Genetic Analysis of Agronomic and Enological Traits in Grapevine

TESIS DOCTORAL

Título Genetic analysis of agronomic and enological traits in grapevine

Autor/es

Shiren Song

Director/es

Cristina Menéndez Menéndez

Facultad

Facultad de Ciencias, Estudios Agroalimentarios e Informática

Titulación

Departamento

Agricultura y Alimentación

Curso Académico

2013-2014 Genetic analysis of agronomic and enological traits in grapevine, tesis doctoral de Shiren Song, dirigida por Cristina Menéndez Menéndez (publicada por la Universidad de La Rioja), se difunde bajo una Licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 3.0 Unported. Permisos que vayan más allá de lo cubierto por esta licencia pueden solicitarse a los titulares del copyright.

© El autor © Universidad de La Rioja, Servicio de Publicaciones, 2014 publicaciones.unirioja.es E-mail: [email protected] Universidad de La Rioja Facultad de Ciencias, Estudios Agroalimentarios e Informática Departamento de Agricultura y Alimentación Instituto de Ciencias de la Vid y el Vino

Genetic Analysis of Agronomic and Enological Traits in Grapevine

Memoria para optar al grado de Doctor en Enología Presentada por SHIREN SONG

Directora: Dra: Cristina MENÉNDEZMENÉNDEZ

Logroño 2014

 This dissertation research has been carried out in the Department of Agriculture and Food, at the Universidad de la Rioja and Instituto de Ciencias de la Vid y el Vino, under the direction of Dr. Cristina Menéndez Menéndez

 This research was funded by the Government of La Rioja through the FOMENTA 04/2008 research project: “Análisis genético de caracteres agronómicos y de calidad en la vid mediante marcadores moleculares”. I was financially supported by a fellowship from the CSC (Chinese Research Council) (2008- 2009) and by a MAEC-AECID (Ministerio de Asuntos Exteriores y de Cooperación, Agencia Española de Cooperación Internacional y Desarrollo) fellowship from the Spanish Government (2010-2013). This research was conducted in collaboration with Viveros Provedo S.A.

To my parents

ACKNOWLEDGMENTS

I would like acknowledge the people who contributed to this work.

First, I am very appreciated to my supervisor Prof. Cristina MenéndezMenéndez, for her support, encouragement and advising during these years. Furthermore, I am thankful to Cristina for being not only a supervisor but also a good friend, for giving me advice and chances to express my own opinon. I am also grateful for her patience and help when I met any problems during the past six years.

I would like to thank Prof.Marta Dizy Soto for accepting me after I got CSC scholarship, also toProf.MaríaDel Mar Hernández,for their special care and help when I arrived Spain in 2007 and last six year´s life in Logroño.

Thanks to Prof. Hua Wang for helping me to apply the CSC scholarship and for recommending me toProf. Marta Dizy Soto when I was in China. Thanks to Prof.Yulin Fang and Prof.Yanlin Liu for their constant greetings and support from China.

Thanks to Prof.PurificaciónFernándezZurbano and Dr. José Miguel for their guidance and assistance to theanalysis of Anthocyanins profile with HPLC-MS of my thesis.

Thanks to Dr. Jose Miguel MartínezZapater for his help in SNP analysis of my thesis.

Thanks to Dr. Vicente S. Marco, Dr. Ignacio Pérez, Dr. Eduardo Prado, Dr. Javier Sáenz de Cabezón and Dra. ElenaMartínez for their warm-hearted care and kindness, for creating and maintaining a cheerful atmosphere in our research group.

Thanks to Mr. Luis A. Montón and Ms. Mª Carmen Moneo, and the personnel of Laboratory Service Centre, for their help in my research work.

I would like to thank my colleaguesAitziber, Ana, Elena, Gonzalo, Gustavo, Isidoro, Javier and Raquel for their careful work and friendly help to part of my research. Thanks to my colleagues Beatriz, Christina, Luz, Miguel, and Rubén for making the group cheerful and bringing a lot of fun both inside and outside the lab.

Especially thanks to Mr. Ignacio Provedo and Mr. Javier Provedo, not only for the collaboration with ViverosProvedo S.A., but also for their kindness help when I was in trouble.

Thanks to my family, especially to my parentsWanmin Song and Tairong Liu, for them infinite patienceand selfless dedication.They neither complaint nor puss me although I have been studying so many years.

Lastly, I want to thank my fiancéeZhao Wufor her activeencouragementwhen I was in trouble with my thesis. Thanks for her patient awaiting more than one year, and for her understanding and support.

Many thanks to my friendsIlzeZigele, Jasmine Leung, Manolo Gallardo, Yongsheng Tao, Yanfang Zhangand Wei Zheng for their friendly help.

Universidad de La Rioja,

Logroño, La Rioja, Spain

September 2014

Shiren Song INDEX

INDEX

Index of Tables ...... i

Index of Figures...... iii

Abbreviations ...... v

Summary ...... vii

Resumen ...... ix

1 General introduction ...... 1 1.1EconomicImportance ...... 1 1.2 Origin and Taxonomy of Vitis ...... 1 1.3 Genetics and Breeding of grapevine ...... 3 1.4 Molecular markers development ...... 6 1.5 Genetic Mapping ...... 8 1.6 QTL analysis in grapevine ...... 11 1.7References ...... 15 3 Material and methods ...... 31 3.1 Plant material ...... 31 3.1.1 Tempranillo ...... 31 3.1.2 Graciano ...... 32 3.2 Climate, soil and management ...... 33 3.3 Phenotypic evaluation ...... 34 3.3.1 Agronomic traits ...... 34 3.3.2 Enological traits ...... 35 3.3.3 Seed traits ...... 36 3.3.4 Statistical analysis for phenotypical traits ...... 37 3.4 Berry skin anthocyanins profile analysis ...... 39 3.4.1 Extraction and calculations of berry skin anthocyanins: ...... 39 3.4.2 HPLC Analysis of anthocyanins profile ...... 39 3.4.3 Statistical analysis for anthocyanins profile ...... 41 3.5 Molecular marker analyses ...... 42 3.5.1 DNA extraction ...... 42 3.5.2 SSR Primer pairs selection ...... 42 3.5.2 CAPS analysis ...... 45 3.5.3 SNPs development and analysis ...... 45 3.6 Genetic mapping and Linkage analysis ...... 48 3.6.1 The construction of map ...... 48 3.6.2 Comparison of male and female recombination rates ...... 49

INDEX

3.6.3 Estimation of genome length and map coverage ...... 49 3.7 QTL analysis ...... 51 3.8 References ...... 52 4 Segregation and associations of enological and agronomic traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) ...... 59 4.1 Introduction ...... 60 4.2 Materials and methods ...... 63 4.2.1 Plant material ...... 63 4.2.2 Phenotypic evaluation ...... 63 4.2.3 Genotypic evaluation of the VvmybA allele with CAPs ...... 65 4.2.4 Statistical analysis ...... 65 4.3 Results ...... 67 4.3.1 Phenotypic evaluation of agronomic traits ...... 67 4.3.2 Phenotypic evaluation of enological traits ...... 70 4.3.3 Phenotypic correlations ...... 72 4.3.4 Association of anthocyanins content with allelic composition for VvmybA ...... 76 4.3.5 Principal Component and Cluster analysis and pre-selection of improved genotypes ...... 77 4.4 Discussion ...... 81 4.5 Conclusions ...... 85 4.6 References ...... 87

5 Anthocyanin composition of a F1population derived from

Graciano x Tempranillo ...... 93 5.1 Introduction ...... 94 5.2 Material and Methods ...... 98 5.2.1 Plant material and microsatellite analyses...... 98 5.2.2 Berry sampling and anthocyanins extraction ...... 98 5.2.3 Reagents, standards and chromatographic analysis...... 100 5.2.4 Statistical data analysis ...... 101 5.3 Results and discussion: ...... 102 5.3.1 Identification of anthocyanin compounds ...... 102 5.3.2 Physicochemical characterization of grape samples ...... 105

5.3.3 Anthocyanin content and fingerprint of parents and F1 population ...... 106 5.3.4 The distribution of individual anthocyanininF1 population ...... 110 5.3.5Contribution of the different anthocyanin group to to the profiles of the F1 population and the parents ...... 111 5.3.6.Relationships between anthocyanins as varietal markers ...... 114 5.3.7 Principal Component Analysis (PCA) ...... 114

INDEX

5.4 Conclusions ...... 117 5.5 References ...... 118 6 A genetic linkage map of a Graciano x Tempranillo wine grape population ...... 125 6.1 Introduction ...... 127 6.2 Materials and methods ...... 131 6.2.1 Plant material ...... 131 6.2.2 Molecular marker analyses ...... 131 6.2.3 Genetic mapping and Linkage analysis ...... 137 6.3Results ...... 140 6.3.1 Polymorphism of markers ...... 140 6.3.2 The frequency of distorted alleles ...... 141 6.3.3Construction of genetic maps ...... 142 6.3.4 Comparison ofparental meiotic recombination rates ...... 145 6.3.5 Genome length and coverage ...... 147 6.4 Discussion ...... 147 6.5 Conclusions ...... 149 6.6 Reference ...... 170 7 QTL analysis of agronomic,enologicaland seedtraits ...... 177 7.1 Introductions ...... 179 7.2 Material and methods ...... 182 7.2.1Plants material ...... 182 7.2.2 Phenotypicevaluation ...... 182 7.2.3 Construction of the genetic map...... 183 7.2.4 QTL detection ...... 184 7.3 Results ...... 185 7.3.1 QTL of productivity traits ...... 185 7.3.2 QTL for enological traits ...... 190 7.3.3 QTLs of Seed traits ...... 193 7.4 Discussion ...... 197 7.4.1 Agronomical traits ...... 197 7.4.2 Phenology traits ...... 198 7.4.3Enological traits ...... 198 7.4.4 Seed traits ...... 199 7.4.5 Pleiotropic effects of the QTL and correlation with traits ...... 200 7.4.6 The consistency of QTL mapping ...... 200 7.5 Conclusions ...... 201 7. 6 References ...... 202 General Conclusions ...... 205

Conclusiones Generales ...... 207 3

INDEX

Index of Table

Index of Tables

Table1-1 A list of all published maps in grapes ...... 9 Table 3-1 Climate data during growing season (from April to October) in the 3 years of study ...... 33 Table 3-2 SSR primers used to amplify the F1 Graciano x Tempranillo population ...... 43 Table 3-3 Number of SNPs per chromosome of 18,071 SNPs ...... 47 Table 4-1 Mean values of 16 agronomic traits evaluated in the Graciano x Tempranillo population ...... 67 Table 4-2 Mean values of 11 enological traits evaluated in the Graciano x Tempranillo population ...... 70 Table 4-3 Phenotypic correlations (Spearman rank coefficient) between years and repeatability for each trait ...... 72 Table 4-4 Phenotypic correlations between traits averaged over three years ...... 75 Table 4-5 Groupings of genotypes obtained from squared Euclidian distance combined with the average linkage clustering methods based on the evaluation of agronomic and enological traits in a Graciano x Tempranillo population ...... 78 Table 5-1 Identification of anthocyanins in the berry skins of Graciano x Tempranillo populations ...... 103 Table 5-2 Analytical parameters of grapes of the F1 population and parents in 2009 and 2010 ...... 105 Table 5-3 Characterization (mg/kg of fresh berry) of the anthocyanins compounds for F1 population, Graciano and Tempranillo in two years ...... 108 Table 5-4 Relative contribution (%) to the anthocyanin profileof progeny and parents ...... 111 Table 5-5 Component score coefficient of principal components 1 and 2 of F1 populations of Graciano x Tempranillo in 2009 and 2010 ...... 116 Table 6-1 SSR primers used to amplify the F1 Graciano x Tempranillo population ...... 132 Table 6-2 Number of SNPs per chromosome of 18,071 SNPs ...... 136 Table 6-3 The number and segregation type of the markers analyzed in the progeny Graciano x Tempranillo ...... 140 Table 6-4 Number of SNPs analyzed and mapped in the progeny Graciano x Tempranillo . 141 Table 6-5 Summarizing outline of Graciano, Tempranillo and consensus maps ...... 142 Table 6-6 Characteristics of consensus map and the Graciano and Tempranillo maps ...... 144 Table 6-7 Estimation of recombination rate in Graciano and Tempranillo linkage maps ..... 146 Table 6-8 Estimated genome length, expected and observed genome coverage ...... 147 Table 7-1 Characteristics of main QTLs detected for agronomic traits ...... 187 Table 7-2 Characteristics of main QTLs detected for Enological traits ...... 191 Table 7-3 Characteristics of main QTLs detected forseed traits ...... 195

Index of Table

Index of Figures

Figure 3-1 The cluster, leaves and shoot of Graciano and Tempranillo ...... 32 Figure 3-2 Scheme for phenol extraction...... 36 Figure 4-1 Distribution of agronomic traits in 2010. a. Distribution of production traits...... 68 Figure 4-1 Distribution of agronomic traits in 2010. b. Distribution of phenology-related traits...... 69 Figure 4-2 Distribution of enological traits in 2010...... 71 Figure 4-3 PCR analysis of VvmybA allele in the F1 population Graciano x Tempranillo .... 76 Figure 4-4 Principal component analysis. Distribution of variables on the score plot ...... 77 Figure 4-5 Cluster analysis of hybrids in a Graciano x Tempranillo wine grape population .. 80 Figure 5-1 The structure of anthocyanins in Vitis ...... 94 Figure 5-2 HPLC chromatograms of Tempranillo and Graciano skin extracts showing all detected anthocyanins in 2010...... 104 Figure 5-3 Distributions of individual anthocyanins for the populations in 2009 and 2010 .. 110 Figure 5-4 Distribution of non-acylated anthocyanins and acylated anthocyanins of F1 populations in 2009 and 2010...... 113 Figure 5-5 Distributions of Dp/Mv and Pn/Mv for the populations in 2009 and 2010 ...... 114 Figure 5-6 Cumulative variances of principal components 1 and 2 for anthocyanin content of F1 hybrids Graciano x Tempranillo in 2009 and 2010 ...... 115 Figure 6-1 The linkage map of F1 population from Graciano x Tempranillo ...... 151

iii

Abbreviations

Abbreviations Full name AFLP Amplified fragment length polymorphism BAC Bacterial artificial chromosome BW Mean berry weight BSAn Berry skin anthocyanins c. century CAPS Cleaved amplified polymorphic sequence Ce Expected genome map coverage CI Color intensity cM centi Morgan CN Cluster number cv. Cultivar CW Mean cluster weight DNA Deoxyribonucleic acid dNTP Deoxynucleotide triphosphate EAn Extractable anthocyanins EI Extractability index EST Expressed Sequence Tag F Flowering F1 First familial generation FAM 6-carboxy-fluorescine FAO Food and agriculture organisation FI Fertility index FP Flowering period FV Flowering-Veraison interval Ge Estimated genome length Go Observed genome map length HPLC High Performance Liquid Chromatography IGGP International Grapevine Genome Program Indel Insertion/deletion KW Kruskal –Wallis test LG Linkage Group LOD Logarithm of odd ratio LRR Leucine-rich repeat MAS Marker assisted selection Mbp Megabase pair MS Mass Spectrometry NCBI NationalCenter for Biotechnology Information PA Proanthocyanins PCA Principal component analysis

Abbreviations

Continued Abbreviations Full name PCR Polymerase chain reaction pH Power of hydrogen QTL Quantitative trait locus r Coefficient of correlation r repeatability R Ripening RAPD Random Amplified Polymorphic DNA REC Recombination frequency thresholds RFLP Restriction Fragment Length Polymorphism S Sprouting SCAR Sequence-characterized amplified region SdI Seed development inhibitor SD Standard deviation SE Standard error SF Sprouting-Flowering interval SM Seed maturity SN Mean seed number SNP Single Nucleotide Polymorphism spp. species SSR Simple Sequence Repeat SR Sprouting-Ripening interval SW Mean seed weight SIM Simple interval mapping TA Total acidity TAn Total anthocyanins TPI Total phenol index TSS Total soluble solid V Veraison var. Variety

VEs Special environment variance

VEg General environment variance

VG Genotypic variance VIVC Vitis International variety catalog VMC Vitis Microsatellite Consortium

VP Phenotypic variance VP Veraison period V-R Veraison-Ripening interval ºBé Degrees Baumé χ2 Chi-squared

Summary

In order to select improved genotypes with potential for producing high-quality wines in a climate change scenario, asegregating F1 population with 151 progeny derived from a cross between Graciano x Tempranillo was studied for14 agronomic traits, 11 enological traits and 5 seed traits for three consecutive years.

All traits presented transgressivesegregation and continuous variation. Significant correlations among traits were observed but most associations were weak.Seven groups of hybrids were distinguished based on ripening time, cluster weight, berry weight and anthocyanins content by cluster analysis; and fourteen genotypes were pre-selected for further research.

In addition, the anthocyanin profilesof the hybrids and parents were determined during 2 growing seasons (2009 and2010)with HPLC-MS. Fifteen monoglucosideanthocyanins were detected with HPLC-MS, including two unidentified compounds. The concentration of 13 identified anthocyanins and the percentage of non acylated, acetyl and coumarylanthocyanins were analyzed to understand the inheritance of the anthocyanin profile in the population.Ratios between different anthocyanins were evaluated for their use as potential varietal markers.

A genetic linkage map was constructed using Joinmap 3.0 software, following a pseudo-testcross strategy. Altogether271 simple sequence repeat (SSR) and 18071 SNP (Single Nucleotide Polymorphism) markers, and one CAPS marker were screened. Finally, a consensus map with a total of 1210 markers (183SSRs, 1 CAPs and 1026 SNPs) was assembled covering 1385.8 cM distributed into 19 linkage groups, with an average interval length of 1.2cM between markers.

Finally a QTL (Quantitative Traits Loci) analysis was carried out using MapQTL 6.0 software with the simple interval mapping (SIM) combined with permutations test and the non-parametric Kruskal-Wallis (KW) for agronomic, enological and seed traits.

vii

viii

Resumen

Con el objetivo de seleccionar nuevos genotipos con el potencial de producir vinos de alta calidad en un escenario de cambio climático, se estudio una población segregante de 151 individuos obtenida del cruzamiento entre Graciano y Tempranillo para 14 caracteres agronómicos, 11 enológicos y cinco caracteres de semilla durante tres años consecutivos.

Todos los caracteres presentaron segregación transgresiva y variación continua. Se observaron correlaciones significativas entre los parámetros estudiados pero la mayoría de las asociaciones fueron débiles. Se diferenciaron siete grupos de híbridos basados en fecha de madurez, peso del racimo, peso de la baya y contenido en antocianos, y se seleccionaron catorce genotipos para futuras investigaciones.

Además se determinaron los perfiles antociánicos de los híbridos y los parentales durante 2 ciclos de cultivo (2009 y 2010) con HPLC-MS. Se detectaron 15 compuestos con HPLC-MS, incluyendo dos compuestos no identificados. La concentración de 13 antocianos identificados y el porcentaje de antocianos no acilados, acetilados y cumarilados fueron analizados para comprender la herencia del perfil antociánico en la población. Se evaluaron diversos ratios entre antocianos para su uso como potenciales marcadores varietales.

Se construyó un mapa de ligamiento usando Joinmap 3.0. En total se evaluaron en la población 271 marcadores microsatélites, 18071 SNPs y un marcador CAPS. Se obtuvo un mapa consenso con un total de 1210 marcadores (183 SSRs, 1 CAPs y 1026 SNPs) que cubre 1385.8 cM distribuidos en 19 grupos de ligamiento, con un intervalo medio entre marcadores de 1.2 cM.

Finalmente se realizó un análisis QTL (Quantitative Traits Loci) con mapeo de intervalos, test de permutaciones y test de Kruskal-Wallis para los caracteres agronómicos, enológicos y de semilla evaluados en la población.

Resumen

1 General introduction

1 GENERAL INTRODUCTION

1 General introduction

1.1EconomicImportance

Grapevine (Vitis vinifera L.) is one of the oldest and the most economically important fruit species in the world, and is today grown throughout temperate and tropical regions. It is mainly used for wine and spirit production but also for fresh fruits, raisins, juice and jams, etc (Bouquet 2011; Soneji and Nageswara-Rao 2011).

According to the OIV year’s summary of 2013, the total cultivated area of grapevine(including areas not yet in production or harvested) in the world reached 7.5 millionhectares. The world production of grape was 60.9 million of tons, and the wine production in 2012 (excluding juice and must) was about 252 million hectoliters. Europe retains more than half of the world´s vineyard surface (56.9%). The expansion of Asian vineyards, which reached more than a fifth of the total surface in 2012 (21.9%), is driven mainly by China, whose vineyards almost doubled in the last decade (+89%). Spain, ranks first in grapevine cultivation in the world with 1.0 million of hectaresand is the third country, after Italy and France, in wine production with 30.4 millionhectoliters (OIV, 2013 http://www.oiv.int). Among 72 Spanish Denominations of Origin,In DOCa (Denominación de Origen Calificada) Rioja, one of 69 Spanish Denominations of Origin, cultivated wine grape extended to 61840 hectares in 2013, producing 337,045tons of red grape and 25578tons of white grape, resulting in 2.53 million of hectoliters of wine(Source: Riojawine 2013, El Rioja en cifras. http://www.riojawine.com).

1.2 Origin and Taxonomy of Vitis

Cultivated grapevines (Vitis vinifera spp.sativa) are thought to have been domesticated from wild populations of Vitis vinifera spp. sylvestris., which are dioecious plants still occurring in small isolated populations along riverbank forests from Western Europe to central Asia and North Africa (Arroyo-García et al. 2006;

1 General introduction

Reisch et al. 2012). The domestication process involved the selection of hermaphrodite genotypes producing larger and sweeter berries of attractive colour and the development of techniques for their vegetative propagation; andis closely linked to the discovery of wine, which probably occurred during the Neolithic period (c.8500-4000 BC) (McGovern 2003; Arroyo-García et al. 2006).

Considerable diversity is present within V. vinifera, the number of cultivars available today is estimated from 6000 to 12217 (prime names appear in the Vitis International variety catalog, VIVC) (This et al. 2011; Maul et al. 2014).

Grape (Vitis) belongs to Vitaceaefamily, which belong to the Rhamnales order in the subclass Rosidae of Eudicots (Bouquet 2011).The genus Vitis is tipically divided into two subgenera, Euvitis Planch.and Muscadinia Planch. Subgenus Euvitis(2n = 2x = 38) contains about 57 speciesare the most important in viticulture, and is dividedinto three major groups of species: an Asian group with approximately30 species, an American groupwith approximately30 species andan European or central Asian group containing thewidely cultivated V. vinifera L. species (Owens 2008). Among the Asian species, only V. amurensis has been domesticated and used for fresh fruit, juice, wine, and jelly production. The American species, including V. aestivalis, V. cinerea, V. labrusca, V. riparia, and V. rupestris, have been extensively used to produce rootstocks and fruiting cultivars with fungal resistance (Owens 2008).

The subgenus Muscadinia (2n = 2x = 40) contains 3 species: M rotundifolia Michx., V. munsoniana Simpson ex Munson, and V. popenoei Fennell, native to the southeastern USA and Central America (Soneji and Nageswara-Rao 2011; Blanc et al. 2012).

Hybrids between species within a subgenus are typically fully fertile and many interspecific hybrids between Euvitisspecies have been developed as scion and rootstock cultivars. Hybrids between the subgenera are usually sterile due to the difference in chromosome number.Backcrossing with partially fertile intersubgeneric hybrids can introduce disease resistance of M. rotundifolia into bunch grape gene pools (Reisch et al. 2012). 2

1 General introduction

Grape has the potential to become a model organism for fruit trees. Compared to other perennials, the genome size of Vitisis relatively small, between 487.1 Mb (Jaillon et al. 2007) and 504.6 Mb (Velasco et al. 2007), about half the size of tomato genome [Solanum lycopersicum, 950 Mb (Lodhi and Reisch 1995)], and similar to rice [Oryza sativa, 430 Mb (Goff et al. 2002)], barrel clover [Medicago truncatula, 500 Mb(Young et al. 2011)] andblack cottonwood[Populus trichocarpa, 465 Mb (Tuskan et al. 2006)], almost 4 times the genome of Arabidopsis [Arabidopsis thaliana, 125 Mb (Kaul et al. 2000)].

1.3 Genetics and Breeding of grapevine

The common objectives of most breeding programs is to obtain locally adapted, good yielding, high quality cultivars adapted to environmental as well asto biotic and abiotic stresses.In practice these objectives are complex given the different characteristics needed for table, raisin and wine grape production.

There are several main constraints to grapevine improvement. Grape is a relatively long-lived perennial and requires time and space for adequate evaluation. In the case of wine grapes, vinification and wine evaluation must be carried out which further complicates and delays selection. Most wine grape cultivars are extremely heterozygous and old varieties carry deleterious alleles that exhibit pronounced inbreeding depression after selfing or sibling mating, although inbreeding effects can vary among cultivars (Winkler et al. 1974). The grape breeding efficiency depends on the screening methods used for fruit quality, yield, disease resistance, winter hardiness and tolerance to other abiotic stresses. Field and laboratory procedures are often performed in order to select for horticultural traits prior to determining enological potential. Wine grape evaluation is again more complex because single seedling vines produce very small amounts of fruit, adding to the difficulty of judging wine making potential. Finally, little is known about the inheritance of wine quality components, which are likely to be quantitatively inherited and under environmental influence (Riaz et al. 2007).

1 General introduction

The breeding of grapevine is a double face effort. On one hand, the wine industry tends to be highly conservative and the most widely-grown grape varieties originated long ago, sometimes many centuries ago, rather than as a product of deﬁned breeding efforts. On the other hand, the table and raisin grape markets are very receptive to new cultivars, and many have rapidly gained market share.

The first documented grape breeding with controlled crosses of V. viniferaseem to be the development of ‘Alicante Bouschet’ and ‘Petit Bouschet.’ which resulted from crosses beginning in 1824 by Louis Bouschet and his son in southern France (Paul 1996) .

In the mid-1800s, viticulture in Europe was afﬂicted with numerous grapevine pests that originated in North America. These included phylloxera (Daktulosphaira vitifoliaeFitch 1855), as well as powdery mildew (Erysiphe necator), downy mildew (Plasmopara viticola), and black rot (Guignardia bidwellii). Viticulturist sought new wine varieties which resistant to phylloxera and to fungaldiseases. Nurserymen and researchers responded with the development the French-American hybrids, which resulted from crosses between American species and the European V.viniferacultivars (Cahoon 1998). These new cultivars became exceedingly popular in France but only for table wine, not quality wine production. Some of French American hybrids, are still used to combat fungal diseases and cold winter weather, however they are generally considered to have inferior fruit quality compared to V. vinifera cultivars(Cahoon 1998).

Breeding of these interspecific hybrids ceased in Europe after the creation and utilization of phylloxera resistant rootstocks took hold. However, the cultivation of disease resistant interspeciﬁc hybridgrape cultivars is still popular in some countries. ‘Regent’ released in 1996 in Germany, is now grown on over 2000 hactares; in Hungary, ‘Bianca’ awhite wine grape was still commercially (Reisch et al. 2012).

While there has been much activity worldwide in breeding interspeciﬁc hybrid cultivars, there has also been notable success in the development of new cultivars of V. vinifera. Among wine grapes, ‘Müller-Thurgau’ was released in 1882 (Reisch and 4

1 General introduction

Pratt 1996) and is now one of the most widely grown cultivars in Germany. ‘Dornfelder,’ a red wine cultivar, was developed at the research institute in Weinsberg, Germany, and released for cultivation in 1979. It is now widely grown in northern Europe as well as in colder regions of the United States (Reisch et al. 2012).‘Cabestrel’ released by INRA, France should be a potential variety (Lacombe et al. 2013).

The new V.vinifera cultivars continue to be developed. The most successful of these are seedless table grapes, while wine grapes have been less successful since their wide utilization is greatly limited by the demands of winemakers and marketers to have traditional varieties with well-documented quality and historical acceptance.

The seedless V. vinifera table grape market has grown rapidly over the past 50 years due to the embryo culture technique, one of the most important contributions to grape breeding in the twentieth century, widely used among table grape breeding programs in the United States, Israel, South Africa, Chile, and Australia (Burger et al. 2009).

Furthermore, the rootstocks breeding has achievedresistance to phylloxera, nematodes, virus disease(Weinberger and Harmon 1966; Walker et al. 1991, 1994;Clark 1997), as well as others abiotic stress resistance(Reisch et al. 2012).

There is no doubt that hybridization played an important role in the diversification of grape varieties (This et al. 2006). Well-known varieties such as ‘Cabernet Sauvignon’, ‘Chardonnay’, ‘Syrah’, ‘Merlot’ and ‘Tempranillo’ are obviously the result of crossings between older varieties (Bowers and Meredith 1997; Bowers et al. 1999a, 2000; Vouillamoz and Grando 2006; Boursiquot et al. 2009; Bouquet 2011; Ibáñez et al. 2012).

An important source of genetic variation in V. vinifera is the presence of numerous somatic mutations. Stable somatic mutants of a relatively subtle nature are typically grouped under the heading of ‘clones’. More substantive mutations, particularly those altering berry pigmentation are often elevated to the state of a new cultivar name (e.g. ‘Pinot noir’, ‘Pinot gris’, ‘Pinot blanc’, and ‘Pinot meunier’ or

1 General introduction

‘Cabernet Sauvignon’, ‘Malian’, and ‘Shalistan’ (Walker et al. 2006).

1.4 Molecular markers development

In the last couple of decades, the development of molecularmarkers has stimulated advances in plant breeding,since these markers directly reveal genetic variabilitythrough DNA analysis, and environmentaleffects don’t influence their detection(Staub et al. 1996).Theprimary use of these molecular markers is in markerassistedselection (MAS) (Paterson et al. 1991), which is one of the most efficient application of biotechnology to plant breeding.

The mostwidely used markers forMAS are Restriction Fragment Length Polymorphism (RFLP), Random Amplified Polymorphic DNA (RAPD), Amplified Fragment Length Polymorphism (AFLP), Short Sequence Repeats (SSR) and Single Nucleotide Polymorphism (SNP). The selection of marker depends on the ease of their detection, the possibility of revealing single or multiple loci, their dominant or co-dominant nature, and their expense.

Until the advance of molecular markers, little was known about the inheritance of grape traits.These molecular markers have multiple uses in grape breeding and genetics, including cultivar identification and germplasm management; mapping of traits of interest; and estimation of genetic diversity (Bowers et al. 1993; Bowers and Meredith 1996; Dalbó et al. 2000; This et al. 2006). The surge of development of grape genetic maps with molecular markers has the potential to greatly expand use of MAS in grape breeding program.

RFLP, RAPD and AFLP analysishave been utilized successfully to investigate genetic relationships, genetic diversity, identify grape accessions and rootstock varieties (Thomas et al. 1993; Bourquin et al. 1995; Fanizza et al. 2000; Kocsis et al. 2005).

SSR have been one of the most widely used molecular marker systems in grapevinesince the early 1990s(Thomas et al. 1993; Thomas and Scott 1993). In

1 General introduction

addition, reproducibility, standardization, and transfer and comparison of data among different labs made SSR markers the choice for fingerprinting and cultivar identification. The number of publicly available microsatellite markers has greatly expanded (Bowers et al. 1996, 1999b;Sefc et al. 1999;Scott et al.2000; Di Gaspero et al. 2005; Merdinoglu et al. 2005; Cipriani et al. 2008;Salmaso et al. 2008). Thedevelopment of the Vitis Microsatellite Consortium (VMC), consisting of 21 different grape research groups from 12 countries, generated a large number of SSR markers (Riaz et al. 2007).

They are now commonly applied to genetic diversity assement (Thomas and Scott 1993; Sefc et al. 2000; This et al. 2006; Laucou et al. 2011); parentage analysis (Sefc et al. 1998c; Bowers and Meredith 1997; Bowers et al 1999a;This et al. 2004;Cipriani et al. 2010; Lacombe et al. 2013); fingerprinting analysis (Grando and Frisinghelli 1998;Lin and Walker 1998; Sefc et al. 1998a, 1998b, 1999)and genetic linkage mapping (Dalbó et al. 2000; Doligez et al. 2002, 2006a, 2006b, 2010; Grando et al. 2003; Adam-Blondon et al. 2004; Fischer et al. 2004;Riaz et al. 2004; Lowe and Walker 2006; Mandl et al. 2006; Troggio et al. 2007; Costantini et al. 2008; Vezzulli et al. 2008; Battilana et al. 2009, 2013; Duchêne et al. 2009; Marguerit et al. 2009; Moreira et al. 2011; Blanc et al. 2012; Correa et al. 2014).

There are four public databases that provide information of grapevine genetic fingerprint with SSR markers: the grape microsatellite collection (GMC) database (http://meteo.iasma.it/genetica/gmc.html) was developed to permit an easy retrieval of grape nuclear microsatellite profiles and related information; the Greek Vitis database (http://www.biology.uch.gr/gvd/) contains nuclear as well as chloroplast SSR profiles of Greek grapevine cultivars, rootstocks, Vitis species and hybrids used as rootstocks; National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/), where most DNA sequcences of SSR and EST developed for grapevine can be found as well as linkage groups informations; and the French national sequencing center (GENOSCOPE) (http://www.genoscope.cns.fr/index.html), where the referencesequence of Pinot Noir

1 General introduction as well as SSR, EST, and candidate gene were available.

With the rapid decrease in the cost of DNA sequencing, molecular markers for grape have begun to shift to large datasets of SNP markers (Myles et al. 2010; Emanuelli et al. 2013).The release of two grapevine genome sequences marked a significant moment in the history of grape genetics and breeding (Jaillon et al. 2007; Velasco et al. 2007), is already having a significant impact upon grapevine improvement efforts (Di Gaspero and Cattonaro 2010).

SNP markers are the newestdeveloped class of markers. They target a single base mutation in the DNA sequence. Small indels (insertion or deletion events) are also assimilated as SNP markers since they can be handled with many of the technologies designed to identify SNPs. In grapevine, the SNP were reported by by Salmaso et al. (2004) and Lijavetzky et al. (2007). The number of potential SNP markers in the Pinot Noir genome was estimated to reach 2 million, with many present in coding regions, covering approximately 87% of annotated genes(Velasco et al. 2007).

SNPs are easily amenable to massive parallel automatic detection, making them particularly useful for saturating grapevine genetic maps (Steemers et al. 2006), especially in coding regions, or as an approach to gene discovery through linkage disequilibrium studies.The transferability of SNPs across species is, therefore, much less likely than the transferability of SSRs and this is a major issue in mapping many traits, such as those for resistance to biotic and abiotic stress which usually entail genes present in wild species. However, due to their high density in the grapevine genome and the flexibility of multiplexing systems, it is possible to envisage working with complementary sets of SNP markers, some of which could be informative in V. vinifera and others in wild relatives (Myles et al. 2010; Emanuelli et al. 2013).

1.5 Genetic Mapping

A genetic linkage map of an organism is an abstract model of the linear arrangement of a group of genes and markers, is based on homologous recombination

1 General introduction

during meiosis, this map is also a meiotic map.

