly Dr. Tony McRae and Mr Peter Cunningham.
Data Archiving Statement
Data for this project were generated using computer simulation. Primary data comprised simulated pedigrees, mating patterns and allocation of trees to test series, replicate and blocks numbers. Secondary data were then generated and comprised sparse matrices containing the coefficient matrix for the mixed model equations. Inverting these matrices derived accuracies and prediction error variances. Primary and secondary data currently exist on the first author’s computer and can be provided to a third party repository database if required.
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Communicated by R. Burdon
This article is part of the Topical Collection on Breeding
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Kerr, R.J., Dutkowski, G.W., Jansson, G. et al. Connectedness among test series in mixed linear models of genetic evaluation for forest trees. Tree Genetics & Genomes 11, 67 (2015). https://doi.org/10.1007/s11295-015-0887-5
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DOI: https://doi.org/10.1007/s11295-015-0887-5