Estimation of genetic parameters for height using spatial analysis in Tsuga heterophylla full-sibling family trials in British Columbia

Eduardo Pablo Cappa 1 , 2 , 3 , A. D. Yanchuk 4  and C. V. Cartwright 4
  • 1 Department of Forest Sciences, 2424 Main Mall, University of British Columbia, , Vancouver, Canada
  • 2 British Columbia Ministry of Forests and Range, Research Branch, PO Box 9519 Stn Prov Govt, , Victoria, Canada
  • 3 Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), , Buenos Aires, Argentina
  • 4 British Columbia Ministry of Forests and Range, Research Branch, PO Box 9519 Stn Prov Govt, , Victoria, Canada

Abstract

Non-spatial and spatial analyses were carried out to study the effects on genetic parameters in ten-year height growth data across two series of 10 large second-generation full-sib progeny trials of western hemlock [Tsuga heterophylla (Raf.) Sarg.] in British Columbia. To account for different and complex patterns of environmental heterogeneity, spatial single trial analyses were conducted using an individual-tree mixed model with a two-dimensional smoothing surface with tensor product of B-spline bases. The spatial single trial analysis, in all cases, showed sizeable lower Deviance Information Criterion values relative to the non-spatial analysis. Also, fitting a surface displayed a consistent reduction in the posterior mean as well as a decrease in the standard deviations of error variance, no appreciable changes in the additive variance, an increase of individual narrow-sense heritability, and accuracy of breeding values. The tensor product of cubic basis functions of B-spline based on a mixed model framework does provide a useful new alternative to model different and complex patterns of spatial variability within sites in forest genetic trials. Individual narrow-sense heritabilities estimates from the spatial single trial analyses were low (average of 0.06), but typical of this species. Estimated dominance relative to additive variances were unstable across sites (from 0.00 to 1.59). The implications of these estimations will be discussed with respect to the western hemlock genetic improvement program in British Columbia.

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  • ANEKONDA, T. S. and W. J. LIBBY (1996): Effectiveness of nearest neighbor data adjustment in a clonal test of Redwood. Silvae Genet. 45(1): 46-51.

  • CANTET, R. J. C., A. N. BIRCHMEIER, A. W. CANAZA CAYO and C. FIORETT (2005): Semiparametric animal models via penalized splines as alternatives to models with contemporary groups. J. Anim. Sci. 83: 2482-2494.

  • CAPPA, E. P. and R. J. C. CANTET (2007): Bayesian estimation of a surface to account for a spatial trend using penalized splines in an individual-tree mixed model. Can. J. For. Res. 37: 2677-2688.

  • CAPPA, E.P., A. D. YANCHUK and C. V. CARTWRIGHT (2012): Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials. Annals of Forest Science 69: 627-640. DOI: 10.1007/s13595-011-0179-7.

  • COSTA E SILVA, J., G. W. DUTKOWSKI and A. R. GILMOUR (2001): Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual. Can. J. For. Res. 31: 1887-1893.

  • DE BOOR, C. (1993): B(asic)-spline basics. Fundamental Developments of Computer-Aided Geometric Modeling. L. Piegl, ed. Academic Press, San Diego, CA.

  • DURBAN, M., I. CURRIE and R. KEMPTON (2001): Adjusting for fertility and competition in variety trials. J. Agric. Sci. (Camb.) 136: 129-140.

  • DUTKOWSKI, G. W., J. COSTA E SILVA, A. R. GILMOUR and G. A. LOPEZ (2002): Spatial analysis methods for forest genetic trials. Can. J. For. Res. 32: 2201-2214.

  • DUTKOWSKI, G. W., J. COSTA E SILVA, A. R. GILMOUR, H. WELLENDORF and A. AGUIAR (2006): Spatial analysis enhances modeling of a wide variety of traits in forest genetic trials. Can. J. For. Res. 36: 1851-1870.

  • FINLEY, A. O., S. BANERJEE, P. WALDMANN and T. ERICSSON (2009): Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial data sets. Biometrics 65: 441-451.

  • FOSTER, G. S. and D. T. LESTER (1983): Fifth-year height variation in western hemlock open pollinated families growing on four test sites. Can. J. For. Res. 13: 251-256.

  • FU, Y. B., A. D. YANCHUK and G. NAMKOONG (1999): Spatial patterns of tree height variations in a series of Douglas-fir progeny trials: implications for genetic testing. Can. J. For. Res. 29: 714-723.

  • FU, Y. B., G. P. Y. CLARKE, G. NAMKOONG and A. D. YANCHUK (1998): Incomplete block designs for genetic testing: statistical efficiencies of estimating family means. Can. J. For. Res. 28: 977-986.

  • GILMOUR, A. R., B. J. GOGEL, B. R. CULLIS and R. THOMPSON (2006): ASReml User Guide Release 2.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK.

  • GREEN, P. J. and B. W. SILVERMAN (1994): Nonparametric Regression and Generalized Linear Model. Chapman & Hall, London, UK.

  • GRONDONA, M. O., J. CROSSA, P. N. FOX and W. H. PFEIFFER (1996): Analysis of variety yield trials using 2-dimensional separable ARIMA processes. Biometrics 52: 763-770.

  • HAMANN, A., M. KOSHY and G. NAMKOONG (2002): Improving precision of breeding values by removing spatially autocorrelated variation in forestry field experiments. Silvae Genet. 51: 210-215.

  • HARVILLE, D. A. (1997): Matrix algebra from a statistician’s perspective. Springer-Verlag. New York.

