naa a22 4500 510730329 CHVBK 20180411082950.0 cr unu---uuuuu 180411e20130201xx s 000 0 eng 10.1007/s11295-012-0540-5 doi (NATIONALLICENCE)springer-10.1007/s11295-012-0540-5 Use of resampling to assess optimal subgroup composition for estimating genetic parameters from progeny trials [Elektronische Daten] [Martin Perron, Josianne DeBlois, Mireille Desponts] 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. Springer-Verlag, 2012 Bootstrap confidence intervals nationallicence Forest trees nationallicence Accuracy of genetic parameters nationallicence Simulation nationallicence Wood traits nationallicence Height nationallicence Perron Martin Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière, 2700, rue Einstein, G1P 3W8, Québec, Québec, Canada aut DeBlois Josianne Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière, 2700, rue Einstein, G1P 3W8, Québec, Québec, Canada aut Desponts Mireille Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière, 2700, rue Einstein, G1P 3W8, Québec, Québec, Canada aut Tree Genetics & Genomes Springer-Verlag 9/1(2013-02-01), 129-143 1614-2942 9:1<129 2013 9 11295 https://doi.org/10.1007/s11295-012-0540-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11295-012-0540-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Perron Martin Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière, 2700, rue Einstein, G1P 3W8, Québec, Québec, Canada aut NATIONALLICENCE 700 1- DeBlois Josianne Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière, 2700, rue Einstein, G1P 3W8, Québec, Québec, Canada aut NATIONALLICENCE 700 1- Desponts Mireille Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière, 2700, rue Einstein, G1P 3W8, Québec, Québec, Canada aut NATIONALLICENCE 773 0- Tree Genetics & Genomes Springer-Verlag 9/1(2013-02-01), 129-143 1614-2942 9:1<129 2013 9 11295 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer