Accesso libero

Two-dimensional penalized splines via Gibbs sampling to account for spatial variability in forest genetic trials with small amount of information available

INFORMAZIONI SU QUESTO ARTICOLO

Cita

Anekonda, T. S. and W. J. Libby (1996): Effectiveness of nearestneighbor data adjustment in a clonal test of Redwood. Silvae Genet. 45(1): 46–51.Search in Google Scholar

Bohmanova, J., I. Misztal and J. K. Bertrand (2005): Studies on multiple trait and random regression models for genetic evaluation of beef cattle for growth. J Anim Sci 83: 62–67.10.2527/2005.83162xSearch in Google Scholar

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: 2482–2494.10.2527/2005.83112482xSearch in Google Scholar

Cappa, E. P. and R. J. C. Cantet (2006): Bayesian inference for normal multiple-trait individual-tree models with missing records via full conjugate Gibbs. Can J For Res 36: 1276–1285.10.1139/x06-024Search in Google Scholar

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: 2677–2688.10.1139/X07-116Search in Google Scholar

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: 1887–1893.10.1139/x01-123Search in Google Scholar

Cornillon, P. A., L. Saint-Andre, J. M. Bouvet, P. Vigneron, A. Saya and R. Gouma (2003): Using B-splines for growth curve classification: applications to selection of eucalypt clones. Forest Ecology and Management 176: 75–85.10.1016/S0378-1127(02)00276-1Search in Google Scholar

De Boor, C. (1993): B(asic)-spline basics. Fundamental Developments of Computer-Aided Geometric Modeling. Edited by L. Piegl, Academic Press, San Diego, CA.Search in Google Scholar

Durban, M., I. Currie and R. Kempton (2001): Adjusting for fertility and competition in variety trials. J Agric Sci (Camb) 136: 129–140.10.1017/S0021859601008541Search in Google Scholar

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: 2201–2214.10.1139/x02-111Search in Google Scholar

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: 1851–1870.10.1139/x06-059Search in Google Scholar

Eilers, P. H. C. and B. D. Marx (1996): Flexible smoothing with B-splines and penalties (with comments and rejoinder). Stat Sci 11: 89–121.10.1214/ss/1038425655Search in Google Scholar

Eilers, P. H. C. and B. D. Marx (2003): Multivariate calibration with temperature interaction using two-dimensional penalized signal regression. Chemometr. Intell Lab Syst 66: 159–174.10.1016/S0169-7439(03)00029-7Search in Google Scholar

El-Kassaby, Y. A. and Y. S. Park (1993): Genetic variation and correlation in growth, biomass traits, and vegetative phenology of a 3-year-old Douglas-fir common garden at different spacings. Silvae Genet 42: 289–297.Search in Google Scholar

Ericsson, T. (1997): Enhanced heritabilities and best linear unbiased predictors through appropriate blocking of progeny trials. Can J For Res 27: 2097–2101.10.1139/x97-153Search in Google Scholar

Federer, W. T. (1998): Recovery of interblock, intergradient, and intervarietal information in incomplete block and lattice rectangle designed experiments. Biometrics 54: 471–481.10.2307/3109756Search in Google Scholar

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: 441–451.10.1111/j.1541-0420.2008.01115.x277509518759829Search in Google Scholar

Gilmour, A. R., B. R. Cullis and A. P. Verbyla (1997): Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2: 269–293.10.2307/1400446Search in Google Scholar

Gezan, S. A., D. A. Huber and T. L. White (2006): Post hoc blocking to improve heritability and precision of best linear unbiased genetic predictions. Can J For Res 36: 2141–2147.10.1139/x06-112Search in Google Scholar

Green, P. J. and B. W. Silverman (1994): Nonparametric Regression and Generalized Linear Model. Chapman & Hall, London, UK.10.1007/978-1-4899-4473-3Search in Google Scholar

Grondona, M. O., J. Crossa, P. N. Fox and W. H. Pfeiffer (1996): Analysis of variety yield trials using two-dimensional separable ARIMA processes. Biometrics 52: 763–770.10.2307/2532916Search in Google Scholar

Henderson, C. R. (1984): Applications of Linear Models in Animal Breeding. Canada, University of Guelph, Guelph, Ont.Search in Google Scholar

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.Search in Google Scholar

Harville, D. A. (1997): Matrix algebra from a statistician’s perspective. Springer-Verlag. New York.10.1007/b98818Search in Google Scholar

Iwaisaki, H., S. Tsuruta, I. Misztal and J. K. Bertrand (2005): Genetic parameters estimated with multi-trait and linear spline-random regression models using Gelbvieh early growth data. J Anim Sci 83: 499–506.10.2527/2005.834757x15753329Search in Google Scholar

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.Search in Google Scholar

Krakowski, J, Y. S. Park and Y. A. El-Kassaby (2005): Early testing of Douglas-fir: wood density and ring width. For Genet 12: 99–105.Search in Google Scholar

