title: Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study.
abstract: Classical methodologies for grapevine selection used in the vine-growing world are generally based on comparisons among a small number of clones. This does not take advantage of the entire genetic variability within ancient varieties, and therefore limits selection challenges. Using the general principles of plant breeding and of quantitative genetics, we propose new breeding strategies, focussed on conservation and quantification of genetic variability by performing a cycle of mass genotypic selection prior to clonal selection. To exploit a sufficiently large amount of genetic variability, initial selection trials must be generally very large. The use of experimental designs adequate for those field trials has been intensively recommended for numerous species. However, their use in initial trials of grapevines has not been studied. With the aim of identifying the most suitable experimental designs for quantification of genetic variability and selection of ancient varieties, a study was carried out to assess through simulation the comparative efficiency of various experimental designs (randomized complete block design, alpha design and row-column (RC) design). The results indicated a greater efficiency for alpha and RC designs, enabling more precise estimates of genotypic variance, greater precision in the prediction of genetic gain and consequently greater efficiency in genotypic mass selection.
authors.affiliation: Centro de Botânica Aplicada à Agricultura (CBAA), Instituto Superior de Agronomia, Technical University of Lisbon, Tapada da Ajuda, Lisboa, Portugal. elsagoncalves@isa.utl.pt
authors.forename: E
authors.initials: E
authors.lastname: Gonçalves
authors.forename: A
authors.initials: A
authors.lastname: St Aubyn
authors.forename: A
authors.initials: A
authors.lastname: Martins
mesh_terms: D001947:Breeding; D003198:Computer Simulation; D014644:Genetic Variation; D015233:Models, Statistical; D012107:Research Design; D012641:Selection, Genetic; D027843:Vitis / Q000235:genetics*