Identity, synonymies and homonynies of minor grapevine cultivars maintained in the portuguese ampelographic collection

Alifragkis, Antonios; Cunha, Jorge; Pereira, Joana; Fevereiro, Pedro; Eiras-Dias, José Eduardo

doi:10.1051/ctv/20153001043

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…The frequency of null alleles was calculated based on the following formula: (He -Ho)/(1 + He) (Brookfield, 1996). PI was defined as the probability that two randomly selected samples have the same SSR profile and was calculated based on the following formula: Σpi 4 + Σ Σ(2pipj) 2 , where pi and pj indicate the frequencies of alleles i and j, respectively (Paetkau et al, 1995).…”

Section: Resultsmentioning

confidence: 99%

Molecular Analysis of Grapevine Germplasm by SSR (Simple Sequence Repeats) in Diyarbakir Province, Turkey

Karataş¹,

Karaagac²,

Karataş³

et al. 2019

Appl. Ecol. Env. Res.

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This study was aimed to identify the grape cultivars growing in Diyarbakır. A total of 45 genotypes were analyzed using 7 microsatellite loci. The numbers of alleles per locus ranged between 7 and 10, whereby VVS2 and VrZAG47 had the highest and VVMD7, VrZAG62, and VrZAG79 had the lowest number of alleles. The expected and observed heterozygosity were 0.77 and 0.73, respectively. Clustering analysis was performed using the UPGMA method (Unweighted Pair-Group Method using Arithmetic means). A dendrogram was constructed based on the genetic similarity among the genotypes, which indicated 5 distinct groups, with each group involving numerous subgroups. Two additional genotypes that are used as reference genotypes around the world, Cabernet Sauvignon and Merlot, were also analyzed and were classified into a separate subgroup and differentiated from the other 43 genotypes. In conclusion, synonyms and homonyms were detected among some of the genotypes analyzed in the study.

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Section: Resultsmentioning

confidence: 99%

Molecular Analysis of Grapevine Germplasm by SSR (Simple Sequence Repeats) in Diyarbakir Province, Turkey

Karataş¹,

Karaagac²,

Karataş³

et al. 2019

Appl. Ecol. Env. Res.

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“…), the selection of the algorithm or method to infer the clustering analysis of populations and cultivars, is critical. Algorithms are, essentially, the equations that various statistical software use to quantify variability and finally present it as a branched tree (dendrogram or phenogram as in Lefort and Roubelakis-Angelakis (2001); Karataşet al (2007); Alifragkis et al (2015)). In the case of molecular marker analysis there is a plethora of different algorithms or methods that may be utilized and can be classified into three main groups, distance methods, parsimony methods and maximum likelihood methods (Nei and Kumar, 2000;Ochieng et al, 2007).…”

Section: Algorithms and Software Packages To Infer Phylogenetic Relat...mentioning

confidence: 99%

“…Algorithms are, essentially, the equations that various statistical software use to quantify variability and finally present it as a branched tree (dendrogram or phenogram as in Lefort and Roubelakis-Angelakis (2001) ; Karataş et al. (2007) ; Alifragkis et al. (2015) ).…”

Section: Algorithms and Software Packages To Infer Phylogenetic Relat...mentioning

confidence: 99%

Vitis vinifera genotyping toolbox to highlight diversity and germplasm identification

et al. 2023

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The contribution of vine cultivation to human welfare as well as the stimulation of basic social and cultural features of civilization has been great. The wide temporal and regional distribution created a wide array of genetic variants that have been used as propagating material to promote cultivation. Information on the origin and relationships among cultivars is of great interest from a phylogenetics and biotechnology perspective. Fingerprinting and exploration of the complicated genetic background of varieties may contribute to future breeding programs. In this review, we present the most frequently used molecular markers, which have been used on Vitis germplasm. We discuss the scientific progress that led to the new strategies being implemented utilizing state-of-the-art next generation sequencing technologies. Additionally, we attempted to delimit the discussion on the algorithms used in phylogenetic analyses and differentiation of grape varieties. Lastly, the contribution of epigenetics is highlighted to tackle future roadmaps for breeding and exploitation of Vitis germplasm. The latter will remain in the top of the edge for future breeding and cultivation and the molecular tools presented herein, will serve as a reference point in the challenging years to come.

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“…All the grapevine plants were genotyped with a set of nine Nuclear SSR Markers (Table 3) (VVS2, VVMD5, VVMD7, VVMD25, VVMD27, VVMD28, VVMD32, VRZAG62, VRZAG79) according to OIV for Vitis characterization [40]. These 9 SSR were divided in two multiplex mixes (Mplex1 SSRs: VVMD27, VVMD25, VVMD28 and VVMD32; Mplex2 SSRs: VVMD5, VVMD7 and VVS2) [41]. Each multiplex reaction was prepared according to the manufacturer's instructions of Maxima Hot Start PCR Master Mix (2×).…”

Section: Genetic Analysismentioning

confidence: 99%

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

Silva

Figueiredo

Cunha³

et al. 2020

Plants

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When a dark-adapted leaf is illuminated with saturating light, a fast polyphasic rise of fluorescence emission (Kautsky effect) is observed. The shape of the curve is dependent on the molecular organization of the photochemical apparatus, which in turn is a function of the interaction between genotype and environment. In this paper, we evaluate the potential of rapid fluorescence transients, aided by machine learning techniques, to classify plant genotypes. We present results of the application of several machine learning algorithms (k-nearest neighbors, decision trees, artificial neural networks, genetic programming) to rapid induction curves recorded in different species and cultivars of vine grown in the same environmental conditions. The phylogenetic relations between the selected Vitis species and Vitis vinifera cultivars were established with molecular markers. Both neural networks (71.8%) and genetic programming (75.3%) presented much higher global classification success rates than k-nearest neighbors (58.5%) or decision trees (51.6%), genetic programming performing slightly better than neural networks. However, compared with a random classifier (success rate = 14%), even the less successful algorithms were good at the task of classifying. The use of rapid fluorescence transients, handled by genetic programming, for rapid preliminary classification of Vitis genotypes is foreseen as feasible.

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Identity, synonymies and homonynies of minor grapevine cultivars maintained in the portuguese ampelographic collection

Cited by 6 publications

References 13 publications

Molecular Analysis of Grapevine Germplasm by SSR (Simple Sequence Repeats) in Diyarbakir Province, Turkey

Molecular Analysis of Grapevine Germplasm by SSR (Simple Sequence Repeats) in Diyarbakir Province, Turkey

Vitis vinifera genotyping toolbox to highlight diversity and germplasm identification

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

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