Kroon, J., B. Andersson and T. J. Mullin (2008): Genetic variation in the diameter-height relationship in Scots pine (Pinus sylvestris). Can J For Res 38: 1493–1503.10.1139/X07-233Search in Google Scholar

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.Search in Google Scholar

Loo-Dinkins, J. A. and C. G. Tauer (1987): Statistical efficiency of six progeny test field designs on three loblolly pine (Pinus taeda L.) site types. Can J For Res 17: 1066–1070.10.1139/x87-163Search in Google Scholar

Loo-Dinkins, J. (1992): Field test design. In: Handbook of quantitative forest genetics. Edited by L. Fins, S. Friedman, and J.V. Brotschol. Kluwer Academic Publishers, Dordrecht, the Netherlands. pp. 96–139.10.1007/978-94-015-7987-2_4Search in Google Scholar

Lopez, G. A., B. M. Potts, G. W. Dutkowski, L. A. Apiolaza and P. Gelid (2002): Genetic variation and intertrait correlations in Eucalyptus globulus base population trials in Argentina. Forest Genetics 9: 223–237.Search in Google Scholar

Magnussem, S. (1990): Application and comparison of spatial models in analyzing tree-genetics field trials. Can J For Res 20: 536–546.10.1139/x90-070Search in Google Scholar

Magnussen, S. (1993): Bias in genetic variance estimates due to spatial autocorrelation. Theor Appl Genet 86: 349–355.10.1007/BF00222101Search in Google Scholar

Magnussen, S. and A. D. Yanchuk (1994): Time trends of predicted breeding values in selected crosses of coastal Douglas-fir in British Columbia: a methodological study. For Sci 40: 663–685.10.1093/forestscience/40.4.663Search in Google Scholar

Meyer, K. (2005): Random regression analyses using B-splines to model growth of Australian Angus cattle. Genet Sel Evol 37: 473–500.10.1186/1297-9686-37-6-473Search in Google Scholar

Rehfeldt, G. E. (1995): Genetic variation, climate models and the ecological genetics of Larix occidentalis. For Ecol Manage 78: 21–37.10.1016/0378-1127(95)03602-4Search in Google Scholar

Ruppert, D. (2002): Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics 11: 735–757.10.1198/106186002853Search in Google Scholar

Ruppert, D., M. P. Wand and R. J. Carroll (2003): Semiparametric Regression. Cambridge Univ Press, Cambridge, UK.10.1017/CBO9780511755453Search in Google Scholar

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.Search in Google Scholar

Schabenberger, O. and C. A. Gotway (2005): Statistical Methods for Spatial Data Analysis. Boca Raton: Chapman & Hall.Search in Google Scholar

Silverman, B. (1986): Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.10.1007/978-1-4899-3324-9Search in Google Scholar

Smith, B. J. (2003): Bayesian Output Analysis Program (BOA) version 1.0 user’s manual. http://www.publichealth.uiowa.edu/boa/Home.html. Cited 14 Aug 2008.Search in Google Scholar

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: 583–639.10.1111/1467-9868.00353Search in Google Scholar

St. Clair, J. B. (2006): Genetic variation in fall cold hardiness in coastal Douglas-fir in western Oregon and Washington. Can J Bot 84: 1110–1121.10.1139/b06-084Search in Google Scholar

Thomson, A. J. and Y. A. El-Kassaby (1988): Trend surface analysis of provenance-progeny transfer data. Can J For Res 18: 515–520.10.1139/x88-075Search in Google Scholar

Verbyla, A. P., B. R. Cullis, M. G. Kenward and S. J. Welham (1999): The analysis of designed experiments and longitudinal data by using smoothing splines (with discussion). Applied Statistics 48: 69–311.Search in Google Scholar

Wand, M. P. (2003): Smoothing and mixed models. Comput Stat 18: 223–249.10.1007/s001800300142Search in Google Scholar

Woods, J. H., D. Kolotelo and A. D. Yanchuk (1995): Early selection of coastal Douglas-fir in a farm-field environment. Silvae Genet 44: 178–186.Search in Google Scholar

White, I. M. S., R. Thompson and S. Brotherstone (1999): Genetic and environmental smoothing of lactation curves with cubic splines. J Dairy Sci 82: 632–638.10.3168/jds.S0022-0302(99)75277-XSearch in Google Scholar

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 Genet Genomics 4: 677–692.10.1007/s11295-008-0142-4Search in Google Scholar

Zas, R. (2006): Iterative kriging for removing spatial autocorrelation in analysis of forest genetic trials. Tree Genet Genomics 2: 177–185.10.1007/s11295-006-0042-4Search in Google Scholar

eISSN:
2509-8934
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Life Sciences, Molecular Biology, Genetics, Biotechnology, Plant Science