Many genetic linkage maps have been constructed for grapevine since the first reported genetic map utilizing DNA based markers (Lodhi et al. 1995). These maps (Table1-1) represent V. vinifera intraspeciﬁc crosses (Doligez et al. 2002, 2006a, 2006b, 2010;Adam-Blondon et al. 2004; Riaz et al. 2004; Mandl et al. 2006; Troggio et al. 2007; Costantini et al. 2008; Vezzulli et al. 2008; Correa et al. 2014) and interspeciﬁc crosses utilizing V. vinifera (Lodhi et al. 1995;Dalbó et al. 2000; Grando et al. 2003; Fischer et al. 2004; Duchêne et al. 2009; Marguerit et al. 2009; Moreira et al. 2011;Battilana et al. 2013), as well as others crosses (Doucleff et al. 2004; Riaz et al. 2006; Lowe and Walker 2006; Blasi et al. 2011; Blanc et al. 2012)

Table1-1 A list of all published maps in grapes

Marker No. of Population Marker system distance Reference genotypes (cM)

Cayuga White (Hybrid of V. vinifera, V. labrusca, V. RAPD, RFLP, 60 6.1 Lodhi et al. 1995 rupestris and V. aestivalis) × Aurora (Hybrid of V. Isozyme vinifera, V. rupestris and V. aestivalis)

Horizon (‘Seyval’ × ‘Schuyler’) × Illinois 547-1 (V. RAPD,SSR, 58 7.8 Dalbó et al. 2000 rupestris × V. cinerea) CAPS, AFLP

MTP2223-2 (Dattier de Beyrouth × Pirovano 75) × AFLP, SSR, 139 6.2 Doligez et al. 2002 ; MTP2121-30 (Alphonse Lavallée × Sultanina) RAPD, SCAR, 2010 Isozymes

Moscato bianco (V. vinifera L.) × SSR, AFLP, 81 8.1 Grando et al. 2003 ; V. riparia Mchx SSCP Battilana et al. 2013

Riesling × Cabernet Sauvignon SSR, EST 153 11 Riaz et al. 2004

V. rupestris and V. arizonica hybrids AFLP, SSR, 116 10.2 Doucleff et al. 2004 RAPD, Syrah × Grenache ISSR, SSR 96 6.4 Adam-Blondon et al. Riesling Self 2004 ; Vezzulli et al. 2008

Regent × Lemberger AFLP, RAPD, 153 5.9 Fischer et al. 2004 SSR,SCAR,

Continued

1 General introduction

Marker No. of Population Marker system distance Reference genotypes (cM)

Ramsey (V. champinii) x Riparia SSR 188 6.8 Lowe and Walker 2006 Gloire (V. riparia).

Welschriesling x Sirius SSR, RAPD 92 15.6 Mandl et al. 2006

MTP2687-85 (Olivette x Ribol) x Muscat of Hamburg SSR 174 9.1 Doligez et al. 2006a; 2010

Five populations SSR, CAPs, 46, 95, 114, 6.2 Doligez et al. 2006b SCAR 139, 153

D8909-15 (V. rupestris x V. arizonica) x F8909-17 (V. SSR, EST, RFLP 181 5.4 Riaz et al. 2006 rupestris x V. arizonica)

Syrah x Pinot Noir EST, SNP, SSCP, 94 1.3 Troggio et al. 2007; SSR, AFLP Vezzulli et al. 2008

Italia (V.vinifera) x Big Perlon(V.vinifera) SSR, AFLP, EST 163 4.2 Costantini et al. 2008

Cabernet Sauvignon (V. vinifera) x Glorie de SSR, SSCP 138 6.7 Marguerit et al. 2009 Montepellier (V. riparia)

Selfing of Muscat Ottonel SSR, SNP 212, 117 8.7 Duchêne et al. 2009 Selfing of Gewurztraminer

Selfing of Rupreche (V.amurensis) SSR, RGA 232 7.3 Blasi et al. 2011

Selfing of Regale (M. Rotundifolia) SSR 191 5.3 Blanc et al. 2012

Moscato Bianco (V.vinifera) x V. riparia SSR, SSCP, SNP 174, 94 6.1 Battilana et al. 2013; VRH3082 1-42 (V. vinifera x V.rotundifolia) x SK77 Moreira et al. 2011 5/3 (V.vinifera x V.amurensis)

Muscat Otteonel x Malvasia aromatica di Candia SSR, SNP 91, 249 Battilana et al. 2013 Moscato Bianco x Vr (V. riparia or V. riparia x V. vinifera)

Ruby seedless x Sultanina SSR, AFLP, SNP, 137 4.9 Correa et al. 2014 SCAR

The most used software for genetic mapping were JoinMap® (Van Ooijen and Voorrips 2001; Van Ooijen 2006), R/QTL (Broman et al. 2003) and Carthagene 0.999R (Givry et al. 2005). Some authors used one or two software for mapping, for example, the maps from JoinMap were further analyzed by R/QTL (Barba et al.

1 General introduction

2014),parental mapsconstructed with Carthagene are integrated into a consensus map with Joinmap (Marguerit et al. 2009), and JoinMap was used to check for segregation distortion, to find genotyping errors and to draw the map after mapping with Carthagene (Duchêne et al. 2009).

1.6 QTL analysis in grapevine

Genetic maps have also been used to find genes controlling various aspects of grapevine composition and development. In grapevine,some traits are controlled by a single gene (major gene), others traits exhibit continuous variation resulting from the action of multiple genes that are subject to environmental modification. Many agriculturally important traits such as yield, quality and some forms of disease resistance are controlledby many genes and are known as quantitative traits (also‘polygenic,’ ‘multifactorial’ or ‘complex’ traits). Theregions within genomes that contain genes associatedwith a particular quantitative trait are known as quantitative trait loci (QTL) (Collard et al. 2005).

Genetic linkagemapshave facilitatedmappingof agriculturallyimportant QTL in grapes, including QTLfor disease resistance, seedlessness and berry weight.

Using QTL mapping, resistance loci whose alleles exertsmaller effects on the phenotype may be manipulatedmore effectively (Young 1996). In the case ofdisease resistance, an obvious goal would be to developgrape cultivarswith resistance alleles at allQTLsof interest. Establishment of generalized genomic regionsthat affect a particular trait within inter-andintra-species grape mapping populations with commonmarkers will help to clarify the relationships ofQTLs in different genetic backgrounds, and promotemarker assisted selection and breeding.

Three widely-used methods for detecting QTLs are single-marker analysis, simple interval mapping and composite interval mapping (Liu 1998; Tanksley 1993).

Single-marker analysis (point analysis) is the simplest method for detecting QTLs associated with single markers. This method does not require a complete linkage map

1 General introduction

and can be performed with basic statistical software programs. However, the major disadvantage is the effect of a QTL to be underestimated. The use of a large number of segregating DNA markers covering the entire genome may minimize problems (Tanksley 1993).

A more powerful method is the Simple Interval Mapping (SIM) method makes use of linkage maps and analyses intervals between adjacent pairs of linked markers along chromosomes simultaneously, instead of analyzing single markers (Lander and Botstein 1989).

Composite interval mapping (CIM) has become popular for mapping QTLs. This method combines interval mapping with linear regression and includes additional genetic markers in the statistical model in addition to an adjacent pair of linked markers for interval mapping (Jansen 1993; Jansen and Stam1994; Zeng 1994). The main advantage of CIM is that it is more precise and effective at mapping QTLs especially when linked QTLs are involved.

Multiple intervals mapping (MIM) uses multiple marker intervals simultaneously to construct multiple putative QTL in the model for QTL mapping (Kao et al. 1999; Zeng et al. 1999). Therefore, when compared with the current methods such as IM and CIM, MIM tends to be more powerful and precise in detecting QTL. In addition, MIM can readily search for and analyze epistatic QTL and estimate the individual genotypic value and the heritabilities of quantitative traits.On the basis of the MIM result, genetic variance components contributed by individual QTL were also estimated, and marker-assisted selection can be performed.

There are numerouspublic and private software packages (linkage analysis and QTL mapping) available to map QTLs and analysis the QTLs.The most commonly used software packages in grapevine are MapMaker/QTL (Lincoln et al. 1993), QTL Cartographer (Basten et al. 2003), Qgene (Nelson 1997), WinQTLCart (Wang et al. 2004) and MapQTL®(Van Ooijen 2009), some authors used more than one software to analyze the QTL, for example Doligez et al. (2010) use three software:MapQTL, WinQTLCart, and QTLCartographer to search the QTLs for fertility index. 12

1 General introduction

Once a genetic map has been constructed and QTL have been defined for a trait of interest, the potential use of the linked marker(s) in MAS needs to be evaluated. For a realistic assessment of marker-assisted selection we need: high power of QTL detection; high accuracy and precision of QTL localization and estimated QTL effects; and validation of results across environmental samples, genotypic samples, generations, and across breeding populations.

Genetics started with the analysis of qualitative traits, so the first geneticlocalizations of agronomic traits were based on the observation of theirsegregation as presence or absence. Applying the genome-spanning genetic maps virtually all polymorphic qualitative traits detected in a segregating population can bepositioned in relation to molecular markers. In grapevine, the major genes considered responsible for qualitative traits such as those governing flower sex (Dalbó et al. 2000; Riaz et al. 2006; Marguerit et al. 2009; Fechter et al. 2012), berry color (Doligez et al. 2002; Fischer et al. 2004; Walker et al. 2006, 2007; Mejía et al. 2007; Salmaso et al. 2008) and seedlessness (Striem et al. 1996; Lahogue et al. 1998; Doligez et al. 2002; Cabezas et al. 2006; Mejía et al. 2007, 2011;Costantini et al. 2008) have been positioned in genetic maps. The berry colour locus was found to co-locate with the gene encoding VvMybA1, a transcription factor regulating the expression of the gene for the enzyme of the last biosynthetic step of anthocyanin formation (Kobayashi et al. 2004; Lijavetzky et al. 2006). A candidate gene for the control of sexual traits in grapevine linked to the sex locus has been proposed (Marguerit et al. 2009; Fechter et al. 2012; Battilana et al. 2013).

And the quantitative traits such as berry weight, berry size (Doligez et al. 2002; Fanizza et al. 2005; Cabezas et al. 2006; Mejía et al. 2007; Constantini et al. 2008), phenology related traits (Fischer et al. 2004; Cabezas et al. 2006; Mejía et al. 2007; Costantini et al.2008; Duchêne et al. 2012 ; Grzeskowiak et al. 2013), monoterpene content (Battilana et al. 2009), terpenol content (Duchêne et al. 2009), moscat flavour (Emanuelli et al. 2010), and proanthocyanindins in berry skin and seed (Huang et al. 2012) were analyzed.

1 General introduction

Moreover, the QTLs for other agronomical traits were detected, such as axillary shoot growth (Fisher et al. 2004), number of cluster per vine, cluster weight and number of berry per cluster (Fanizza et al. 2005), fertility (Doligez et al. 2010; Grzeskowiak et al. 2013), inflorescence morphology (Marguerit et al. 2009), leaf morphology (Welter et al. 2007), seed number and seed weight (Doligez et al 2002; Cabezas et al. 2006; Costantini et al. 2008).

Furthermore, the QTLs analysis for resistance were studied especially with interspecies crosses, such as resistance to fungal diseases (Dalbó et al. 2001;Fisher et al. 2004; Welter et al. 2007; Moroldo et al. 2008; Bellin et al. 2009; Marguerit et al. 2009; Moreira et al. 2011; Riaz et al. 2011; Barba et al. 2014), the phylloxera root resistance (Zhang et al. 2009), Pierce´s disease resistance (Riaz et al. 2006, 2008), resistance to Xiphinema index (Xu et al. 2008).

The availability of grapevine whole-genome sequences and several physical mapping also offers new opportunities to search for genes encoding for proteins containing both nucleotide binding sites (NBS) and leucine rich repeats (LRR) domains, to identify candidate genes and to better understand the molecular and physiological basis of traits of interest (Jaillon et al. 2007; Velasco et al. 2007; Moroldo et al. 2008;Riaz et al. 2011).

In Spain, despite the large area devoted to viticulture and the existence of many native varieties, little progress in grapevine breeding has been made. In order to develop genotypes more adapted to climate change and to satisfy the consumer demand for quality wine, a breeding program with native wine grape varieties, Tempranillo and Graciano was started in 2004

1 General introduction

1.7 References

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Fischer BM, Salakhutdinov I, Akkurt M, Eibach R, Edwards KJ, Töpfer R,Zyprian EM(2004) Quantitative trait locus analysis of fungal disease resistance factors on a molecular map of grapevine. Theor Appl Genet 108:501-515

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1 General introduction

2 Objectives

2 OBJECTIVES

2 Objectives

2 Objetives

In the context of climate change and consumer demands of quality wine,a segregating progeny was obtained between two Spanish varieties, Graciano and Tempranillo with the following objectives:

 Study the segregation of phenology, production, berry quality and enological

traits present in the F1 population, evaluate the correlations among traits, and perform a pre-selection of superior hybrids.

 Study the segregation of anthocyanin content and composition in progeny, identify the anthocyanins or anthocyanin groups most relevant in the variability present, determine the anthocyanins profile of F1 population and parents, and evaluate the environmental factor in the amount and composition of anthocyanin.

 Construct a genetic map of Graciano x Tempranillo population with SSRs

markers and SNPs

 Identify QTL for agronomical trait, berry quality traits and seed traits on the gentic map, and propose marker for future use in marker-assisted selection

(MAS).

2 Objectives

3 Material and Methods

3 MATERIAL AND METHODS

3 Material and Methods

3 Material and methods

3.1 Plant material

An intraspecific hybrid population (163 genotypes) derived from controlled crosses between two Spanish wine grape varieties, Graciano and Tempranillo (Vitis vinifera L.), was used for this study. The individual hybrids (one plant per genotype) have been grown on their own roots since 2004 in Varea (Logroño, La Rioja, Spain) on a sandy-loam soil, in East-West orientation with 3 m spacing between rows and 1 m between plants and trained to double Royat cordon. Standard irrigation, fertilization and plant protection practices for La Rioja region were performed. The plants first flowered and fruited in 2007.

In order to discard individuals resulting from self-pollinations and foreign pollen sources, leaf samples of Graciano, Tempranillo and all individuals of the progeny were collected in the field, frozen in liquid nitrogen and stored at -80ºC. The population was genotyped for 5 polymorphic SSR (Simple Sequence Repeats) markers: VVS2 (Thomas and Scott 1993), VrZAG62, VrZAG79 (Sefc et al. 1999), VVMD6, VVMD34 (Bowers et al. 1996; 1999). The microsatellite analysis revealed incompatible results for 12 genotypes that were discarded, resulting in a final population of 151 plants.

3.1.1 Tempranillo

Tempranillo is a variety native to La Rioja and Aragon, is currently the most cultivated red wine variety in Spain, as a preferential or authorized variety in 56 out of the 69 Spanish Denominations of origin (Ibáñez et al. 2012), cultivated with 61% of vineyard in DOCa Rioja region (García-Mario et al. 2010). Tempranillo is a very suitable variety to elaborate wines to be aged, the wine with intense color represents a good base wine for blending (Escudero-Gilete et al. 2010). It is the one of the great grape varieties in the world, has been planted in Argentina, Australia, Canada, Chile, Dominican Republic, Lebanon, Mexico, Morocco, New Zealand, South Africa,

3 Material and Methods

Turkey, Uruguay and USA (Ibáñez et al. 2012). There are 74 synonyms for Tempranillo reported in the Vitis International Variety Catalogue (VIVC) (Maul et al. 2014 http//:www.vivc.de). Some of the more common synonyms in Spain are Tinta del País (Ribera del Duero), Tempranillo de Rioja, Aragonés, Cencibel (La Mancha), Chinchillana, Escobera (Badajoz), Tinto Fino de Madrid (Madrid), Tinta de Toro (Zamora), Tinto Aragonés (Aragón) and Ull de Llebre (Cataluña). In addition, it is known in Portugal under the names Aragonez and Tinta Roriz (Ibáñez et al. 2012; Riojawine http//:www.riojawine.com).

Regarding its agronomic performance, it sets well but is highly sensitive to pests and disease and not very resistant to drought or high temperatures, mature quite early as the name which comes from the Spanish “temprano” meaning “early”. (Riojawine http//:www.riojawine.com).

Figure 3-1 The cluster, leaves and shoot of Graciano (Left) and Tempranillo (Right)

3.1.2 Graciano

Graciano is red grape variety native to Rioja and Navarra region, with a very limited production in Spain (0.02% of the total grape varieties grown) (Figure 3-1). The richly coloured, perfumed black grape variety offers wines with a marked acidity and polyphenolic content, ideal for ageing, with a unique aroma that is much more intense than those of other varieties in Rioja.Traditionally it has been used to improve the characteristics of Tempranillo as an excellent complement, affording a long 32

3 Material and Methods

shelf-life, higher color intensity, complex aroma, and aging potential to the blend (Chomé et al. 2006; Núñez et al. 2004; García-Marino et al. 2013). It is fairly resistant to mildew and powdery mildew, with low fertility rates and late maturing. This variety is known as Morratel (France), Xeres (California) and Tinta Miúda (Portugal) (Monagas et al. 2006; Riojawinehttp//:www.riojawine.com), with 78 synonyms recorded in the Vitis International Variety Catalogue (VIVC) (Maul et al. 2014 http//:www.vivc.de).

These two varieties were selected as parental genotypes in the present study as they show complementary agronomic (phenology, fertility, berry weight, seed number, seed weight) and oenological (total acidity, anthocyanins content) characteristics. Graciano clone-117 was used as female parent, Tempranillo clone-43 was used as pollen parent, the cross was developed in Viveros Provedo (Varea, La Rioja, Spain).

3.2 Climate, soil and management

The experimental field was located in Viveros Provedo (Varea, La Rioja, Spain) which belong to the Rioja Alta with an annual mean temperature of 14 ºC, 433 mm annual rainfall and 2474.5 sunshine hours. The climate data during growing season (from April to October) in 2008, 2009 and 2010 are showed in Table 3-1.

Table 3-1 Climate data during growing season (from April to October) in the 3 years of study

Temperature (º C) Total Total Year rainfall Sunshine Mean Max (Month) Min (Month) (mm) (hours)

2008 17.9 35.9 (August) -0.1 (April) 425.8 1743.8

2009 19.3 37.8 (August) -0.9 (October) 163.9 1911.4

2010 18.1 38.9 (August) -0.2 (April) 193.8 1778.2

Source: Agencia Estatal de Meteorologia, Spain, collected from climate station in Agoncillo, which locate 9 km far from Varea, La Rioja, Spain.

The soil type was sandy to sandy-loam with 35% carbonate, pH 7.8, and 1.5% organic matter. Viticultural practices were the same as for the rest of the nursery.

3 Material and Methods

Vines were irrigated in order to satisfy water demand in the key phases of grapevine growth using a drip irrigation system (4 L/heach 80 cm) with a frequency of 3-5 times for 24 hours (100-150 L/m2/ year).

3.3 Phenotypic evaluation

Twenty-seven agronomic and enological traits, overall, were evaluated in the hybrid population during three growing seasons (2008-2010). The number of genotypes that bore fruit varied each year due to hail damage during flowering, and bird damage during veraison-ripening stages. Thus, 116, 123, and 132 genotypes were harvested in 2008, 2009, and 2010 respectively.

3.3.1 Agronomic traits

Phenology traits

Dates of sprouting (S), considered as when 50% of the buds were in Baggiolini stage C; flowering time (T, 50% flowering in Baggiolini stage I) (Baggiolini 1952), veraison time (V, 50% berry veraison), and ripening time (R) were scored every 3 days from 1st March. Ripening stage was established as the date when random grapes picked from the top, medium and bottom of the clusters reached 13°Baumé.

Flowering period (FP, time between the opening of first flower and that of all flowers), veraison period (VP, time between the veraison of first berry and that of all the berries), interval from sprouting to flowering (S-F), interval from flowering to veraison (F-V), and interval from veraison to ripening (V-R) were calculated as described by Duchêne and Schneider (2005) and Costantini et al. (2008).

Productivity traits

For each genotype, yield per vineand number of clusters per vine (CN) were measured at harvest, and average weight of clusters (CW) was calculated. The fertility index (FI) was calculated as the number of inflorescences per young shoot.

One hundred berries were randomly sampled from 2-3 representative ripen

3 Material and Methods

clusters and mean berry weight (BW) was calculated. Additionally two sets of 60 berries were picked to measure seed traits and berry skin anthocyanins contents respectively, those samples were stored at -20 ºC until analysis.

3.3.2 Enological traits

Two sets of data were generated for the enological analysis, one related to the technological parameters and another related to the phenolic maturity indices.

Technological maturity parameter

For the first set, 200 whole berries from each genotype were sampled at random, from different positions within the cluster, to avoid effects due to sun exposition. Grapes were squeezed and parameters of the resulting musts were evaluated by triplicate. Sugar content [Total Soluble Solids (TSS), expressed as degree Baumé] was measured with an Atago Master-Baume refractometer (Atago, Tokyo, Japan); pH and total acidity (expressed as g/L tartaric acid) were measured with a TitroMatic 1S-1B (Crison, Barcelona, Spain). In addition, 60 randomly selected berries were frozen to measure berry skin anthocyanin content (mg/g) by triplicate as described in Nadal and Lampreave (2007).

Phenolic maturity indixes

The criteria for estimating optimal maturity in red grapes are complex. The ratio sugars/acids do not give enough information to determine precisely the date of harvesting. The accumulation of phenolic compounds depends on climate, soil, genetics and cultural practices.

The phenolic maturity indeces were determined following Saint-Criq et al. (1998) as described in Nadal (2010) (Figure 3-2). Two hundred whole berries were sampled and crushed to obtain a homogeneous mixture, and then macerated for 1 h at two different pH values [pH=1.0 (A) and pH=3.6 (B)]. The total anthocyanin content (TAn) was determined with extract A; the extractable anthocyanin (EAn), colour intensity (CI), total polyphenols index (TPI) and tannin contents (TC) were determined with extract B. 35

3 Material and Methods

Figure 3-2 Scheme for phenol extraction (Nadal 2010).

The homogenate is shaked in plastic tubes, with buffers at pH 1.0 (Tube A) and pH 3.6 (Tube B) for 1 h, at room temperature. TA = Total Anthocyanins; EA = Extractable Anthocyanins

The determination of anthocyanins is done according to the method of

decolouration by bisulphite (NaHSO3) (Ribéreau-Gayon and Stonestreet 1966); total potential anthocyanins extracted at pH = 1.0 (Tube A) are called total anthocyanins and the anthocyanins extracted at pH = 3.6 (Tube B) are called extractable anthocyanins.

The extractability index (%EI) and Seed maturity (%SM) were calculated as follows (Nadal 2010):

(Ant − Ant ) 100* %EI = pH 0.1 pH 6.3 Ant pH 0.1

[Abs280 − (Ant )] 100*40* %SM = pH 6.3 pH 6.3 Abs280 pH 6.3

3.3.3 Seed traits

Seed traits, including mean seed number per berry (SN), and mean seedfresh weight (SW), were evaluated by triplicate from a sample of 20 berries randomly selected.Then, seedsare frozen until the measurement of seed polyphenoles by triplicate: total polyphenol index (TPI) of seed, seed tannins content (TC) and seed catechin contents.

The seed were took out from grape berry carefully, cleaned with absorbent paper dried at room temperature for 24 hours, and then were weighted with an analytical

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balance.

The extraction of seed polyphenol as performed as described in Nadal (2010). The seeds were ground with liquid nitrogen; then 1 g of seed powder was extracted with 50 mL methanol (99.8%, Panreac, Barcelona, Spain) in an ultrasonic bath (P-Selecta Ultrasons, Barcelona, Spain) for 15 min, the temperature was controlled to be below 14 ºC. The supernatant was centrifuged at 8,000 rpm for 15 min at 5 ºC (Sorvall RC 6 Plus, Thermo Fisher Scientific, USA) and the extract was used for following measurements. Seed Total Polyphenol Index (TPI) and seed tannins were determined by spectrophotmetry from the aborbances at 280 nm and 550 nm respectively.

Seed catechin (flavan-3-ols) content was measured with DMACH (4-dimethylamino cinnamaldehyde, Sigma-Aldrich, USA) index as described in Vivas et al. (1994).

3.3.4 Statistical analysis for phenotypical traits

Descriptive statistics for all traits were conducted. A t-test was carried out to detect differences between both parents.

Analysis of variance with LSD test was used to evaluate mean value differences among the 3 years. The normality of each trait distribution was evaluated by the Kolmogorov-Smirnov test. Data that significantly deviated from normality were transformed (square-root or logarithm) to fit a normal distribution.

Phenotypic correlations between traits were determined in each year with the Spearman rank-correlation coefficient (P < 0.05). Correlation analysis between years was used to evaluate the genotype stability across years for each trait. Year effect was tested with analysis of variance and non parametric Kruskal-Wallis test (Fanizza 2005). Based on the multiple measurements, the repeated-measures analysis of variance (years, genotypes within year) was applied to estimate the repeatability over years for each trait (Falconer 1989; Fanizza 2005). The repeatability was calculated

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+VV r = G Eg V GP ++= VVVV EgEs as: P , , where r is repeatability; VG is genotypic variance; VP is phenotypic variance, VEs is special environment variance, (within individual variance component); VEg is general environment variance, (environmental variance contributing to the between-individual component).

Finally, the factor analysis of the quantitative phenotypic data was carried out using the principal components extraction method to identify the main components, then the gained matrix were analyzed with Hierarchical cluster methods to pre-select the optimum hybrids.

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3.4 Berry skin anthocyanins profile analysis

3.4.1 Extraction and calculations of berry skin anthocyanins:

For grape skin anthocyanins determinations, three representative samples of 20 berries each were weighed with a precision balance, manually peeled with a scalpel and the residual pulp/flesh was removed carefully. Skin anthocyanins extraction was performed in 40 mL acidified ethanol solution (ethanol: HCl= 4:1, v/v) in hermetically closed dark bottles, and placed on a stirring plate (Rotabit, J.P. Selecta, S.A. Spain) at 120 rpm at room temperature for 48 hours (Nadal and Lampreave 2007; Cáceres et al. 2012)

Once the extraction of anthocyanins was finished, the total anthocyanins of skin will be measured with spectrophotometer (Agilent 8453 with Hewlett Packard UV-Visible Chemstation, Agilent Technologies, USA) at 520 nm.

The total anthocyanin concentration was obtained from the following equation:

Total anthocyanins content (mg/g) = 35.58 x Abs 520 nm/weight of 20 berries (g)

3.4.2 HPLC Analysis of anthocyanins profile

The identification and quantification of anthocyanins from extracts above were analyzed by HPLC using the method described by Sáenz-Navajas et al. (2011).

Grape berry skin extract samples were diluted 1:10 in formic acid (Formic acid : ddH2O = 42.5 : 1000, v/v). Subsequently, samples were centrifuged at 12000 rpm for 5 min at room temperature using an Eppendorf centrifuge (Hamburg, Germany); and the supernatant was collected and filtered before analysis through a 0.45 µm pore size membrane.

Reagents, standards and chromatographic analysis

All chemicals used were of analytical reagent grade. Ultrapure water was obtained from a Milli-Q purification system (Millipore, Molsheim, France). Acetonitrile, ethanol, methanol (HPLC grade) and formic acid were purchased from

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Scharlab (Barcelona, Spain). Oenin chloride standard was supplied by Sigma-Aldrich (St Louis, MO, USA).

Analysis was carried out on a modular Agilent 1200 liquid chromatograph (Waldbronn, Germany), equipped with a G1329A automatic injector, a G1311A HPLC quaternary pump, an online G1379A degasser, a G1316A oven, a G1315B photodiode array detector, and Agilent Chemstation software. The separation of anthocyanins was accomplished on a Nucleosil 120-C18 column (250 x 46 mm, 5μm) from Teknokroma, (San Cugat del Valles, Spain). The column temperature was maintained at 40ºC. The injection volume was 30μL with an isocratic flow rate of 1 mL/min and the total run time was 93 min. Chromatographic separation was carried out following the method described by Cacho et al. (1992) with some gradient modifications. Solvents were (A) water/formic acid (95:5 v/v) and (B) acetonitrile establishing the following gradient: at 0 min 10%B, at 10 min 15% B, at 17 min 19%B, at 34 min 40% B, at 35 min 50% B, at 37 min 100% B, at 37-44 min isocratic 100%B, at 46 min 10% B, at 46-55 min isocratic 10% B. The chromatograms were recorded at 520 nm and the UV spectra were collected from 200 to 650 nm. Identification of the component peaks was performed by the UV/Vis, MS and MS/MS spectra and retention times of the available standard. The HPLC system was coupled to a micrOTOF-Q High-Resolution Mass Spectrometer (Bruker Daltonik, Bremen, Germany) equipped with an Apollo II ESI/APCI multimode source. All mass spectrometry data were acquired in positive ionization profile mode. For quantification, calibration curves ranging from 0.01 mg/Lto 1.0 mg/L for the lower concentration of compounds and from 1.0 to 100 mg/L for the higher concentration of compounds were obtained from external standard of oenin malvidinchloride≥ 90 % (Sigma-Aldrich, St Louis, MO, USA). The calibration curves obtained were: y = 111.61x + 50.86 (lower concentration) and y = 95.82x + 73.96 (higer concentration). Once the areas of all compounds were obtained for all samples, the concentration was calculated based on the calibration curve and the dilution factor already mentioned. All analyses were carried out in duplicate. The results are expressed in mean values.

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The concentration of each anthocyanins component was expressed as malvidin mg/Kg of fresh berry.

3.4.3 Statistical analysis for anthocyanins profile

Descriptive statistics of the anthocyanins concentrations were used for the statistical analysis. The descriptive were estimated for the F1 population and the parents.

Principal component analysis (PCA) and cluster analysis were performed to understand the anthocyanin profiles. Analysis of variance and non-parametric Kruskal-Wallis test were used to estimate the year effect.

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3.5 Molecular marker analyses

3.5.1 DNA extraction

For DNA extraction, 4 discs (2 cm2) of young, healthy leaves (about 200 mg) were collected from all genotypes in a 2 ml Eppendorf tube, and frozen immediately in liquid nitrogen in the field. Leaf samples were stored at -80 ºC until used.

Leaf samples were ground to a fine powder with a Tissuelyser (QIAGEN GmbH, Germany) and genomic DNA was extracted using DNeasy plant Mini Kit (QIAGEN GmbH, Germany) with slight modifications (step 2, 600 μl AP1; step 3, 195 μl AP2) of the manufacturer’s protocol to enhance extraction efficiency. The concentration of genomic DNA was quantified with a NanoDrop 1000 (Thermo Scientific Inc. USA). The amount and integrity of resulting genomic DNA was checked on 0.8% agarose gel prepared in 1 x TBE buffer.

Genotyping of the individual F1 plants was performed by screening a variety of PCR (Polymerase Chain Reaction) based SSRs markers (Thomas et al. 1993), CAPS (Cleaved Amplified Polymorphic Sequence) marker (Walker et al. 2007), and 18K SNPs (Single Nucleotide Polymorphism) markers (Lijaveztky et al. 2007; Cabezas et al. 2011).

3.5.2 SSR Primer pairs selection

First of all, the 163 F1 plants were genotyped for 5 SSRs markers: VVS2 (Thomas and Scott 1993), VrZAG62, VrZAG79 (Sefc et al. 1999), VVMD6, VVMD34 (Bowers et al. 1996, 1999), in order to discard individuals resulting from self-pollinations and foreign pollen sources, resulting in a final population of 151 plants as mentioned above.

SSRs Primer selection

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(Merdinoglu et al. 2005), 14 UDV (Di Gaspero et al. 2005), 4ScuVV (Scott et al. 2000) and 130 VMC (Vitis Microsatellite Consortium, AgroGene S.A. Moissy Cramayel, France) (Salmaso et al. 2008; Costantini et al. 2008). Only 188 of SSR markers were selected for linkage mapping according to their segregation type. The SSR markers were selected to be well-spread over the 19 linkage groups according to the last available version of the reference map of Doligez et al. (2006) and Vezzulli et al. (2008). Primer pairs were synthesized (Eurofins MWG systhesis GmbH, Germany) and (IDT® Integrated DNA Technologies, Inc., Belgium) from published sequences (Table 3-2) most of them are available at the UniSTS database of NCBI GeneBank (www.ncbi.nlm.nih.gov).

Table 3-2 SSR primers used to amplify the F1 Graciano x Tempranillo population

SSR Loci screened Origen Reference

VVS V. vinifera cv Sultana Tomas and Scott, 1993 VVMD V. vinifera cv Pinot noir;V. riparia Bowers et al. 1996, 1999

VrZAG V. riparia Sefc et al. 1999 Vchr V. vinifera;V. berlandieri x V. riparia Cipiani et al. 2008

VVI V. vinifera Merdinoglu et al. 2005 ScuVV V. vinifera cv Chardonnay Scott et al. 2000

UDV V. vinifera; V. riparia Di Gaspero et al. 2005 VMC V. vinifera; V.rupestris x V. lincecumii Salmaso et al. 2008;

Costantini et al. 2008

For most SSR primers, the forward primer for each primer pair was synthesized with an additional modifier 19 bp M13 tail (CACGACGTTGTAAAACGAC) added to the 5’ end of the oligonucleotide (Oetting et al. 1995), an M13 primer that has the same sequence and that is directly labeled to the infrared fluorophore IRD-700, was the sole type of labeled primer for the detection of the SSR.

Additionally, 9SSRs (VrZAG64, VrZAG67, VMC2H4, VMC8G8, VVIH01, VVIM42a, VVIN61, VVIV05, and VVIV24) wereamplified with multiplex PCR as described by Ibañez et al. (2009). The forwards primerof each pair was fluorescently labeled with 6-carboxyfluorescein (6-FAM®). 43

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PCR Amplification protocol of SSR primers

Polymerase chain reaction (PCR) amplification was performed in GeneAmp® PCR System 9700 thermo cycler and VeritiTM 96 Well Thermal Cycler (Applied Biosystems, USA) with 96 well plates with 15-20 ng DNA, 0.2 µM of each primer 1x PCR buffer, 2.0 mM MgCl2, 0.2 mM of each dNTP, 1 unit AmpliTaq Gold® (Applied Biosystems) or Immolase DNA Polymerase (LABOLAN, Navarra, Spain) and 0.12 µM M13-700 IRD.

The PCR program of VVS, VVMD, VrZAG and ScuVV wasfollowing: 5 min at 95 ºC; 30 cycles 94 ºC for 10 s, Ta for 45 s, and 72ºC for 1 min; 7 min at 72ºC. Different annealing temperatures (Ta) were applied according to the primers sequence and the manufacturers’ instructions.

The PCR program of Vchr, VMC, and VVI series were carried out following a ‘touch-down’protocol (Don et al. 1991). For Vchrs, thermal cycling conditions were: one cycle at 95 ºC for 5 min, followed by 10 touch-down cycles at 94 ºC for 20 s, 55 - 0.5 ºC/cycle for 20 s, 65 ºC for 40 s, followed by 15 cycles at 94 ºC for 20 s, 50 ºC for 20 s, 65 ºC for 40 s, and a final step of 1 hour at 65 ºC. For VMCs, thermal cycling conditions were: one cycle at 94 ºC for 5 min, followed by 10 touch-down cycles at 94 ºC for 30 s, 59 - 0.3 ºC/cycle for 30 s, 72 ºC for 45 s, followed by 24 cycles at 94 ºC for 30 s, 56 ºC for 30 s, 72 ºC for 45 s, and a final step of 5 min at 72 ºC. For VVIs, thermal cycling conditions were established as one cycle at 94 ºC for 5 min, followed by 6 touch-down cycles at 92 ºC for 45 s, 60 - 0.5 ºC/cycle for 1 min, 72 ºC for 1 min 30 s, followed by 24 cycles at 92 ºC for 45 s, 57 ºC for 1 min, 72 ºC for 1 min 30 s, and a final step of 5 min at 72 ºC.

Amplified products were denatured and immediately loaded on acrylamide gels [(8% acrylamide:bis-acrylamide = 19:1, 1 x TBE, and 7 mol/L Urea(InvitrogenTM, Thermo Fisher Scientific, USA)] in a LI-COR 4200 DNA Analyzer (LI-COR, Inc., USA). The identification and sizing of alleles for each genotype was performed automatically with SAGA software (LI-COR, Inc., USA).

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Additionally, for the 9 SSRs labeled with FAM®, a multiplex PCR was carried out with 3 primers pairs in the same PCR reaction according the size of the amplified fragment. The reaction mixes and thermal cycler conditions of multiplex PCR were described by Ibañez et al. (2009). The separation of fragments and data analysis was carried out in anABI PRISM 3130 Genetic Analyzer (Applied Biosystems®, Thermo Fisher Scientific, USA), and GeneMaper® softwareusing GS500LIZ (Applied Biosystems®, Thermo Fisher Scientific, USA) as an internal marker to size the fragments.

