High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

The genetic differentiation of grapevine rootstock varieties was inferred by the Artificial Neural Network approach based on the Self-Organizing Map algorithm. A combination of RAPD and SSR molecular markers, yielding polymorphic informative loci, was used to determine the genetic characterization among the rootstock varieties 420-A, Schwarzmann, IAC-766 Campinas, Traviú, Kober 5BB, and IAC-572 Jales. A neural network algorithm, based on allelic frequency, showed that the individual grapevine rootstocks (n = 64) were grouped into three genetically differentiated clusters. Cluster 1 included only the Kober 5BB rootstock, Cluster 2 included rootstocks of the varieties Traviú and IAC-572, and Cluster 3 included 420-A, Schwarzmann and IAC-766 plants. Evidence from the current study indicates that, despite the morphological similarities of the 420-A and Kober 5BB varieties, which share the same genetic origin, two new varieties were generated that are genetically divergent and show differences in performance.

Keywords:
clustering methods; RAPD-SSR loci; self-organizing map algorithm

Authorship

Marcia Oliveira Costa

Programa de Pós-Graduação em Genética e Melhoramento, Universidade Estadual de Maringá, Maringá, Paraná, Brazil. Universidade Estadual de MaringáBrazilMaringá, Paraná, BrazilPrograma de Pós-Graduação em Genética e Melhoramento, Universidade Estadual de Maringá, Maringá, Paraná, Brazil.

Livia Santos Capel

Carlos Maldonado

Instituto de Ciências Biológicas, Universidade de Talca, Talca, Chile. Universidade de TalcaChileTalca, ChileInstituto de Ciências Biológicas, Universidade de Talca, Talca, Chile.

Freddy Mora

Instituto de Ciências Biológicas, Universidade de Talca, Talca, Chile. Universidade de TalcaChileTalca, ChileInstituto de Ciências Biológicas, Universidade de Talca, Talca, Chile.

Claudete Aparecida Mangolin

Departmento de Biotecnologia, Genética e Biologia Celular, Universidade Estadual de Maringá, Avenida Colombo, 5790, 87020-900, Maringá, Paraná, Brazil.Universidade Estadual de MaringáBrazilMaringá, Paraná, BrazilDepartmento de Biotecnologia, Genética e Biologia Celular, Universidade Estadual de Maringá, Avenida Colombo, 5790, 87020-900, Maringá, Paraná, Brazil.

http://orcid.org/0000-0002-1653-3106

Maria de Fátima Pires da Silva Machado ^**Author for correspondence. E-mail: mfpsmachado@uem. br

*Author for correspondence. E-mail: mfpsmachado@uem. br

SCIMAGO INSTITUTIONS RANKINGS

Instituto de Ciências Biológicas, Universidade de Talca, Talca, Chile. Universidade de TalcaChileTalca, ChileInstituto de Ciências Biológicas, Universidade de Talca, Talca, Chile.

Figures | Tables

Figures (3)
Tables (3)

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Figure 1
Results from the clustering procedure, based on an artificial neural network analysis with RAPD and SSR markers assessed in 6 varieties of grapevine rootstocks, which evidenced three genetically differentiated groups.

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Figure 2
Principal coordinate analysis of genetic distances with RAPD and SSR primers in 6 varieties of grapevine rootstocks. Circles denote the genetically differentiated clusters, according to the artificial neural network model.

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Figure 3
UPGMA dendrogram based on Nei’s genetic distances, built with SSR and RAPD markers, assessed for the 6 varieties of grapevine rootstocks. Values at the nodes indicate the proportions of bootstrap runs (10,000) in a particular node.

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Table 1
Nucleotide sequences of the microsatellite primers, simple sequence repeated for each primer, number of alleles (NA) detected by each primer in the rootstock varieties 420-A, Schwarzmann, IAC-766 Campinas, Traviú, IAC-572 Jales and Kober 5BB.

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Table 2
Parameters revealing the discriminatory capacity of RAPD and SSR markers and the genetic variability within rootstocks IAC-766 Campinas, IAC-572 Jales, 420-A, Schwarzmann, Traviú and Kober 5BB.

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Table 3
Genetic differentiation among varieties of grapevine rootstocks IAC-766 Campinas, IAC-572 Jales, 420-A, Schwarzmann, Traviú and Kober 5BB, based on F_ST rates (above the diagonal) and Nei´s Genetic Distances (below the diagonal).

Figure 1 Results from the clustering procedure, based on an artificial neural network analysis with RAPD and SSR markers assessed in 6 varieties of grapevine rootstocks, which evidenced three genetically differentiated groups.

Figure 2 Principal coordinate analysis of genetic distances with RAPD and SSR primers in 6 varieties of grapevine rootstocks. Circles denote the genetically differentiated clusters, according to the artificial neural network model.

Figure 3 UPGMA dendrogram based on Nei’s genetic distances, built with SSR and RAPD markers, assessed for the 6 varieties of grapevine rootstocks. Values at the nodes indicate the proportions of bootstrap runs (10,000) in a particular node.

Table 1 Nucleotide sequences of the microsatellite primers, simple sequence repeated for each primer, number of alleles (NA) detected by each primer in the rootstock varieties 420-A, Schwarzmann, IAC-766 Campinas, Traviú, IAC-572 Jales and Kober 5BB.

