Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management

Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in or...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Alba, Vittorio [verfasserIn] Bergamini, Carlo [verfasserIn] Genghi, Rosalinda [verfasserIn] Gasparro, Marica [verfasserIn] Perniola, Rocco [verfasserIn] Antonacci, Donato [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Ampelometry SSR Trait selection CHAID Grape biodiversity

Übergeordnetes Werk:	Enthalten in: Molecular biotechnology - New York, NY : Springer, 1994, 57(2015), 8 vom: 25. März, Seite 709-719
Übergeordnetes Werk:	volume:57 ; year:2015 ; number:8 ; day:25 ; month:03 ; pages:709-719

Links:	Volltext

DOI / URN:	10.1007/s12033-015-9862-5

Katalog-ID:	SPR023912952

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520			\|a Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results.
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matchkey_str	article:15590305:2015----::meoercefriadslcslcinoautvraettsiaapocivtsi
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allfields	10.1007/s12033-015-9862-5 doi (DE-627)SPR023912952 (SPR)s12033-015-9862-5-e DE-627 ger DE-627 rakwb eng 570 ASE 58.30 bkl 42.13 bkl Alba, Vittorio verfasserin aut Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results. Ampelometry (dpeaa)DE-He213 SSR (dpeaa)DE-He213 Trait selection (dpeaa)DE-He213 CHAID (dpeaa)DE-He213 Grape biodiversity (dpeaa)DE-He213 Bergamini, Carlo verfasserin aut Genghi, Rosalinda verfasserin aut Gasparro, Marica verfasserin aut Perniola, Rocco verfasserin aut Antonacci, Donato verfasserin aut Enthalten in Molecular biotechnology New York, NY : Springer, 1994 57(2015), 8 vom: 25. März, Seite 709-719 (DE-627)343966271 (DE-600)2073594-7 1559-0305 nnns volume:57 year:2015 number:8 day:25 month:03 pages:709-719 https://dx.doi.org/10.1007/s12033-015-9862-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.30 ASE 42.13 ASE AR 57 2015 8 25 03 709-719
spelling	10.1007/s12033-015-9862-5 doi (DE-627)SPR023912952 (SPR)s12033-015-9862-5-e DE-627 ger DE-627 rakwb eng 570 ASE 58.30 bkl 42.13 bkl Alba, Vittorio verfasserin aut Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results. Ampelometry (dpeaa)DE-He213 SSR (dpeaa)DE-He213 Trait selection (dpeaa)DE-He213 CHAID (dpeaa)DE-He213 Grape biodiversity (dpeaa)DE-He213 Bergamini, Carlo verfasserin aut Genghi, Rosalinda verfasserin aut Gasparro, Marica verfasserin aut Perniola, Rocco verfasserin aut Antonacci, Donato verfasserin aut Enthalten in Molecular biotechnology New York, NY : Springer, 1994 57(2015), 8 vom: 25. März, Seite 709-719 (DE-627)343966271 (DE-600)2073594-7 1559-0305 nnns volume:57 year:2015 number:8 day:25 month:03 pages:709-719 https://dx.doi.org/10.1007/s12033-015-9862-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.30 ASE 42.13 ASE AR 57 2015 8 25 03 709-719
allfields_unstemmed	10.1007/s12033-015-9862-5 doi (DE-627)SPR023912952 (SPR)s12033-015-9862-5-e DE-627 ger DE-627 rakwb eng 570 ASE 58.30 bkl 42.13 bkl Alba, Vittorio verfasserin aut Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results. Ampelometry (dpeaa)DE-He213 SSR (dpeaa)DE-He213 Trait selection (dpeaa)DE-He213 CHAID (dpeaa)DE-He213 Grape biodiversity (dpeaa)DE-He213 Bergamini, Carlo verfasserin aut Genghi, Rosalinda verfasserin aut Gasparro, Marica verfasserin aut Perniola, Rocco verfasserin aut Antonacci, Donato verfasserin aut Enthalten in Molecular biotechnology New York, NY : Springer, 1994 57(2015), 8 vom: 25. März, Seite 709-719 (DE-627)343966271 (DE-600)2073594-7 1559-0305 nnns volume:57 year:2015 number:8 day:25 month:03 pages:709-719 https://dx.doi.org/10.1007/s12033-015-9862-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.30 ASE 42.13 ASE AR 57 2015 8 25 03 709-719
allfieldsGer	10.1007/s12033-015-9862-5 doi (DE-627)SPR023912952 (SPR)s12033-015-9862-5-e DE-627 ger DE-627 rakwb eng 570 ASE 58.30 bkl 42.13 bkl Alba, Vittorio verfasserin aut Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results. Ampelometry (dpeaa)DE-He213 SSR (dpeaa)DE-He213 Trait selection (dpeaa)DE-He213 CHAID (dpeaa)DE-He213 Grape biodiversity (dpeaa)DE-He213 Bergamini, Carlo verfasserin aut Genghi, Rosalinda verfasserin aut Gasparro, Marica verfasserin aut Perniola, Rocco verfasserin aut Antonacci, Donato verfasserin aut Enthalten in Molecular biotechnology New York, NY : Springer, 1994 57(2015), 8 vom: 25. März, Seite 709-719 (DE-627)343966271 (DE-600)2073594-7 1559-0305 nnns volume:57 year:2015 number:8 day:25 month:03 pages:709-719 https://dx.doi.org/10.1007/s12033-015-9862-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.30 ASE 42.13 ASE AR 57 2015 8 25 03 709-719
allfieldsSound	10.1007/s12033-015-9862-5 doi (DE-627)SPR023912952 (SPR)s12033-015-9862-5-e DE-627 ger DE-627 rakwb eng 570 ASE 58.30 bkl 42.13 bkl Alba, Vittorio verfasserin aut Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results. Ampelometry (dpeaa)DE-He213 SSR (dpeaa)DE-He213 Trait selection (dpeaa)DE-He213 CHAID (dpeaa)DE-He213 Grape biodiversity (dpeaa)DE-He213 Bergamini, Carlo verfasserin aut Genghi, Rosalinda verfasserin aut Gasparro, Marica verfasserin aut Perniola, Rocco verfasserin aut Antonacci, Donato verfasserin aut Enthalten in Molecular biotechnology New York, NY : Springer, 1994 57(2015), 8 vom: 25. März, Seite 709-719 (DE-627)343966271 (DE-600)2073594-7 1559-0305 nnns volume:57 year:2015 number:8 day:25 month:03 pages:709-719 https://dx.doi.org/10.1007/s12033-015-9862-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.30 ASE 42.13 ASE AR 57 2015 8 25 03 709-719
