title: A strategy on constructing core collections by least distance stepwise sampling.
abstract: A strategy was proposed for constructing core collections by least distance stepwise sampling (LDSS) based on genotypic values. In each procedure of cluster, the sampling is performed in the subgroup with the least distance in the dendrogram during constructing a core collection. Mean difference percentage (MD), variance difference percentage (VD), coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were used to evaluate the representativeness of core collections constructed by this strategy. A cotton germplasm collection of 1,547 accessions with 18 quantitative traits was used to construct core collections. Genotypic values of all quantitative traits of the cotton collection were unbiasedly predicted based on mixed linear model approach. By three sampling percentages (10, 20 and 30%), four genetic distances (city block distance, Euclidean distance, standardized Euclidean distance and Mahalanobis distance) combining four hierarchical cluster methods (nearest distance method, furthest distance method, unweighted pair-group average method and Ward's method) were adopted to evaluate the property of this strategy. Simulations were conducted in order to draw consistent, stable and reproducible results. The principal components analysis was performed to validate this strategy. The results showed that core collections constructed by LDSS strategy had a good representativeness of the initial collection. As compared to the control strategy (stepwise clusters with random sampling strategy), LDSS strategy could construct more representative core collections. For LDSS strategy, cluster methods did not need to be considered because all hierarchical cluster methods could give same results completely. The results also suggested that standardized Euclidean distance was an appropriate genetic distance for constructing core collections in this strategy.
authors.affiliation: Department of Agronomy, Zhejiang University, 310029 Hangzhou, China.
authors.forename: J C
authors.initials: JC
authors.lastname: Wang
authors.forename: J
authors.initials: J
authors.lastname: Hu
authors.forename: H M
authors.initials: HM
authors.lastname: Xu
authors.forename: S
authors.initials: S
authors.lastname: Zhang
mesh_terms: D003198:Computer Simulation; D005828:Genetics, Population / Q000706:statistics & numerical data*; D003368:Gossypium / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D025341:Principal Component Analysis