3.5.2 CAPS analysis

The CAPS (Cleaved Amplified Polymorphic Sequence) marker 20D18CB9 was tested using the primers 20D18CB9f (5’-GATGACCAAACTGCCACTGA-3’) and 20D18CB9r (5’-ATGACCTTGTCCCACCAAA A-3’) as described in Walker et al. (2007). PCR was conducted using 20 ng of genomic DNA plus Platinum Taq (InvitrogenTM, Thermo Fisher Scientific, USA) in accordance with the manufacturer’s instructions in 50 μl reactions using cycling condition of 94 ºC for 2 min, 35 cycles of 94 ºC for 30 s, 55 ºC for 30 s, 72 ºC for 1min, followed by 72 ºC for 10 min. The amplification product was then restricted with DdeI and separated by gel electrophoresis on 2.5% agarose gels, using 1 x TBE buffer (Bayo-Canha et al. 2012). Gels were stained with Midori green Advanced DNA stain (Nippon Genetics EUROPE GmbH, Germany), the DNA fragments were photographed under UV light with CHEMI GENIUS Bio Imaging System (Syngene, Cambridge, UK), and documented with GeneSnap from SynGene software (Syngene, Cambridge, UK).

3.5.3 SNPs development and analysis

A 20K genotyping chip was generated from the paired end re-sequenced of 43 Vitis vinifera ssp vinifera, four V. vinifera ssp sylvestris, three V. cinerea, three V. berlandieri, three V. aestivalis, three V. labrusca, one V. lincecumii, five M. rotundifolia genotypes, using Illumina platforms. An average of 4.3 and of 3.4 millions SNP were detected respectively per V. vinifera and per other Vitis species

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genotypes. SNPs were first filtered upon technical criteria: Illumina score > 0.9 and class I type. Two subsets of SNPs were used in the chip construction: a V. vinifera specific subset and a general Vitis species subset. For the V. vinifera subset, SNPs in regions involved in structural variations and repetitions were filtered out and the remaining SNPs were then selected based on their even physical repartition along the genome together with their MAF (Minimum Allele Frequency). For the Vitis species subset, SNPs in repeated regions were filtered out and the remaining SNPs were chosen based on their level of heterozygosity and evenly distributed along the genome. In the end, 14,817 V. vinifera SNPs and 4,978 Vitis species SNPs were selected along with 205 control SNPs to design a 20K grapevine Infinium genotyping chip Illumina designed an 18,071 SNP chip: (http://urgi.versailles.inra.fr/Species/Vitis/GrapeReSeq_Illumina_20K).

This chip was used to genotype the 151 hybrids derived from the cross Graciano x Tempranillo as well as the two progenitor genotypes. Genotyping was performed at Genoscope (Evry, France) using Illumina protocols. The results were filtered for those SNPs providing consistent segregations along the 19 linkage group of the segregating population (Table 3-3).

The filtering process was performed as described (Barba et al. 2014), filtering was based on parental information, physical distance, physical location and missing data.

First, 18,071 SNPs were selected based on parental information, markers that were homozygous (AA x BB or AA x AA), had more than 10% missing data or were heterozygous (AB x AB) in both parents were discarded. After this step, 4656 SNP were left.

Secondly, 50% of markers in each chromosome were discarded according to physical distance. For example, in chromosome 1, there are 255 polymorphic SNPs distributing in 22,964,178 bp physical distance, among them 130 were discarded every 177 kbp, and the same procedure was applied to other chromosomes.

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Thirdly, according to physical location, neighbour markers were left as AB x BB and AA x AB one by one as possible.

Table 3-3 Number of SNPs per chromosome of 18,071 SNPs

Chromosome Number of SNPs Chromosome Number of SNPs

Chr1 878 Chr18 1109

Chr2 682 Chr19 873

Chr3 734 PLTD 24

Chr4 902 Chr_un 1555

Chr5 906 Chr1_random 30

Chr6 783 Chr3_random 53

Chr7 788 Chr4_random 20

Chr8 850 Chr5_random 28

Chr9 856 Chr7_random 60

Chr10 675 Chr9_random 7

Chr11 727 Chr10_random 43

Chr12 864 Chr11_random 29

Chr13 893 Chr12_random 53

Chr14 1080 Chr13_random 121

Chr15 756 Chr16_random 12

Chr16 794 Chr17_random 34

Chr17 653 Chr18_random 199

Total 18,071

Chr indicates chromosome location, and suffix ´_random´ corresponds with unassembled portions of the indicated reference chromosome.

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3.6 Genetic mapping and Linkage analysis

3.6.1 The construction of map

Genetic maps for Graciano and Tempranillo and a consensus linkage map for the cross were independently generated using 151 F1 individual population and two way pseudo-test cross strategy. Genotypes with more than 10% missing data were not considered for linkage analysis. The mapping software Joinmap® 3.0 (Van Ooijen and Voorrips 2001) was used with a cross-pollination (CP) population type, excluding bands heterozygous in both parents (hk x hk segregation type). The segregations thatcould not be handled directly by Joinmap (a0 x cd and ab x c0, where 0 represents a null allele) were included in a duplicated form, as described as Doligez et al. (2002). They were treated as two separate loci, one segregating only in the one-banded parent and the other one segregating only in the two banded parent.

For each locus, the goodness-of-fit of the observed segregation ratio to the appropriate expected ratio was tested using a χ2 test for both parental and consensus map. We decided to keep the distorted markers unless they were of low quality or they significantly affected the order of their neighbours.

In order to construct the map of Graciano, the maternal population loci with segregation type and were translatedto loci . Moreover, and type loci are ignored for maternal population.

In order to construct the map of Tempranillo, the paternal population loci with segregation type and were translated to . Moreover, and type loci are ignored for maternal population.

The χ2 test was applied to test the segregation ratio of the F1 population. All the

3 Material and Methods statistical analyses were performed with software SPSS V. 14.0 and STATGRAPHICS 16.0

3.6.2 Comparison of male and female recombination rates

To compare recombination rates between Graciano and Tempranillo, new parental maps were constructed based on 211 common markers. For these markers two data sets were prepared: one in which the maternal parent was coded as homozygous and the paternal parent was coded as heterozygous and a second data set in which the coding was reversed, as described in Riaz et al. (2004) and Lowe & Walker (2006). Marker order was fixed according to the original parental maps. A total number of 64 pairs of strong linked markers were considered. Joinmap software allows us to compared two maps under the “Join-combine groups for map integration” function. Here, the “Heterogeneity test” function, which lists all pair-wise groups of common markers, their recombination frequency and LOD values, was used to identify pairs of common markers showing significant differences based on χ2 test in recombination frequencies between the two parents. Two point estimates of recombination and LOD scores were supplied by JoinMap for each marker pair in both parents. Mean recombination frequencies with their error values were calculated for each parent in Excel. A genome wide test for differences in mean maternal and paternal recombination rates was performed using a Z test for comparisons between two population means.

3.6.3 Estimation of genome length and map coverage

Estimated genome length (Ge) was calculated by using the method of moment estimator, Ge= N(N-1)X/K (Hulbert et al. 1988), where N is the number of markers, X is the maximum observed map distance between marker pairs above a threshold LOD Z (Chakravarti et al. 1991), Z = 4 in this study, and K is the number of locus pairs having LOD values at or above Z. The value of X and K were obtained from Joinmap using Kosambi mapping function.

The confidence interval for Ge, Iα(Ge) was calculated from the equation:

3 Material and Methods

-1/2 -1 Iα(Ge) = Ge(1 ± nαK ) , where nα = 1.96 for an α of 5% (Gerber and Rodolphe 1994).

The expected genome map coverage (Ce, %) for each parent was calculated following Bishop et al. (1983) from the equation: Ce = 1-P1,N

N+1 N+1 N and P1, N = 2R/(N+1) [(1-X/2G) -(1-X/G) ]+ [(1-RX/G)(1-X/G)] , where R is the haploid number of chromosomes, N is the number of markers and X is the maximum centiMorgan distance when LOD = 4.

The observed genome map length (Go, cM) was calculated based on the sum length of all linkage groups for each linkage map (Lowe and Walker 2006).

Finally the observed genome map coverage was the ratio between observed and estimated genome length (Go/Ge, %). The above calculations were performed using all mapped loci.

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3.7 QTL analysis

Many traits of agronomic and berry quality are complex, but arescored as discrete classes or categories. They are treated as quantitativetraits, though some of them do not showcontinuous variation(Falconer 1989). Simple interval mapping(SIM) (Lander and Botstein 1989) handles the analysis of ordinal or discretephenotypic categories.In this study, QTL analysis was carried out using MapQTL® 6.0 software (Van Ooijen 2009) with SIM.

QTL were declared signiﬁcant when the maximum LOD exceeded the linkage group and/or genome-wide LOD threshold (calculated using 1,000 permutations) and mean error rate was lower than 0.05 (p < 0.05).

First, the non-parametric Kruskal-Wallis (KW) rank sum test, designed for categorical data, was applied to the global segregation of each locus, and then, Simple interval mapping (SIM) was used. A stringency significance level of P = 0.05 was used for the KW test. Maximum LOD values were used to estimate QTL peak position, the conﬁdence intervals (1-LOD) were estimated in cM and corresponded to an LOD score drop of one on either side of the likelihood peak. A QTL was considered significant only when it was detected by both methods.

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3.8 References

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Maul E, Töpfer R, Eibach R (2014) Vitis International Variety Catalogue. Julius Kühn Institut. www.vivc.de. Accessed June 2014

Monagas M, Bartolomé B, Gómez-Cordovés C (2006) Effect of the modifier (Graciano vs. Cabernet Sauvignon) on blends of Tempranillo wine during ageing in the bottle. I. Anthocyanins, pyranoanthocyanins and non-anthocyanin phenolics. LWT- Food Sci Technol 39:1133-1142.

Nadal M (2010) Phenolic maturity in red grapes. In: Delrot S, Medrano H, Etti O, Bavaresco L, Grando S (eds) Methodologies and results in grapevine research. Springer Science, Heidelberg, Germany, pp: 389-411

Nadal M and Lampreave M (2007) Influencia del riego en la maduración polifenólica de las bayas y la calidad de los vinos. Experiencia del riego en la comarca del Priorato, DO Montsant. In: Baeza- Trujillo P, Lissarrague JR, Sánchez de Miguel P (eds) Fundamentos, aplicación y consecuencias del riego en la vid. Editorial Agrícola Española, Madrid, Spain, pp: 231-256,

NCBI UniSTS http://www.ncbi.nlm.nih.gov/

Núñez V, Monagas M, Gomez-Cordovés MC, Bartolomé B (2004) Vitis vinifera L. cv. Graciano grapes Characterized by its anthocyanin profile. Postharvest Biol Tec 31: 69-79

Oetting WS, Lee HK, Flanders DJ, Wiesner GL, Sellers TA, King RA (1995) Linkage analysis with multiplexed short tandem repeat polymorphisms using infrared fluorescence and M13 tailed primers. Genomics 30: 450-458

Riaz S, Dangl GS, Edwards KJ, Meredith CP (2004) A microsatellite marker based framework linkage map of Vitis vinifera L. Theor Appl Genet 108:864-872.

Ribereau-Gayon P, Stonestreet E (1966) Dosage des tanins du vin rouge et détermination de leur structure. Chimie Anal 48: 188-196

Riojawine http://www.riojawine.com

Sáenz-Navajas MP, Echavarri F, Ferreira V, Fernandez-Zurbano P (2011) Pigment composition and color parameters of commercial Spanish red wine samples: linkage to quality perception. Eur Food Res Technol 232: 877-887

Saint-Criq de Gaujelac N, Vivas N, Glories Y (1996) Maturité phénolique: définition et contrôle. Revue Française d œnologies 173: 22-25

3 Material and Methods

Scott KD, Eggler P, Seaton G, Rossetto M, Ablett EM, Lee LS, Henry RJ (2000) Analysis of SSR derived from grape ESTs. Theor Appl Genet100:723-726

Song S,Hernández MM, Provedo I, Menéndez C (2014) Segregation and associations of enological and agronomic traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) Euphytica 195:259-277

Thomas MR, Matsumoto S, Cain P, Scott NS (1993) Repetitive DNA of grapevine: classes present and sequences suitable for cultivar identification. Theor Appl Genet 86: 173-180

Thomas MR and Scott NS (1993) Microsatellite repeats in grapevine reveal DNA polymorphisms when analysed as sequence-tagged sites (STSs). Theor Appl Genet 86:985-991

Voorrips RE (2002) MapChart: Software for the graphical presentation of linkage maps and QTLs.J Hered 93 (1): 77-78.

Van Ooijen JW (2009) MapQTL® 6. Software for the mapping of quantitative trait loci in experimental populations of diploid species. Kyazma BV: Wageningen, Netherlands

Van Ooijen JW and Voorrips RE (2001) JoinMap®3.0, Software for the calculation of genetic linkage maps. Plant Research International, Wageningen, Netherlands.

Vezzulli S, Troggio M, Coppola G, Jermakow A, Cartwright D, Zharkikh A, Stefanini M, Grando MS, Viola R, Adam-Blondon AF (2008) A reference integrated map for cultivated grapevine (Vitis vinifera L.) from three crosses, based on 283 SSR and 501 SNP-based markers. Theor Appl Genet 117: 499-511

Vivas N, Glories Y, Lagune L, Saucier C, Agustin M (1994) Estimation of the polymerisation level of procyanidins from grapes and wines by use of p-dimethylaminocinnamaldehyde. J Int Sci Vigne Vin 28:319-336

Walker A, Lee E, Bogs J, Mc David DJ, Thomas MR, Robinson SP (2007) White grapes arose through the mutation of two similar and adjacent regulatory genes. Plant J 49:772-785

3 Material and Methods

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4 SEGREGATION AND ASSOCIATIONS OF ENOLOGICAL AND AGRONOMICAL TRAITS IN GRACIANO X TEMPRANILLO WINE GRAPE PROGENY

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) 4 Segregation and associations of enological and agronomic traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

Abstract

The main objective of this research was the evaluation of the variability present in a segregating wine grape population derived from a cross between Graciano x Tempranillo, two Spanish varieties, in order to select improved genotypes with potential for producing high-quality wines in a climate change scenario.For that purpose, the phenotypic segregation of 16 agronomic traits related to production and phenology and 11 enological traits related to technical and phenolic maturity was studied in the progeny for three consecutive years.

All traits presented transgressive segregation and continuous variation. Year effect was significant for all traits except total, extractable and skin anthocyanins content. However, a high level of genotype consistency for enological traits was revealed by repeatabilities and correlations between years. Significant correlations among traits were observed but most associations were weak.

Furthermore, the CAPS (Cleaved Amplified Polymorphic Sequence) marker for the VvmybA genotype was tested to determine whether it would be useful in indirect selection for berry anthocyanins content. The results showed that the number of homozygous and heterozygous genotypes for the functional colour allele adjusted to a 1:1 segregation ratio, and that homozygous genotypes had significantly higher anthocyanins content.

Principal component analysis found eight variables that contributed up to 80% of the phenotypic variability present in the population. Seven groups of hybrids were distinguished based on ripening time, cluster weight, berry weight and anthocyanins content by cluster analysis; and fourteen genotypes were pre-selected for further research.

Key words: Grape breeding, Phenolic maturity, Climate conditions, Berry quality,

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4.1 Introduction

Grapevine (Vitis vinifera L.) is one of the most important fruit species in the world. The use of grapevine for fruit, juice and wine production can be traced back more than 8,000 years (This et al. 2006). During this time, numerous cultivars have been selected for their quality and adaptation to different climatic conditions.

Many studies have documented a correlation between climate change and agronomical traits in recent years (Hall and Jones 2009). Climate affects grapevine growth and fruit and wine production in many ways.Temperature is widely accepted as being the primary climatic factor affecting the quality of viticultural production (Jackson and Lombard 1993; Gladstones 2004). Besides, the length of the growing season is considered an important determinant of grape quality and consequent wine value (Jackson and Lombard 1993; Coombe and Iland 2004) because air temperature during ripening affects the composition of harvested grapes (Mullins et al. 1992; Webb et al. 2006, 2007).

During maturation, the total berry phenolic concentration slowly increases until a maximum is reached 1 or 2 weeks before harvest. Phenolic maturity is attained when the concentration of grape phenolics is maximal (Ortega-Regules et al. 2006). Grape phenolics are structurally diverse and have variable extraction potentials. Anthocyanin pigments and tannins are particularly important in red wine quality. In addition, the ratio of skins and seeds to berry size, has an important role in the extraction of phenolics on wine and therefore in wine quality. Several factors contribute to berry phenolic maturity, including variety, climatic conditions and cultural practices (González et al. 1990; Cacho et al. 1992; Jordao et al. 1998; Vivas et al. 2001; Harbertson and Adams 2002; Downey et al. 2006). Furthermore, the time at which ripening takes place can determine potential wine quality for a particular vintage. The temperature of the final ripening month is regarded as a particularly important factor influencing wine styles. Many studies have demonstrated that temperature influences many components of grape development, including the breakdown of acids (Buttrose et al. 1971) and berry colour development (Bergqvist et al. 2001; Buttrose et al. 1971; Kliewer 1977). In particular, prolonged periods with temperatures above 30°C can induce heat stress, which may lead to premature veraison, berry abscission, enzyme inactivation and reduced flavour development (Mullins et al. 1992). 60

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

In traditional wine-growing regions in Europe, the limiting factor for producing high-quality wines is the level of ripeness of the grapes at high latitudes. Unripe grapes give green, acidic wines, with low alcohol levels, as a result of insufficient sugar accumulation in the berry. For this reason, early ripening varieties such as Pinot noir and Chardonnay are grown to optimize the chances of attaining correct ripeness. At lower latitudes, where the climate is warmer, grapes might reach ripeness early in the summer but quick ripening of the grapes reduces aromatic expression in the wines (Van Leeuwen and Seguin 2006). Moreover, the earlier occurrence of phenological events and a compression of the growth period affect bothwine production and quality. The disruption in flavor and colour development occurswith significant warming during maturation and especially at night, finally upsetting the wine typicity and quality (Webb et al. 2007; Ramos et al. 2008).

Regarding climate change, one of the most important concerns for the enologists is the ability of berries to complete phenolic maturity when the growing season is shortened by the effect of higher temperatures and lower and/or erratic rainfall (Nadal 2010) especially at low latitudes. Besides, the high sugars/acids ratio derived from higher temperatures results in high alcoholic wines with low acidity, undesirableto the consumer. Moreover, increasing temperatures during ripening could affect dramatically the synthesis of phenol compounds in the berry. Therefore it is important to select genotypes that are able to mature slowly in order to attain complete phenolic maturity at the date of harvesting. Recent studies have addressed the selection of novel grape genotypes for a climate change scenario (Viana el al. 2011a; Bayo-Canha et al. 2012). Nevertheless none has evaluated the ability of genotypes to complete phenolic maturity, and therefore their potential for vinification in such setting.

Although viticulture has a history of over 8000 years, grape breeding was only started in the early 1950s (Mullins et al. 1992). So far, the best method for breeding new varieties of grape is crossbreeding. Therefore, interspecific hybridization, inter-varietal hybridization and bud mutation, as well as ploidy breeding are carried out widely at different institutions all over the world (Clark 2010; Liang et al. 2011). However, due to the rigorous regulatory classification system (Appellation in France and Denominación de Origen in Spain) in wine, very few new varieties have been released in the traditional wine regions of Europe.

The changing climatology, especially during the growing season, may alter the

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

specific optimum climate of a traditional variety, hence hindering its ability to ripen balanced fruit. Producing or preserving current wine styles will consequently become more challenging. Thus, it is convenient to breed and select new high quality varieties suited to the new climatic conditions, as well as satisfactory to consumer demands.

The heterozygous nature of grapevine is a complicating feature for any effective breeding program, hence the requirement for an investigation on the inheritance of desirable traits within the species. Genetic analysis in grapevine is not easy, due to its long life cycle, large number of chromosomes, partial sterility of ovules, and low seed germination. Moreover, many genes which belong to a polygenic series affect anthocyanin formation, chlorophyll formation, shape and structure of leaves, habit and vigor, etc (Alleweldt andPossingham 1988). Quantitative genetics (Falconer 1989) provided us with theoretical base to obtain useful information on the contribution of genetic and environmental factors to phenotypic expression. From a series of quantitative genetics studies on grapevine, it is known that the year effects largely account for the environmental variation of individual fruit traits (Shiraishi 2011).

The main objective of the present study was to analyze a segregating hybrid population obtained from two Spanish relevant wine varieties, Tempranillo and Graciano; in order to evaluate the variability present and to select improved genotypes, with the greatest potential for producing high-quality wines in a climate change scenario.

Tempranillo is the main wine grape variety grown in Spain so as in Denominación de Origen Calificada Rioja (DOCa); and is native to La Rioja and Aragón (Ibáñez et al. 2012). Graciano is a variety native to La Rioja, usually blended with Tempranillo to strengthen the wine with colour, acidity, elegant aroma, and aging potential (Chomé et al. 2006; Núñez et al. 2004). These varieties were selected as parental genotypes in the present study as they show complementary agronomic and enological characteristics. The breeding program at Viveros Provedo aims to identify superior genotypes based on Tempranillo´s genetic background; well adapted to La Rioja growing region and with wider adaptation to the new climatic conditions. In that framework, several F1 progenies with Tempranillo as parental genotype were developed.

In this work, the segregation of phenology, production, berry quality and

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

enological traits present in the F1 population developed from a Graciano x Tempranillo cross was studied. In addition, correlations among traits were evaluated and a pre-selection of superior hybrids was performed.

4.2 Materials and methods

4.2.1 Plant material

A F1 population of 163 plants obtained from controlled crosses between the wine grape cultivars Graciano (female parent) and Tempranillo (male parent) was used for our investigation. The individual hybrids (one plant of each genotype) have been grown on their own roots since 2004, on a sandy-loam soil, in East-West orientation with 3 m spacing between rows and 1 m between plants in double Royat cordon. Standard irrigation, fertilization and plant protection practices for La Rioja region were performed. The plants first flowered and fruited in 2007.

The population was genotyped for 5 SSRs markers: VVS2 (Thomas and Scott 1993), VrZAG62, VrZAG79 (Sefc et al. 1999), VVMD6, VVMD34 (Bowers et al. 1996; 1999), in order to discard individuals resulting from self-pollinations and foreign pollen sources, resulting in a final population of 151 plants. DNA was extracted from 200 mg frozen leaves using a DNAeasy Plant Mini kit (Quiagen, Germany) following the manufacturer’s protocol. The microsatellite analysis was conducted following the methods described by Martín et al. (2003). The fragments were separated on a LICOR 4200 DNA Analyzer (LI-COR, Inc., USA) on 8% denaturing polyacrylamide gels and sized with SAGA software (LI-COR, Inc., USA).

4.2.2 Phenotypic evaluation

Agronomic traits

Regarding phenology, the dates of sprouting (S, considered as when 50% of the buds were in Baggiolini stage C), flowering time (F, 50% flowering in Baggiolini

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

stage I, Baggiolini 1952), veraison time (V, 50% berry veraison), and ripening time (R) were scored. Ripening time was established as the date when random grapes picked from the top, medium and bottom of the clusters reached 13°Baumé and consequently harvest started on September the 23rd, 9th and 10th in 2008, 2009 and 2010 respectively.

For each genotype, yield per vine and number of clusters per vine (CN), were measured at harvest, and the average weight of clusters (CW) was calculated. The fertility index (FI) was scored as the number of inflorescences per young shoot. Mean berry weight (BW) was estimated from a sample of 100 berries randomly taken from each genotype. Seed traits, including mean seed number per berry (SN) and mean fresh seed weight (SW), were evaluated by triplicate from samples of 20 berries randomly selected.

Enological quality traits

Two sets of data were generated for the enological analysis, one related to the technological parameters and another related to the phenolic maturity indices. For the first set, 200 whole berries from each genotype were sampled at random, from different positions within the cluster, to avoid effects due to sun exposition. Grapes were squeezed and parameters of the resulting musts were evaluated by triplicate. Sugar content (TSS, expressed as degree Baumé) was measured with an Atago Master-Baume refractometer (Atago, Tokyo, Japan); pH and total acidity (TA, expressed as g/L tartaric acid) were measured with a TitroMatic 1S-1B(Crison, Barcelona). In addition, 60 randomly selected berries were frozen to measure berry skin anthocyanin content (BSAn, mg/g) by triplicate as described in Nadal and Lampreave (2007).

The phenolic maturity indexes were determined following Saint-Criq et al. (1998) as described in Nadal (2010). Two hundred whole berries were sampled and crushed to obtain a homogeneous mixture, and then macerated for 4h at two different pH

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) values (pH=1.0 (A) and pH=3.6 (B)). The total anthocyanins content (TAn) was determined with extract A; the extractable anthocyanins (EAn), colour intensity (CI), total polyphenols index (TPI) and tannin contents (TC) were determined with extract B.

The extractability index (%EI) and seed maturity (%SM) were calculated as follows (Nadal, 2010):

[(Ant − Ant ) 100* ] %EI = pH 0.1 pH 6.3 Ant pH 0.1

[Abs280 − (Ant )] 100*40* %SM = pH 6.3 pH 6.3 Abs280 pH 6.3

4.2.3 Genotypic evaluation of the VvmybA allele with CAPs

To establish whether the total anthocyanins content was correlated with the VvmybA genotype, the CAPS (Cleaved Amplified Polymorphic Sequence) marker 20D18CB9 (Walker et al. 2007) was tested against the progeny and parental plants, using a PCR assay. The amplification product obtained using the primers 20D18CB9f (5´-GATGACCAAACTGCCACTGA-3´) and 20D18CB9r (5´-ATGACCTTGTCCCACCAAA A-3´) was then restricted with DdeI and separated by gel electrophoresis on 2.5% agarose gels, using 1x TBE buffer (Bayo-Canha et al. 2012). Gels were stained with Midori green Advanced DNA stain (Nippon Genetics EUROPE GmbH, Germany), the DNA fragments were photographed under UV light with CHEMI GENIUS Bio Imaging System (Syngene, Cambridge, UK), and documented with GeneSnap from SynGene software(Syngene, Cambridge, UK).

4.2.4 Statistical analysis

Descriptive statistics for all traits were conducted. A t-test was carried out to detect differences between both parents. Analysis of variance with LSD test was used to evaluate mean value differences among the 3 years. The normality of each trait distribution was evaluated by the Kolmogorov-Smirnov test. Data that significantly deviated from normality were transformed (square-root or logarithm) to fit a normal distribution.

Phenotypic correlations between traits were determined in each year with the Spearman rank-correlation coefficient. Correlation analysis between years was used to

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

evaluate the genotype stability across years for each trait. Year effect was tested with analysis of variance and non-parametric Kruskal-Wallis test (Fanizza et al. 2005). Based on the multiple measurements, the repeated-measures analysis of variance (years, genotypes within year) was applied to estimate the repeatability over years for each trait (Falconer 1989; Fanizza et al. 2005). The repeatability was calculated as: +VV r = G Eg ++= VVVV VP , GP EgEs , where r is repeatability; VG is genotypic variance; V VP is phenotypic variance; Es is special environment variance (within individual variance component); VEg is general environment variance (environmental variance contributing to the between-individual component).

A factor analysis of the phenotypic data was carried out using the principal components extraction method (PCA) to identify the main variance components contributing to the variability among hybrids. Then the gained matrix was analyzed with hierarchical cluster methods to pre-select the best suitable hybrids. A hierarchical cluster analysis was carried out using the squared Euclidian distance combined with the average linkage clustering methods.

All the statistical analyses were performed with software SPSS V. 14.0 and STATGRAPHICS 16.0.

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4.3 Results

For this research, wine grape varieties that differ in several agronomic (phenology, fertility, berry weight, seed number, seed weight) and enological attributes (total acidity, anthocyanins content) were chosen as parents. A t-test detected significant differences between both progenitors in mean berry weight, pH, total acidity, fertility index, mean seed number and mean seed weight (p<0.01) as expected.

4.3.1 Phenotypic evaluation of agronomic traits

Segregating agronomic traits relating to phenology, production and fruit characteristics were scored in the population over three years (2008-2010) (Table 4-1). The number (N) of plants producing enough berries for evaluation each year varied from 116 to 132, as a result of hail damage during flower bud formation in 2008, and bird damage during veraison-ripening.

Table 4-1 Mean values of 16agronomic traits evaluated in the Graciano x Tempranillo population

Year Total Agronomic traits N 2008 N 2009 N 2010 Mean Min Max Yield per vine (Kg) 116 0.8a 123 2.1b 132 2.5c 1.8 0.1 10.8 Number of clusters per vine 116 9.5a 123 11.5a 132 20.4b 14.0 1 57 Mean cluster weight (g) 116 84.7a 123 173.7b 132 121.8c 127.3 15.0 487.0 Fertility index 116 0.8a 123 0.9a 132 1.2b 0.9 0.1 2.4 Mean berry weight (g) 116 1.5a 123 1.7b 132 1.5a 1.6 0.7 2.8 Mean seed number per berry 114 2.0a 123 2.4b 132 2.0a 2.2 1.1 3.5 Mean seed weight (mg) 114 33.1a 123 32.1a 132 30.1b 31.8 9.9 51.8 Sprouting (days from March 1st) - 151 35a 150 49b 42 20 60 Flowering (days from March 1st) 140 108a 132 93b 136 99c 97 80 113

Veraison (days from March 1st) 137 171a 131 158b 134 174c 167 142 185

Ripening (days from March 1st) 137 216a 129 201b 132 223c 214 189 240 Flowering Period (days) 140 15a 131 13b 134 13b 14 4 28 Veraison Period (days) 137 9a 131 11b 134 15c 12 2 27

Sprouting-Flowering interval (days) - - 131 58a 136 49b 53 38 70

Flowering-Veraison interval (days) 137 63a 131 65b 134 75c 68 51 86

Veraison-Ripening interval (days) 137 45a 129 43a 132 50b 46 13 72

Values with different letters show significant differences between years (p< 0.05) according to the LSD test. N, number of genotypes evaluated each year. Mean values (Mean), minimum (Min) values and maximum (Max) values for the 3 years Phenotypic data distributions were similar in the three years of study. Figure 4-1 shows the distributions for year 2010. Continuous variation and transgressive segregation were observed for all traits in the three growing seasons evaluated,

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) indicating that genetic variability is present in the population and confirming the quantitative nature of the traits evaluated.

The Kolmogorov-Smirnov test indicated departures from normality for yield per vine, cluster number, mean seed number and all phenology-related traits (S, F, V, R, FP, VP, S-F, F-V, and V-R) (p<0.05 in all years).

Figure 4-1 Distribution of agronomic traits in 2010. Parental values are indicated by GR (Graciano) and TE (Tempranillo). a. Distribution of production traits. Yield mean value and mean cluster weight showed significant differences in the 3 years (Table 4-1). The distribution of production traits in the progeny in 2010 growing season is shown in figure 4-1a. Forty four percent of the genotypes (59/132) showed low production (below 2.0 kg), 71% of the genotypes (94/132) displayed low cluster weight (below 150 g) and 38% (50/132) exhibited low fertility (below 1.0). The standard values were set according to DOCa Rioja Regulation for grafted vines in commercial fields. Mean cluster number (14) and mean fresh seed weight (31.8 mg) showed intermediate values between the parents. Mean seed number per berry (2.2) was the only trait with mean values higher than the parental varieties, and mean berry weight (1.6 g) showed lower values than the progenitors. Yield, cluster weight and

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

fertility index increased gradually from 2008 to 2010, as grapevines became older and production became more stable.

Figure 4-1 Distribution of agronomic traits in 2010. Parental values are indicated by GR (Graciano) and TE (Tempranillo). b. Distribution of phenology-related traits. The growing season (from sprouting to ripening) lasted on average 172 days, the same as Graciano, but 8 days longer than Tempranillo. Dates for sprouting, flowering, veraison, ripening time (days from March 1st), and veraison period, sprouting-flowering interval, and flowering-veraison interval showed significant differences among the 3 years (Table 4-1). Phenology-related traits were the most influenced by the environment, and significant differences among years were attributed to the temperature variation in the three years of study. Mean temperatures during the three growing seasons (April-October) obtained from the closest weather station, varied significantly between 17.9 ºC in 2008 and 19.3 ºC in 2009, with a 18.1

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

ºC mean temperature in 2010. The 2008 growing season was cool and rainy (425.8 mm) for La Rioja conditions and total sunshine hours were lower (1743.8) compared to 2009 (163.9 mm and 1911.4 h) and 2010 (193.8 mm and 1778.2 h) seasons. The early occurrence of rainfall in 2008 had a strong effect on yield likely due to flower drop and lower fertility but did not influence ripeness (Table 4-1 and 4-2). Accordingly, sugar content was the greatest in the 2009 growing season as temperatures and sunshine hours were also the highest (Table 4-2).

Tempranillo was earlier than Graciano for all phenology-related traits, and transgressive segregation was observed in the progeny for all traits (Figure 4-1b). Sprouting ranged between 20 days and 60 days, 15% of the population being earlier than Tempranillo and only 5% of the genotypes later than Graciano in 2010. For flowering date, 60% of the genotypes were earlier than Tempranillo and 5% of them later than Graciano. The veraison- ripening interval varied between 13 and 72 days and approx. 30% of the progeny showed a longer V-R than Graciano (Figure 4-1b).

4.3.2 Phenotypic evaluation of enological traits

Traits related to both technical and phenolic maturity were evaluated in the population over 3 years and mean values and ranges are shown in Table 4-2. Only pH and colour intensity showed significant differences among the 3 years studied (Table 4-2).

Table 4-2 Mean values of 11enological traits evaluated in the Graciano x Tempranillo population

Year Total Enological traits N 2008 N 2009 N 2010 Mean Min Max Total soluble solids (º Baumé) 116 12.4a 123 12.8b 132 12.2a 12.4 8.4 15.6 pH 116 3.5a 123 3.6b 132 3.5c 3.5 3.1 4.2 Total acidity (g/L tartaricacid) 116 7.4a 123 5.9b 132 6.1b 6.5 2.9 12.4 Skin anthocyanins content (mg/g) 95 1.3ab 123 1.4a 132 1.2b 1.3 0.3 3.5 Colour intensity 68 16.2a 112 19.1b 123 21.7c 19.5 6.6 56.3 Total Polyphenol Index 68 51.6a 112 61.7b 123 58.7 58.2 18.7 107.6 Tannin content (mg/L) 68 295.8a 112 355.1b 123 333.9b 333.4 77.3 665.9 Total anthocyanins (mg/L) 68 587.8a 110 763.0b 123 727.7b 709.0 142.6 1903.1 Extractable anthocyanins (mg/L) 68 398.1a 110 434.7ab 123 475.0bc 442.9 108.5 1114.7 Seed maturity (%) 68 68.9a 110 72.2b 123 68.0a 69.8 35.2 90.6 Extractability index(%) 68 29.9a 110 39.1b 123 31.2a 33.8 0.8 70.4

Values with different letters show significant differences between years (p< 0.05) according to the LSD test. N, number of genotypes evaluated each year. Mean values (Mean), minimum (Min) values and maximum (Max) values for the 3 years Phenotypic data of enological traits showed similar distributions in the three years of study and therefore distributions are reported only for 2010 (Figure 4-2).

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

Total acidity ranged between 2.9 and 12.4 g/L, with an average value in the progeny intermediate between the parental values. Total polyphenol index and total tannin content showed also intermediate values in the progeny compared to both progenitors (Figure 4-2).

Figure 4-2 Distribution of enological traits in 2010. Parental values are indicated by GR(Graciano) and TE (Tempranillo). However, mean skin anthocyanins (1.3 mg/g), colour intensity (19.5) and both total and extractable anthocyanins (709.0 and 442.9 mg/L) were lower in the progeny than in both parental genotypes. In all cases, there were genotypes showing higher values than Graciano, the parent with the highest polyphenols content.

