The use of AFLP and SSR molecular markers to decipher homonyms and synonyms in grapevine cultivars: the case of the varietal group known as ’’Schiave’’.

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Title: The use of AFLP and SSR molecular markers to decipher homonyms and synonyms in grapevine cultivars: the case of the varietal group known as ’’Schiave’’.
Authors: Fossati, T.1, Labra, M., Castiglione, S.1, Failla, O.2, Scienza, A.2, Sala, F. francesco.sala@unimi.it
Source: Theoretical & Applied Genetics. Feb2001, Vol. 102 Issue 2/3, p200-205. 6p.
Subjects: Genetic markers, Cultivars, Grapes, Genetics, Nucleic acids, DNA
Abstract: ’’Schiave’’ collectively refers to grapevine cultivars presently grown on the Southern and Northern slopes of the Eastern Alps and bearing different names (Schiava, Trollinger, Rossara, Rossola, Geschlafene, Gansfüsser, Urban and others). Their common origin has been suggested by historic, linguistic and ampelographic considerations. This hypothesis has now been assayed by using more direct approaches based on AFLP and SSR analysis. The present paper shows: (1), that AFLP and SSR give comparable results when used to study genomic similarity among the Schiave grapevine cultivars, and (2) that ’’Schiave’’ is used to group grapevine cultivars that are genetically heterogeneous. In fact, a dendrogram constructed from an AFLP analysis of the 33 best-known Schiave (or correlated) cultivars, shows different, and in some cases relevant, degrees of genomic dissimilarity. The analysed cultivars cluster into at least five taxonomic groups with specific geographic distribution along the valleys of Valtellina, Bergamo and Brescia and those of South Tyrol and Swabia. It is concluded that the common definition ’’Schiave’’ refers to a similar cultivation practice in contiguous regions rather than to a common genetic background. [ABSTRACT FROM AUTHOR]
Copyright of Theoretical & Applied Genetics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The use of AFLP and SSR molecular markers to decipher homonyms and synonyms in grapevine cultivars: the case of the varietal group known as ’’Schiave’’.
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  Data: ’’Schiave’’ collectively refers to grapevine cultivars presently grown on the Southern and Northern slopes of the Eastern Alps and bearing different names (Schiava, Trollinger, Rossara, Rossola, Geschlafene, Gansfüsser, Urban and others). Their common origin has been suggested by historic, linguistic and ampelographic considerations. This hypothesis has now been assayed by using more direct approaches based on AFLP and SSR analysis. The present paper shows: (1), that AFLP and SSR give comparable results when used to study genomic similarity among the Schiave grapevine cultivars, and (2) that ’’Schiave’’ is used to group grapevine cultivars that are genetically heterogeneous. In fact, a dendrogram constructed from an AFLP analysis of the 33 best-known Schiave (or correlated) cultivars, shows different, and in some cases relevant, degrees of genomic dissimilarity. The analysed cultivars cluster into at least five taxonomic groups with specific geographic distribution along the valleys of Valtellina, Bergamo and Brescia and those of South Tyrol and Swabia. It is concluded that the common definition ’’Schiave’’ refers to a similar cultivation practice in contiguous regions rather than to a common genetic background. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Theoretical & Applied Genetics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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