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Peter J. Leonard, Mark H. Brand, Bryan A. Connolly, and Samuel G. Obae

Asian Sorbus species, simple-leaved European Sorbus , or × Sorbaronia species derived from these Sorbus . Fig. 1. Phenogram created using the unweighted pair group method with arithmetic averages (UPGMA) based on Jaccard’s coefficient of similarity

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Jacob Mashilo, Hussein Shimelis, Alfred Odindo, and Beyene Amelework

bottle gourd using 14 simple sequence repeat markers based on districts of collection. Cluster analysis. Jaccard’s coefficient of similarity values ranged from 0.07 to 1.0, with a mean of 0.63 among the 67 landraces (data not shown). Among the test bottle

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Mozhgan Zangeneh and Hassan Salehi

were scored and the data matrices of the ISSR fragments were accumulated for further investigation. These data were applied to generate a similarity matrix based on Jaccard’s coefficients by using the SIMQUAL program in the NTSYS-PC software package

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Xuejuan Chen, Ming Sun, Jianguo Liang, Hui Xue, and Qixiang Zhang

Chrysanthemums have beautiful flowers with high ornamental value and rich genetic diversity. Amplified fragment length polymorphism (AFLP) markers were used to detect the relationships among 12 wild accessions and 62 groundcover chrysanthemum cultivars. Nineteen EcoRI/MseI primer combinations revealed 452 informative polymorphic bands with a mean of 23.8 bands and 71.5% polymorphic rate per primer pair. Jaccard’s coefficient of similarity varied from 0.64 to 0.89, indicating much genetic variation in chrysanthemums. The 74 accessions were classified into two major groups by unweighted pair group method with the arithmetic averages (UPGMA). The dendrogram showed that AFLP variability was closely correlated with both geographic distribution and traditional classification of the wild accessions. Among all accessions, genetic relationship was the most relevant factor in AFLP-marker clustering, whereas petal type was also informative. AFLP technology could be very efficient for discriminating species of chrysanthemum and its related genera and reconstruct their genetic relatedness.

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Justin A. Porter, Hazel Y. Wetzstein, David Berle, Phillip A. Wadl, and Robert N. Trigiano

present (1) or absent (0). Similarity matrices were generated with NTSYSpc Version 2.20q (Applied Biosystems, Carlsbad, CA) based on band scoring. Jaccard’s coefficient of similarity was used to show similarity between samples. Cluster analysis using

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Jericó J. Bello-Bello, Lourdes G. Iglesias-Andreu, Susana A. Avilés-Viñas, Eunice Gómez-Uc, Adriana Canto-Flick, and Nancy Santana-Buzzy

) somatic embryos obtained from embryogenic callus. Table 2. Effectiveness of ISSR markers for detecting polymorphism in Habanero pepper somaclonal variation. ISSR = intersimple sequence repeat. Fig. 2. Genetic similarity dendrogram (Jaccard’s coefficient

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Ghazal Baziar, Moslem Jafari, Mansoureh Sadat Sharifi Noori, and Samira Samarfard

similarity matrix based on Jaccard’s coefficient. The partitioning of molecular variance and correlation coefficients between the similarity matrix were analyzed according to Mantel (1967) using XLSTAT Pro version 7.5, a Microsoft Excel © add-in. Finally

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José R. Bautista-Aguilar, Lourdes G. Iglesias-Andreu, Jaime Martínez-Castillo, Marco A. Ramírez-Mosqueda, and Matilde M. Ortiz-García

number and frequency of alleles for each locus based on their total size in bp. A similarity matrix was calculated using Jaccard’s coefficient and the dendrogram was generated using the unweighted pair group method (UPGMA). The clustering method was

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Fanjuan Meng, Ruoding Wang, Mu Peng, Chao Wang, Zhongkui Wang, Fachun Guan, and Yajun Li

clustering overall. Fig. 2. Dengrogram based on Jaccard’s coefficient. s1–s15: samples from Jiazha village, k1–k29: samples from Langkazi village, m1–m26: samples from Zhaxue village. To analyze hierarchical population structure of all samples, a Bayesian

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Sima Taheri, Thohirah Lee Abdullah, Nur Ashikin Psyquay Abdullah, and Zaiton Ahmad

Taxonomy and Multivariate Analysis System (NTSYSpc2.10e; Rohlf, 2002 ). The genetic similarity for all pairwise comparisons was computed using Jaccard’s coefficient. The similarity matrix was used to create the dendrogram using the unweighted paired group