The mean extractability index (33.8%) in the progeny showed intermediate values between the parentsand 32.5% of the progeny showed values between 30 and 40% which are optimal values for wine grape. Seed maturity was higher in the progeny than in both parental genotypes. 71

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4.3.3 Phenotypic correlations

In order to evaluate the effect of genetic and environmental factors on agronomic traits; the year effect, the correlation coefficient of the same traits between years and the repeatability were estimated. Analysis of variance and Kruskal-Wallis test revealed a highly significant year effect (p<0.01) for all traits studied except berry skin anthocyanins content, total anthocyanins and extractable anthocyanins.

Table 4-3 Phenotypic correlations (Spearman rank coefficient) between years and repeatability for each trait

Coefficients between years Traits 2008 and 2008 and r 2009 and 2010 2009 2010 Yield per vine (Kg) 0.55** 0.44** 0.57** 0.29 Number of clusters per vine 0.58** 0.55** 0.63** 0.21 Mean cluster weight (g) 0.52** 0.41** 0.54** 0.17 Fertility index 0.62** 0.49** 0.67** 0.31 Mean berry weight (g) 0.51** 0.38** 0.70** 0.52 Mean seed number per berry 0.56** 0.57** 0.80** 0.47 Mean seed weight (mg) 0.38** 0.39** 0.75** 0.29 Sprouting (days from March 1st) - - 0.47** 0.0 Flowering (days from March 1st) 0.23* 0.30** 0.34** 0.0 Veraison (days from March 1st) 0.65** 0.48** 0.58** 0.0 Ripening (days from March 1st) 0.57** 0.51** 0.52** 0.0 Flowering period (days) 0.32** 0.25** 0.38** 0.26 Veraison period (days) 0.39** 0.19 0.36** 0.02 Sprouting-Flowering interval (days) - - 0.26** 0.0 Flowering-Veraison interval (days) 0.63** 0.35** 0.57** 0.0 Veraison-Ripening interval (days) 0.50** 0.36** 0.45** 0.28 Total soluble solids (º Baumé) 0.70** 0.40** 0.50** 0.43 pH 0.68** 0.59** 0.64** 0.55 Total acidity (g/L ) 0.64** 0.63** 0.71** 0.39 Berry skin anthocyanins (mg/g) 0.43** 0.54** 0.88** 0.54 Colour intensity 0.80** 0.72** 0.78** 0.72 Total polyphenol index 0.48** 0.34** 0.55** 0.36 Tannin content (mg/L) 0.42** 0.45** 0.49** 0.45 Total anthocyanins (mg/L) 0.81** 0.84** 0.90** 0.82 Extractable anthocyanins (mg/L) 0.82** 0.79** 0.83** 0.81 Seed maturity (%) 0.79** 0.83** 0.79** 0.33 Extractability index (%) 0.47** 0.37** 0.46** 0.74

r: repeatability; * and ** mean correlations significant at the 0.05 and 0.01 level, respectively. “-“ means missing data

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

The differential response of genotypes to year-environment variation was estimated by phenotypic correlations of the same trait between years (Table 4-3). Correlations were highly significant (p<0.01) for all traits, except for flowering time and veraison period (VP). The highest coefficient was observed for total anthocyanins content between year 2009 and year 2010 (r=0.90, p<0.01). This result confirms the year effect analysis previously reported.

Production traits (yield, cluster number, mean cluster weight, fertility index and mean berry weight) showed correlations ranging from 0.41 to 0.67 (p<0.01). Enological traits (sugar content, pH, total acidity, skin anthocyanins content and phenolic maturity indexes) were significantly (p<0.01) correlated in the three years with coefficients ranging from 0.37 to 0.88. Seed traits among years were also related with values ranging from 0.38 to 0.80 (p<0.01). The phenology-related traits showed the lowest correlation coefficients (0.25 - 0.63) (p<0.01).

The repeatability, that sets the upper limit to the broad-sense heritability (Falconer 1989; Fanizza et al. 2005), was estimated for all traits over 3 years (Table 4-3). Agronomic traits showed low repeatabilities (0.17 to 0.31) and those were even lower for phenology-related traits indicating a strong environmental effect, as expected. On the other hand, traits related to enological potential, berry and seed parameters showed the highest repeatabilities (up to 0.82).

Several associations between traits were revealed within each year, using Spearman rank correlation coefficient (Table 4-4). Overall, coefficients observed in the three years (2008, 2009 and 2010) were similar and the values reported in the table correspond to the correlations averaged over 3 years, indicating also if the correlation was found only in two years or if there were contradictory results.

As expected, many of the observed associations concerned the component variables of the same trait. Among those; yield relatedtraits,harvesting related factors and colour derived parameters. High significant (p<0.01) correlations were detected between vine yield and cluster number (r=0.77), cluster weight (0.65) and fertility index (0.73).

Positive significant correlations were found between colour intensity and berry skin anthocyanins (0.81), and total and extractable anthocyanins (0.91 and 0.94, respectively). In addition, significant (p<0.01) high correlations were revealed

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

between veraison-ripening interval and ripening date (0.78) and between flowering-veraison interval and veraison date (0.85).

However, correlations between different groups of traits were also detected in at least two years (Table 4-4). Production traits (yield, cluster weight) showed negative correlations with enological quality traits (skin, total and extractable anthocyanins content, total polyphenol index) as expected, but coefficients were low, ranging from 0.30 to 0.35.

Berry weight is one of the most relevant quality parameters in grape. A positive significant correlation was found between berry weight and seed number (0.25) but lower than the value (0.41) reported by Costantini et al. (2008). Berry weight also showed significant negative correlations with colour parameters as expected, but associations were weak (-0.24 to -0.32). The number of seeds per berry correlated positively with productivity traits such as yield (0.25) and cluster weight (0.38) and negatively with seed fresh weight (-0.48) and anthocyanins content, both skin and total (-0.25 and -0.26) indicating that the larger the seed number, and consequently the berry weight, the lower the anthocyanin content.The negative correlation between seed number and seed fresh weight contradicts the results of Costantini et al. (2008) that reported a positive correlation between SN and SW (0.36).

Regarding phenology traits, few relevant correlations were observed. Low significant (p<0.01) correlations were revealed between flowering period and veraison period with yield (0.34 and 0.33 respectively), mean cluster number (0.28 and 0.31), and fertility index (0.31 and 0.29).This result is expected as genotypes with higher productivity would take longer to complete reproductive developmental stages. Sprouting showed a high negative correlation with the sprouting-flowering interval (-0.85) and a moderate correlation with flowering time (0.45), indicating that early-sprouting genotypes will take longer to reach flowering. Ripening and veraison-ripening interval exhibited high negative correlations with total soluble solids (-0.76 and -0.59) andlower with tanins content (-0.37 and -0.30 respectively). Wei et al. (2002) and Bayo-Canha et al. (2012) reported a negative correlation (-0.62 and -0.50) between ripening and acidity in contrast with the results of Jones and Davis (2000). However, in our work, only a low negative correlation between ripening time and pH (-0.24) was found in 2 years. These results indicate that late-ripening genotypes in our growing conditions cannot reach maturity.

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

Table 4-4 Phenotypic correlations between traits averaged over three years

CN CW FI BW SN SW S F V R FP VP S-F F-V V-R TSS pH TA BSAn CI TPI TC TAn EAn SM EI

Yield 0.77 0.65 0.73 0.36 0.32 ns ns -0.28b ns ns 0.34 0.33b ns ns ns ns ns 0.29b ns ns -0.32 -0.33b ns ns ns ns CN 1 ns 0.89 ns ns ns ns -0.26b ns ns 0.33 0.31b ns ns 0.19b -0.23b -0.21b 0.29b ns ns ns -0.27b ns ns ns ns CW 1 ns 0.35 0.38 -0.32b ns ns ns ns ns ns ns ns ns ns ns ns -0.34b -0.31b -0.30b ns -0.35b -0.30b ns -0.34b FI 1 ns ns ns ns -0.24b ns 0.23b 0.30 0.28b ns ns ns -0.26b ns ns ns ns ns -0.31b ns ns ns ns BW 1 0.25 0.24 ns ns ns ns ns ns ns ns ns ns -0.24b ns -0.32b -0.24b -0.35b ns -0.26b -0.24b ns ns SN 1 -0.48 ns ns ns ns ns ns ns ns ns ns ns ns -0.25b -0.27b ns ns -0.26b -0.25b 0.29 ns SW 1 ns ns ns ns ns -0.24b ns ns ns ns ns 0.30b ns ns ns ns ns ns ns ns S 1 0.46b ns ns c ns -0.85 ns ns ns ns ns ns -0.20b ns ns -0.21b ns ns ns F 1 ns ns -0.33b ns ns -0.30b ns ns ns ns ns ns ns ns ns ns ns ns V 1 ns -0.19b 0.35b ns 0.85 -0.48b ns ns ns ns ns ns ns ns ns ns ns R 1 ns ns ns ns 0.78 -0.49 -0.25b ns ns ns ns -0.40 ns ns ns ns FP 1 ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns VP 1 ns ns ns ns ns ns ns ns ns ns ns ns ns ns S-F 1 ns ns ns ns ns ns ns ns ns 0.22b ns ns ns F-V 1 -0.45 ns ns ns ns ns ns ns ns ns ns ns V-R 1 -0.59 ns ns ns ns ns -0.30 ns ns ns ns TSS 1 0.31 ns ns ns ns 0.39 ns ns ns ns pH 1 -0.54 ns -0.25b ns ns -0.23b ns ns -0.30b TA 1 ns 0.23b ns ns ns ns ns ns BSAn 1 0.81 0.53 ns 0.84 0.83 -0.71 0.59b CI 1 0.68 ns 0.91 0.94 -0.65 0.49b TPI 1 0.62 0.60 0.62 -0.24b ns TC 1 ns ns 0.25b ns TAn 1 0.95 -0.84 0.61 EAn 1 -0.85 0.47b SM 1 -0.41

Bold and normal font indicate correlations significant at the 0.01 and 0.05 significance level, respectively; ns = not significant; b = correlation significant in two years; c = contradictory result. CN: Cluster number; CW: Mean cluster weight; FI: fertility index; BW: Mean berry weight (g);SN: Mean seed number; SW: Mean seed weight, S: sprouting, F: Flowering, V: Veraison, R: Ripening, FP: Flowering period, VP: Veraison period, S-F: sprouting-flowering interval, F-V: Flowering – veraison interval, V-R: Veraison-ripening interval, TSS: total soluble solids; TA: Total acidity; BSAn: Berry skin anthocyanins content; CI: Colour Index, TPI: Total polyphenol index; TC: Tannins content; TAn: Total anthocyanins; EAn: Extractable Anthocyanins; SM: seed maturity, EI: Extractability index. 75

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

Correlations observed in only 1 year, as well as discordant correlations over different years, were not considered.

4.3.4 Association of anthocyanins content with allelic composition for VvmybA

The 151 F1 plants together with the Graciano and Tempranillo progenitors were tested also for colour genotype with the CAPS marker 20D18CB9, which flanks the VvmybA genes (Walker et al. 2007). These genes co-segregate with the berry colour locus mapped in linkage group 2 (Riaz et al. 2004). An example of a gel used to score the marker is presented in Figure4-3a. Tempranillo and 74 genotypes (49%) carried one copy of the functional allele (heterozygous red); Graciano and 77 genotypes (51%) carried two copies of the functional allele (homozygous red). Thus, the PCR-established VvmybA genotypeadjusts to a 1:1 segregation ratio (χ 2 = 0.062, p> 0.5).

a) b)

M TE GR001 002 004 005 008 009

Berry skin anthocyanins content (mg/g) 30 homozygous hybrids 25 heterozygous hybrids

15 400bp % Hybrids 10

5 200bp 0

0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 >2.50

Figure 4-3 PCR analysis of VvmybA allele in the F1 population Graciano (GR) x Tempranillo (TE) and the distribution of 123 hybrids (year 2010) evaluated for skin anthocyanins content. a). Functional VvmybA allele (248bp) indicated homozygous red genotypes (GR, TG008, TE020 with 248/248/329bp) and heterozygous genotypes (TE, TG011, TG015, TG 023 and TG026 with 213/248/329bp). b). Distribution of 123 genotypes for berry skin anthocyanins content.

The LSD test for the average berry skin anthocyanin content, as well as total and extractable anthocyanins of the corresponding genotypic classes showed significant differences (p<0.01) between homozygous and heterozygous plants in the three years. In 2009 and 2010, 138 of the 151 F1 plants were analyzed for berry skin anthocyanins content. Although skin anthocyanins differed even within the same genotypic classes 76

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) (Fig4-3b), hybrids with two copies of the functional allele (homozygous) had significantly higher anthocyanin contents (average value of 1.74 mg/g) than hybrids with only one copy (heterozygous) (average of 0.86 mg/g). Eighty six percent of hybrids homozygous for the functional allele were distributed in the high anthocyanin content range (above 1.25 mg/g), in comparison with 9% of the heterozygous hybrids.

4.3.5 Principal Component and Cluster analysis and pre-selection of improved genotypes

A principal component analysis was conducted with the aim of elucidating which variables accounted for the phenotypic variability present in the F1 population. Eight components were extracted in all three years with PCA, and 78.3%, 79.5% and 80.9 % of the variance was explained in 2008, 2009 and 2010 respectively. The first principal component (explaining 22.4% of the variance) was strongly associated with the group of enological traits (CI, TPI, BSAn, EAn, TAn) and negatively correlated with a group of productivity traits (yield, CW, BW). The second component (explaining 14.4% of the variance) was negatively correlated with a group of phenology traits (V, R, VP and F-V) (Figure 4-4).

Figure 4-4 Principal component analysis. Distribution of variables on the score plot

The F1 population wasclassifiedbased on all traits in each year using only hybrids that had no missing values.The genotypes were grouped based on the most relevant 77

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) criteria for thewine grape breeding program in all analyzed years: production, anthocyanin content, and ripening time.

The groups obtainedbased on the 2010 evaluation, are shown in Figure 4-5. The 132 hybrids were grouped into two main clusters and seven subgroups, in agreement with the above mentioned criteria. Our aim was to preselect hybrids which not only showed high quality (low berry weight, low seed number, high berry skin anthocyanin content), but also relatively high production as well as an earlier maturation date for La Rioja, a Mediterranean climate region with pronounced continental influencein northern Spain.

Table 4-5 Groupings of genotypes obtained from squared Euclidian distance combined with the average linkage clustering methods based on the evaluation of agronomic and enological traits in a Graciano x Tempranillo population

R BSAn CW BW Cluster Subgroup Genotypes N (days) (mg/g) (g) (g)

149, 151, 1, 138, 145, 130, 135, 120, 121, 91, 94, 80, 1 I 17 234.5 1.6 67.1 1.3 84, 41, 72, 19, 25

II 141, 150, 20, 109, 110, 61, 67, 48, 51, 35, 45, 26 12 235.1 1.0 75.9 1.6

131, 146, 2, 125, 129, 114, 116, 104, 106, 99, 103, 96, III 26 233.8 1.4 137.6 1.5 98, 92, 93, 85, 87, 63, 65, 44, 62, 33, 43, 22, 24, 21

140, 148, 3, 132, 137, 127, 128, 113, 123, 107, 112, IV 22 235.0 0.7 160.8 1.6 101, 102, 68, 89, 47, 66, 34, 46, 10, 12, 4

136, 143, 5, 118, 133, 111, 115, 100, 105, 81, 82, 73, 2 V 25 210.2 0.8 129.2 1.6 79, 64, 69, 50, 56, 36, 37, 23, 31, 15, 18, 7, 13

VI 108, 144, 71, 78, 83 5 205.0 2.3 44.8 1.2

126, 139, 8, 122, 124, 117, 119, 90, 95, 75, 88, 59, 60, VII 25 206.6 1.5 135.7 1.5 57, 58 52, 55, 40, 42, 32, 39, 29, 30,9, 17

Bold font numbers correspond to pre-selected genotypes. R: ripening (days from March 1st); BSAn: berry skin anthocyanins; CW: cluster weight; BW: berry weight

Cluster 1 includes 77 genotypes with a longer harvesting time (average of 234 days after March 1st) compared to cluster 2 (average of 207 days). In this last cluster, three subgroups are present (Table 4-5; Figure 4-5). The first one, subgroup V (25 78

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) genotypes; 136 to 13), shows the lowest anthocyanins content (average of 0.8 mg/g) and the second one (subgroup VI including genotypes 108 to 83) presents the highest values (average of 2.3 mg/g) but displays the lowest cluster weight. Most of the selected hybrids fall in the third subgroup, subgroup VII including genotype numbers 126 to 17, which exhibit both moderate anthocyanins content and cluster weight. Late-maturing genotypes were classified in cluster 1, and among them genotypes 131 to 21 could be interesting for warmer climate regions based on their higher anthocyanins content and their optimal cluster and berry weight values. Moreover, those genotypes would be useful in a climate change scenario, where both average and maximum temperatures will increase.

On the basis of those criteria, we selected 14 hybrids from the population: genotypes 136, 115 and 13, belonging to subgroup V; and 8, 122, 124, 119, 57, 58 40, 39, 29, 30, and 17 from subgroup VII (Table 4-5). All 14 pre-selected genotypes were grafted on rootstock (R-110) and planted in different experimental fields to confirm their aptitude for wine grape breeding, and further research is focused on evaluating the quality of winesproduced and theirpotential for aging.

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

Figure 4-5 Cluster analysis of hybrids in a Graciano x Tempranillo wine grape population

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4.4 Discussion

Grapevine is a perennial crop from which a high-value product, wine is obtained. For that reason, it is also one of the most studied in the context of climate change (Fraga et al. 2013; Hall and Jones 2009). In this study we conducted an analysis of a progeny between two Spanish relevant wine varieties in order to evaluate the phenotypic variability present in the population and the possibility of selecting improved hybrids, which combine features of both parents, for a climate change scenario.

Significant differences between parental genotypes were found only for six traits. Although differences are essential when using inbred lines, in allogamous species such as grapevine, the presence of enough variability in the progeny is not conditioned by the differences in the parental values due to the heterozygous nature of grape varieties. All traits studied showed transgressive segregation indicating that recombinant allelic combinations were generated in the population.

The 27 traits evaluated in the present study exhibited continuous variation in the segregating progeny, indicating a polygenic quantitative nature as previously reported (Fanizza et al. 2005; Liang et al. 2012; Costantini et al. 2008; Bayo-Canha et al. 2012; Liang et al. 2011; Liu et al. 2007). Distributions of several parameters evaluated in the progeny fitted an additive model of inheritance, among them mean seed number per berry, seed weight, veraison and ripening date, flowering- veraison interval and veraison-ripening interval. Quality characteristics such as berry weight, ripening date, acidity, total soluble solids, and anthocyanins content were reported to be under strong additive genetic control (Wei et al. 2002 and Liang et al. 2009). In our population, the total acidity and total polyphenols and tannins content were intermediate between the parental values but the mean total soluble solids and anthocyanins content (skin, total and extractable) were lower than the values of both progenitors. This fact is due to the late-ripening genotypes being unable to reach maturity. However, 20-30% of the progeny showed high total and extractable anthocyanins content (above 1000 81

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) mg/Land 600 mg/L, respectively). Production-related variables that affect wine quality such as lower cluster weight and fertility index, showed a dominant model of inheritance towards low values, in agreement with the results of Bayo-Canha et al. 2012. The phenotypic variability observed in our population confirms that it is feasible to select advanced breeding materials in an intra-specific cross with the final aim of obtaining new improved varieties.

Another issue relevant for selection is the consistency of genotype performance over years. All traits showed a significant year effect except those related to anthocyanins content. Despite the year effect observed, phenotypic correlations between years were moderate for fruit yield components (yield per vine, cluster weight, number of clusters per vine, and berry weight (0.38 to 0.70) (Table 4-4) and higher than those reported by Fanizza et al. 2005. Higher values (0.43 to 0.90) were observed for enological parameters such as total acidity and both total and skin anthocyanins contents (Table 4-4).

Therefore, the genotype x year interaction affects moderately the yield components and much less the anthocyanins content. This was confirmed by the QTL analysis carried out by Fanizza et al. 2005, that reported no stable QTLs across years for each of the components of fruit yield (yield, cluster number, cluster weight, number of berries per cluster and berry weight). Poor fruit-set caused by environmental factors such as hail, has been reported by other authors as the main cause of yield variability among years (Mullins et al. 1992) and may be, besides juvenility, in the basis of the much lower correlations found in our study between years 2008 and 2010.

Phenology-related traits and indices such as flowering time, flowering period and veraison period showed low phenotypic correlations between years and very low repeatabilities confirming the results of Costantini et al. (2008) that reported a highly significant year effect for all phenological traits and no correlations between years for flowering time. However, moderate correlations were observed in our study for

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) flowering- veraison interval and veraison-ripening interval (0.45 to 0.63, respectively). The same authors identified QTLs for phenology-related traits in three chromosomic regions and a clear association, both at the correlation coefficient and QTL localization level, between flowering-veraison interval and veraison time and a less clear between veraison- ripening interval and flowering or veraison time. The onset of the different developmental stages is strongly influenced by environmental factors and particularly by temperature (Cleland et al. 2007) and selection for both veraison and ripening time would be more effective.

In order to obtain a balanced wine, it is essential to have a relatively long veraison-ripening interval, which allows sugars to accumulate to optimal levels, maintaining acid structure, and producing an optimum profile for polyphenols and flavor and aroma compounds (Jackson and Lombard 1993; Jones et al. 2005). The results in this work indicate that long veraison-ripening intervals reduce the sugar and tannin content (r = -0.59 and -0.30, respectively), meaning that a subset of the progeny is unable to reach maturity, neither technical, nor phenolic, in agreement with the seed maturity values observed. Therefore in our conditions, a continental Mediterranean climate region, early genotypes with a long veraison-ripening interval would be now the most promising. However in the context of climate change with higher temperatures, late-ripening genotypes such as those useful in Rioja Baja and Southern Spain would be more desirable, as they would show better quality attributes. In our population 19 genotypes (14%) showed earlier ripening time than Tempranillo and 57% were later than Graciano. Besides, 30% of the progeny showed a V-R interval longer than Graciano, indicating that it is possible to obtain offspring with better adaptation to La Rioja climatic conditions in the future.

An estimate of the correlation between traits is of fundamental importance in breeding programs, especially if selected traits have negative correlations, low heritability or are difficult to quantify (Viana et al. 2011b). Moreover, indirect selection for low heritability traits based on other correlated and highly heritableis particularly relevant in woody species with long generation times. 83

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) Significant correlations among different groups of traits were observed in our population, but most coefficients were low and similar to those reported for table and wine-grape segregating populations (Wei et al. 2002; Fanizza et al. 2005; Costantini et al. 2008; Viana et al. 2011a, 2011b; Bayo-Canha et al. 2012).

Low seed number is associated with fruit quality in apple and kiwi, by reducing the growth of the fruits and the fruit development time (Lai et al. 1989). This consideration arises from the role of seeds as both sinks and sources of hormones, and therefore inductors of increased cell division and expansion, resulting in larger fruits. In grape, seed number has been related to fertility and maturation rate, and productive clones are usually associated with a larger number of seeds per berry. A positive correlation between berry weight and seed number (Doligez et al. 2002) and a negative correlation with seedlessness was reported (Wei et al 2002). In this work, the association between mean berry weight and mean seed number per berry was weak (r=0.25), in consonance with the findings reported by Coombe and Hale (1973) in table grape and Viana et al (2012) in wine grape but contrary to Costantini et al (2008) in table grape. These contradictory results could be explained by the existence of at least 3 minor independent QTLs for seed number and for berry weight reported by Doligez et al. 2002. Furthermore, seed number was negatively correlated with anthocyanins content (skin, total and extractable) as well as colour intensity, which are considered maturity parameters for wine grapes. However no correlation was found between veraison-ripening interval and seed number. Given the correlations detected in this research, seed number could be a better parameter than seed weight for indirect selection of berry quality.

Climate, soil, cultivation and biology are some of the most relevant factors affecting synthesis and concentration of phenols in berries (Downey et al. 2006). Nadal (2010) reported that when analyzing the phenolic maturity on the whole berry, the total polyphenol content in grapes gave a better approach than anthocyanins for predicting the polyphenol extraction and content in wines. In our study, total polyphenols exhibited moderate correlations (0.53 to 0.62) with anthocyanins 84

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) contents, colour intensity showing the highest coefficient (0.68).

One way to adapt grapevine to climate change is to breed new varieties. This study found that crossing Graciano x Tempranillo generated a large phenotypic variability which may be useful for the selection of new improved genotypes. Hybrids could be selected from the segregating population with a ripening date earlier than Tempranillo or later than Graciano. The selection of prime hybrids should be based also on low cluster weight, high sugar content, moderate acid content and high anthocyanins content and extractability. The CAPS VvmybA marker genotype would be a useful marker in indirect selection for anthocyanins content as reported also by Bayo-Canha et al. (2012).The value of the 14 pre-selected hybrids as grafted genotypes should be further evaluated for berry quality and production in different wine regions. In addition, micro-vinifications should be made to assess the organoleptic features of the produced wines before considerating these genotypes for high quality production systems.

Our study confirmed that it is feasible to select advanced breeding materials in an intra-specific cross with the final aim of obtaining new improved varieties more adapted to future climate conditions.

4.5 Conclusions

The results of CAPs marker analysis for the VvmybA genotypeshowed that the number of homozygous and heterozygous genotypes for the functional colour allele adjusted to a 1:1 segregation ratio, and that homozygous genotypes had significantly higher anthocyanins content. 85

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) Principal component analysis found eight variables that contributed up to 80% of the phenotypic variability present in the population. Seven groups of hybrids were distinguished based on ripening time, cluster weight, berry weight and anthocyanins content by cluster analysis; and fourteen genotypes were pre-selected for further research.

4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.)

4.6 References

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Baggiolini M (1952) Les stades repères dans le développement annuel de la vigne et leur utilisation pratique. Rev Rom Agric Vitic 8:4-6

Bergqvist J, Dokoozlian N, Ebisuda N (2001) Sunlight exposure and temperature effects on berry growth and composition of Cabernet Sauvignon and Grenache in the central San Joaquin Valley of California. Am J Enol Viticult 52:1-7

Bowers JE, Dangl GS, Vignani R, Meredith CP (1996) Isolation and characterization of the new polymorphic simple sequence repeat loci in grape (Vitis vinifera L.). Genome 45:1142-1149

Bowers JE, Dangl GS, Meredith CP (1999) Development and characterization of additional microsatellite DNA markers for grape. Am J Enol Viticult 50:243-246

Buttrose MS, Hale CR, Kliewer WM (1971) Effect of temperature on the composition of ‘Cabernet-Sauvignon’ berries. Am J Enol Vitic22:71-75

Cacho J, Fernández P, Ferreira V, Castells JE (1992) Evolution ofﬁve anthocyanidin -3-glucosides in the skin of the Tempranillo, Moristel, and Garnacha grape varieties and ﬂuencein of climatological variables. Am J Enol Vitic43:244-248

Chomé P, Sotés V, Benayas F, Cayuela M, Hernández M, Sáenz F, Ortiz J, Rodríguez I, Chaves J (2006) Variedades de Vid. Registro de Variedades Comerciales. MAPA, Madrid.

Clark JR (2010) Eastern United States table grape breeding. J Am Pomol Soc 64:72-77

Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22: 357-365

Coombe BG and Hale CR (1973). The hormone content of ripening grape berries and the effect of growth substance treatments. Plant Physiol 51:629-634.

Coombe BG and Iland PG (2004) Grape berry development and winegrape quality. In: Dry PR and Coombe BG (eds) Viticulture volume 1-Resources. Winetitles, Adelaide, pp: 210-248.

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4 Segregation and Associations of Enological traits and Agronomical traits in Graciano x Tempranillo wine grape progeny (Vitis vinifera L.) Duchêne E and Schneider C (2005) Grapevine and climatic changes: a glance at the situation in Alsace. Agron Sustain Dev 25:93-99

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5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

5ANTHOCYANIN COMPOSITION OF A

F1POPULATION DERIVED FROM GRACIANO X TEMPRANILLO

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

5 Anthocyanin composition of a F1population derived from Graciano x Tempranillo

Abstract

Grape berry skin anthocyanins content contribute to wine colour and polyphenol content, and vary with variety, climatic conditions, cultivation technique and maturity.

In this work, the anthocyanins profile of a 151 hybrid population obtained by crossing Graciano and Tempranillo was studied fortwo growing seasons (2009 and 2010)with HPLC-MS.

Fifteen monoglucosideanthocyaninswere detected with HPLC-MS, including two unidentified compounds. The concentration of 13 identified anthocyanins and the percentage of non acylated, acetyl and coumarylderivatives anthocyanins were analyzed to understand the inheritance of the anthocyanins profile in the population. Transgressive segregation was observed for lower values for all anthocyanins except peonidin that showed intermediate values.

Significant year effect was detected for all anthocyaninsindicating environmental influence but the ratios of Dp/Mv and Pn/Mvwere consistent over the years confirming that they can be considered as potential varietal markers.

Two main principal components (PC) were identifiedthat explained 94.8% and 96.8% of the total variance in 2009 and 2010 respectively. Overall more than 95% of the total variance observed can be explained by fiveanthocyanins.

Key words: Anthocyanin profile; inheritance; hybrids; colour

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

5.1 Introduction

Grapevine is considered one of the main species cultivated by humans, not only for being the most widespread fruit in the world but also by the global economic importance of fruits and by-products on the market. In Spain, grapevines are even of greater importance, as it is the country with the largest area devoted to grapevine cultivationabout 1,018 thousand hectares (OIV 2013), followed by France and Italy. It should also be highlightedthat almost 98.11% of Spanish vineyards are allocated towinemaking (Annual Statistics, 2009, MAPA, Spain).

Colour is one of the main parameters used to assess the quality of wine, especially of red wine (Sáenz-Navajas et al. 2011). Anthocyaninsare the main compounds responsible for thecolourof red berriesand red wines. They are located in vacuoles or specialized structures in the skin of grape berries(Pazmino-Duran et al. 2009). The type and content of berry skin anthocyaninsare highly variable, depending on the grape variety and maturity.Anthocyanincomposition has been broadly studied in order to characterize the variety or establish the origin of grapes(Ortega-Regules et al. 2006; Figueiredo-González et al. 2012).

Anthocyanins Abbreviation 3’(R1) 5’(R2)

Pelargonidin- 3-O-Glucoside Pg3-O-Glu H H Delphnidin- 3-O-Glucoside Dp 3-O-Glu OH OH Cyanidin- 3-O-Glucoside Cy 3-O-Glu OH H Petunidin- 3-O-Glucoside Pt 3-O-Glu OMe OH Peonidin- 3-O-Glucoside Pn 3-O-Glu OMe H Malvinidin- 3-O-Glucoside Mv 3-O-Glu OMe OMe

Figure 5-1The structure of anthocyanins in Vitis (Annika-Nyman 2001; Marquez et al. 2012)

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

Chemically, anthocyanins derive fromanthocyanidins, which belong to the group of flavonoids. Specially, anthocyanins are a group of flavonoids with cationflavylium structure. Then, anthocyanins are derivatives of polyhydroxy and polymethoxy phenyl - 2 - benzopyrilium or flavylium salt. The chemical structure of anthocyanidins found in grapesisshown in Figure 5-1.Based on their particular chemical structure and pH of the solution in which they are present, they appear in different colours.

There are several hundred known anthocyanins and anthocyanin-derived pigmentswithdiverse structure and properties that have been detected in different wines. Anthocyanins can be differentiated by the number of hydroxyl groups present in the molecule, their degree of methylation, the nature and number of sugars bound to the molecule, the position of the link and the type and number of aliphatic or aromatic acids bound to the sugar present in the molecule. There are minor variations of the red type depending on the specific anthocyanin, but considering the high prevalence of malvidin derivatives, this is the main anthocyanin contributing to colour. The derivatives of malvidin and petunidin are purple in colour, whereas derivatives of cyanidin and peonidin are reddish and the tone of delphinidin is bluish-red.

Each anthocyanidin can be acetylated or glycosylated by acids and sugars in different positions. Because of this, the number of anthocyanins is between 15 and 20 times greater than the number of anthocyanidins (Mazza et al. 1995; Riberau-Gayon, 1966). Most anthocyanins have a glycosidic linkage at position 3, binding to a sugar molecule. The monosaccharide always sets binding hydroxyl via its carbon 1, as shown in Figure 5-1. The glycosylation of the hydroxyl in position 3 is very important for the stability of the compound. This is the way in whichanthocyanins are present in Vitisvinifera.In the formation of these complexes is critical the activity of the enzymes flavonoid 3'-hydroxylase (F3'H) and flavonoid 3', 5'-hydroxylase(F3'5'H), since these are responsible for carrying out the hydroxylation of the ring B. Thus the relative activity of these enzymes with their substrate specificity determines the composition of anthocyanins and the differences in the ratio of dihydroxylatedanthocyanins and trihydroxylatedanthocyanins (Gómez-Plaza et al. 2008). Genes associated with these 95

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

enzymes have been mapped in linkage groups 6 and 17 of the grapevine map, respectively (Castellarin et al. 2006).

Several genes involved in anthocyanin biosynthesishave been identified in grapevine,by confirming that when the expression of these genes is impaired by a retrotransposon Gret1, it causes a loss of colour and the berry turns white (Kobayashi et al 2002 and 2005;Lijavetzky et al. 2006; Walker et al. 2008). The VvmybAgenes have been mapped on chromosome 2 in the genetic map of Vitisvinifera. These results are very relevant for wine research and selection of new varieties. Knowledge of the determination of grape colour can facilitate the choice of crosses between cultivars to obtain new varieties with specific colours.

HPLC analysis allows not only recognizing the presence of these compounds in grape berry skin, but also performing a quantitative analysis, in order to compare their concentrations and understand their importance in the manifestation of berry colour. The numerous studies performed by HPLC for grapevine varieties and their anthocyanin profiles show that there are differences between varieties (Burns et al. 2002; Núñez et al.2004; Liang et al.2009; Cook-Papini et al. 2010). As a consequence, anthocyanin profiles or some relationshipbetween some of them can be used as a valid tool for the differentiation of grapes or wine of different varieties (García et al. 2003; Núñez et al. 2004). Genetically, the presence or absence of anthocyanins in grape skin is inheritedas a qualitative character controlled by oligogenes (Luo and He 2004). However,anthocyanin content is a quantitative trait controlled bypolygenes(Liang et al. 2009). Although the concentrations of anthocyanin compounds vary with variety, maturity, climate conditions (temperature, rainfall), the production region and cultivation techniques (Nadal and Arola 1995; Guidoni et al. 2008), it is commonly accepted that the anthocyanin profile of varieties is genetically inherited (Regules Ortega et al., 2006) . Only few studies have focused on assessing the skin anthocyanin content of the hybrids resulting from crosses between wine varieties, such as Monastrell x Cabernet Sauvignon (Gomez-Plaza et al. 2008) andBarbera x Nebbiolo 96

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di Dronero (Cook-Papini et al. 2010). Tempranillo is the most widely grown red grape variety in Spain and it is cultivated in 75% of the Denominación de Origen Calificada (DOCa) Rioja. It is considered native to Rioja and it is the wine region’s most typical grape being the origin of the identity of its wines and one of the great grape varieties in the world. From an enological viewpoint it is very versatile as is capable of producing wines that can withstand long ageing periods, with a good balance of alcohol content, colour and acidity, and an honest, smooth, fruity mouthfeel that turns velvety as it ages. Graciano is an indigenous grape variety, less known as its cultivation is very limited in other areas. Shown to be an excellent complement to Tempranillo in the ageing process, its planted surface area has increased significantly in the last few years. It offers wines with a marked acidity and polyphenolic content, ideal for ageing, with a unique aroma that is much more intense than those of other varieties in Rioja. Recent studies showed that co-pigmentation and self-association processes are improved with Graciano grapes added to Tempranillo and that pigment extraction and retention in Tempranillo wines is increased by the incorporation of the Graciano variety during the pre-fermentative maceration step (García-Marino et al. 2010).

The objective of this work was to analyse the anthocyanin fingerprint of a population of interspecific hybrids obtained from thesetwo grape varieties (Tempranillo and Graciano) during two growing seasons. In this work, we aimed to determine which anthocyanins or anthocyanin groups were responsible for the variability present in the F1 population and to evaluate the relative contribution of environmental and genetic factors in the amount and composition of anthocyanins in two consecutive vintages.