language	English
source	Enthalten in Molecular biotechnology 57(2015), 8 vom: 25. März, Seite 709-719 volume:57 year:2015 number:8 day:25 month:03 pages:709-719
sourceStr	Enthalten in Molecular biotechnology 57(2015), 8 vom: 25. März, Seite 709-719 volume:57 year:2015 number:8 day:25 month:03 pages:709-719
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Ampelometry SSR Trait selection CHAID Grape biodiversity
dewey-raw	570
isfreeaccess_bool	false
container_title	Molecular biotechnology
authorswithroles_txt_mv	Alba, Vittorio @@aut@@ Bergamini, Carlo @@aut@@ Genghi, Rosalinda @@aut@@ Gasparro, Marica @@aut@@ Perniola, Rocco @@aut@@ Antonacci, Donato @@aut@@
publishDateDaySort_date	2015-03-25T00:00:00Z
hierarchy_top_id	343966271
dewey-sort	3570
id	SPR023912952
language_de	englisch
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author	Alba, Vittorio
spellingShingle	Alba, Vittorio ddc 570 bkl 58.30 bkl 42.13 misc Ampelometry misc SSR misc Trait selection misc CHAID misc Grape biodiversity Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management
authorStr	Alba, Vittorio
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topic_title	570 ASE 58.30 bkl 42.13 bkl Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management Ampelometry (dpeaa)DE-He213 SSR (dpeaa)DE-He213 Trait selection (dpeaa)DE-He213 CHAID (dpeaa)DE-He213 Grape biodiversity (dpeaa)DE-He213
topic	ddc 570 bkl 58.30 bkl 42.13 misc Ampelometry misc SSR misc Trait selection misc CHAID misc Grape biodiversity
topic_unstemmed	ddc 570 bkl 58.30 bkl 42.13 misc Ampelometry misc SSR misc Trait selection misc CHAID misc Grape biodiversity
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title	Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management
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title_full	Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management
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author_browse	Alba, Vittorio Bergamini, Carlo Genghi, Rosalinda Gasparro, Marica Perniola, Rocco Antonacci, Donato
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title_sort	ampelometric leaf trait and ssr loci selection for a multivariate statistical approach in vitis vinifera l. biodiversity management
title_auth	Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management
abstract	Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results.
abstractGer	Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results.
abstract_unstemmed	Abstract High estimated heritability values were recently revealed for mature leaf traits in grape (Vitis vinifera L.), thus redeeming ampelography in the era of molecular markers. The “Organisation Internationale de la Vigne et du Vin (OIV)” set a list of hundreds of descriptors for grapevine in order to standardize ampelographic and ampelometric scores. Therefore, the selection and reduction of the number of OIV codes can represent a major goal for leaner biodiversity assessment studies. The identification of ampelometric traits associated with grape diversity allows to construct Classification Trees with chi squared automatic interaction detection (CHAID) algorithm, a stepwise model-fitting method that produces a tree diagram in which at each step the sample pool is splitted based on the independent variables statistically different for the dependent variable. A collection of 100 table and wine grapevines (Vitis vinifera L.) was characterized and evaluated by means of six microsatellites and twenty-two ampelometric traits on mature leaves. Nine ampelometric traits were selected by principal component analysis and employed to build the classification trees based on CHAID algorithm. The strategy can represent an effective tool for grape biodiversity management, right allocations, and identification of new grape genotypes, implemented by a further microsatellite investigation only when unsolved cases occur, allowing faster and cheaper results.
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container_issue	8
title_short	Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management
url	https://dx.doi.org/10.1007/s12033-015-9862-5
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author2	Bergamini, Carlo Genghi, Rosalinda Gasparro, Marica Perniola, Rocco Antonacci, Donato
author2Str	Bergamini, Carlo Genghi, Rosalinda Gasparro, Marica Perniola, Rocco Antonacci, Donato
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doi_str	10.1007/s12033-015-9862-5
up_date	2024-07-03T22:17:17.563Z
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Ampelometric Leaf Trait and SSR Loci Selection for a Multivariate Statistical Approach in Vitis vinifera L. Biodiversity Management

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