Primer	Nucleotide Sequence (5’→ 3’)	Simple Sequence Repeated	NA
Udv34	AAGAGACCAAGGATAGATCAAACA^(F) AAATGCAACGGGAGATGGTA^(R)	(TG) _n(AG) _n(AT)_n(AGAGG)_n	4
Vmc4d4	GTCTTGTAATGGAACCAACTGC^(F) AGATTGACCTGGACCTGAAACT^(R)	(GCT)₉	3
Udv127	CGTCAATCGCTTGTAATCTGC^(F) TGAGCATTTTGCTCCTTCATT^(R)	(CA)_n	4
Udv32	CATGCGTATGTGTTAGAGAGCA^(F) CATGGCATGTGCTTTGTTAT^(R)	(AC)_n(AT)_n	4
Vvmd8	TAACAAACAAGAAGAGGAAT^(F) AGCACATCCACAACATAATG^(R)	(TC)_{12. 5} (TA)₈	3
Udv01	TCATTTTCTTGATCGAAGTCCA^(F) TGAGCATCAAAACAGGAAGC^(R)	(TA/TG)_n	4
Udv11	TTTATGGCAACCCTCCAATC^(F) TTGATGGTCCACTGGAACTG^(R)	(CT/CA)_n	4
Vvmd6	ATCTCTAACCCTAAAACCAT^(F) CTGTGCTAAGACGAAGAAGA^(R)	(CT)₄C(CT)₂TTAG(CT)TAAT(CT)₆C(CT)₂C(CT)₂	4
Scu14vv	CTGCACTTGAATACGAGCAGGTC^(F) TGTTATATGATCCTCCCCCTCCTC^(R)	(GAA)_n	3
Udv108	TGTAGGGTTCCAAAGTTCAGG^(F) CCTTTTTATATGTGGTGGAGCA^(R)	(CT)_n(CA)_n(GT)_n	4
Vvmd28	AACAATTCATGAAAAGAGAGAGAGAGA^(F) TCATCAATTTCGTATCTCTATTTGCTG^(R)	(CT)₈	3
Vvmd7	AGAGTTGCGGAGAACAGGAT^(F) CGAACCTTCACACGCTTGAT^(R)	(CT)_{14. 5}	5

Table 2 Parameters revealing the discriminatory capacity of RAPD and SSR markers and the genetic variability within rootstocks IAC-766 Campinas, IAC-572 Jales, 420-A, Schwarzmann, Traviú and Kober 5BB.

Marker	N	A	PIC	H	I	P_ST	Nm
RAPD
OPB - 01	12	10	0.69	0.31	0.46	0.77	0.15
OPB - 03	15	15	0.61	0.39	0.56	0.54	0.43
OPB - 04	17	17	0.66	0.29	0.46	0.72	0.20
OPB - 05	12	10	0.60	0.25	0.39	0.76	0.16
OPB - 07	16	13	0.61	0.33	0.48	0.64	0.29
OPB - 08	14	13	0.50	0.35	0.52	0.59	0.59
OPB - 10	12	7	0.75	0.23	0.34	0.67	0.24
OPB - 11	18	17	0.62	0.38	0.55	0.90	0.90
OPB - 15	11	4	0.53	0.14	0.21	0.93	0.93
OPB - 17	16	15	0.72	0.34	0.50	0.68	0.24
OPB - 18	12	9	0.46	0.27	0.41	0.67	0.25
OPC - 02	15	13	0.57	0.29	0.43	0.72	0.19
OPC - 04	16	13	0.55	0.28	0.42	0.68	0.24
OPC - 07	17	16	0.54	0.31	0.46	0.54	0.42
SSR
Udv34	4	4	0.67	3.58	1.32	0.14	1.60
Vmc444	3	3	0.46	2.31	0.91	0.27	0.68
Udv127	4	4	0.58	2.78	1.13	0.09	2.50
Udv32	4	4	0.68	3.67	1.34	0.16	1.33
Vvmd08	3	3	0.32	1.86	0.81	0.41	0.36
Udv001	4	4	0.56	2.66	1.11	0.22	0.89
Udv011	4	3	0.57	2.61	1.11	0.13	1.67
Vvmd06	4	4	0.59	2.72	1.13	0.24	0.77
Scu14vv	3	3	0.46	2.30	0.93	0.14	1.58
Udv108	4	4	0.56	2.81	1.16	0.20	1.03
Vvm428	3	3	0.52	2.64	1.03	0.13	1.70
Vvm407	3	3	0.54	2.77	1.05	0.16	1.32

N: total number of bands; A: number of polymorphic bands; H: Nei’s gene diversity; I: Shannon’s information index; P_ST: Genetic differentiation coefficient between populations and Fixation index for RAPD and SSR, respectively; Nm: Gene flow.

Table 3 Genetic differentiation among varieties of grapevine rootstocks IAC-766 Campinas, IAC-572 Jales, 420-A, Schwarzmann, Traviú and Kober 5BB, based on F_ST rates (above the diagonal) and Nei´s Genetic Distances (below the diagonal).

	420-A	Schwarzmann	Traviú	IAC-766	IAC-572	Kober 5BB
420-A	-	0.22	0.34	0.27	0.42	0.49
Schwarzmann	0.12	-	0.41	0.28	0.53	0.56
Traviú	0.27	0.28	-	0.34	0.49	0.45
IAC-766	0.19	0.15	0.23	-	0.41	0.46
IAC-572	0.32	0.37	0.37	0.28	-	0.57
Kober 5BB	0.46	0.43	0.34	0.37	0.43	-

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High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

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[1] *Author for correspondence. E-mail: mfpsmachado@uem. br