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

5.2 Material and Methods

5.2.1 Plant material and microsatellite analyses

An intraspecific hybrid population (163 genotypes) derived from controlled crosses between two Spanish wine grape varieties, Tempranillo and Graciano, was used for this study. The individual hybrids (one plant per genotype) have been grown on their own roots since 2004 inVarea (Logroño, La Rioja, Spain) on a sandy-loam soil, in East-West orientation with 3 m spacing between rows and 1 m between plants and trained to double Royat cordon. Standard irrigation, fertilization and plant protection practices for La Rioja region were performed. The plants first flowered and fruited in 2007.

Leaf samples of Tempranillo, Graciano and all individuals of the progeny were collected in the field, frozen in liquid nitrogen and transferred to be stored at -80ºC. DNA was extracted from 200 mg frozen leaves using a DNAeasy Plant Mini kit (Quiagen, Germany) following the manufacturer´s protocol. The population was genotyped for 5 polymorphic SSRs markers: VVS2 (Thomas and Scott 1993), VrZAG62, VrZAG79 (Sefc et al. 1999), VVMD6, VVMD34 (Bowers et al. 1996; 1999), in order to discard individuals resulting from self- pollinations and foreign pollen sources, resulting in a final population of 151 plants. The microsatellite analysis was conducted following the methods described by Martín et al. (2003). The fragments were separated on a LICOR 4200 DNA Analyzer (LI-COR, Inc., USA) on 8% denaturing polyacrylamide gels and sized with SAGA software (LI-COR, Inc., USA).

5.2.2 Berry sampling and anthocyanins extraction

Berry samples were harvested from the vineyard at ripening in two consecutive years, 2009 and 2010, corresponding to the third and fourth year of fruiting of the population, respectively. Ripening time was established as the date when random grapes picked from the top, medium and bottom of the clusters reached 13°Baumé.

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

Only 123 and 132 of the population bore fruit in 2009 and 2010 respectively, due to hail damage during flowering and bird damage during veraison-ripening.

Berries for anthocyanins analysis were sampled at random from different positions (shoulders, tip, and the external and internal regions) within the cluster, to avoid effects due to sun exposition. Overall, three replicates of 100 berries each, were collected per genotype as previously described, immediately weighted, frozen and stored at -20° C until processing.

For grape skin anthocyanin determinations, three representative samples of 20 berries each were weighed with a precision balance, manually peeled with a scalpel and the residual pulp/flesh was removed carefully. Skin anthocyanins extraction was performed in 40 mL acidified ethanol solution (ethanol: HCl= 4:1, v/v) in hermetically closed dark bottles, and placed on a stirring plate (Rotabit, J.P. Selecta, S.A. Spain) at 120 rpm at room temperature for 48 hours (Cáceres et al. 2012; Nadal 2010).

Once the extraction of anthocyanins was finished, total anthocyanins of skin will be quantified with spectrophotometry (Agilent 8453 with Hewlett Packard UV-VisibleChemstation) at 520 nm. The total anthocyanin concentration was obtained from the following equation:

Total anthocyanins content (mg/g) = 35.58 x Abs 520nm /weight of 20 berries (g)

Skin extract samples were diluted 1:10 in formic acid (Formic Acid:ddH2O= 42.5:1000, v/v). Subsequently, samples were centrifuged at 12,000rpm for 5 minutes at room temperature using an Eppendorf centrifuge (Hamburg, Germany); and the supernatant was collected and filtered before analysis through a 0.45 µm pore size membrane. The identification and quantification of anthocyanins from extracts above were analyzed by HPLC using the method described by Sáenz-Navajas et al. (2011).

Additionally, for each genotype, yield per vine was recorded and 100 random berries were weighed and crushed for determining sugar concentration of musts by refractometry (Atago Master-Baumé refractometer (Atago, Tokyo, Japan)) and total 99

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo acidity by potentiometry with a TitroMatic 1S-1B(Crison, Barcelona).

5.2.3 Reagents, standards and chromatographic analysis

All chemicals used were of analytical reagent grade. Ultrapure water was obtained from a Milli-Q purification system (Millipore, Molsheim, France). Formic acid (analytical grade), acetonitrile, and ethanol (HPLC grade) were purchased from Scharlab (Barcelona, Spain). Malvidin chloride standard was supplied by Sigma-Aldrich (St Louis, MO, USA).

Analysis was carried out on a modular Agilent 1200 liquid chromatograph (Waldbronn, Germany), equipped with a G1329A automatic injector, a G1311A HPLC quaternary pump, an online G1379A degasser, a G1316A oven, a G1315B photodiode array detector, and Agilent Chemstation software The separation of anthocyanins was accomplished on a Nucleosil 120-C18 column (250 x 46 mm, 5μm) from Teknokroma (San Cugat del Valles, Spain). The column temperature was maintained at 40ºC. The injection volume was 30 μL with an isocratic flow rate of 1 mL/min and the total run time was 55 min. Chromatographic separation was carried out following the method described by Cacho et al. (1992) with some gradient modifications. Solvents were (A) water/formic acid (95:5 v/v) and (B) acetonitrile establishing the following gradient: 0 min 10 % B, at 10 min 15% B, at 17 min 19% B, at 34 min 40% B, at 35 min 50% B, at 37 min 100% B, at 37-44 min isocratic 100% B, at 46 min 10% B, at 46-55 min isocratic 10% B. The chromatograms (Figure 5-2) were recorded at 520 nm and the UV spectra were collected from 200 to 650 nm. Identification of the component peaks was performed by the UV/Vis, MS and MS/MS spectra and retention times of the available standard. The HPLC system was coupled to a micrOTOF-Q High-Resolution Mass Spectrometer (Bruker Daltonik, Bremen, Germany) equipped with an Apollo II ESI/APCI multimode source. All mass spectrometry data were acquired in positive ionization profile mode. For quantification, calibration curves ranging from 0.01 mg/Lto 1.0 mg/L for the lower concentration of compounds and from 1.0 to 100 mg/L for the higher concentration of

100

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compounds were obtained from external standard of oeninmalvidinchloride≥ 90% (Sigma-Aldrich, St Louis, MO, USA). The calibration curves obtained were: y = 111.61x + 50.86 (lower concentration) and y = 95.82x + 73.96 (higher concentration). Once the areas of all compounds were obtained for all samples, the concentration was calculated based on the calibration curves and the dilution factor already mentioned. All analyses were carried out in duplicate. The results are expressed in mean values. The concentration of each anthocyanins component was expressed as malvidin mg/Kg of fresh berry.

5.2.4 Statistical data analysis

Descriptive statistics of the anthocyaninsconcentrations were used for the statistical analysis. Mean values and ranges were estimated for the F1population and the parents.

All the statistical analyses were performed with SPSS 14.0 and STATGRAPHICS

16.0.

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5.3 Results and discussion:

5.3.1 Identification of anthocyanin compounds

The HPLC-DAD chromatograms of skin extracts from Graciano, Tempranillo and the intraspecific hybrid population were recorded at 520 nm and different anthocyanins were identified corresponding to the different absorption maximum registered. In total, 15anthocyanins were detected in the berry skin according to data obtained from MS and HPLC profiles which included the peak number, retention time, molecular ions, and UV-Vis spectra maximum absorption (Table 5-1). On the basis of their chemical structure, 13 identified anthocyanins were classified into five groups: delphinidin (Dp), cyanidin (Cy), petunidin (Pt), peonidin (Pn) and malvidin (Mv); corresponding to their glucoside, acetylglucoside and p-coumaroylglucoside derivatives and two unidentified compounds.

Anthocyanins have inherent positive charge, so they have maximum sensitivity in the positive modes of the mass spectrometer, and their detection and identification can be done by comparing fragmentation data of these anthocyanins from previous literature reports (Ivanova et al. 2011; Alcalde-Eon et al. 2006).Maximum absorption, molecular and fragment ions considered for the assignment of anthocyanins peaks are shown in Table 5-1. Compounds corresponding to derivatives of the fivecommon aglycones in grapes from V. viniferaL, i.e., delphinidin (m/z 303), cyanidin (m/z 287), petunidin (m/z 317), peonidin (m/z 301) and malvidin (m/z 331), were identified by mass spectrometry. The glucoside, acetylglucoside and p-coumaroylglucoside derivatives had similar fragmentation patterns containing two signals, the original M+ molecular ion, and the fragments [M−162] +, [M−204] + and [M−308] + which are the result of elimination of glucose, acetylglucose and p-coumaroylglucose residues, respectively (Baldi et al. 1995; Vivar-Quintana et al. 2002).

Peak 4, that by MS analysis revealed an [M+] ion at m/z 655 and fragment at m/z 303, could not be assigned to any anthocyanin and remains unidentified.

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The chromatographic features of peak 7, a [M+] ion at m/z 561 and an aglycone fragment at m/z 399, corresponding to loss of acetylglucosidemoiety with m/z 204, are consistent with those of Vitisin A. Vitisins are adducts resulting from the cycloaddition of a molecule of pyruvic acid to one of anthocyanin, and are usually found in wines (Bakker et al. 1997). In previous reports this molecular and fragment ions had been found only in wines and in musts of dried grapes (Monagas et al. 2003; Marquez et al. 2012).

Table 5-1 Identification of anthocyanins in the berry skins of Graciano x Tempranillo populations

Rt [M+]( Fragments Peak λmax(nm) Compound (min) m/z) (m/z) 1 13.61 465 303 276, 526 Delphinidin-3-O-glucoside 2 17.80 449 287 280, 518 Cyanidin-3-O-glucoside 3 19.34 479 317 278, 527 Petunidin-3-O-glucoside 4 21.28 655 303 279, 533 Unidentified 5 22.39 463 301 276, 516 Peonidin-3-O-glucoside 6 23.90 493 331 277, 527 Malvidin-3-O-glucoside 7 25.81 561 399 236, 507 Unidentified 8 27.31 507 303, 465 277, 526 Delphinidin-3-O(6´-O-acetyl)glucoside 9 30.02 521 317, 479 279, 520 Petunidin-3-O(6´-O-acetyl)glucoside 10 30.85 611 303, 465 279, 310, 529 Delphinidin-3-O(6´-O-p-coumaroyl)glucoside 11 31.53 535 331, 493 278, 530 Malvidin-3-O(6´-O-acetyl)glucoside 12 32.78 595 287, 449 279, 310, 521 Cyanidin-3-O(6´-O-p-coumaroyl)glucoside 13 33.28 625 317, 479 279, 310, 530 Petunidin-3-O(6´-O-p-coumaroyl)glucoside 14 35.20 609 301, 463 281, 312, 518 Peonidin-3-O(6´-O-p-coumaroyl)glucoside 15 35,53 639 331, 493 284, 309, 533 Malvidin-3-O(6´-O-p-coumaroyl)glucoside

The elution order of the anthocyanidins was monoglucoside

HPLC chromatograms of Graciano and Tempranillo skin extracts (2010) are

103

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo shown in Figure 5-2.

6 7 8 9 1110 14 15

300,00 6 4,00

13 mUA

1,50 200,00 22,00 27,00 32,00 37,00 Time (min) mUA

5 11 100,00

1 3 10 4 2 7 8 15 9 1413 0,00 5,00 25,00 45,00 Time (min)

Figure 5-2 HPLC chromatograms of Tempranillo (above) and Graciano (below) skin extracts showing all detected anthocyanins in 2010

In Vitis species, 6 types of anthocyanins have been described (Annika Nyman2001; Marquez et al 2012), besides the 5 found in our study, the presence of pelargonidin-3-glucoside (Pg) was observed in Concord(V. labrusca), and in Rubi Red and Salvador grape juice (V. vinifera x V. rupestris)(Wang et al. 2003), and in

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5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

Vitisviniferateinturier grapes (GarnachaTintorera) as reported by Castillo-Muñoz et al. 2009. The presence of Pg was confirmed in the skin of CabernetSauvignon and Pinot Noir (He et al. 2010) and Merlot and Syrah (Marquez et al. 2012), but only in trace amounts in comparison with other anthocyanins. In our research, no Pg was identified.

5.3.2 Physicochemical characterization of grape samples

The physicochemical data of the grapes of all genotypes were studied at the moment of harvest during two growing seasons (Table 5-2). Harvest time was established when random sampled berries reached 13°Baumé.

Table 5-2Analytical parameters of grapes of the F1population and parents in 2009 and 2010

F1 population TE GR Traits Year N Range Mean SD Mean±SD Mean±SD

Berry weight (g) 2009 123 0.86-2.78 1.66 0.37 2.24±0.14 1.72±0.07 2010 131 0.71-2.49 1.50 0.33 2.09 ±0.03 1.32±0.06 Total soluble solids (ºBaumé) 2009 123 10.00-15.60 12.77 1.06 13.50±0.53 12.99±0.48 2010 131 8.40-14.20 12.15 1.22 13.04±0.38 12.97±0.31 Total acidity (g/Ltartaric acid) 2009 123 2.90-10.40 5.92 1.23 5.15±0.55 7.01±0.25 2010 131 2.95-9.83 6.13 1.34 5.38±0.46 7.64±0.29 pH 2009 123 3.31-4.07 3.61 0.16 3.74±0.04 3.38±0.06 2010 131 3.07-4.24 3.49 0.19 3.60±0.12 3.47±0.07 Skin anthocyaninscontent (mg/g) 2009 123 0.31-3.50 1.43 0.68 1.32±0.07 1.38±0.17

2010 131 0.41-2.60 1.21 0.49 1.49±0.04 2.23±0.19

Berry weight ranged widely from 0.71 to 2.78g in the hybrid population. Total soluble solids (expressed as degree Baumé) of grape musts ranged from 8.4 to 15.6 and were usually lower in 2010 due to lower temperatures and rainy conditions. Although harvest time was set at 13ºBaumé, there were genotypes in both years that could not reach maturity in our conditions. Total acidity ranged from 2.9 to 10.4 and mean pH was around 3.5.

Significant differences were detected between mean berry weight, sugar content, pH and total berry skin anthocyanins content with the ANOVA analysis and Kruskal-Wallis analysis. All studied characters showed continuous variationtypical of 105

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo quantitative traits.

5.3.3 Anthocyanin content and fingerprint of parents and F1 population

All genotypes in the progeny had red coloured fruits, indicating that the F1 population does not segregate for that character. This result was expected as Graciano is homozygous red for the colour locus, meaning it has two copies of the colour allele (Song et al. 2014). Former studies had pointed to the heterozygosity of Tempranillo (Martinez Toda et al. 2004) for the colour trait, and this fact was confirmed by the segregation observed in other crosses with Tempranillo (white genotypes were segregated from Cabernet Sauvignon x Tempranillo, data not published). More recently Ibáñez et al. (2012) identified Albillo Mayor (white grape) and Benedicto (red colour grape) as the parents of Tempranillo, validating the observations mentioned above. Consequently, the total anthocyanin content of the grape skins of Graciano (2.30 ± 0.10 mg/g) were significantly higher than those of Tempranillo (1.48 ± 0.13mg/g) (p < 0.01).

There were significant differences among hybrids and years in total anthocyanins, but in general total anthocyanin contents were higher in 2009 than in 2010. These differences can be attributed to the different growing conditions, as in 2009 growing conditions were hotter and drier compared to 2010.

To evaluate the anthocyanin fingerprint of genotypes and elucidate their inheritance, 13 identified anthocyanins were considered: the monoglucosides of delphinidin (Dp), cyanidin (Cy), petunidin (Pt), peonidin (Pn) and malvidin (Mv); the acetylated derivatives of delphinidin (DpAc), petunidin (PtAc) and malvidin (MvAc) and the p-coumarylated derivatives (DpCum, CyCum, PtCum, PnCum, and Mv Cum).

Concentrations and the pattern ofanthocyanins for Tempranillo, Graciano and the

F1 population, expressed as malvidin-3-O-glucoside, are shown in table 5-3. The identified anthocyanins are identical in the two parents and the population as expected, but quantitative differences were found in their anthocyanic profiles.

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Total anthocyanin contents ranged in the hybrid population from 93.6 to 1583.4 (2009) and 78.5 to 1035.5 mg/kg berry in 2010. The most abundant anthocyanin compounds in grape skins were malvidin derivatives, which represented from 67.7% and 62.8% (Graciano) to 62.0% and 60.4% (Tempranillo)of the total anthocyanins in 2009 and 2010, respectively. This group of compounds accounted in average for 60.0% and 62.8% of the total anthocyanins content in the F1 population in 2009 and 2010, respectively. Malvidin 3-O-glucoside was also the main anthocyanin, independent of the analyzed genotype, and concentrations in the F1 population were intermediate between Graciano and Tempranillo values.

Peonidin derivatives represented the second group of the total anthocyanins in Graciano (14.7%in 2009 and 17.8% in 2010) and in the F1 population (16.9% in 2009 and 19.1% in 2010). However, in Tempranillo they only accounted in average for 4.1% and 4.8% of the total anthocyanins content, being much lower than delphinidin or petunidin. Peonidin-3-O-glucoside is the second most important anthocyanin in Graciano and its relative concentration in the hybrid population was even higher than in Graciano, indicating transgressive segregation. These data agree with previous studies conducted with both parental varieties (Núñez et al. 2004).

Peonidin-coumarylcontent of Graciano was higher than Tempranillo, and the F1 population showed much lower values than the parents. Moreover,Graciano had a high value for bothmalvidin-coumaryland delphinidin-coumaryl derivatives. These results coincide with previous reports (Núñez et al. 2004).The high level of peonidin could be due to a high activity of flavonoid-3´-hydroxylase (F3´H) which control the formation of the di-hydrolatedanthocyanins (cyanidin and peonidin), and high activity of methyl-transferases that convert cyanidin into peonidin in this variety (Castellarin et al. 2006). Furthermore, the content of cyanidin-3-glucoside and acetylated anthocyanin content were reported as a useful marker for differentiation between Cabernet Sauvignon, Merlot and Tempranillo varieties (Lorrain et al. 2011)

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Table 5-3 Characterization (mg/kg of fresh berry) of the anthocyanins compounds for F1 population, Graciano and Tempranillo in two years

F1 Population Graciano Tempranillo Anthocyanins 2009 2009 2010 2010 2009 2010 2009 2010 Mean Range Mean Range Mean±SD Mean±SD Mean±SD Mean±SD Malvidin-3-O-glucoside 261.8 28.5-793.2 279.1 43.5-687.5 984±69.8 753±120.9 375±11.6 394±91.1 Malvidin-3-O(6´-O-acetyl) 52.8 0.51-233.2 10.7 0.00-70.9 50.1±0.19 47.7±17.3 16.7±0.03 16.4±11.5 glucoside Malvidin-3-O (6´-O-p-coumaroyl) 0.37 0.00-3.39 9.0 0.17-36.4 106±1.24 29.7±22.3 95.8±5.4 25.9±17.7 glucoside Σ Malvidin forms 315.0 29.0-986.2 302.2 46.4-743.9 1140±71.2 830±154.9 488±16.9 436±108.7 % Malvidin forms 60.1 62.8 67.7 62.8 62.0 60.4

Peonidin-3-O-glucoside 83.3 14.8-237.2 90.6 12.1-202.9 213±3.2 227±38.3 25.3±1.6 32.6±13.4 Peonidin-3-O (6´-O-p-coumaroyl) 0.72 0.00-5.85 1.49 0.03-10.23 34.9±0.36 8.1±6.5 6.9±1.2 2.0±1.6 glucoside Σ Peonidin forms 84.1 14.8-237.2 92.1 12.9-204.7 248±3.6 235±39.2 32.2±2.8 34.6±14.1 % Peonidin forms 16.0 19.1 14.7 17.8 4.1 4.8

Petunidin-3-O-glucoside 37.6 0.00-144.8 32.3 0.45-113.8 106±1.4 94.6±10.9 92.5±5.4 96.7±23.9 Petunidin-3-O 5.19 0.00-27.4 2.12 0.05-7.36 1.1±0.45 6.6±1.6 0.39±0.04 2.6±1.2 (6´-O-acetyl) glucoside Petunidin-3-O (6´-O-p-coumaroyl) 0.09 0.00-3.75 0.70 0.00-2.97 8.4±1.9 3.1±1.8 20.6±2.1 5.2±3.4 glucoside Σ Petunidin forms 42.9 4.13-151.9 35.2 0.83-122.2 115±0.05 104±12.0 113±3.2 104±26.7 % Petunidin forms 8.2 7.3 6.9 7.9 14.4 14.5

Delphinidin-3-O-glucoside 39.1 0.74-182.5 36.1 0.19-154.3 123±1.6 105±9.9 130±7.8 126±35.3 Delphinidin-3-O 8.71 0.00-48.1 2.19 0.01-13.1 6.6±0.06 6.2±5.3 4.8±0.99 4.5±3.2 (6´-O-acetyl) glucoside Delphinidin-3-O (6´-O-p-coumaroyl) 23.7 0.00-96.2 6.53 0.07-24.9 22.9±0.29 19.7±16.7 5.5±0.30 6.9±4.7 glucoside Σ Delphinidin forms 71.5 1.21-267.0 44.9 1.64-177.0 152±1.9 131±15.9 140±8.5 137±38.2 % Delphinidin forms 13.7 9.3 9.1 9.9 17.8 19.0

Cyanidin-3-O-glucoside 10.3 0.00-41.3 6.40 0.12-37.3 25.7±0.37 21.1±5.6 10.0±0.65 8.2±3.5 Cyanidin-3-O (6´-O-p-coumaroyl) 0.07 0.00-2.60 0.00 0.00-1.32 2.74±0.05 1.2±0.78 3.1±1.9 1.2±0.89 glucoside Σ Cyanidin forms 10.5 0.00-41.5 6.65 0.00-38.3 28.4±0.42 22.4±5.5 13.1±1.2 9.5±3.7 % Cyanidin forms 2.0 1.4 1.7 1.7 1.7 1.3

Total anthocyanins 523.9 93.6-1583.4 480.9 78.5-1035.5 1684±77.1 1323±153.5 787±30.2 722±184.8

Petunidin and delphinidin-derived compounds were quantitatively similar in each parental variety, but contributions were different for both cultivars, being lower in 108

5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo

Graciano (6.9-7.9% for petunidin and 9.1- 9.9% for delphinidin) than inTempranillo (14.4-14.5% and 17.8-19.0% respectively).

Cyanidin derivatives represented a minor group in both varieties as well as in their progeny. It has been reported that low values of cyanidin in red grape premium varieties are expected as this anthocyanin is the precursor of all the others (Núñez et al 2004). The cyanidin content in the F1 population was intermediate.

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5.3.4 The distribution of individual anthocyanininF1 population

The distributions of 5 individual anthocyanins are shown in Figure 5-3, there were differences between two years studied with the ANOVA/ non-parametric Kruskal Wallis analysis. The mean values of 2010 were significantly higher than 2009 for 5 anthocyanins (P<0.05). All the F1 population showed continuous variation in the contents for all non-acetylated anthocyanins.

Figure 5-3 Distributions of individual anthocyanins for the populations in 2009 and 2010

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5.3.5Contribution of the different anthocyanin group to to the profiles of the F1 population and the parents

The different types of acylatedanthocyaninspresent in nature depend onthe acids which esterified with the hydroxyl of glucose; among themcaffeic, p-coumaric, ferulic, malic, succinic and pyruvicacid, and, less frequently, other organic acids (He et al. 2012; Kondo et al. 1989; Yositama et al. 1977). In grapes the most common are the p-coumaric, caffeic and acetic acid. Their relative contribution to the total acylatedanthocyaninsvary from one variety to another and isconsidered a good indicator ofintervarietal differences (Mazza 1995).

Table 5-4 Relative contribution (%) to the anthocyanin profileof progeny and parents

% Relative to non acylated Non Acetyl Coumaryl Genotype Year -acylated derivatives derivatives Dp Cy Pt Pn Mv

F1 2009 82.5 12.74 4.78 7.87 2.65 7.82 22.32 59.54

2010 92.4 3.83 3.73 6.87 1.34 6.30 22.56 62.93

Graciano 2009 86.2 3.43 10.40 8.48 1.77 7.31 14.67 67.77

2010 90.8 4.57 4.67 8.82 1.79 7.89 19.05 62.45

Tempranillo 2009 80.5 2.78 16.76 20.52 1.58 14.60 4.00 59.29

2010 91.1 3.24 5.69 19.08 1.22 14.72 4.90 60.07

As shown in table 5-4, the different anthocyanins chemical groups showed similar patterns in both years, the non-acylatedanthocyanins group was the main group for F1 population and their parents ranging from 86.2-90.8% in Graciano, 80.5-91.1% in Tempranillo, and 82.5-92.4% in the progeny. The coumarylglucoside group was the second group: 4.7-10.4% in Graciano, 5.7-16.8% in Tempranillo, and 3.7-4.8% in the F1 population. The acetyl glucoside group, which participate in an intra-molecular copigmentation process increasing the wine colour intensity (Ortega-Regules et al. 2006), is aminor group in Graciano and Tempranillo, but ranks similar to the coumaryl derivatives in the F1 population.

Among the five non-acylatedanthocyanins, similar to the results reported in most wine grape varieties, the most abundant compound in grape berry skin is malvidin-3-glucoside,(62.5-67.8% in Graciano, 59.3-60.1% in Tempranillo, and 111

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59.5-62.9%in F1 populations)(Table 5-4) relative to total non acylatedanthocyanins(Castillo-Muñoz et al. 2009; Ortega-Regules et al. 2006). In general, malvidin derivative forms are stable molecules and their presence would give stability to the wine during winemaking, because these compounds are relatively resistant to oxidation (Ribereau-Gayon et al. 2006).

Peonidin-3-glucoside is the second most abundant anthocyanin in Graciano and the F1 population ranging from 14.6-19.1% in Graciano to 22.3-22.6% in the progeny. These values were high when compared to those of most representative Vitisvinifera L. such as Cabernet Sauvignon and Merlot (Hermosín Gutiérrez and García-Romero 2004; Ortega-Regules et al. 2006), and similarto Pinot Noir (Dimitrovska et al. 2011).This fact doesn´t occur in Tempranillo, in which the peonidin-3-glucoside occupies the fourth position after two relatively abundant anthocyanins as delphinidin-3-glucoside (19.1-20.5%) and petunidin-3-glucoside (14.6-14.7%), whose concentrations appear even five times higher than peonidin-3-glucoside. These results are in agreement with previous findings(Castillo-Muñoz et al. 2009; Ortega-Regules et al. 2006). The percentage of petudinin-3-glucoside and delphinidin-3-glucoside were similar in the F1 population and Graciano:(6.3-7.8% and 6.9-7.9%, in the F1 respectively;and for Graciano, 7.3-7.9% and 8.5-8.8%, respectively).

High concentrations of petunidin-3-O-glucoside and delphinidin-3-O-glucoside are characteristic of the Tempranillo grapes. However, petunidin and delphinidin derivative levels were lower than malvidin levels. These results are consistent with others reported previously (Boss et al. 1996; Roggero et al. 1986), which considered delphinidin-3-O-glucoside to be a primitive pigment, taking into account the biosynthetic pathways that lead to the formation of different anthocyanins in grapes. Roggero and co-workers (1988) also postulated that petunidin-3-O-glucoside is formed from delphinidin and is later converted to malvidin via reactions mediated by the enzyme methyl transferase.

Cyanidin-3-glucoside represented the smallest contribution to the anthocyanin

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5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo profile both in F1 population and parents. Some authors stated that it is usual to find low concentrations of cyanidin-3-O-glucosides in red grape varieties since this anthocyanin is the precursor of all the others (Nuñez et al. 2004).

The distribution of non-acylated and acylatedantocyaninsin the F1 population are shown in Figure 5-4, differences between the two vintages were detected with the ANOVA/non-parametric Kruskal Wallis analysis. The mean values of 2009 were significantly higher than those of 2010 for acetyl anthocyanins, and coumarylanthocyanins (P < 0.05).

Figure 5-4 Distribution of non-acylatedanthocyanins and acylatedanthocyanins of F1 populations in 2009 and 2010

Summarizing, anthocyanin content and composition was influenced by the environment as a significant year effect was observed in our experiment.

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5.3.6.Relationships between anthocyanins as varietal markers

Anthocyanin composition has been studied by many authors in order to characterise the variety or establish the origin of grapes by using different anthocyanin relationships as varietal markers. Since peonidin-3-O-glucoside and malvidin-3-O-glucoside are stable anthocyanins and they are the ultimate forms in the anthocyanin biosynthesis (Roggero et al. 1986),the relationship between them was used as a varietal marker (Dimitrovska et al. 2011; Monagas et al. 2003). In this work, the ratios of Dp/Mv and Pn/Mv were chosen to study their distributions in the F1 population. The results showed that there were no significant differences between the two vintages for Dp/Mv and Pn/Mv (Figure 5-5).

Figure 5-5 Distributions of Dp/Mv and Pn/Mv for the populations in 2009 and 2010

One of the main differences between the parents in this study is thatGraciano has a higher value for the ratio of peonidin-3-glucoside and malvidin-3-glucoside (Pn/Mv = 0.255) than Tempranillo (0.089). This resultagrees with the report of Núñez (2004), Graciano showed a high proportion of peonidin derivatives, and peonidin-3-glucoside/malvidin-3-glucoside (Pn/Mv) can be considered as a potential marker for the characterization of this variety.

5.3.7 Principal Component Analysis (PCA)

In order to assess the relationship between berry colour and its composition and to examine the similarity among genotypes, PCA was calculated on the

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13anthocyanins compounds identified by HPLC-MS. In 2009, the first two PCs accounted for 94.8% of the total variance present in the population. The first component (PC1) explained more than 89.5% of the total variance (Figure 5-6) and was clearly connected with the content of Mv-3-glu, Mv-3-act, Dp-3-glu, Pt-3-glu (Table 5-5). On the other hand, the second component (PC2) was clearly connected with the content of Peo-3-glu. In 2010, the first 2 PCs accounted for 96.8% of the total variance. PC1 explained more than 87% of total variance (Figure 5-6), and PC1 and PC2 were associatedwith the same anthocyanins as in 2009.

2009 2010

100 100 96.82 94.79

89.54 90 90 87.24

80 80 Cumulative variance (%) Cumulative variance (%) 70 70 1 2 1 2 Principal component Principal component

Figure 5-6 Cumulative variances of principal components 1 and 2 for anthocyanin content of F1hybrids Graciano x Tempranillo in 2009 and 2010

According to Luo and He (2004), the anthocyanin content had a direct relationship with berry skin colour. Furthermore, the relative content of a given anthocyanin is stable in grape skin of the progeny. Using PC analysis in this study, we could confirm that the absolute content of only five anthocyaninsdetermined berry skin colour.

For a given segregating population, Liang et al. (2009)stated that the anthocyanins content depends on both the composition of anthocyanins in the parents and the heritability of each of the crucial anthocyanins.

It was supposed that the presence or absence of anthocyanins in grape skin was controlled by major genes in maternal parents, and the quantities of these anthocyanins were controlled by minor genes. Both the maternal and paternal parents may contain minor genes that contribute colour in grape. Therefore a breeder should

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5 Anthocyanin Composition of a F1 Population derived from Graciano x Tempranillo choose a suitable maternal parent that has a relatively high level of one of more specific anthocyanins. For example, in our study, the high colored variety Graciano was selected as maternal parent being the high MV-derivatives variety Tempranillothe male parent; this should result in an attractive ruby red or purple wine. At the same time, the paternal parent should be considered in a cross to enhance the level of anthocyanins in the progeny.

Table 5-5 Component score coefficient of principal components 1 and 2 of F1 populations of Graciano x Tempranillo in 2009 and 2010

2009 2010

1 2 1 2

Delphinidin-3-O-glucoside 0.662 0.001 0.680 0.165 Cyanidin-3-O-glucoside 0.103 0.095 .083 0.174 Petunidin-3-O-glucoside 0.591 -0.019 0.586 0.113 Peonidin-3-O-glucoside 0.465 0.847 0.366 1.165 Malvidin-3-O-glucoside 3.703 -0.163 3.505 -0.177 Carboxypyrano Malvidin-3-O-glucoside 0.044 0.042 0.021 0.008

Delphinidin-3-O(6´-O-acetyl)glucoside 0.141 0.031 0.042 0.006 Petunidin-3-O(6´-O-acetyl)glucoside 0.055 0.030 0.036 -0.002 Malvidin-3-O(6´-O-acetyl)glucoside 0.764 0.141 0.219 0.000 Delphinidin-3-O(6´-O-p-coumaroyl)glucoside 0.162 0.287 0.037 0.063

Cyanidin-3-O(6´-O-p-coumaroyl)glucoside 0.004 0.001 0.001 0.000 Petunidin-3-O(6´-O-p-coumaroyl)glucoside 0.004 0.001 0.009 -0.002 Peonidin-3-O(6´-O-p-coumaroyl)glucoside -0.009 0.011 0.018 0.008 Malvidin-3-O(6´-O-p-coumaroyl)glucoside 0.231 0.210 0.152 -0.009

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5.4 Conclusions

In our work, 15anthocyanins were identified and quantified from the berry skins of a progeny of Graciano x Tempranillo with HPLC-MS. There were no significant differences of physico-chemical profiles between 2 years analyzed.

Anthocyaninscontent showed continuous variation in the F1 population, typical of quantitative traits. Transgressive segregation was observed in major individual anthocyanin concentration, with a tendency towards lower values than both parental varieties except forpeonidincontent that showed intermediate values.

Although year effect was not observed for total berry skin anthocyanins between the two years analyzed, significant differences were found for all 13 identified anthocyanins, confirming that the anthocyaninscontent is a character conditioned by the environment. However the ratios Dp/Mv and Pn/Mv were consistent in the different vintages allowing its use as a varietal marker.

Two main principal components (PC) explained 94.8% and 96.8% total variance in 2009 and 2010 respectively. The first onewas clearly related with the content of Mv-3-glu, Mv-3-act, Dp-3-glu and Pt-3-glu; the second component was positively associated with the content of Pn-3-glu in both years. Overall more than 95% of the total variance observed can be explained by 5 anthocyanins.

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Cluster 11

Cluster 2 Cluster 2

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6 A GENETIC LINKAGE MAP OF A GRACIANO X TEMPRANILLO WINE GRAPE POPULATION

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6 A genetic linkage map of a Graciano x Tempranillo wine grape population

Abstract

A genetic linkage map was constructed using 151 progeny derived from two Spanish wine grapes Graciano x Tempranillo (Vitis viniferaL.). Of 271 simple sequence repeat (SSR) markers tested, 188were polymorphic for at least one parent, and 53.2% were fully informative (4 allele or 3 allele markers).In addition, one CAPs (Cleaved Amplified Polymorphic Sequences) marker wasmapped.

The population was genotyped with18071 SNP (Single Nucleotide Polymorphism) markers, of which, 4656 were polymorphic for at least one parent, 535 and 491 of them were filtered to saturate the maternal (Graciano) map and paternal (Tempranillo) map, respectively

Maternal, paternal and consensus maps were generated using Joinmap 3.0 software, following a pseudo-testcross strategy.The maternal map consisted of 147 SSR markers and 535 SNP markers assembled into 19 linkage groups spanning 1264.4 cM. The average distance between markers was 1.9 cM.The paternal mapconsisted of 136 SSR markers and 491 SNP markers aligned into 19 linkage groups covering 1220.5 cM with an average distance between markers of2.0 cM. Finally, a consensus map with a total of 1210markers (183SSRs, 1 CAPs and 1026SNPs) wasassembled covering 1385.8 cM distributed into 19 linkage groups, with an average interval length 1.2cM between markers.

Strong segregation distortion was observed in LG17 of Graciano parental map for all SSR markers and SNP markers (p < 0.001).

No statistically significant difference in recombination rate was observed between Graciano and Tempranillo based on 64 common strong linked marker intervals.The expected genome coverage of maternal, paternal and consensus map wasaround 86%, but differences for estimated genome length and observed genome map coverage were observed.

Key words: SSRs, SNPs, segregation distortion, recombination rate

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6.1 Introduction

Grapevine (Vitis vinifera L.) is an important crop worldwide in terms of economic value used mainly for wine and spirit production but also for fresh fruit, raisins, fruit juices, jams, etc (Bouquet 2011).

