Use of resampling to assess optimal subgroup composition for estimating genetic parameters from progeny trials
2013
Perron, Martin | DeBlois, Josianne | Desponts, Mireille
Estimations of genetic parameters of wood traits based on reduced sample populations are widely reported in the literature, but few investigations have considered the consequences of these small populations on the precision of parameter estimates. The purpose of this study was to determine an optimal strategy for sampling subgroups, by varying either the number of families or the number of individuals (trees) per family, and by verifying the accuracy of certain genetic parameters (across-trials analysis). To achieve this, simulations were conducted using random resampling without replacement (k = 1,000/pair of varying factors) on datasets containing 10-year total height of two coniferous species (Larix laricina and Picea mariana), as well as pilodyn measurements of wood density evaluated on a 26-year-old population of P. mariana. SAS® 9.2 Macro Language and Procedures were used to estimate confidence intervals of several genetic parameters with different reduced samplings. Simulation results show that reducing the number of trees per family per site had more impact on the magnitude and precision of genetic parameter estimates than reducing the number of families, especially for half-sib heritability and type B genetic correlations for height and wood density. A priori determination of an optimal subsampling strategy to evaluate the accuracy of genetic parameters should become common practice before assessing wood traits, in tree breeding studies or when planning juvenile retrospective progeny trials for forest tree species.
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