title: A comparison of experimental designs for selection in breeding trials with nested treatment structure.
abstract: Plant breeders frequently evaluate large numbers of entries in field trials for selection. Generally, the tested entries are related by pedigree. The simplest case is a nested treatment structure, where entries fall into groups or families such that entries within groups are more closely related than between groups. We found that some plant breeders prefer to plant close relatives next to each other in the field. This contrasts with common experimental designs such as the alpha-design, where entries are fully randomized. A third design option is to randomize in such a way that entries of the same group are separated as much as possible. The present paper compares these design options by simulation. Another important consideration is the type of model used for analysis. Most of the common experimental designs were optimized assuming that the model used for analysis has fixed treatment effects. With many entries that are related by pedigree, analysis based on a model with random treatment effects becomes a competitive alternative. In simulations, we therefore study the properties of best linear unbiased predictions (BLUP) of genetic effects based on a nested treatment structure under these design options for a range of genetic parameters. It is concluded that BLUP provides efficient estimates of genetic effects and that resolvable incomplete block designs such as the alpha-design with restricted or unrestricted randomization can be recommended.
authors.affiliation: Fachgebiet Bioinformatik, Institut für Pflanzenbau und Grünland, Universität Hohenheim, 70593, Stuttgart, Germany. piepho@uni-hohenheim.de
authors.forename: H P
authors.initials: HP
authors.lastname: Piepho
authors.forename: E R
authors.initials: ER
authors.lastname: Williams
mesh_terms: D001947:Breeding; D003198:Computer Simulation; D010375:Pedigree; D010944:Plants / Q000235:genetics*; D011897:Random Allocation; D012107:Research Design; D012641:Selection, Genetic