The domestication of grapevine is thought to have occurred approximately 6000 to 8000 years ago (McGoven 2003), and several studies have addressed the origin and the genetic relationships between wild and cultivated grapevines (Arroyo-Garcia et al. 2006;De Mattia et al. 2008).

In the past decades, while innovative viticulture management technologies were developed, such as grafting on rootstocks resistant to Phylloxera and adapted to different types of soil; the catalog of grown varieties was by contrast greatly reduced. Grapevine breeding was poorly encouraged over the last century, due to its long growth cycle and the development of wine trade based onthe few cultivars authorized by the Appellations of Origin. This reduction of the varietal choice resulted in part from the widespread ignorance about the genetic diversity that ensured the success of viticulture. The most important will obviously by the influence of climate changes and global warming on the geographical distribution of vineyards and steadiness of wine quality (Jones et al. 2005).

During the first half of the 20th century, the breeding and selection of winegrape cultivars resistant to phylloxera,powdery mildew and downy mildew was considerably extended in France by means of hybridization between V. vinifera and many American wild species. However, the resultant hybrids have been generally unaccepted due to the low quality of the wine produced and consequently the production of quality wine with these hybrids was forbidden by law. This was an immense drawback causing the end of grapevine breeding in most countries (Cahoon 1998).

In Germany,the situation was quite different as resistance breeding was still

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supported by the governmentandcurrently anewly bred red variety ‘Regent’has becomeone of the top five most planted cultivars in Germany. Regent is a hybrid between ‘Diana’ (Vitis vinifera variety Silvaner x Müller-Thurgau) and the interspecific hybrid ‘Chambourcin’ (Eibach and Töpfter 2003). It is considered amajor fungalresistant quality grape variety world-wide, especially in German wineregions.

Furthermore, the grapevine breeding programs were and still are carried out in table grape for seedleesness (Doligez et al. 2002), muscat flavor (Emanuelli et al. 2010), resistance diseases(Fisher et al. 2004; Riaz et al. 2011).However, because of the heterozygous nature of grape,many agronomical important traits are quantitatively inherited and thus difficult to control in grapevine breeding.

The inheritance of complex characteristics can be addressed by establishing their association with linked molecular markers. Genetic factors involved in the variation of traits can be localized as quantitative trait loci (QTL) on the basis of a molecular map as introduced into plant genetics by Paterson et al. (1988) and later modified into QTL interval mapping (Lander and Botstein 1989). Efficient QTL analysis requires a densely covered genetic map with good saturation and an even distribution of markers. Once the correlation between a molecular marker and a specific phenotype has been established, the inheritance of a trait of interest can be scored in a progeny at very early stages of plant development (marker assisted selection (MAS), Lande and Thompson 1990). In addition, such markers may serve as anchors for inter-varietal comparative studies, for the molecular characterization of genetic resources and for positional cloning of corresponding genetic regions. With these approaches, candidate genes can be identified and become available for the improvement of traditional grapevine cultivars after further functional characterization (Fisher et al. 2004).

Progress in grape breeding will be obviously reinforced by the two high-quality sequences of grapevine genome that have been recently published (Jaillon et al. 2007; Velasco et al. 2007).

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Model cross populations of numbers as large as possible are desirable for QTL mapping, but, with grapevine as with other woody crops, there are practical limits. Genetic profiling of individuals is nowadays based on SSR (simple sequence repeat) markers which have a number of positive features that make them superior to any other type of molecular markers developed so far for DNA finger printing (Schlterer 2004). Several genetic linkage maps have already been published for grape (Vitis spp.). The older ones were mainly based on RAPD or AFLP markers and aimed at the detection of QTLs for specific traits (Lodhi et al. 1995; Dalbó et al. 2000; Doligez et al. 2002; Grando et al. 2003; Doucleffet al. 2004; Fischer et al. 2004). More recently, two maps containing mainly SSRs were developed to serve as reference maps (Adam-Blondon et al. 2004; Riaz et al. 2004). SSR markers are highly transferable among grapevine genotypes and a large set of these markers are now publicly available (Thomas and Scott 1993; Bowers et al. 1996, 1999; Sefc et al. 1999; Scott et al. 2000; Di Gaspero et al. 2005; Adam-Blondon et al. 2004; Merdinoglu et al. 2005; NCBI UniSTS; The Greek Vitis Database).

Since grapevine is highly heterozygous, most existing maps were based on full-sib populations from crosses between two heterozygous parents. Although individual maps were developed for different purposes, all of them shared the pseudo-testcross mapping strategy(Grattapaglia and Sederoff1994) and partially overlapping sets of SSR markers.

The relevance of molecular markers to grapevine genetics has driven the development of a common set of markers and genetic maps (Riaz et al. 2004; Adam-Blondon et al. 2004; Doligez et al. 2006). Nowadays, the International Grape Genome Program refers to an integrated map containing more than 400 SSR markers (http://www.vitaceae.org/index.php/Maps_and_Markers) in addition to a dense genetic linkage map anchored to the ‘Pinot noir’ genome with 1,006 markers (Troggio et al. 2007). Physical maps have also been developed, such as the V. vinifera grapevinereference genome for a nearly homozygous selection, PN40024 (Jaillon et al. 2007), ‘Cabernet Sauvignon’ (Moroldo et al. 2008) and ‘Pinot noir’ (Velasco et al. 129

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2007). Next-generation sequencing (NGS) has been employed recently for the construction of a genetic map with 1,643 SNPs (Single Nucleotide Polymorphism) derived from a cross of Z180 (V. monticola × V.riparia) and Beihong (V. vinifera × V. amurensis) (Wang et al. 2012) and to develop a SNP chip with an array of nearly 9,000 SNPs based on the sequence of 10 cultivated V. vinifera varieties and 7 wild species (Myles et al. 2010).

Nowadays, genotyping-by-sequencing (GBS) provides a simple and robust procedure for simultaneous SNP discovery and genotyping through pooled barcoded RRLs, Illumina sequencing and SNP calling based on alignment of short reads. As a result, thousands of markers with low coverage are obtained (Elshire et al. 2011), which should be sufficient to infer linkage in biparental populations and for QTL mapping (Davey et al. 2011). Due to its speed, low cost, and reduced ascertainment bias, GBS is a good strategy for simultaneous discovery and assay of SNPs suitable for rapid development of dense maps in segregating populations.

The objective of this research is to construct a genetic map of Spanish wine grape Graciano x Tempranillo population withSSRs markers and SNPs; to explore the recombination rate of Graciano and Tempranillo. This map is the first map utilizing a large set of SSR and SNPs(18K). It will be an important tool for identify major genes or QTLs for traits (agronomical traits, berry quality traits and seed traits).

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6.2 Materials and methods

6.2.1 Plant material

The mapping population consistedof 163 plants obtained from controlled crosses between the wine grape cultivars Graciano (female parent) and Tempranillo (male parent).

6.2.2 Molecular marker analyses

DNA extraction

SSR Primer pairs selection

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self-pollinations and foreign pollen sources, resulting in a final population of 151 plants as mentioned above..

SSRs Primer selection

A total of 271 SSR primers pairs were tested on the parents and six offspring to select useful polymorphisms: 2 VVS (Thomas and Scott 1993), 13 VVMD (Bowers et al. 1996,1999), 13 VrZAG (Sefc et al. 1999), 35 VChr (Cipriani et al. 2008), 60 VVI (Merdinoglu et al. 2005), 14 UDV (Di Gaspero et al. 2005), 4ScuVV (Scott et al. 2000) and 130 VMC (Vitis Microsatellite Consortium, AgroGene S.A. Moissy Cramayel, France) (Salmaso et al. 2008; Costantini et al. 2008). Only 188 of SSR markers were selected for linkage mapping according to their segregation type. The SSR markers were selected to be well-spread over the 19 linkage groups according to the last available version of the reference map of Doligez et al. (2006) and Vezzulli et al. (2008). Primer pairs were synthesized (Eurofins MWG systhesis GmbH, Germany) and (IDT® Integrated DNA Technologies, Inc., Belgium) from published sequences (Table 6-) most of them are available at the UniSTS database of NCBI GeneBank (www.ncbi.nlm.nih.gov).

Table 6-1 SSR primers used to amplify the F1 Graciano x Tempranillo population

SSR Loci screened Origen Reference

VVS V. vinifera cv Sultana Tomas and Scott, 1993 VVMD V. vinifera cv Pinot noir;V. riparia Bowers et al. 1996, 1999 VrZAG V. riparia Sefc et al. 1999 Vchr V. vinifera;V. berlandieri x V. riparia Cipiani et al. 2008 VVI V. vinifera Merdinoglu et al. 2005 ScuVV V. vinifera cv Chardonnay Scott et al. 2000 UDV V. vinifera; V. riparia Di Gaspero et al. 2005 VMC V. vinifera; V.rupestris x V. Lincecumii Salmaso et al. 2008;

For most SSR primers, the forward primer for each primer pair was synthesized with an additional modifie r 19 bp M13 tail (CACGACGTTGTAAAACGAC) added to the 5’ end of the oligonucleotide (Oetting et al. 1995), an M13 primer that has the same sequence and that is directly labeled to the infrared fluorophore IRD-700, was

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the sole type of labeled primer for the detection of the SSR.

PCR Amplification protocol of SSR primers

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CAPS analysis

The CAPS (Cleaved Amplified Polymorphic Sequence) marker 20D18CB9 was tested using the primers 20D18CB9f (5’-GATGACCAAACTGCCACTGA-3’) and 20D18CB9r (5’-ATGACCTTGTCCCACCAAA A-3’) as described in Walker et al. (2007). PCR was conducted using 20 ng of genomic DNA plus Platinum Taq (InvitrogenTM, Thermo Fisher Scientific, USA) in accordance with the manufacturer’s instructions in 50 μl reactions using cycling condition of 94 ºC for 2 min, 35 c ycles of 94 ºC for 30 s, 55 ºC for 30 s, 72 ºC for 1min, followed by 72 ºC for 10 min. The amplification product was then restricted with DdeI and separated by gel electrophoresis on 2.5% agarose gels, using 1 x TBE buffer (Bayo-Canha et al. 2012). Gels were stained with Midori green Advanced DNA stain (Nippon Genetics EUROPE GmbH, Germany), the DNA fragments were photographed under UV light with CHEMI GENIUS Bio Imaging System (Syngene, Cambridge, UK), and documented with GeneSnap from SynGene software (Syngene, Cambridge, UK).

SNPs development and analysis

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SNP data for our progeny were kindly provided by JM Martínez-Zapater at IC V V. A 20K genotyping chip was generated from the paired end re-sequenced of 43 Vitis vinifera ssp vinifera, four V. vinifera ssp sylvestris, three V. cinerea, three V. berlandieri, three V. aestivalis, three V. labrusca, one V. lincecumii, five M. rotundifolia genotypes, using Illumina platforms. An average of 4.3 and of 3.4 millions SNP were detected respectively per V. vinifera and per other Vitis species genotypes. SNPs were first filtered upon technical criteria: Illumina score > 0.9 and class I type. Two subsets of SNPs were used in the chip construction: a V. vinifera specific subset and a general Vitis species subset. For the V. vinifera subset, SNPs in regions involved in structural variations and repetitions were filtered out and the remaining SNPs were then selected based on their even physical repartition along the genome together with their MAF (Minimum Allele Frequency). For the Vitis species subset, SNPs in repeated regions were filtered out and the remaining SNPs were chosen based on their level of heterozygosity and evenly distributed along the genome. In the end, 14,817 V. vinifera SNPs and 4,978 Vitis species SNPs were selected along with 205 control SNPs to design a 20K grapevine Infinium genotyping chip Illumina designed an 18,071 SNP chip: (http://urgi.versailles.inra.fr/Species/Vitis/GrapeReSeq_Illumina_20K).

The filtering process was performed as described (Barba et al. 2014), filtering was based on parental information, physical distance, physical location and missing data.

First, 18,071 SNPs were selected based on parental information, markers that were homozygous (AA x BB or AA x AA), had more than 10% missing data or were

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6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population heterozygous (AB x AB) in both parents were discarded. After this step, 4656 SNP were left.

Thirdly, according to physical location, neighbour markers were left as AB x BB and AA x AB one by one as possible.

Table 6-2 Number of SNPs per chromosome of 18,071 SNPs

Chromosome Number of SNPs Chromosome Number of SNPs

Chr1 878 Chr18 1109 Chr2 682 Chr19 873 Chr3 734 PLTD 24 Chr4 902 Chr_un 1555 Chr5 906 Chr1_random 30 Chr6 783 Chr3_random 53 Chr7 788 Chr4_random 20 Chr8 850 Chr5_random 28 Chr9 856 Chr7_random 60 Chr10 675 Chr9_random 7 Chr11 727 Chr10_random 43 Chr12 864 Chr11_random 29 Chr13 893 Chr12_random 53 Chr14 1080 Chr13_random 121 Chr15 756 Chr16_random 12 Chr16 794 Chr17_random 34 Chr17 653 Chr18_random 199 Total 18,071 Chr indicates chromosome location, and suffix ´_random´ corresponds with unassembled portions of the indicated reference chromosome.

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6.2.3 Genetic mapping and Linkage analysis

The construction of map

Logarithm of the odds (LOD) and recombination frequency thresholds (REC) were fixed at 3.0 and 0.45, respectively, to assign markers to Linkage Groups and establish marker order. Kosambi mapping function (Kosambi 1944) wasused for the estimation of map distances. When three rounds of mapping were performed the second-round map was chosen, except in a few cases where the order of markers in the third-round map was confirmed by other mapping experiments reported in literature.

In order to construct the map of Graciano, the maternal population loci with segregation type and were translatedto loci . Moreover, and type loci are ignored for maternal population.

In order to construct the map of Tempranillo, the paternal population loci with segregation type and were translated to. Moreover,

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x hk> and type loci are ignored for maternal population.

The χ2 test was applied to test the segregation ratio of the F1 population. All the statistical analyses were performed with software SPSS V. 14.0 and STATGRAPHICS 16.0

Comparison of male and female recombination rates

Estimation of genome length and map coverage

Estimated genome length (Ge) was calculated by using the method of moment

estimator, Ge= N(N-1)X/K (Hulbert et al. 1988), where N is the number of markers, X is the maximum observed map distance between marker pairs above a threshold LOD Z (Chakravarti et al. 1991), Z = 4 in this study, and K is the number of locus pairs having LOD values at or above Z. The value of X and K were obtained from

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Joinmap using Kosambi mapping function.

The confidence interval for Ge, Iα(Ge) was calculated from the equation:

-1/2 -1 Iα(Ge) = Ge(1 ± nαK ) , where nα = 1.96 for an α of 5% (Gerber and Rodolphe 1994).

The expected genome map coverage (Ce, %) for each parent was calculated following Bishop et al. (1983) from the equation: Ce = 1-P1,N

The observed genome map length (Go, cM) was calculated based on the sum length of all linkage groups for each linkage map (Lowe and Walker 2006).

Finally the observed genome map coverage was the ratio between observed and estimated genome length (Go/Ge, %). The above calculations were performed using all mapped loci.

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6.3Results

6.3.1 Polymorphism of markers

Of the 271 SSR markers tested, 188 of them segregated in the progeny of Graciano x Tempranillo for at least one of the parents (Table 6-3). The number and segregation type of the markers used to generate the maps of Graciano and Tempranillo are shown in Table 6-3. In total, 188 microsatellites and one CAPs marker were polymorphic, resulting in a 65.1%degree of polymorphism. Among them there were4 SSRs with presence of a null allele (3 in Graciano and 1 in Tempranillo ), 7 SSRs with segregation pattern and 11 SSRs showing segregation.

Furthermore, 28 homomorphic SSR markers were discarded after screening, because they can only be used to construct the consensusmap. The CAPS marker segregated 1:1 in the progeny. Finally, 189 markers were used to construct the linkage map, 100 of them segregated for two alleles in the parents, representing the most informative segregation types and . Of the 89 markers segregating for one parent ( or ), 50 segregated for Graciano , and a smaller number of markers (39) were placed on the Tempranillo parental map.

Table 6-3 The number and segregation type of the markers analyzed in the progeny Graciano x Tempranillo

Segregation Type Numbers recode comments ab x cd 1:1:1:1 39 ef x eg 1:1:1:1 57 lm x ll 1:1 39 nn x np 1:1 32 1 CAPS ab x c0 1:1:1:1 1 ab x cd a0 x cd 1:1:1:1 3 ab x cd aa x cd 1:1 7 nn x np ab x cc 1:1 11 lm x ll Total 189

Of 189 selected markers, VMC2A10, VMC3A9 and VMC5G1.1 couldn´t be 140

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mapped in Graciano, while VMC2A7, VMC2A10, and VMC5G1.1 could notbe mapped in Tempranillo. One hundred and eighty three SSRs and one CAPS were finally integrated in the map, as Vchr10b, Vchr13c, VMC2A10, VMC3A9, and VMC5G1.1 were not mapped.

SSR-based linkage maps were saturated with SNPs. Of 18071 analyzed SNPs, 25.8% of them (4656) were polymorphic, 54.2% of them (2523) segregated in Graciano, and 45.8% of them (2133) segregated in Tempranillo (Table 6-4).

Table 6-4 Number of SNPs analyzed and mapped in the progeny Graciano x Tempranillo

Number Polymorphic SNPs Type Percentage of SNPs Segregation type Number Used Percentage Homozygous 11234 62.2% Homomorphic 2181 12.1% Polymorphic 4656 25.8% lm x ll 2523 535 21.6% nn x np 2133 491 22.3% Total 18071 100% 4656 1026 22.0%

6.3.2 The frequency of distorted alleles

A χ2 testperformed with JoinMap 3.0 (Van Ooijen and Voorrips 2001) revealed that 16SSR markers showed a distorted segregation (p<0.01). Among them, 3 had a 1:1 segregation type, 13 had a 3:1 segregation type and only VMC2A10 could not be positioned. Four markers mapping together on LG17 of the consensus map showed a highly distorted segregation (p<0.001). The distortion ratio was 8.4%.

The frequency of distorted alleles was faintly higher for the female parent5.3% (8/150in Graciano) than for the male parent.Only 3.6% (5/139) of SSR markers showed segregation distorsionin Tempranillo. Among them, fourhighly distorted markers (p<0.001) on LG17, all of them with 3:1 segregation pattern, were distorted onlyin Graciano. The only SSR marker with no segregation distortion (VMC3A9) could not be mapped.

For SNP markers, all distorted loci were discarded for the analysis except the ones in LG17, all of them segregating for the Graciano parent () with high segregation distortion (p<0.001). 141

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

6.3.3Construction of genetic maps

For the maternal map (Graciano), 147SSR markers and 535 SNP markers were assembled into 19 linkage groups spanning 1264.4cM, with an average number of 35.7 markers per group.The average length of linkage groups was 66.5 cM, ranging from 36.8 cM(LG17) to 102.1 cM (LG5).The average distance between markers was 1.9cM; only 6 gaps larger than 10cM were present, one of them larger than 20cM.The largest gap is22.5 cM between VVIO61 and chr1_15909374 in LG1(Table 6-5, Table 6-6 and Figure 6-1).

Table 6-5 Summarizing outline of Graciano, Tempranillo and consensus maps

Consensus Graciano Tempranillo N. of polymorphic markers 1215 685 629 N. of mapped markers 1210 682 627 N. of ungrouped markers 2 2 2 N. of unpositioned markers 3 1 0 N. of linkage groups (LG) 19 19 19 N. of makers/LG range 47-90 24-62 22-46 Mean number of markers/LG 63.7 35.7 33.0 Total length (cM) 1385.8 1264.4 1221.0 Mean LG length (cM) 72.9 66.5 64.2 LG length range (cM) 58.8-100.1 42.5-102.1 37.1-101.7 Average distance between loci (cM) 1.2 1.9 2.0 N. of gaps between 10 and 20 cM 2 5 4 N. of gaps >20 cM 0 1 1

The paternal map (Tempranillo) consisted of 136 SSR markers and 491 SNP markers which were positioned on 19 linkage groups and covered altogether 1220.5 cM with an average of33.0 markers per group. Linkage group sizes ranged from 37.1 cM (LG10) to 101.7 cM (LG18) with an average length of64.2 cM. There were 5 gaps greater than 10cM, and the average distance between markers was 1.8cM. The largest gap was 43.1 cM between Chr12_2034441 and Chr12_13597099 in LG12 (Table 6-5, Table 6-6 and Figure 6-1).

Correspondence between linkage group (LG) and chromosome number wereestablished by the international consensus map (Doligez et al. 2007; Vezzulli et

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The integrated map consisted of 183 SSR markers, one CAPS and 1026 SNP markers on19 linkage groups with an average of 63.7 markers per linkage group. The map covered1385.6 cM, with an average interval length1.2cM between markers.Linkage groups size ranged from 58.8 cM (LG10) to 100.1 cM (LG18) with an average size of 72.9 cM. There were 2 marker free regions longer than 10 cM. The largest gap is 16.3 cM between Chr17_8394730 and Chr17_5927601 in LG17(Table 6-5, Table 6-6 and Figure 6-1).

The total number of positioned markers per linkage group were between 24 (LG8 and LG17) and 62 (LG5) for Graciano, between 22 (LG12) and 46 (LG8) for Tempranillo, and between 47 (LG4) and 90 (LG18) for the consensus map.The average length between markers of linkage group ranged between1.5 cM and 3.2 cM for Graciano, between 1.2 cM and 3.2 cM for Tempranillo, and between 0.9 cM to 1.5 cM for consensus map (Table 6-6).There were loci that could not be assigned to any linkage group (ungrouped markers), a possible explanation is that they locate in regions of the genome not yet covered by the present map; and some loci could be assigned but not placed on the maps (unpositioned markers), most likely due to insufficient linkage or location conflicts.

As reported by other authors, most distorted markers mapped together on certain linkage groups (in this study on LG7, LG17 and LG18). Markers with distorted segregation were reported on LG7 in the Italiax Big Perlon map (Costantini et al., 2008) and on LG 18 in the Dominga x Autumn seedless (Cabezas et al. 2006).

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Table 6-6 Characteristics of consensus map and the Graciano and Tempranillo maps

Consensus map Map of Graciano Map of Tempranillo Linkage Length Marker Mean 10-20 >20 Length Marker Mean 10-20 >20 Length Marker Mean 10-20 >20 Group (cM) SSR SNP (cM) cM cM (cM) SSR SNP (cM) cM cM (cM) SSR SNP (cM) cM cM

LG1 73.3 12 47 1.2 0 0 66.1 9 18 2.4 0 1 71.4 10 29 1.8 0 0 LG2 78.3 10 43 1.5 0 0 67.9 7 26 2.1 0 0 53.4 6 17 2.3 0 0 LG3 69.9 6 59 1.1 0 0 69.9 6 31 1.9 0 0 54.3 5 28 1.6 0 0 LG4 69.6 8 39 1.5 0 0 61.6 7 21 2.2 0 0 43.1 4 18 2.0 1 0 LG5 98.9 10 76 1.2 0 0 102.1 9 53 1.6 0 0 61.9 6 23 2.1 0 0 LG6 63.4 9 60 0.9 0 0 61.3 8 29 1.6 0 1 64.4 9 31 1.6 0 0 LG7 90.6 11 48 1.5 0 0 74.9 8 17 3.0 1 0 89.4 11 31 2.1 0 0 LG 8 84.9 12 53 1.3 0 0 76.4 7 17 3.2 0 0 83.9 10 36 1.8 0 0 LG 9 62.7 8 51 1.1 0 0 54.4 6 24 1.8 0 0 57.0 6 27 1.7 0 0 LG 10 58.8 13 54 0.9 0 0 64.1 11 32 1.5 0 0 37.1 8 22 1.2 0 0 LG 11 63.6 9 55 1.0 0 0 63.8 8 28 1.8 0 0 57.9 5 27 1.8 0 0 LG 12* 77.9 10 54 1.2 1 0 77.8 10 36 1.7 1 0 70.6 4 18 3.2 0 1 LG 13 63.2 5 52 1.1 0 0 42.5 4 23 1.6 0 0 66.0 5 29 1.9 0 0 LG 14 70.5 14 52 1.1 0 0 61.3 12 30 1.5 0 0 73.4 11 22 2.2 1 0 LG 15 59.7 6 59 0.9 0 0 62.0 6 32 1.6 0 0 54.9 5 27 1.7 0 0 LG 16 67.7 10 52 1.1 0 0 74.5 5 27 2.3 1 0 54.5 9 25 1.6 0 0 LG 17 66.9 9 40 1.4 1 0 36.8 4 20 1.5 0 0 63.7 8 20 2.3 1 0 LG 18 100.1 15 75 1.1 0 0 85.9 14 40 1.6 0 0 101.7 7 35 2.4 1 0 LG 19 65.8 7 57 0.9 0 0 61.1 6 31 1.6 0 0 61.9 7 26 1.9 0 0 Total 1385.8 184 1026 2 0 1264.4 147 535 3 2 1220.5 136 491 4 1 Mean 72.9 9.9 53.8 1.2 66.5 7.7 28.2 1.9 64.2 7.2 25.8 2.0 Note: * LG12 of Tempranillo was mapped with LOD = 3.0

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Marker order of SSR markers was highly conserved between Graciano and Tempranillo maps, with only 4 rearrangements present on LG2, LG7, LG8, and LG10. Two of them were less than 6cM. Differences in linkage group size and coverage had been reported before and are most likely due to the different number of markers used to construct the map (Lowe and Walker 2006).

Marker order was generally consistent between homologs from the parental and the consensus maps. Most of the inversions presented on several linkage groups occurred between closely linked markers(VMC9D3 and Chr1_22466694 on LG1 for example). There are causal relationships between distorted markers and rearrangements, because allinversions may be accounted for by segregation distortion (LG17 of Graciano with significant distorted segregation).

When comparing our maps to the first (Doligez et al. 2006) and last (Vezzulli et al. 2008) published integrated maps,a complete agreement is present with respect to linkage groups, while marker order of SSR markers is similar but less consistent. There are discrepancies in maker order between our consensus map and Doligez et al (2006) (152 shared SSRs) for LG2, LG6, LG9, LG10 and LG18. Vezzulli et al (2008) map shared 110 SSRs with our map but discrepancies are shown only in linkage group 18. InconsistenciesinLG9, LG10 andLG18 were reported between Costantini et al (2008) consensus map and the map of Doligez et al (2006)both maps sharing 109 SSRs. Discrepancies not only come from the population size on which the map is based(Constantini et al, 2008) but also the number of shared markers which decided by the statistical methods used to perform linkage analysis. Our map include SSRs markers from the VChr type because they were polymorphic in our progeny, could be grouped and showed linkageto other markers as reported (Cipriani et al. 2008).

The LG12 of Tempranillo grouped in two partswhen LOD 4.0 was established as threshold with JoinMap. AllSSR marker available in NCBI database were screenedand were homozygous except the five mapped loci, besides all SNPs markers showed homozygosity in this big gap (43.1 cM when LOD =3.0with JoinMap)too indicating that ahighly homozygous region is present inchromosome 12 of Tempranillo.

6.3.4 Comparison ofparental meiotic recombination rates

When comparing both parental maps on the basis of 211 common markers in

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JoinMap3.0, 64 marker pairs exhibited statistically different recombination rates. The recombination rate of Graciano wasnot significantly different from Tempranilloatp=0.05 level based on a Z test (-0.183) (Table 6-7).

Table 6-7 Estimation of recombination rate frequencies in Graciano and Tempranillo linkage maps

Mean Marker Standard Standard Parent recombination Z test P value interval deviation error frequency

Graciano 64 0.176 0.111 0.014 -0.183 0.4274

Tempranillo 64 0.179 0.120 0.015 Recombination was higher in the maternal parent for 5 marker pairs (VVIN52-VMC5F5, VVIN52-VMC5A1 in LG16; VMC1C10-VMC4A5 in LG9, VrZAG79-VMC3C7, VrZAG79-Vchr5a, VrZAG79-VMC9B5 in LG5) and higher in the paternal parent for 11marker pairs (VMC6C7-Vchr19a in LG19, VMCNG2F12-VVIM93, UDV134-VVIM93, VVIU04-VVIM93 in LG18, VVIN52-VMC5F5, VVIN52-VMC5A1 in LG16; VMCNG1E1-VrZAG112, VMCNG1E1-VMC5B3, Vchr14b-VrZAG112 in LG14; VMC5C1-VMC1C10, VMC4A5-VMC1C10 in LG9).

Fortyfour percent（28/64）of marker intervals on LG5, LG6 and LG14 had statistically different recombination rates, suggesting that there may be hot spots for recombination across the genome. The linkage groups with more distorted markers and rearrangements on average indicate that the local recombination rate difference between parents may account for problems with segregation distortion and marker order discrepancies (Lowe and Walker, 2006).

Among the three linkage groups with highest number of distorted markers (LG7, LG17 and LG18), only LG18 showed statistically significant differences in parental recombination rates indicating that only in some cases differences in recombination rates may account for segregation distortion.

Given that the recombination rate in Graciano does not differ from that of Tempranillo, there are no significant differencesin length betweenmaternal and paternal maps (1264.4cM vs 1220.5cM). So, the length of the maternal map with respect to that of paternal map is presumably due to the number of markers rather than to differences in the recombination rate between parents as reported by Costantini et al. (2008).The

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parental segregation data produced slightly longer estimated of genome length than maternal data as reported by Riaz et al. (2004).

6.3.5 Genome length and coverage

The estimated genome length (cM) of Tempranillo is greater (2763.3 cM) than that of Graciano and consensus map (1442.8 cM and1284.6 cM respectively) as expected due to the large gap in chromosome 12 (Table 6-8). As mentioned above (Table 6-7), only5, 6 and 2 gaps (Table 6-7) over 10 cM were present in linkage groups of Tempranillo, Graciano and consensus map , indicating uneven coverage.

Following Bishop’s methods (1983), expected genome map coverage based on these lengths was around 85% for all maps (Table 6-8). Observed coverage ofTempranillo map is lower (46.7%) than that of Graciano and consensus maps (89.7% and 107.9%respectively).

Table6-8 Estimated genome length, expected and observed genome coverage calculated with map distance based on Kosambi´s mapping function

GR TE Consensus map Number of mapped markers (M) 682 627 1210

Max observed map distance (X, cM) 22.5 43.1 16.3 Number of strong linkage (K)a 7243 6122 18562 1442.8 2763.3 1284.6 Estimated genome length (Ge, cM ) Confidence interval (95%) 1379-1444 2335-2972 1264-1305 86.3% 84.5% 87.2% Expected genome map coverage (Ce, %) b 1264.4 1220.5 1385.6 Observed genome length (Go, cM) 89.7% 46.7% 107.9% Observed genome map coverage (Go/Ge, %) a LOD threshold of 4.0 b Based on the sum length of all linkage groups for each linkage map

6.4 Discussion

Co-dominant microsatellite markers can provide good coverage of the Vitis genome with a smaller number of markers than dominant markers of other types (Riaz et al. 2004). SNPs can also be suitable for rapid development of dense maps in segregating populations. Therewere three maps constructed with larger number of microsatelite markers: the linkage map using Syrah x Grenache and selfing of Riesling

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published by Adam-Blondon et al. (2004), the one using Riesling x Cabernet Sauvignon reported by Riaz et al. (2004), and last one reported by Lowe and Walker (2006) with Vitis champinii x V. riparia populations. Adam-Blondon reported 1406cM with 220 SSRs; Riaz et al. observed 1728 cM with 152 SSRs; Low and Walker constructed a 1304cM map with 205 SSRs. By contrast, the consensus map presented here covers only 1385.6 cM with 183 SSR markers and 1026 SNPs. From the comparison, the length of genome was not always correlated with the number of linkage markers, nor with the polymorphic ratio of tested markers.

When comparing our maps with integrated map published by Doligez et al. (2006) and Vezzulli et al. (2008), complete agreement existed with respect to linkage groups, although the marker order is similar but less consistent.It provided evidence for the reproducibility of SSR locations across Vitis genus, as demonstrated in several publications (Lin and Walker 1998; Tessier et al.1999; Di Gasperoet al. 2000; Fernández et al.2007). Our map could be a useful resource from which markers can be selected for other mapping projects and as the basis for an eventual high-density consensus map. The VChrs markers published by Cipriani et al.(2008) were verified in our work and they were positioned with the same order as reported. This set of marker was only used by Riaz et al.(2011), it should be added to NCBI and be used to fill the conserved region of an integrated map. Moreover, an efficient approach would be to consult previously published maps, and identified five to ten primer pairs per linkage groups to screen for polymorphism in order to quickly develop framework maps representing all 19 linkage groups.

The SNPs used in our research showed a strong coverage to saturate the gaps between SSRs loci, but the processes of SNPs filtration may eliminate possible useful information.

The marker order of SSRs on the consensus map differ from the parental maps in 4 linkage groups (LG2, LG7, L8 and LG10) for some makers pairs (Figure 6-1). It happened almost in all published mapping experiments (Riaz et al. 2004; Lowe and Walker 2006; Costantini et al. 2008). The order differences might be related to differences in recombination frequencies in some regions, although parental recombination differences were significant only in LG5, LG6 and LG 18. Furthermore,

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the marker order will be affected by population size, differences in recombination rates between parents, and loci exhibiting segregation distortion etc(Malliepard et al. 1997). Where markers are closely linked and population size are small, there is a limited statistical power to determine the correct order of loci. With the two published integrated maps, the presence of real chromosomal rearrangements is sure, the researchers can compare the result with the maps to order loci within linkage groups.

The estimated genome size of Graciano (1442.8 cM) was evidently shorter than that estimated in Vitis vinifera Syrah (1,708cM) and Grenache (1,778 cM) (Adam-Blondon et al. 2004), slightly smaller than that estimated in V. champinii , Ramsy (1468.7 cM) and V. riparia (1588.3cM) (Lowe and Walker, 2006). But he estimated genome length (cM) of Tempranillo (2763.3 cM) is greater than that observed in Cabernet Sauvignon (2,374 cM) and Riesling (2,385 cM) (Riaz et al. 2004). This discrepancy may be due to the size of the largest marker gap on each of the maps, as genome size estimations based on Hulbert’s equation (1988) will inflate with higher maximum observed map distances (X). Riaz et al. (2004) reported maximum distances between markers of 49.0 and 44.7 cM, while the value for the our consensus map were 43.1 cMfor Tempranillo map.

The estimated genome length of consensus map (1284.6 cM) is smaller than observed genome length (1385.6 cM), it caused a observed genome map coverage more than 1 (107.9%). It may be due to there are repeated SSRs or SNPs almost all linkage groups of consensus map except LG2, LG12, LG13 and LG17 (Figure 6-1).

The strong distorted segregation in LG17 of Graciano cannot be attributed to the type of molecular markers, nor to genotyping errorsbecauseboth SSR and SNPs conducted as separate experiments showed distorted segregation in the same region. It could be concluded Segragation DistortionLoci (SDL) in LG17 of Graciano (Liu et al. 2010), which can be defined as a Segragation Distortion Rigions for more research, for the reason of there are no SDL reported in grapevine, only one segragation distortion rigions was reported on LG14 (Riaz et al. 2008).

6.5 Conclusions

In this work, a genetic linkage map was constructed using 151 progeny derived from

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two Spanish wine grapes Graciano x Tempranillo (Vitis vinifera L.) with 183 polymorphic SSRs, one CAPS marker, and 1026 SNPs. Maternal, paternal and consensus maps were assembled into 19 linkage groups, spanning 1264.4 cM, 1220.5 cM, and 1385.8 cM respectively. The level of polymorphism present was not enough to construct a Tempranillo map with even genome coverage, and LG12 is split in two parts at LOD >3. These maps will be usefulin identifying QTL responsible for agronomic, enological and seed traits that have been evaluated in this progeny.

