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C.A. Weber

Lack of variation among black raspberry cultivars is thought to be a limiting factor in fruit production and in breeding improved cultivars. An assessment of the available diversity in black raspberry is needed to effectively develop improved cultivars. Such an assessment was done to estimate the genetic similarities for RAPD markers in 16 black raspberry genotypes and to determine the genetic diversity among these genotypes based on these markers. In addition, the ability to distinguish between the black raspberry genotypes, two red raspberry cultivars (Rubus idaeus L.), and a blackberry cultivar (Rubus hybrid) was determined. A similarity matrix from 379 RAPD markers was calculated, and a phylogenetic tree was constructed using the PHYLIP suite of phylogeny software, which revealed the relationship among the genotypes. An average of 81% similarity was calculated among 16 black raspberry genotypes with a maximum similarity of 98% and a minimum of 70%. The average similarity between black raspberry and red raspberry was 41% and was 26% between black raspberry and blackberry. Combined marker profiles from six RAPD primers could be used to distinguish between the 16 black raspberry genotypes. Red raspberry and blackberry could be distinguished from black raspberry by 27 and 29 of 30 RAPD primers tested, respectively. Genetic diversity was most prominent in genotypes from the extremes of the black raspberry indigenous range. Diversifying the germplasm pool for black raspberry cultivar improvement can be achieved through utilizing genotypes from the extremes of the black raspberry range and through interspecific hybridization.

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Şurhan Göl, Sami Doğanlar, and Anne Frary

distance matrix using Dice’s coefficient and clustering analysis was done with the unweighted neighbor-joining algorithm. These analyses were done with the DARwin5 (Dissimilarity Analysis and Representation for Windows) software program ( http

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Phillip A. Wadl, Xinwang Wang, Andrew N. Trigiano, John A. Skinner, Mark T. Windham, Robert N. Trigiano, Timothy A. Rinehart, Sandra M. Reed, and Vincent R. Pantalone

analysis (PCA) plots were produced from the shared allele distance matrix using NTSYS software. A molecular key was developed respectively from genotyping the C. florida cultivars and breeding lines and the C. kousa cultivars. Four polymorphic SSR loci

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David M. Czarnecki II, Madhugiri Nageswara Rao, Jeffrey G. Norcini, Frederick G. Gmitter Jr, and Zhanao Deng

phenotypes and pairwise Apostol (or simple match) genetic distances, or based on the allele frequencies within populations and the pairwise Nei's genetic distances among populations. From the Apostol distance matrix, a principal coordinate analysis (PCoA) was

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Phillip A. Wadl, Mark T. Windham, Richard Evans, and Robert N. Trigiano

-sharing distance matrix for the C. kousa genotypes and then a UPGMA tree was generated ( Fig. 4 ). The pairwise similarity coefficients ranged from 0.40 to 1.00 and 155 of 406 pairwise comparisons were 0.75 or higher. The lower the value the more similar, the

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Chalita Sriladda, Heidi A. Kratsch, Steven R. Larson, and Roger K. Kjelgren

correlation tests. Fifteen field populations were used for the correlation tests. The Euclidean distance matrix of morphology and NEI-72 distance matrix of genetics were computed using NTSYSpc 2.2N software (Exeter Software, Setauket, NY). Geographic distance

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Sandra M. Reed and Timothy A. Rinehart

using Nei's minimum genetic distance matrix for 26 single-sequence repeat markers among 36 Hydrangea paniculata genotypes. Bootstrap values out of 1000 replicates are shown if 50% or higher. Three cultivars are listed by their trademarked names: Early

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Cecil Pounders, Tim Rinehart, and Hamidou Sakhanokho

alleles were represented as diploid. Populations version 1.2.28 was used for phenetic analyses ( Langella, 2002 ). Genetic distances between individual samples were calculated using allele sharing distance to create a distance matrix ( Jin and Chakraborty

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Nathan C. Phillips, Steven R. Larson, and Daniel T. Drost

molecular variance based on Euclidean distances were computed using Arlequin ( Excoffier et al., 1992 ). Neighbor-joining trees were generated by PAUP* version 4.0b8 ( Swofford, 2000 ) using the distance matrix generated by Arlequin ( Excoffier et al

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Sara Melito, Angela Fadda, Emma Rapposelli, and Maurizio Mulas

molecular variance (AMOVA) as implemented in Arlequin Version 3.5.1.2 ( Excoffier et al., 2005 ). Population genetic distance matrix ( Nei, 1973 ) was performed with Arlequin to explore the pairwise relationships among the M. communis accessions analyzed