  • HENDERSON, C. R. (1984): Applications of Linear Models in Animal Breeding. Canada, University of Guelph, Guelph, Ont.

  • HYNDMAN, R. J., M. L. KING, I. PITRUN and B. BILLAH (2005): Local lineal forecasts using cubic smoothing splines. Aust. N. Z. J. Stat. 47: 87-99.

  • JAYAWICKRAMA, K. J. S. (2003): Genetic improvement and deployment of western hemlock in Oregon and Washington: Review and Future Prospects. Silvae Genet. 52(1): 26-36.

  • JOYCE, D., R. FORD and Y. B. FU (2002): Spatial patterns of tree height variations in a black spruce farm-field progeny test and neighbors-adjusted estimations of genetic parameters. Silvae Genet. 51: 13-18.

  • KING, J. N. (1990): The significance of geographic variation patterns for western hemlock genetic improvement. Technical Report, B.C. Ministry of Forests Research Branch, 12 p.

  • KUSER, J. E. and K. K. CHING (1981): Provenance variation in seed weight, cotyledon number, and growth rate of western hemlock seedlings. Forest Science, 26: 463-470.

  • KUSER, J. E. and K. K. CHING (1981): Provenance variation in seed weight, cotyledon number, and growth rate of western hemlock seedlings. Can. J. For. Res. 11: 662-670.

  • KUSNANDAR, D. and N. GALWEY (2000): A proposed method for estimation of genetic parameters on forest trees without raising progeny: critical evaluation and refinement. Silvae Genet. 49: 15-21.

  • MAGNUSSEN, S. (1993): Bias in genetic variance estimates due to spatial autocorrelation. Theor. Appl. Genet. 86: 349-355.

  • MAGNUSSEN , S. (1994): A method to adjust simultaneously for statial microsite and competition effects. Can. J. For. Res. 24: 985-995.

  • POJAR, J. and A. MACKINNON (1994): Plants of the Pacific Northwest Coast, Washington, Oregon, British Columbia & Alaska. Lone Pine Publishing, Vancouver, British Columbia.

  • POLLARD, D. F. W. and F. T. PORTLOCK (1986): Intraspecific variation in stem growth of western hemlock. Can. J. For. Res. 16: 149-151.

  • R DEVELOPMENT CORE TEAM (2011): R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.

  • RUPPERT, D., M. P. WAND and R. J. CARROLL (2003): Semiparametric Regression. Cambridge Univ. Press, Cambridge, UK.

  • SAENZ-ROMERO, C., E. V. NORDHEIM, R. P. GURIES and P. M. CRUMP (2001): A Case Study of a Provenance/ Progeny Test Using Trend Analysis with Correlated Errors and SAS PROC MIXED. Silvae Genet. 50: 127-135.

  • SCHUTZ, W. M. and C. C. COCKERHAM (1966): The Effect of Field Blocking on Gain from Selection. Biometrics 22(4): 843-863.

  • SILVERMAN, B. (1986): Density Estimation for Statistics and Data Analysis, Chapman and Hall, London. SMITH, B. J. (2003): Bayesian Output Analysis Program (BOA) version 1.0 user’s manual. Available from http://www.public-health.uiowa.edu/boa/Home.html.

  • SPIEGELHALTER, D. J., N. G. BEST, B. P. CARLIN and A. VAN DER LINDE (2002): Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society Series B 64: 583-639.

  • WALDMANN, P., J. HALLANDER, F. HOTI and M. J. SILLANPÄÄ (2008): Efficient MCMC implementation of Bayesian analysis of additive and dominance genetic variances in non-inbred pedigrees. Genetics 179: 1101-1112.

  • WEBBER, J. E. (2000): Western hemlock: a manual for tree improvement seed production. Res. Br., B. C. Min. For., Victoria, B.C.Work Pap. 44/2000.

  • WHITE, T. L. (1996): Genetic parameter estimates and breeding value predictions: issues and implications in tree improvement programs. In: DIETERS, M. J., MATHESON, A. C., NIKLES, D. G., HARWOOD, C. E., WALKER, S. M. (eds) Proceedings of the QFRI-IUFRO Conference Tree Improvement for Sustainable Tropical Forestry. Caloundra, Queensland, Australia, pp 110-117.

  • WILLIAMS, E. R. and A. C. MATHESON (1994): Experimental Design and Analysis for use in Tree Improvement. CSIRO, Melbourne, Australia.

  • WU, H. X. and A. C. MATHESON (2004): General and specific combining ability from artial diallels of radiata pine: implications for utility of SCA in breeding and deployment populations. Theor. Appl. Genet. 108: 1503-1512.

  • YANCHUK, A. (1996): General and specific combining ability from disconnected partial diallels of coastal Douglas-fir. Silvae Genet. 45: 37-45.

  • YE, T. Z. and K. J. S. JAYAWICKRAMA (2008): Efficiency of using spatial analysis in firest-generation coastal Douglas-fir progeny tests in the US Pacific Northwest. Tree Genetics and Genomes 4: 677-692.

  • ZAS, R. (2006): Iterative kriging for removing spatial autocorrelation in analysis of forest genetic trials. Tree Genetics and Genomes 2: 177-185.

  • ZHELEV, P., I. EKBERG, G. ERIKSSON and L. NORELL (2003): Genotype environment interactions in four full-sib progeny trials of Pinus sylvestris (L.) with varying site indices. Forest Genetics 10: 93-102.

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