Tree Genetics & Genomes

, 11:67 | Cite as

Connectedness among test series in mixed linear models of genetic evaluation for forest trees

  • Richard J. Kerr
  • Gregory W. Dutkowski
  • Gunnar Jansson
  • Torgny Persson
  • Johan Westin
Original Paper
Part of the following topical collections:
  1. Breeding


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.


Connectedness Genetic evaluation BLUP Genetic progress 


  1. Danell, O (1991) Survey of past, current and future Swedish forest tree breeding. Silva Fenn 25:241–247CrossRefGoogle Scholar
  2. Eberhart SA, Russell WA (1966) Stability Parameters for Comparing Varieties. Crop Sci 6:36–40. doi:10.2135/cropsci1966.0011183X000600010011x CrossRefGoogle Scholar
  3. 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 40:145–159PubMedCentralPubMedGoogle Scholar
  4. 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 277–308CrossRefGoogle Scholar
  5. Foulley JL, Hanocq E, Boichard D (1992) A criterion for measuring the degree of connectedness in linear models of genetic evaluation. Genet Sel Evol 24:315–330PubMedCentralCrossRefGoogle Scholar
  6. Henderson CR (1988) Use of an average numerator relationship matrix for multiple-sire joining. J Anim Sci 66:1614–1621Google Scholar
  7. 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, DordrechtGoogle Scholar
  8. Johnson GR (2004) Common families across test series - how many do we need? For Genet 11:103–112Google Scholar
  9. Kennedy BW, Trus D (1993) Considerations on Genetic Connectedness between Management Units under an Animal-Model. J Anim Sci 71:2341–2352PubMedGoogle Scholar
  10. 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 PubMedCrossRefGoogle Scholar
  11. Laloe D (1993) Precision and Information in Linear-Models of Genetic Evaluation. Genet Sel Evol 25:557–576PubMedCentralCrossRefGoogle Scholar
  12. Laloe D, Phocas F, Menissier F (1996) Considerations on measures of precision and connectedness in mixed linear models of genetic evaluation. Genet Sel Evol 28:359–378PubMedCentralCrossRefGoogle Scholar
  13. 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 87–88Google Scholar
  14. Quaas RL, Pollak EJ (1980) Mixed Model Methodology for Farm and Ranch Beef-Cattle Testing Programs. J Anim Sci 51:1277–1287Google Scholar
  15. 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 CrossRefGoogle Scholar
  16. Searle SR (1987) Linear models for unbalanced data. John Wiley and Sons, New YorkGoogle Scholar
  17. Westell RA, Quaas RL, Vanvleck LD (1988) Genetic Groups in an Animal-Model. J Dairy Sci 71:1310–1318CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Richard J. Kerr
    • 1
  • Gregory W. Dutkowski
    • 1
  • Gunnar Jansson
    • 2
  • Torgny Persson
    • 3
  • Johan Westin
    • 3
  1. 1.PlantPlan Genetics Pty Ltd, School of Biological SciencesUniversity of TasmaniaHobartAustralia
  2. 2.SkogforskUppsalaSweden
  3. 3.SkogforskSävarSweden

Personalised recommendations