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.

Establishing a core collection from the integration of morphoagronomical, phytopathological and molecular data

“…According to Hu, Zhu and Xu (2000) and Wang et al (2007), a sub-collection is representative when its amplitude coincidence index is at least 80%. Subcollections established from each set of characteristics, QUANT-20 MULT-20 or MOL-20, were therefore not considered representative, as they presented values for AC of 0.79, 0.77 and 0.78 for the molecular, quantitative and multi-category groups of characteristics respectively.…”

“…The amplitude coincidence index (AC) for each sub-collection was obtained for each group of characteristics, whether quantitative, multi-category or molecular, by means of the equation (HU; ZHU; XU, 2000;WANG et al, 2007): (3) where: AC is the amplitude coincidence index; A i SC is the amplitude of the i-th characteristic in the sub-collection; AiCI is the amplitude of the i-th characteristic in the initial collection; and n is the number of characteristics for a particular group.…”

“…Much work is done in order to establish core collections, and several strategies have been outlined for their formation, which include from simple random, non-random, and stratified sampling (UPADHYAYA et al,2007;XU et al, 2006), to more sophisticated sampling methods (JANSEM; VAN HINTUM, 2007;VASCONCELOS et al, 2007;WANG et al, 2007).…”

Development of Japanese Persimmon Core Collection by Genetic Distance Sampling Based on SSR Markers

“…For the final choice of entries, stepwise clustering and least distance stepwise clustering proposed by Hu et al (19) and Wang et al (31), respectively, seemed to be the appropriated method, but they require prior arbitrary determination of core size and complicated computation of cluster analysis for many times. Mstrat software developed by Gouesnard et al (12) also could obtain precise result.…”

Effect of the scale of quantitative trait data on the representativeness of a cotton germplasm sub-core collection

“…The least distance stepwise sampling (LDSS) method (Wang et al, 2007) was adopted to construct sub-core collections. The procedure was: (1) The genetic distances among accessions were calculated and accessions were classified by hierarchical cluster analysis based on their genetic distance; (2) One accession from a subgroup with the least distance was randomly removed and another accession of the subgroup was sampled; (3) The genetic distances among the remaining accessions were calculated, and the sampling was repeated in the same way.…”