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Figure 6-1 The linkage map of F1 population from Graciano x Tempranillo GR1 LG1GRTE TE1

0.0 vmc4f8 3.8 chr1_980292_C_T 5.3 chr1_1302400_C_T 7.4 chr1_2477665_C_T 8.8 chr1_2653364_A_C 12.5 chr1_946801_C_T 14.0 chr1_3260597_A_C 14.3 chr1_1198061_A_C 16.3 chr1_1372222_C_T 16.8 chr1_3797506_C_T 0.0 chr1_946801_C_T 17.9 vmc8a7 0.0 vmc4f8 1.8 chr1_1198061_A_C 18.2 chr1_4217401_A_G 3.9 chr1_980292_C_T 3.8 chr1_1372222_C_T 18.9 chr1_2145603_A_C 5.4 chr1_1302400_C_T 5.9 chr1_2145603_A_C 19.4 chr1_4542269_A_G 7.5 chr1_2477665_C_T 8.4 vvip12 21.0 vvip12 8.9 chr1_2653364_A_C 8.9 vmc8a7 21.7 chr1_4153036_C_T 14.0 vmc8a7 9.2 chr1_4153036_C_T 22.2 chr1_4969240_A_G 14.5 chr1_3260597_A_C 11.2 chr1_6218393_C_T 23.5 chr1_5577208_C_T 17.4 chr1_3797506_C_T 12.8 chr1_6673914_C_T 23.7 chr1_6218393_C_T 18.6 chr1_4217401_A_G 14.2 chr1_7251013_C_T 25.4 chr1_6673914_C_T 19.9 chr1_4542269_A_G 14.7 vmcng2g7 25.5 chr1_SNP1517_271 22.7 chr1_4969240_A_G 15.8 chr1_7536794_G_T 26.8 chr1_7251013_C_T 24.0 chr1_5577208_C_T 17.4 chr1_7741364_C_T 27.3 chr1_7288880_C_T 26.2 chr1_SNP1517_271 20.7 vvip60 27.7 vmcng2g7 28.0 chr1_7288880_C_T 22.7 vvin61 28.4 chr1_7536794_G_T 29.1 vmcng2g7 23.3 chr1_10136235_A_G 29.8 chr1_7349729_C_T 30.5 chr1_7349729_C_T 26.0 chr1_10422513_C_T 30.0 chr1_7741364_C_T 32.6 chr1_8249755_A_G 28.8 vvis21 31.9 chr1_8249755_A_G 34.6 vvip60 30.2 vchr1b 33.6 vvip60 35.5 chr1_8467057_G_T 32.0 chr1_15605027_A_G 34.7 chr1_8467057_G_T 37.1 chr1_9000381_A_C 33.7 chr1_15909374_C_T 35.4 chr1_10136235_A_G 39.1 chr1_9996086_C_T 36.2 chr1_9000381_A_C 41.5 vvin61 37.8 chr1_10422513_C_T 42.7 chr1_11027925_C_T 38.2 chr1_9996086_C_T 44.2 vchr1b 38.9 vvin61 45.1 chr1_12046683_A_G 41.6 vvis21 45.9 chr1_14163808_C_T 41.8 chr1_11027925_C_T 47.3 chr1_17513172_A_G 43.3 vchr1b 49.9 chr1_18182887_A_C vmc8d1 56.2 vvio61 44.2 chr1_12046683_A_G 50.9 58.4 chr1_20944785_A_G 44.5 chr1_15605027_A_G vmc2b3 60.7 chr1_21505077_C_T 45.0 chr1_14163808_C_T 52.1 chr1_18404031_A_G 62.7 chr1_22190393_G_T 46.2 chr1_15909374_C_T 53.7 chr1_19009322_A_G 64.1 chr1_22466694_A_C 46.4 chr1_17513172_A_G 55.3 chr1_19715227_C_T 66.1 vmc9d3 49.1 chr1_18182887_A_C 61.4 chr1_21007656_A_G vmc8d1 62.8 vvio61 50.1 vmc2b3 64.1 chr1_21104634_A_C 51.3 chr1_18404031_A_G 66.5 chr1_21713586_A_G 52.9 chr1_19009322_A_G 69.5 chr1_22750062_C_T 54.5 chr1_19715227_C_T 71.4 vmc9d3 60.8 chr1_21007656_A_G 62.7 chr1_21104634_A_C 64.0 vvio61 66.0 chr1_21713586_A_G 67.3 chr1_20944785_A_G 69.0 chr1_22750062_C_T 69.4 chr1_21505077_C_T 71.6 vmc9d3 72.4 chr1_22190393_G_T 73.3 chr1_22466694_A_C

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GR2 LG2GRTE TE2

0.0 chr2_856575_C_T 0.8 chr2_1201275_C_T 1.7 chr2_1449046_A_G 3.3 chr2_1539000_A_G 4.6 chr2_1808997_A_C 7.4 chr2_2266367_A_G 9.5 chr2_2811785_C_T 16.2 chr2_4014240_A_G 0.0 chr2_856575_C_T 17.1 chr2_4166541_A_G 0.8 chr2_1201275_C_T 0.0 chr2_453380_A_G 23.6 chr2_5236271_G_T 2.6 chr2_938214_C_T 1.7 chr2_1449046_A_G 25.2 chr2_5279601_C_T 3.4 chr2_1539000_A_G 4.8 chr2_1459108_A_C 26.7 vvmd34 5.8 chr2_1486301_A_G 4.7 chr2_1808997_A_C 28.6 chr2_12966_C_T 7.5 chr2_2266367_A_G 7.7 chr2_1886239_A_G 29.4 chr2_5418217_A_C 9.5 chr2_2200380_C_T 9.7 chr2_2811785_C_T 32.6 vvio55 16.3 vvmd34 11.1 vvib01 34.9 vmc2c10 12.0 chr2_2486995_C_T 17.3 chr2_4014240_A_G 37.5 caps 18.4 chr2_4166541_A_G 14.9 chr2_2828762_A_G 37.8 chr2_6501610_C_T 15.6 chr2_3106328_A_C 26.6 chr2_5236271_G_T 38.5 chr2_14057713_G_T 28.8 chr2_5279601_C_T 17.7 chr2_3134968_A_G 39.1 chr2_6790442_C_T 19.7 chr2_3511534_A_C 33.3 chr2_12966_C_T 40.4 chr2_7943169_A_C 34.1 chr2_5418217_A_C 21.1 udv027 40.9 chr2_7006798_A_G 22.4 chr2_3744081_C_T 38.3 vvio55 41.9 chr2_7161346_A_G 41.4 vmc2c10 27.6 chr2_4137690_A_G 43.6 vmc5g7 31.6 vvmd34 44.0 chr2_6501610_C_T 44.0 vmc6b11 45.5 chr2_6790442_C_T 46.2 chr2_6040324_A_G 47.0 chr2_7006798_A_G 46.4 chr2_7539910_A_C 40.0 chr2_5662969_C_T 49.0 chr2_7161346_A_G 47.1 chr2_5662969_C_T 41.6 chr2_6040324_A_G 52.1 chr2_7539910_A_C 48.7 chr2_10510317_A_G 53.6 vmc5g7 49.6 chr2_10345624_C_T 49.3 chr2_7943169_A_C 53.8 vmc6b11 51.2 chr2_13148623_A_G 55.9 chr2_10510317_A_G 50.3 vmc5g7 52.1 chr2_14954503_C_T 50.6 vmc6b11 56.8 chr2_10345624_C_T 52.6 chr2_4137690_A_G 58.4 chr2_13148623_A_G 52.3 chr2_14057713_G_T 54.0 chr2_16619946_C_T 53.4 caps 59.3 chr2_14954503_C_T 55.9 chr2_18523796_C_T 61.2 chr2_16619946_C_T 56.1 chr2_3744081_C_T 63.1 chr2_18523796_C_T 56.9 chr2_18192295_G_T 64.2 chr2_18192295_G_T 57.0 udv027 64.9 chr2_18726871_A_G 57.6 chr2_18726871_A_G 66.2 vmc8c2 58.6 chr2_3511534_A_C 67.9 vmc7g3 58.9 vmc8c2 60.6 vmc7g3 60.7 chr2_3134968_A_G 62.7 chr2_3106328_A_C 63.4 chr2_2828762_A_G 66.4 chr2_2486995_C_T 67.1 vvib01 68.7 chr2_2200380_C_T 70.6 chr2_1886239_A_G 72.5 chr2_1486301_A_G 73.5 chr2_1459108_A_C 75.6 chr2_938214_C_T 78.3 chr2_453380_A_G

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6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR3 LG3GRTE TE3

0.0 chr3_257067_A_G 3.9 chr3_1255784_A_G 8.7 chr3_1672399_C_T 9.6 chr3_621609_C_T 10.2 chr3_724399_C_T 10.4 chr3_1680734_C_T 12.3 chr3_1362562_G_T 13.6 chr3_1652982_G_T 13.8 chr3_1890842_A_G 16.4 chr3_2159367_A_G 16.7 chr3_2634543_C_T 18.0 chr3_3006386_A_G 18.8 chr3_2415247_A_G 0.0 chr3_621609_C_T 20.7 chr3_2841435_C_T 0.6 chr3_724399_C_T 0.0 chr3_257067_A_G 21.4 chr3_2949498_C_T 2.7 chr3_1362562_G_T 3.9 chr3_1255784_A_G chr3_3220272_A_G 22.9 4.1 chr3_1652982_G_T 8.7 chr3_1672399_C_T chr3_3083557_A_G 6.8 chr3_2159367_A_G 10.4 chr3_1680734_C_T 24.1 chr3_3144062_G_T 9.2 chr3_2415247_A_G 13.8 chr3_1890842_A_G 24.6 chr3_3366191_A_G 11.1 chr3_2841435_C_T 16.7 chr3_2634543_C_T 25.4 chr3_3460123_A_G 11.8 chr3_2949498_C_T 18.0 chr3_3006386_A_G 25.8 vmc8f10 12.9 chr3_3083557_A_G 22.9 chr3_3220272_A_G 27.3 chr3_3243826_A_G 15.0 chr3_3144062_G_T 24.3 chr3_3366191_A_G 28.2 chr3_5519531_A_G 16.8 chr3_3243826_A_G 25.2 vmc8f10 28.7 chr3_5463233_A_G 18.2 vmc8f10 26.1 chr3_3460123_A_G 28.9 chr3_5116559_A_G 19.6 chr3_5463233_A_G 28.2 chr3_5519531_A_G 30.4 chr3_4520158_A_G 21.7 chr3_5202339_A_G 28.9 chr3_5116559_A_G 31.2 chr3_5202339_A_G 23.2 chr3_4466725_C_T 30.4 chr3_4520158_A_G 31.8 chr3_4354008_A_G 26.0 chr3_3877259_A_G 31.8 chr3_4354008_A_G 32.8 chr3_4466725_C_T 27.4 chr3_6074070_C_T 33.1 chr3_3869027_A_G 33.1 chr3_3869027_A_G 28.2 vmc1a5 38.1 vmc1a5 35.5 chr3_3877259_A_G 30.9 chr3_6551533_C_T 39.3 chr3_5968917_C_T 37.0 chr3_6074070_C_T 32.5 chr3_6812820_C_T 41.5 chr3_6512337_G_T 38.2 vmc1a5 34.3 chr3_7003964_C_T 44.7 chr3_6811622_A_G 39.7 chr3_5968917_C_T 37.7 vvmd36 46.4 chr3_7157449_A_G 39.8 chr3_6551533_C_T 40.3 vmc1g7 47.0 vvmd36 41.1 chr3_6512337_G_T 41.4 chr3_8560917_C_T 48.2 chr3_7468704_A_C 42.0 chr3_6812820_C_T 43.8 vchr3a 49.7 vmc1g7 43.8 chr3_7003964_C_T 45.0 chr3_9704632_C_T 51.4 vchr3a 44.6 chr3_6811622_A_G 46.0 chr3_10844728_A_G 52.2 vvmd28 46.4 chr3_7157449_A_G 46.7 chr3_9992068_C_T 52.9 chr3_11238850_G_T 47.3 vvmd36 50.4 chr3_16946190_A_G 54.1 chr3_10713706_A_G 47.7 chr3_7468704_A_C 51.2 chr3_17217402_G_T 55.3 chr3_11382276_A_G 50.0 vmc1g7 52.4 chr3_17820622_C_T 56.4 chr3_r_294211_A_G 51.6 chr3_8560917_C_T 53.5 chr3_SNP1219_191 59.0 chr3_14579673_A_C 51.8 chr3_11238850_G_T 54.3 chr3_18009357_A_G 60.2 chr3_16593567_A_G 53.1 vchr3a 61.5 chr3_16721456_A_G 54.5 chr3_11382276_A_G 64.3 chr3_17800427_A_G 54.6 chr3_9704632_C_T 65.0 chr3_17882474_A_G 54.9 chr3_10713706_A_G 67.7 chr3_18460170_C_T 55.5 chr3_r_294211_A_G 69.9 chr3_18461496_C_T 55.6 chr3_10844728_A_G 56.3 chr3_9992068_C_T 57.2 vvmd28 59.0 chr3_14579673_A_C 60.0 chr3_16946190_A_G 60.2 chr3_16593567_A_G 60.8 chr3_17217402_G_T 61.5 chr3_16721456_A_G 62.1 chr3_17820622_C_T 63.1 chr3_SNP1219_191 63.9 chr3_18009357_A_G 64.2 chr3_17800427_A_G 64.9 chr3_17882474_A_G 67.6 chr3_18460170_C_T 69.9 chr3_18461496_C_T

153

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR4 LG4GRTE TE4

0.0 vchr4a 1.7 chr4_165191_C_T 3.6 chr4_583500_A_G 4.8 chr4_694382_A_G 6.7 chr4_1159330_C_T 7.1 vmcng2c2.1 0.0 vchr4a 0.0 vmcng2c2.1 8.3 chr4_1299324_C_T 1.8 chr4_165191_C_T 9.0 chr4_1402616_C_T 3.6 chr4_583500_A_G 3.9 chr4_52836_C_T 10.4 chr4_1721980_A_G 4.8 chr4_694382_A_G 6.8 chr4_836438_C_T 11.2 chr4_1827376_C_T 6.6 vmcng2c2.1 11.5 chr4_52836_C_T 7.0 chr4_1159330_C_T 10.2 chr4_2234711_G_T 12.3 chr4_2047733_G_T 8.3 chr4_1299324_C_T 9.0 chr4_1402616_C_T 13.0 chr4_3819604_A_G 14.4 chr4_836438_C_T chr4_3612913_A_C 10.4 chr4_1721980_A_G 17.8 chr4_2234711_G_T 11.2 chr4_1827376_C_T 18.2 chr4_5758095_G_T 20.7 chr4_3819604_A_G 12.3 chr4_2047733_G_T 25.9 chr4_5758095_G_T 17.8 chr4_3612913_A_C 25.6 chr4_7636564_A_G 30.5 chr4_9115304_C_T 30.6 vrzag21 31.8 chr4_6959925_C_T 32.0 chr4_9494155_C_T 33.1 chr4_10021568_G_T 33.5 vmc2b5 33.4 chr4_7636564_A_G 30.8 chr4_9115304_C_T 34.2 chr4_14224653_C_T 35.0 chr4_11091087_C_T 32.1 chr4_6959925_C_T 36.4 chr4_14330886_A_G 36.2 chr4_14294357_C_T 33.4 chr4_10021568_G_T 37.4 vvin75 37.2 chr4_15212585_A_G 35.3 chr4_11091087_C_T 38.8 chrUn_13374874_A_G 38.3 chr4_16393344_C_T 36.4 chr4_14294357_C_T 39.5 chr4_16835940_A_C 39.3 chr4_9494155_C_T 37.4 chr4_15212585_A_G 41.1 vvip37 39.8 vrzag21 38.5 chr4_16393344_C_T 42.2 chr4_18327828_A_G 41.4 vmc2b5 40.6 chr4_18047362_A_G 44.3 vrzag83 42.1 chr4_14224653_C_T 42.0 vmc2b5 45.9 chrUn_24135523_A_G 42.8 chr4_18047362_A_G 43.1 vrzag21 49.0 chr4_21058901_A_G 44.3 chr4_14330886_A_G 50.3 chr4_21809735_A_C 45.4 vvin75 52.9 chr4_22126828_A_G 46.8 chrUn_13374874_A_G 55.1 chr4_22591836_C_T 47.5 chr4_16835940_A_C 55.7 chr4_22273921_C_T 49.1 vvip37 56.5 chr4_22810713_C_T 50.2 chr4_18327828_A_G 57.8 chr4_23048673_C_T 52.3 vrzag83 59.0 chr4_23381611_C_T 53.8 chrUn_24135523_A_G 61.6 vmc6g10 56.9 chr4_21058901_A_G 58.2 chr4_21809735_A_C 60.9 chr4_22126828_A_G 63.0 chr4_22591836_C_T 63.6 chr4_22273921_C_T 64.4 chr4_22810713_C_T 65.7 chr4_23048673_C_T 66.9 chr4_23381611_C_T 69.6 vmc6g10

154

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR5 LG5GRTE TE5

0.0 chr5_1707161_A_G 1.7 chr5_1249211_A_C 3.7 chr5_1141397_A_G 6.4 chr5_368005_C_T 7.8 chr5_2300500_C_T 8.9 chr5_2406415_C_T 10.2 chr5_2560157_C_T 12.2 chr5_2723418_G_T 15.1 chr5_2969847_A_G 15.8 chr5_3110237_C_T 16.3 chr5_1809400_G_T 17.3 chr5_3635981_C_T 18.9 chr5_SNP1071_151 0.0 chr5_1707161_A_G 19.1 chr5_1471041_C_T 1.7 chr5_1249211_A_C 20.2 chr5_4373666_A_G 3.7 chr5_1141397_A_G 21.0 vrzag47 6.4 chr5_368005_C_T 21.6 vchr5c 7.8 chr5_2300500_C_T 22.3 chr5_565115_A_C 9.0 chr5_2406415_C_T 22.9 chr5_4865007_A_G 10.2 chr5_2560157_C_T 23.1 chr5_474400_C_T 0.0 chr5_1809400_G_T 12.2 chr5_2723418_G_T 24.4 chr5_5042229_C_T 2.8 chr5_1471041_C_T 15.2 chr5_2969847_A_G 25.9 chr5_SNP1053_81 15.9 chr5_3110237_C_T 26.5 chr5_5570145_A_G 6.0 chr5_565115_A_C 17.3 chr5_3635981_C_T 27.8 chr5_5778744_C_T 6.8 chr5_474400_C_T 19.0 chr5_SNP1071_151 28.1 chr5_2790839_C_T 9.6 chr5_SNP1053_81 20.3 chr5_4373666_A_G 28.9 chr5_5952091_G_T 11.7 chr5_2790839_C_T 21.0 vrzag47 30.1 chr5_3285139_A_G 13.8 chr5_3285139_A_G 21.6 vchr5c 30.9 chr5_6584828_A_G 22.9 chr5_4865007_A_G 32.5 chr5_6694704_A_G 21.7 vrzag79 24.5 chr5_5042229_C_T 33.2 chr5_6925484_G_T 26.6 chr5_5570145_A_G 26.4 Vvi_5316 34.7 vrzag79 27.7 chr5_5708246_A_G 28.0 chr5_5778744_C_T 35.7 chr5_7191560_C_T 29.1 chr5_5952091_G_T 29.0 chr5_6343083_A_G 37.9 chr5_7367897_C_T 33.0 vvit68 30.4 vrzag79 39.3 chr5_7571058_A_C 31.7 chr5_6584828_A_G 34.5 chr5_7253933_G_T 42.2 vvit68 36.7 chr5_8009885_A_G 33.4 chr5_6694704_A_G 43.3 chr5_7992188_C_T 40.2 chr5_r_249870_C_T 34.0 chr5_6925484_G_T 43.8 Vvi_5316 36.1 chr5_7191560_C_T 42.3 vmc3c7 44.0 chr5_8029875_A_G 42.7 vchr5a 37.3 vvit68 45.0 chr5_5708246_A_G 39.3 chr5_7367897_C_T 43.1 chr5_13749881_C_T 45.2 chr5_8091466_C_T 43.8 chr5_14739326_A_G 40.8 chr5_7571058_A_C 46.4 chr5_6343083_A_G 44.4 chr5_7992188_C_T 46.3 vmc9b5 47.2 chr5_8522692_G_T 46.9 chr5_19770665_A_G 45.1 chr5_8029875_A_G 50.9 chr5_7253933_G_T 48.1 chr5_19979526_A_G 46.4 chr5_8091466_C_T 51.5 chr5_8835832_C_T 48.6 chr5_8522692_G_T 49.0 chr5_20046991_A_G 53.4 chr5_8009885_A_G 51.2 chr5_22325190_A_G 53.4 chr5_8835832_C_T 54.0 chr5_9105135_A_C 56.2 chr5_9105135_A_C 52.3 chr5_23291717_A_C 56.2 chr5_9640285_C_T 55.2 chr5_23411191_G_T 58.6 chr5_9640285_C_T 56.6 vchr5b 59.0 vchr5b 60.2 chr5_24472817_A_G 57.3 chr5_r_249870_C_T 61.5 vmc4c6 61.1 chr5_r_361363_A_G 58.5 chr5_r_361363_A_G 61.9 chr5_24787430_A_C 62.1 chr5_10288195_A_G 59.5 chr5_10288195_A_G 64.4 vchr5a 59.9 chr5_13749881_C_T 65.0 vmc3c7 60.8 vmc3c7 65.8 chr5_14699639_C_T 61.1 chr5_14739326_A_G 67.0 vvmd14 61.3 vchr5a 67.2 chr5_17966666_A_C 63.0 chr5_14699639_C_T 69.2 chr5_18607493_C_T 63.9 chr5_17966666_A_C 71.0 chr5_19068202_G_T 64.0 chr5_19770665_A_G 73.0 chr5_19693160_C_T 65.1 chr5_19979526_A_G 73.7 chr5_19906220_C_T chr5_20046991_A_G 74.4 vmc9b5 65.8 chr5_18607493_C_T 77.4 chr5_20536827_C_T 67.3 vmc9b5 79.9 chr5_20817657_A_C 68.1 chr5_19068202_G_T 80.8 chr5_21133172_A_G 68.6 chr5_22325190_A_G 85.2 chr5_22156335_C_T 69.8 chr5_23291717_A_C 86.0 chr5_22582065_A_G 69.9 chr5_19693160_C_T 87.2 chr5_22461042_C_T 70.4 chr5_19906220_C_T 88.8 chr5_22680146_C_T 72.7 chr5_23411191_G_T 90.0 chr5_23345127_A_G 73.8 chr5_20536827_C_T 91.0 chr5_23367056_A_C 76.4 chr5_20817657_A_C 94.4 chr5_23454965_A_G 77.3 chr5_21133172_A_G 94.9 chr5_23478053_C_T 77.7 chr5_24472817_A_G 97.0 chr5_24239258_A_G 79.0 vmc4c6 98.4 chr5_24297333_G_T 79.1 vvmd14 102.1 chr5_24781748_C_T 79.4 chr5_24787430_A_C 81.8 chr5_22156335_C_T 82.6 chr5_22582065_A_G 83.8 chr5_22461042_C_T 85.4 chr5_22680146_C_T 86.6 chr5_23345127_A_G 87.6 chr5_23367056_A_C 91.0 chr5_23454965_A_G 91.5 chr5_23478053_C_T 93.6 chr5_24239258_A_G 95.0 chr5_24297333_G_T 98.7 chr5_24781748_C_T

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6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR6 LG6GRTE TE6

0.0 chr6_164244_C_T 1.4 chr6_627563_C_T 1.5 chr6_311434_C_T 2.5 chr6_487780_C_T 3.1 chr6_1695062_G_T 3.7 chr6_854671_A_G 4.6 chr6_969829_C_T 5.2 chr6_1253753_A_G 5.9 chr6_1585014_A_G 7.0 chr6_2606549_A_G 0.0 chr6_311434_C_T 7.6 chr6_2460525_G_T 0.0 chr6_164244_C_T 1.0 chr6_487780_C_T 9.4 chr6_3197255_C_T 1.4 chr6_627563_C_T 2.2 chr6_854671_A_G 10.7 chr6_3444326_C_T 3.1 chr6_1695062_G_T 3.7 chr6_1253753_A_G 11.0 chr6_3043358_C_T 5.5 chr6_2606549_A_G 4.6 chr6_969829_C_T 11.6 chr6_3481146_C_T 5.9 chr6_1585014_A_G 6.1 chr6_2460525_G_T 14.5 chr6_4173691_C_T 7.9 chr6_3197255_C_T 15.2 chr6_3701206_A_C 9.2 chr6_3444326_C_T 15.6 chr6_4348319_A_G 10.0 chr6_3481146_C_T 16.7 chr6_4525685_G_T 11.0 chr6_3043358_C_T 17.2 chr6_3853012_C_T 13.6 chr6_3701206_A_C 18.1 chr6_4794688_C_T 19.2 chr6_4927847_A_G 14.5 chr6_4173691_C_T 15.6 chr6_3853012_C_T 20.0 chr6_4433397_A_G 15.6 chr6_4348319_A_G 16.7 chr6_4525685_G_T 17.7 chr6_4433397_A_G 22.6 chr6_5375964_G_T 23.4 chr6_5187299_G_T 18.2 chr6_4794688_C_T 24.7 vmc5c5 19.3 chr6_4927847_A_G 26.1 vmc2h9 22.4 chr6_5187299_G_T 27.0 chr6_6505368_C_T 22.7 chr6_5375964_G_T 27.8 vchr6a vmc5c5 25.1 chr6_5703589_A_G 28.1 chr6_5703589_A_G 24.4 vmc2h9 26.2 chr6_6141654_C_T 29.0 chr6_6141654_C_T 27.2 chr6_6505368_C_T 27.6 chr6_6469843_A_G 29.3 chr6_6963055_A_G 28.4 vmc2h9 28.0 vchr6a 30.6 chr6_6469843_A_G 29.0 chr6_6963055_A_G 28.9 vmc5c5 32.0 chr6_7164977_C_T 30.8 chr6_7164977_C_T 32.4 chr6_7770420_A_G 33.4 vmc2f10 33.7 vmc2f10 32.9 vmc2f10 34.6 chr6_8428465_C_T 35.2 chr6_8128583_C_T 33.9 chr6_7770420_A_G 35.8 chr6_12279868_A_C 36.0 chr6_8838324_A_G 35.5 chr6_8128583_C_T 37.1 chr6_14068682_A_C 36.3 chr6_8428465_C_T 36.9 chr6_8838324_A_G 38.4 vmc3f12 37.6 chr6_12279868_A_C 38.3 chr6_9423425_A_G vmc4g6 39.9 38.0 chr6_9423425_A_G vmc3a8 38.9 chr6_14068682_A_C 40.9 chr6_12698902_C_T 41.3 chr6_16895611_A_G 39.7 chr6_12698902_C_T 42.0 chr6_12803613_G_T 43.6 chr6_13325126_C_T 44.0 chr6_17621861_C_T 40.7 vmc3f12 41.6 chr6_16895611_A_G 45.1 chr6_15613114_A_G 46.0 chr6_17828680_C_T vmc3a8 42.0 chr6_12803613_G_T 45.8 47.2 chr6_17897551_A_G 43.2 vmc3a8 vmc4g6 48.4 chr6_18189932_G_T 43.3 vmc4g6 47.4 vmc3f12 49.6 vmcng4b9 44.2 chr6_13325126_C_T 48.8 chr6_14784507_A_G 51.6 vvim43 45.0 chr6_15613114_A_G 50.3 chr6_17112506_A_G 52.1 chr6_19044643_C_T 46.0 chr6_17621861_C_T 51.7 chr6_17515249_C_T 47.6 chr6_14784507_A_G 53.3 chr6_18029643_C_T 54.8 chr6_19623776_G_T 48.0 chr6_17828680_C_T 56.6 chr6_18279334_C_T chr6_17112506_A_G 57.1 vmcng4b9 49.2 57.5 chr6_18497326_A_G 58.5 chr6_20877816_C_T chr6_17897551_A_G 50.1 chr6_17515249_C_T 58.0 vvim43 50.9 chr6_18189932_G_T 59.1 chr6_19095204_A_G 61.3 chr6_21182099_G_T 51.9 chr6_18029643_C_T 62.4 chr6_19995259_G_T 52.6 chr6_19044643_C_T 53.8 vmcng4b9 64.4 chr6_21342791_G_T 55.0 vvim43 55.6 chr6_18279334_C_T 56.1 chr6_18497326_A_G 56.9 chr6_19623776_G_T 57.6 chr6_19095204_A_G 60.6 chr6_20877816_C_T 60.8 chr6_19995259_G_T 62.8 chr6_21342791_G_T 63.4 chr6_21182099_G_T

156

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR7 LG7GRTE TE7

0.0 chr7_190897_A_G 4.4 chr7_393512_A_G 5.7 chr7_159555_C_T 9.2 vvmd7 12.1 chr7_1127987_A_G 13.7 chr7_1720231_C_T 16.5 chr7_1923765_C_T 17.5 vrzag62 18.7 chr7_51263_C_T 0.0 chr7_190897_A_G 19.9 chr7_851402_A_G 4.4 chr7_393512_A_G 0.0 chr7_51263_C_T 20.4 chr7_3316137_A_C 5.7 chr7_159555_C_T 1.4 chr7_851402_A_G 22.7 vvmd6 9.2 vvmd7 4.6 vrzag62 24.4 chr7_1339430_C_T 12.1 chr7_1127987_A_G 6.4 chr7_1339430_C_T 25.2 chr7_4151125_C_T 13.8 chr7_1720231_C_T 8.8 chr7_1653105_A_G 26.7 chr7_1653105_A_G 16.2 vrzag62 11.5 chr7_4569169_A_G 27.7 vvmd31 17.2 chr7_1923765_C_T 12.1 vmc5h5 28.4 vmc5h5 20.4 chr7_3316137_A_C 13.6 vvmd6 29.3 chr7_4619472_A_G 22.8 vvmd6 15.6 chr7_6360389_A_G 29.9 chr7_4569169_A_G 25.3 chr7_4151125_C_T 17.6 vmc1a2 30.9 chr7_4984109_A_G 27.2 vmc5h5 18.1 vmc7a4 32.4 chr7_6360389_A_G 28.6 chr7_4619472_A_G 19.2 chr7_8522438_A_G 33.2 chr7_6004128_C_T 29.5 vvmd31 21.8 chr7_10425536_C_T 34.8 vmc7a4 31.0 chr7_4984109_A_G 24.6 chr7_r_672819_A_G 35.6 vmc1a2 33.3 chr7_6004128_C_T 27.9 chr7_r_387208_C_T 36.0 chr7_8522438_A_G 35.3 vmc7a4 36.7 chr7_7065478_C_T 36.2 chr7_7065478_C_T 38.3 chr7_8211796_A_G 37.3 vmc1a2 38.7 chr7_10425536_C_T 38.5 chr7_8211796_A_G 40.7 chr7_10409620_C_T 40.9 chr7_10409620_C_T 47.6 chr7_r_158057_A_G 41.4 chr7_r_672819_A_G 43.4 chr7_11482500_A_C 48.7 vchr7b 43.2 chr7_11482500_A_C 44.9 chr7_13477822_C_T 52.9 vmc1a12 44.6 chr7_r_387208_C_T 50.2 chr7_r_560185_C_T 54.4 chr7_r_935700_C_T 44.7 chr7_13477822_C_T 51.7 chr7_r_1335243_C_T 55.8 chr7_15331303_C_T 50.0 chr7_r_560185_C_T 57.1 chr7_14386112_C_T 58.5 chr7_16669818_C_T 51.5 chr7_r_1335243_C_T 59.6 vchr7b 62.5 vviv04 57.0 chr7_14386112_C_T 61.1 chr7_r_9164_A_G 60.0 chr7_r_9164_A_G 65.7 chr7_18116772_A_G 69.4 chr7_15235038_C_T 62.5 vchr7b 73.1 vmc1a12 63.5 chr7_r_158057_A_G 74.6 chr7_15683821_C_T chr7_r_935700_C_T 74.1 chr7_20849686_A_G 69.2 75.8 chr7_16135908_A_G 74.9 chr7_20686646_A_G chr7_15235038_C_T 77.1 vchr7a 70.5 chr7_15331303_C_T 78.2 vviv04 72.1 vmc1a12 79.0 chr7_17285718_A_G 74.1 chr7_16669818_C_T 80.5 chr7_17906761_A_G 74.3 chr7_15683821_C_T 81.4 chr7_17841371_C_T 75.5 chr7_16135908_A_G 84.8 chr7_19260167_C_T 76.8 vchr7a 85.6 chr7_18958948_A_G 78.3 vviv04 86.7 chr7_19791356_A_G 78.8 chr7_17285718_A_G 88.1 chr7_20282778_C_T 80.2 chr7_17906761_A_G 89.4 chr7_20759134_A_G 81.1 chr7_17841371_C_T 81.4 chr7_18116772_A_G 84.5 chr7_19260167_C_T 85.3 chr7_18958948_A_G 86.4 chr7_19791356_A_G 87.8 chr7_20282778_C_T 89.2 chr7_20759134_A_G 89.8 chr7_20849686_A_G 90.6 chr7_20686646_A_G

157

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR8 LG8GRTE TE8

vmc2f12 0.0 vmc1f10 1.4 chr8_130628_A_G 2.9 chr8_53721_C_T 3.2 vchr8a 4.2 chr8_699182_G_T 4.4 chr8_819802_A_G 5.2 vmc6g8 6.0 chr8_1300165_C_T 13.5 vmcng2h2.2 13.9 vchr8b 0.0 chr8_130628_A_G 14.4 chr8_4740668_C_T 1.7 vmc2f12 16.1 chr8_3328580_C_T 2.0 vmc1f10 16.3 chr8_5969102_C_T 2.4 vchr8a 17.4 chr8_9471590_A_G 4.4 chr8_819802_A_G 0.0 vmc1f10 18.2 chr8_9038752_A_C 5.3 vmc6g8 0.5 vmc2f12 19.9 chr8_9906730_A_G 6.1 chr8_1300165_C_T 4.7 chr8_53721_C_T 24.2 udv126 11.4 chr8_5969102_C_T 6.1 chr8_699182_G_T 25.4 chr8_10929180_G_T 13.7 chr8_4740668_C_T 26.8 chr8_11100449_A_G 15.1 vmcng2h2.2 27.5 chr8_11139859_C_T 15.4 vchr8b 30.2 chr8_11785715_A_G 23.6 udv126 17.2 vmcng2h2.2 31.3 chr8_12264242_A_C 24.9 chr8_10929180_G_T 19.7 chr8_3328580_C_T 31.6 chr8_11860475_C_T 26.2 chr8_11100449_A_G 21.2 chr8_9471590_A_G 32.8 chr8_11969014_C_T 26.9 chr8_11139859_C_T 21.9 chr8_9038752_A_C 33.8 chr8_12293738_C_T 29.5 chr8_11785715_A_G 23.8 chr8_9906730_A_G 35.9 chr8_SNP1323_155 31.0 chr8_11860475_C_T 37.2 chr8_14172672_A_G 32.1 chr8_11969014_C_T 38.9 chr8_14748169_C_T 35.1 chr8_SNP1323_155 40.3 chr8_14968275_G_T 36.4 chr8_14172672_A_G 41.6 chr8_13954154_A_G 38.1 chr8_14748169_C_T 38.1 chr8_12264242_A_C 43.2 chr8_15496987_C_T 39.5 chr8_14968275_G_T 41.0 chr8_12293738_C_T 45.4 chr8_14569602_C_T 42.3 chr8_15496987_C_T 45.5 chr8_15939465_C_T 44.6 chr8_15939465_C_T 46.9 chr8_SNP865_80 46.0 chr8_SNP865_80 50.1 chr8_13954154_A_G 48.0 chr8_15687049_A_G 47.4 chr8_16374635_A_C 54.2 chr8_14569602_C_T 48.4 chr8_16374635_A_C 49.7 chr8_16688802_A_G 57.0 chr8_15687049_A_G 48.8 vmc5h2 51.1 chr8_16983472_A_C 57.9 vmc5h2 50.0 chr8_16092315_A_G 52.4 vmc3c9 59.0 chr8_16092315_A_G 50.7 chr8_16688802_A_G 52.9 chr8_17456803_A_G 65.5 chr8_18915165_A_C 52.1 chr8_16983472_A_C 54.4 chr8_17688015_A_C 65.9 vmc4d3 53.6 chr8_17456803_A_G 55.7 vmc4d3 66.0 chr8_17780974_C_T 54.7 vmc3c9 56.4 chr8_18215325_A_G 66.6 vmc3c9 55.4 chr8_17688015_A_C 59.7 chr8_18899443_A_G 67.0 vmc1e8 55.7 vmc1e8 61.9 chr8_19089523_A_G 72.1 chr8_21030434_C_T 56.6 chr8_17780974_C_T 62.7 chr8_19189267_A_G 73.9 chr8_21486439_C_T 57.0 chr8_18215325_A_G 67.7 chr8_19730855_G_T 76.4 chr8_22330438_C_T 57.5 vmc4d3 70.6 chr8_20163834_C_T 59.6 chr8_18915165_A_C 72.0 chr8_20273978_A_G 60.8 chr8_18899443_A_G 75.4 chr8_20746592_C_T 63.0 chr8_19089523_A_G 76.7 chr8_21237701_C_T 63.2 chr8_21030434_C_T 77.7 vmc2h10 63.8 chr8_19189267_A_G 80.7 chr8_21609656_G_T 65.0 chr8_21486439_C_T 81.6 chr8_21757936_C_T 67.4 chr8_22330438_C_T 83.0 chr8_21825217_A_G 68.8 chr8_19730855_G_T 83.9 chr8_22257532_A_G 71.6 chr8_20163834_C_T 73.1 chr8_20273978_A_G 76.5 chr8_20746592_C_T 77.8 chr8_21237701_C_T 78.8 vmc2h10 81.8 chr8_21609656_G_T 82.7 chr8_21757936_C_T 84.0 chr8_21825217_A_G 84.9 chr8_22257532_A_G

