title: High throughput analysis of grape genetic diversity as a tool for germplasm collection management.
abstract: Using 20 SSR markers well scattered across the 19 grape chromosomes, we analyzed 4,370 accessions of the INRA grape repository at Vassal, mostly cultivars of Vitis vinifera subsp. sativa (3,727), but also accessions of V. vinifera subsp. sylvestris (80), interspecific hybrids (364), and rootstocks (199). The analysis revealed 2,836 SSR single profiles: 2,323 sativa cultivars, 72 wild individuals (sylvestris), 306 interspecific hybrids, and 135 rootstocks, corresponding to 2,739 different cultivars in all. A total of 524 alleles were detected, with a mean of 26.20 alleles per locus. For the 2,323 cultivars of V. vinifera, 338 alleles were detected with a mean of 16.9 alleles per locus. The mean genetic diversity (GDI) was 0.797 and the level of heterozygosity was 0.76, with broad variation from 0.20 to 1. Interspecific hybrids and rootstocks were more heterozygous and more diverse (GDI = 0.839 and 0.865, respectively) than V. vinifera cultivars (GDI = 0.769), Vitis vinifera subsp. sylvestris being the least divergent with GDI = 0.708. Principal coordinates analysis distinguished the four groups. Slight clonal polymorphism was detected. The limit between clonal variation and cultivar polymorphism was set at four allelic differences out of 40. SSR markers were useful as a complementary tool to traditional ampelography for cultivar identification. Finally, a set of nine SSR markers was defined that was sufficient to distinguish 99.8% of the analyzed accessions. This set is suitable for routine characterization and will be valuable for germplasm management.
authors.affiliation: INRA Montpellier SupAgro, UMR DIAPC, Equipe Génétique de la Vigne, Montpellier, France. valerie.laucou@supagro.inra.fr
authors.forename: V
authors.initials: V
authors.lastname: Laucou
authors.forename: T
authors.initials: T
authors.lastname: Lacombe
authors.forename: F
authors.initials: F
authors.lastname: Dechesne
authors.forename: R
authors.initials: R
authors.lastname: Siret
authors.forename: J-P
authors.initials: JP
authors.lastname: Bruno
authors.forename: M
authors.initials: M
authors.lastname: Dessup
authors.forename: T
authors.initials: T
authors.lastname: Dessup
authors.forename: P
authors.initials: P
authors.lastname: Ortigosa
authors.forename: P
authors.initials: P
authors.lastname: Parra
authors.forename: C
authors.initials: C
authors.lastname: Roux
authors.forename: S
authors.initials: S
authors.lastname: Santoni
authors.forename: D
authors.initials: D
authors.lastname: Varès
authors.forename: J-P
authors.initials: JP
authors.lastname: Péros
authors.forename: J-M
authors.initials: JM
authors.lastname: Boursiquot
authors.forename: P
authors.initials: P
authors.lastname: This
mesh_terms: D000483:Alleles; D018556:Crops, Agricultural / Q000235:genetics; D030541:Databases, Genetic; D005819:Genetic Markers; D014644:Genetic Variation; D018895:Microsatellite Repeats; D011110:Polymorphism, Genetic; D012984:Software; D027843:Vitis / Q000033:anatomy & histology* / Q000235:genetics*
chemical_list: D005819:Genetic Markers