158

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR9 LG9GRTE TE9

0.0 chr9_122035_A_G 1.1 chr9_290178_C_T 2.6 chr9_457915_A_C 5.9 vmc1c10 7.9 chr9_1317148_A_C 8.5 chr9_72274_A_G 9.8 chr9_1233128_A_G 10.3 chr9_476129_A_G 10.6 chr9_1621121_C_T chr9_1379768_C_T 0.0 chr9_72274_A_G 11.8 chr9_2107772_A_C 0.0 chr9_122035_A_G 1.5 vmc1c10 13.1 chr9_1399157_A_G 2.9 chr9_476129_A_G 1.1 chr9_290178_C_T 13.2 chr9_2650925_A_G 2.6 chr9_457915_A_C 4.3 chr9_1379768_C_T 15.2 chr9_1679575_C_T 5.6 chr9_1399157_A_G 3.8 vmc1c10 16.6 chr9_2947014_A_G 8.2 chr9_1317148_A_C 7.6 chr9_1679575_C_T 16.8 chr9_2448248_C_T 9.1 chr9_2448248_C_T 10.1 chr9_1233128_A_G 17.8 chr9_3157020_C_T 10.9 chr9_1621121_C_T 11.2 chr9_2957825_A_G 18.7 chr9_3297341_A_G 13.7 chr9_3436753_A_G 12.2 chr9_2107772_A_C 18.9 chr9_2957825_A_G 13.6 chr9_2650925_A_G 15.3 chr9_4877122_A_G 21.5 chr9_3436753_A_G 16.4 chr9_3823194_A_G 17.0 chr9_2947014_A_G 22.0 chr9_3405399_A_G 18.4 chr9_3157020_C_T 18.0 vmc3g8.2 22.6 vviu37 19.0 chr9_3928908_A_G 19.3 chr9_3297341_A_G 23.2 chr9_4877122_A_G 22.6 chr9_3405399_A_G 21.0 chr9_5608664_A_C 24.3 chr9_3823194_A_G 22.3 chr9_5889908_C_T 23.4 vviu37 24.8 chr9_3650890_A_C 25.5 chr9_3650890_A_C 23.8 chr9_6263865_A_G 25.8 vmc6d12 24.8 chr9_6361386_C_T 26.6 vmc6d12 25.9 vmc3g8.2 27.7 chr9_3837013_A_C 25.8 vchr9a chr9_3928908_A_G 26.9 29.5 chr9_4195384_A_G 27.6 chr9_6524069_C_T chr9_3837013_A_C 28.9 chr9_6900298_A_G 30.4 chr9_4457964_G_T 28.6 chr9_4195384_A_G 31.9 chr9_5437172_A_C 30.7 chr9_7063034_A_G 29.0 chr9_5608664_A_C 32.2 chr9_7439368_C_T 33.2 chr9_5511405_G_T 29.5 chr9_4457964_G_T 33.8 chr9_5800178_C_T 33.7 vmc5c1 30.3 chr9_5889908_C_T 35.1 chr9_9646625_C_T 35.8 vchr9a 31.0 chr9_5437172_A_C 37.0 chr9_6318512_G_T 36.4 chr9_10245498_C_T 31.8 chr9_6263865_A_G 37.0 chr9_10284861_C_T 38.1 chr9_6634888_C_T 32.2 chr9_5511405_G_T 39.5 chr9_7093179_C_T 38.9 chr9_10751092_G_T chr9_6361386_C_T 42.5 chr9_13695918_C_T 32.9 41.6 chr9_8317218_C_T chr9_5800178_C_T 42.9 chr9_8958030_A_C 46.3 vmc4a5 34.3 vchr9a 45.4 vmc5c1 35.7 chr9_6524069_C_T 46.1 chr9_9650098_A_G 36.0 chr9_6318512_G_T 47.8 chr9_10684474_G_T 36.9 chr9_6900298_A_G 50.6 chr9_10827524_C_T 37.1 chr9_6634888_C_T 54.4 vmc2e11 38.5 chr9_7093179_C_T 38.8 chr9_7063034_A_G 57.0 vmc4a5 40.4 chr9_7439368_C_T 40.5 chr9_8317218_C_T 41.8 chr9_8958030_A_C 43.0 vmc5c1 43.3 chr9_9646625_C_T 44.6 chr9_10245498_C_T 44.9 chr9_9650098_A_G 45.1 chr9_10284861_C_T 46.6 chr9_10684474_G_T 47.2 chr9_10751092_G_T 49.3 chr9_10827524_C_T 50.8 chr9_13695918_C_T 54.9 vmc4a5 62.7 vmc2e11

159

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR10 LG10GRTE TE10

0.0 chrUn_1429004_C_T 1.9 chr10_17403870_C_T 2.8 chr10_17281922_A_G 4.5 chr10_15521152_C_T 5.8 chrUn_1530187_A_G 6.9 chrUn_1431220_G_T 7.5 chr10_10784022_A_C 8.6 vviv37 9.9 chr10_8198035_A_G 12.4 chr10_7426153_A_G 13.1 chr10_6826760_A_G 13.8 vvin78 15.0 chr10_6726486_G_T 0.0 vmc8d3 0.0 chr10_125104_A_G 15.8 chr10_6482366_C_T 0.6 chr10_6482366_C_T 4.3 vmc3d7 17.1 vmc8d3 4.2 chr10_5199136_G_T 6.2 chr10_918773_C_T 17.8 udv059 4.9 chr10_5033489_A_G 7.5 vvih04 18.7 chr10_5451601_A_G 5.8 chr10_4716067_C_T 8.6 chr10_1197827_A_G 19.7 chr10_5356065_C_T 7.2 chr10_3722750_C_T 10.7 chrUn_10557449_A_G 19.8 chr10_5199136_G_T 8.0 chr10_3565911_A_G 12.2 chrUn_10652040_A_G 21.0 chr10_4716067_C_T 9.9 vrzag64 14.1 chrUn_29523002_A_G 21.4 chr10_5033489_A_G 11.7 vrzag25 15.3 chrUn_25661491_A_G 22.5 vchr10a 13.0 chr10_2473876_C_T 18.0 chrUn_12012200_C_T 23.5 chr10_3722750_C_T 14.6 chr10_1674923_A_G 21.9 chrUn_29210164_A_G chr10_5252431_C_T 23.8 15.4 vvir21 23.8 chr10_r_233796_G_T chr10_3565911_A_G vrzag67 25.5 udv073 24.7 chr10_SNP397_331 16.6 vmc4f9.2 26.6 chrUn_30130559_A_G 25.4 chr10_4876135_C_T 17.0 vchr10a 28.5 chr10_1616598_C_T 26.9 vrzag25 17.7 chr10_1327527_C_T 28.8 vrzag64 27.7 chr10_4037020_C_T 19.1 udv073 29.2 vvir21 28.2 chr10_2473876_C_T 20.0 chr10_1277204_C_T 29.4 vrzag67 29.6 chr10_3149377_C_T 22.2 chr10_r_249601_A_G 30.6 chr10_2590369_A_G 30.1 chr10_1674923_A_G 23.0 chr10_r_341718_A_G 31.8 chr10_2924682_A_C chr10_2924682_A_C 30.9 24.0 chrUn_38947038_C_T 33.1 chr10_3149377_C_T chr10_1327527_C_T 24.9 chrUn_39007685_C_T 35.1 chr10_4037020_C_T 31.8 vvir21 26.2 chrUn_7950570_C_T 37.1 chr10_4876135_C_T 32.2 vmc4f9.2 27.6 chrUn_7638675_C_T 38.0 chr10_SNP397_331 32.4 vrzag67 29.7 chrUn_11885470_A_G 40.1 chr10_5252431_C_T 33.2 chr10_2590369_A_G 32.5 chrUn_38718713_C_T 43.0 chr10_5356065_C_T 33.5 vrzag64 33.8 chr10_1262661_A_C vmc8d3 44.5 34.1 chr10_1616598_C_T 35.0 chr10_r_207287_A_G udv059 35.1 chr10_1277204_C_T 35.8 chrUn_18379786_G_T 45.4 chr10_5451601_A_G 35.8 udv073 37.1 chrUn_8998494_C_T 48.1 chr10_6726486_G_T 36.3 chrUn_30130559_A_G 49.3 vvin78 37.4 chr10_r_249601_A_G 49.9 chr10_6826760_A_G 38.0 chr10_r_341718_A_G 51.2 chr10_7426153_A_G 38.7 chr10_r_233796_G_T 53.4 vchr10a 39.2 chrUn_38947038_C_T 54.1 chr10_8198035_A_G 40.1 chrUn_39007685_C_T 55.8 chr10_10784022_A_C 40.6 chrUn_29210164_A_G 57.0 chrUn_1431220_G_T 41.3 chrUn_7950570_C_T 57.8 chrUn_1530187_A_G 42.7 chrUn_7638675_C_T 59.1 chr10_15521152_C_T 44.4 chrUn_12012200_C_T 60.2 chr10_17281922_A_G 44.7 chrUn_11885470_A_G 61.7 vviv37 47.1 chrUn_25661491_A_G 63.0 chr10_17403870_C_T 47.5 chrUn_38718713_C_T 64.1 chrUn_1429004_C_T 48.2 chrUn_29523002_A_G 48.9 chr10_1262661_A_C 50.0 chr10_r_207287_A_G chrUn_18379786_G_T 50.9 chrUn_10652040_A_G 51.6 chr10_125104_A_G 52.1 chrUn_8998494_C_T 52.4 chrUn_10557449_A_G 54.4 chr10_1197827_A_G 55.7 vvih04 56.7 chr10_918773_C_T 58.8 vmc3d7

160

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR11 LG11GRTE TE11

0.0 chr11_427464_C_T 0.9 vvim04 1.2 chr11_322317_A_G 2.1 vmcng2h1 2.8 chr11_493263_A_G chr11_799567_A_G 4.8 chr11_1176772_A_G 5.7 chr11_1548729_C_T 6.1 vmc2a12 6.4 chr11_1073005_C_T chr11_2028061_A_G 9.1 chr11_1820630_C_T 9.7 chr11_1811149_A_G 0.0 chr11_427464_C_T 12.6 chr11_2841764_C_T 0.9 vvim04 13.0 chr11_2466738_A_G 0.0 chr11_322317_A_G 2.1 vmcng2h1 14.0 vvmd25 0.8 vmcng2h1 4.8 chr11_1176772_A_G 16.3 chr11_3521933_C_T 1.6 chr11_493263_A_G 5.7 chr11_1548729_C_T 16.4 chr11_3319204_A_G 3.6 chr11_799567_A_G 6.1 vmc2a12 19.3 chr11_3781637_A_G 5.2 chr11_1073005_C_T 9.1 chr11_1820630_C_T 20.0 chr11_3661147_A_G 7.9 chr11_2028061_A_G 9.7 chr11_1811149_A_G 20.5 chr11_4137446_A_G 12.6 chr11_2841764_C_T 21.6 vvs2 11.9 chr11_2466738_A_G 14.1 vvmd25 23.0 chr11_4885995_A_G 13.2 vvmd25 16.3 chr11_3521933_C_T 23.4 chr11_3693100_A_C 15.3 chr11_3319204_A_G 19.3 chr11_3781637_A_G 24.5 chr11_4928633_A_C 18.8 chr11_3661147_A_G 20.4 vvs2 25.5 chr11_5020655_C_T 20.7 vvs2 21.0 chr11_4137446_A_G 25.7 chr11_3932187_A_G 22.2 chr11_3693100_A_C 23.1 chr11_4885995_A_G 28.2 chr11_4476380_C_T 24.5 chr11_3932187_A_G 24.5 chr11_4928633_A_C 28.9 chr11_5427850_A_G 27.0 chr11_4476380_C_T 25.5 chr11_5020655_C_T 30.2 chr11_5088655_A_G 28.9 chr11_5088655_A_G 29.0 chr11_5427850_A_G 31.1 chr11_5873119_C_T 30.1 chr11_5295818_C_T 31.2 chr11_5873119_C_T 31.4 chr11_5295818_C_T 32.0 chr11_6749504_C_T 32.7 chr11_6409009_A_G 32.6 chr11_6409009_A_G 33.5 chr11_7647278_G_T 34.0 chr11_6483081_A_G 33.3 chr11_6749504_C_T 34.8 chr11_7841231_C_T 37.6 chr11_r_65847_A_G 33.9 chr11_6483081_A_G 35.8 chr11_8613276_A_G 38.6 chr11_7623774_C_T 34.8 chr11_7647278_G_T 37.0 udv017 39.9 vchr11a 36.1 chr11_7841231_C_T 39.0 chr11_9376176_G_T 41.1 udv017 37.0 chr11_8613276_A_G 40.8 chr11_9644687_A_C 43.1 chr11_9161865_C_T 37.5 chr11_r_65847_A_G 44.3 chr11_15623525_A_G 46.8 chr11_9816477_C_T 38.4 chr11_7623774_C_T 45.8 udv048 48.1 chr11_13832606_C_T 39.5 chr11_9376176_G_T 47.6 chr11_17451665_A_G 49.2 chr11_14080414_A_G 39.7 udv017 49.3 chr11_17710544_C_T 49.9 chr11_17051504_C_T 40.7 vchr11a 51.4 chr11_18318407_A_C 51.3 vchr11b 42.0 chr11_9644687_A_C 52.1 chr11_18428521_C_T 42.9 chr11_9161865_C_T 54.3 chr11_18812572_C_T 58.1 chr11_18295660_C_T 45.5 chr11_15623525_A_G 57.2 chr11_19350926_A_C 59.4 chr11_18907256_A_G 46.6 chr11_9816477_C_T 57.9 chr11_19692807_C_T 62.3 chr11_19032965_C_T 47.1 udv048 62.9 chr11_19235777_C_T 47.9 chr11_13832606_C_T 63.8 chr11_19306558_A_G 48.8 chr11_17451665_A_G 49.0 chr11_14080414_A_G 49.7 chr11_17051504_C_T 50.6 chr11_17710544_C_T 51.1 vchr11b 52.6 chr11_18318407_A_C 53.3 chr11_18428521_C_T 55.6 chr11_18812572_C_T 57.9 chr11_18295660_C_T 58.4 chr11_19350926_A_C chr11_19692807_C_T 59.2 chr11_18907256_A_G 62.1 chr11_19032965_C_T 62.7 chr11_19235777_C_T 63.6 chr11_19306558_A_G

161

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR12 LG12GRTE TE12

0.0 chr12_593945_A_G 1.8 chr12_359038_A_G 2.5 chr12_SNP557_104 2.6 chr12_142586_A_G 3.3 chr12_74800_C_T 4.4 chr12_1145771_G_T 4.8 chr12_989873_G_T 5.5 chr12_1648693_A_G 5.6 chr12_1741119_A_G 0.0 chr12_593945_A_G 7.3 vmc8g6 1.8 chr12_359038_A_G 7.9 chr12_2757585_A_G 2.6 chr12_142586_A_G 8.0 chr12_2091978_A_G 4.8 chr12_989873_G_T 8.7 chr12_2093062_A_G 5.6 chr12_1741119_A_G 9.7 chr12_2989610_C_T 0.0 chr12_SNP557_104 7.3 vmc8g6 10.3 chr12_2867057_C_T 0.8 chr12_74800_C_T 8.0 chr12_2091978_A_G 11.6 chr12_3089570_G_T 2.0 chr12_1145771_G_T 8.7 chr12_2093062_A_G 12.9 chr12_2034441_A_C 3.1 chr12_1648693_A_G 9.7 chr12_2989610_C_T 13.1 chr12_3303460_A_G 4.7 chr12_2757585_A_G 11.6 chr12_3089570_G_T 13.7 chr12_3409163_C_T 5.7 vmc8g6 13.1 chr12_3303460_A_G 14.5 chr12_3634144_C_T 7.8 chr12_2867057_C_T 13.7 chr12_3409163_C_T 16.5 chr12_4293556_A_G 10.5 chr12_2034441_A_C 14.5 chr12_3634144_C_T 20.0 chr12_5332930_A_G 16.5 chr12_4293556_A_G 20.7 vmc3b8 20.0 chr12_5332930_A_G 21.6 chr12_5428824_C_T 20.7 vmc3b8 22.8 vmc2h4 21.6 chr12_5428824_C_T 24.8 chr12_5597636_A_G 22.8 vmc2h4 25.9 chr12_5893238_C_T 24.8 chr12_5597636_A_G 27.5 chr12_6209397_C_T 25.9 chr12_5893238_C_T 29.1 vchr12a 27.5 chr12_6209397_C_T 34.8 chr12_6729463_A_C 29.1 vchr12a 35.8 chr12_6939918_A_G 34.8 chr12_6729463_A_C 47.9 chr12_8743886_A_G 35.8 chr12_6939918_A_G 49.2 chr12_8908147_A_G 47.9 chr12_8743886_A_G 50.9 scu05 49.2 chr12_8908147_A_G 51.5 chr12_8967843_C_T 50.9 scu05 55.4 chr12_9304147_A_G 53.6 chr12_13597099_A_G 51.5 chr12_8967843_C_T 57.1 vmcng2d11 54.3 chr12_14056587_A_G 55.4 chr12_9304147_A_G 57.3 chr12_13597099_A_G 55.7 vmc4f3.1 57.1 vmcng2d11 58.0 chr12_14056587_A_G 57.9 chr12_13224272_A_G 58.4 chr12_9696429_A_G 58.4 chr12_9696429_A_G 59.7 chr12_16950713_A_G 59.1 udv024 59.1 udv024 61.6 chr12_17430195_C_T 59.9 chr12_r_205913_A_G 59.9 chr12_r_205913_A_G 62.3 vviv05 60.7 chr12_12132083_G_T 60.7 chr12_12132083_G_T 62.8 chr12_18281945_C_T 62.1 chr12_13292076_A_G 61.0 vmc4f3.1 63.6 chr12_19602649_A_G 62.5 vmc4f3.1 61.6 chr12_13224272_A_G 64.8 chr12_20234675_A_G 64.3 chr12_17932801_A_G 62.4 chr12_13292076_A_G 65.7 chr12_21905574_C_T 64.9 vviv05 63.3 chr12_16950713_A_G 66.8 chr12_22601976_A_C 66.7 chr12_18843270_A_G 64.3 chr12_17932801_A_G 67.3 chr12_22535561_C_T 69.5 vmc8g9 65.2 vviv05 70.6 vmc8g9 70.5 chr12_20278712_A_G 65.8 chr12_17430195_C_T 72.2 chr12_21797697_C_T 66.4 chr12_18281945_C_T 73.7 chr12_21997669_A_G 66.7 chr12_18843270_A_G 74.8 chr12_22272818_A_G 67.2 chr12_19602649_A_G 76.3 chr12_22089935_C_T 68.4 chr12_20234675_A_G 77.8 chr12_20386067_C_T 69.2 chr12_21905574_C_T 69.9 chr12_20278712_A_G 70.3 chr12_22601976_A_C 70.9 chr12_22535561_C_T 71.4 chr12_21797697_C_T 72.5 vmc8g9 73.7 chr12_21997669_A_G 74.8 chr12_22272818_A_G 76.4 chr12_22089935_C_T 77.9 chr12_20386067_C_T

162

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR13 LG13GRTE TE13

0.0 chr13_603590_A_C 0.6 chr13_960637_C_T 3.5 chr13_1446367_A_G 4.6 chr13_1662986_A_G 6.1 chr13_1899681_A_C 6.3 chr13_275408_C_T 7.5 chr13_2103615_A_G 8.2 chr13_638880_G_T 10.3 chr13_2065690_A_G 11.4 chr13_1218676_C_T 0.0 chr13_275408_C_T 11.8 chr13_2265895_G_T 1.9 chr13_638880_G_T 12.7 chr13_1919094_C_T 0.0 chr13_603590_A_C 4.0 chr13_2065690_A_G 13.2 chr13_2552278_A_C 0.6 chr13_960637_C_T 5.1 chr13_1218676_C_T 15.3 chr13_2619357_A_G 3.5 chr13_1446367_A_G 6.4 chr13_1919094_C_T 15.5 chr13_2879235_C_T 4.6 chr13_1662986_A_G 9.0 chr13_2619357_A_G 16.8 chr13_2724670_C_T 6.1 chr13_1899681_A_C 10.4 chr13_2724670_C_T 17.0 chr13_3138348_G_T 7.5 chr13_2103615_A_G 11.3 chr13_2922475_A_G 17.7 chr13_2922475_A_G 11.8 chr13_2265895_G_T 14.0 chr13_3918645_A_G 19.1 chr13_3452695_G_T 13.2 chr13_2552278_A_C 15.3 chr13_4243525_C_T 20.3 chr13_3918645_A_G 15.5 chr13_2879235_C_T 16.1 chr13_4277087_C_T 21.0 chr13_3951265_C_T 17.0 chr13_3138348_G_T 17.4 chr13_4433482_A_G 21.6 chr13_4243525_C_T 19.1 chr13_3452695_G_T 19.5 chr13_5125130_C_T 21.9 chr13_4305456_A_C 21.2 chr13_3951265_C_T 21.7 chr13_6463193_A_G 22.4 chr13_4277087_C_T 22.0 chr13_4305456_A_C 23.8 chr13_6906550_A_G 23.7 chr13_4433482_A_G 23.0 vchr13a 26.5 chr13_7847968_C_T 24.2 chr13_4801491_A_G 24.9 chr13_4801491_A_G 27.3 vchr13a 25.3 chr13_5348792_A_G 26.3 chr13_5348792_A_G 28.4 chr13_8179675_C_T 25.9 chr13_5125130_C_T 32.1 vviv61 26.4 vchr13a 33.0 chr13_9770777_A_C 28.5 chr13_6463193_A_G 34.7 vmc9h4.2 30.7 chr13_6906550_A_G 34.6 chr13_6716593_C_T 34.9 chr13_SNP351_85 33.2 chr13_6716593_C_T 38.9 chr13_10142668_A_G 36.9 chr13_18824885_C_T 33.3 chr13_7847968_C_T 39.4 vviv61 38.9 chr13_r_1620637_A_G 34.8 chr13_8179675_C_T 42.0 chr13_15914815_C_T 40.7 chr13_20599551_G_T 37.1 chr13_10142668_A_G 44.2 chr13_16597047_A_G 41.4 vmc2c7 38.1 vviv61 46.8 vmc9h4.2 42.5 chr13_21350738_C_T 39.6 chr13_9770777_A_C 47.6 chr13_r_1137256_C_T 40.0 chr13_15914815_C_T 48.8 chr13_r_386355_G_T 41.5 chr13_SNP351_85 51.6 chr13_19564663_C_T 42.0 chr13_16597047_A_G 52.9 chr13_19790934_A_C 43.1 vmc9h4.2 55.4 vmc2c7 43.7 chr13_18824885_C_T 56.9 chr13_r_3138200_C_T 45.2 chr13_r_1137256_C_T 58.0 chr13_r_3054098_A_G 45.7 chr13_r_1620637_A_G 58.9 chr13_20966778_A_G 46.4 chr13_r_386355_G_T 60.3 chr13_21596725_A_G 47.7 chr13_20599551_G_T 63.0 chr13_22067532_A_G 48.8 chr13_21350738_C_T 64.4 vmc8e6 49.1 chr13_19564663_C_T 66.0 chr13_23155679_C_T 50.5 vmc2c7 51.1 chr13_19790934_A_C 54.2 chr13_r_3138200_C_T 55.3 chr13_r_3054098_A_G 56.2 chr13_20966778_A_G 57.5 chr13_21596725_A_G 60.2 chr13_22067532_A_G 61.7 vmc8e6 63.2 chr13_23155679_C_T

163

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR14 LG14GRTE TE14

0.0 chr14_611632_A_C 1.2 chr14_517339_C_T 2.6 chr14_1527129_A_G 4.3 chr14_1027333_A_G 5.7 vmcng1e1 7.1 chr14_1562032_G_T 8.3 vchr14b 9.7 chr14_1898044_C_T 11.7 vmc8h10 12.4 chr14_3218096_A_G 0.0 chr14_1027333_A_G 13.4 chr14_2975911_G_T 2.1 vmcng1e1 14.2 vmc9c1 2.7 chr14_1562032_G_T 15.2 chr14_6342640_A_G 4.6 vchr14b 17.4 chr14_6398046_A_G 5.3 chr14_1898044_C_T 19.2 chr14_6746866_A_G 7.3 vmc8h10 23.1 chr14_9064962_A_G 7.9 chr14_3218096_A_G 25.4 vmc2h12 0.0 chr14_611632_A_C 9.7 vmc9c1 26.6 chr14_11803886_C_T 1.2 chr14_517339_C_T 10.7 chr14_6342640_A_G 28.0 chr14_6991825_A_G 2.6 chr14_1527129_A_G 12.8 chr14_6398046_A_G 29.0 chr14_10902962_A_G 4.8 vmcng1e1 13.9 chr14_6746866_A_G 29.5 chr14_12840882_A_G 7.6 vchr14b 18.8 vmc2h12 vmc2c3 19.7 chr14_9064962_A_G 31.5 chr14_17454496_G_T 11.9 vmc8h10 22.0 chr14_11803886_C_T 32.2 chr14_18042657_G_T 14.1 chr14_2975911_G_T 24.9 chr14_12840882_A_G 33.8 chr14_19726771_A_G 26.2 vrzag112 34.7 vrzag112 27.2 vmc2c3 36.0 chr14_22675729_G_T 28.2 chr14_18042657_G_T 36.8 chr14_19756741_A_G 29.1 chr14_19726771_A_G 37.9 vmc2b11 30.0 vmc2h12 31.4 chr14_22675729_G_T 38.5 chr14_23522081_C_T 31.1 chr14_6991825_A_G 34.0 chr14_23522081_C_T 38.8 chr14_20675721_A_G 32.1 chr14_10902962_A_G 35.0 vmc5b3 39.5 chr14_20786114_G_T 34.7 chr14_17454496_G_T 35.9 chr14_23045539_G_T 40.4 chr14_23045539_G_T 39.3 chr14_19756741_A_G 38.6 chr14_24021046_G_T 41.0 chr14_21295670_C_T 40.5 chr14_20786114_G_T 39.6 chr14_24088161_G_T 41.8 vmc5b3 41.2 vmc2b11 43.7 chr14_25300201_A_G 42.7 chr14_22534912_A_G 42.1 chr14_20675721_A_G 45.2 chr14_26172964_A_G 42.9 chr14_24021046_G_T 43.4 vrzag112 47.2 vvin64 43.8 chr14_22978848_G_T 44.3 chr14_21295670_C_T 47.7 chr14_26527394_A_C 43.9 chr14_24088161_G_T 45.6 chr14_22534912_A_G 48.9 chr14_26948704_C_T 46.6 chr14_25199144_A_G 46.7 chr14_22978848_G_T 50.2 chr14_27571526_A_G 47.8 chr14_25300201_A_G 47.8 vmc5b3 50.5 vmc6e1 48.0 chr14_26710454_A_C 49.7 chr14_25199144_A_G 50.6 vvis70 49.0 chr14_26809933_A_G 51.1 chr14_26710454_A_C 51.2 vmcng1g1.1 49.2 chr14_26172964_A_G 51.4 vvin64 52.2 chr14_27761507_A_G 50.3 vvin64 52.4 chr14_26809933_A_G 53.5 chr14_28447066_C_T 51.2 chr14_26527394_A_C 54.7 chr14_27652060_A_G 54.3 chr14_28794351_A_G 51.8 chr14_27652060_A_G 55.4 vmcng1g1.1 56.3 chr14_28222915_C_T 52.3 chr14_26948704_C_T 55.9 vmc6e1 57.7 chr14_29333037_A_C 53.6 vmc6e1 60.5 chr14_28055030_C_T 58.5 chr14_29440153_C_T 53.9 vmcng1g1.1 64.7 chr14_28950013_A_G 59.6 chr14_29533991_C_T 54.1 vvis70 65.7 vvii51 61.3 chr14_30206391_C_T 54.7 chr14_27571526_A_G 68.1 chr14_29643103_A_G 56.0 chr14_27761507_A_G 70.7 chr14_29770277_C_T 57.2 chr14_28447066_C_T 73.4 chr14_30022737_A_G 57.6 chr14_28055030_C_T 58.1 chr14_28794351_A_G 60.1 chr14_28222915_C_T chr14_29333037_A_C 61.8 chr14_28950013_A_G 62.1 chr14_29440153_C_T 62.9 vvii51 63.0 chr14_29533991_C_T 65.0 chr14_30206391_C_T 65.2 chr14_29643103_A_G 67.8 chr14_29770277_C_T 70.5 chr14_30022737_A_G

164

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR15 LG15GRTE TE15

0.0 chr15_1076855_C_T 1.3 chr15_2904233_A_C chr15_8204460_A_G 2.6 chr15_356487_C_T 4.0 chr15_8655099_C_T 5.0 chr15_1791572_G_T 5.4 chr15_6824635_A_C 6.8 vviv67 8.2 chr15_1109421_A_G 8.6 chr15_9668745_C_T 9.6 chr15_1308142_A_C 10.4 chr15_8692106_A_C 10.6 chr15_10443234_G_T 12.1 chr15_10896524_A_G 0.0 chr15_1076855_C_T 12.3 chr15_6920720_C_T 1.3 chr15_2904233_A_C 14.1 chr15_11453926_C_T 2.6 chr15_8204460_A_G 14.2 chr15_10932946_A_C 0.0 vviv67 4.0 chr15_8655099_C_T 16.0 chr15_11992275_C_T 4.2 chr15_356487_C_T 5.4 chr15_6824635_A_C 17.2 chr15_11902885_A_G 6.5 chr15_1791572_G_T 7.4 chr15_9668745_C_T 18.9 chr15_13004700_A_G 9.0 chr15_1109421_A_G 9.5 vviv67 20.8 chr15_13264066_A_G 9.9 chr15_1308142_A_C 11.1 chr15_10443234_G_T 21.0 chr15_14352726_A_G 11.6 chr15_8692106_A_C 12.2 chr15_10896524_A_G 22.2 chr15_13645913_C_T 13.1 chr15_6920720_C_T 14.2 chr15_11453926_C_T 23.5 chr15_14603759_C_T 14.9 chr15_10932946_A_C 16.0 chr15_11992275_C_T 23.8 chr15_14344407_C_T 17.9 chr15_11902885_A_G 18.8 chr15_13004700_A_G 24.4 chr15_15005972_A_C 21.6 chr15_13264066_A_G 21.0 chr15_14352726_A_G 25.2 vmc5g8 23.0 chr15_13645913_C_T 22.2 vmc5g8 27.7 chr15_15102375_G_T 24.6 chr15_14344407_C_T 23.7 chr15_14603759_C_T 27.8 chr15_15216602_A_G 26.4 vmc5g8 25.1 chr15_15005972_A_C 29.3 vviv24 29.2 chr15_15102375_G_T 27.7 chr15_15216602_A_G 29.8 chr15_16004756_A_G 32.8 vvim42a 29.0 vvim42a 30.9 vvim42a 33.4 vmc4d9.2 29.3 vviv24 31.4 chr15_16209401_A_G 34.1 chr15_15838840_C_T 30.0 chr15_16004756_A_G 32.7 chr15_16458936_C_T 34.7 chr15_16527445_C_T 31.4 chr15_16209401_A_G 32.8 chr15_15838840_C_T 36.7 chr15_17007714_G_T 32.6 chr15_16458936_C_T 33.6 chr15_16527445_C_T 38.1 chr15_17373824_C_T 34.0 chr15_16618969_C_T 33.9 vmc4d9.2 39.4 chr15_17651852_C_T 34.5 vmc4d9.2 34.2 chr15_16618969_C_T 40.8 chr15_18150736_G_T chr15_17007714_G_T 41.9 chr15_18049623_A_C 35.9 chr15_17088101_A_C 35.9 37.5 chr15_17275357_A_C chr15_17088101_A_C 43.3 chr15_18466081_A_C 38.9 chr15_17365510_A_G 37.3 chr15_17373824_C_T 44.3 chr15_18728309_A_G 40.0 chr15_17688852_A_G 37.5 chr15_17275357_A_C 45.0 chr15_18774361_C_T 41.8 chr15_18382830_G_T 38.6 chr15_17651852_C_T 46.4 chr15_18903503_A_G 42.9 chr15_18171756_A_G 38.8 chr15_17365510_A_G 47.2 chr15_19318357_A_G 45.4 chr15_18757538_G_T 40.1 chr15_18150736_G_T 49.2 chr15_19681072_A_G 47.0 chr15_18972075_G_T 40.3 chr15_17688852_A_G 52.0 chr15_19709422_A_G 49.1 chr15_19189885_A_G 41.2 chr15_18049623_A_C 53.3 vchr15a 52.6 chr15_19340957_A_C 41.3 chr15_18382830_G_T 54.9 chr15_20128152_A_G 56.0 chr15_20021255_C_T 42.5 chr15_18466081_A_C 57.8 chr15_20073433_A_G 42.9 chr15_18171756_A_G 58.7 chr15_20177207_C_T 43.6 chr15_18728309_A_G 62.0 vchr15a 44.2 chr15_18774361_C_T 45.2 chr15_18757538_G_T 45.7 chr15_18903503_A_G 46.5 chr15_19318357_A_G 46.8 chr15_18972075_G_T 48.5 chr15_19681072_A_G 48.9 chr15_19189885_A_G 51.4 chr15_19709422_A_G 52.3 chr15_19340957_A_C 53.3 chr15_20128152_A_G 54.7 vchr15a 56.5 chr15_20021255_C_T 58.6 chr15_20073433_A_G 59.7 chr15_20177207_C_T

165

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR16 LG16GRTE TE16

0.0 chr16_564726_A_G 1.2 chr16_390838_A_G 7.2 vvin52 9.4 chr16_247768_C_T 10.8 chr16_2071561_A_G 12.0 chr16_1046232_A_C 13.0 chr16_5831871_C_T 14.2 udv032 14.3 chr16_2628110_A_G 15.3 chr16_2398048_A_G 15.9 chr16_6635670_A_G 16.7 chr16_2777528_A_C 0.0 chr16_247768_C_T 18.7 chr16_3076923_A_C 1.1 vvin52 24.6 chr16_6156372_A_G 3.2 chr16_1046232_A_C 27.2 chr16_8312253_C_T 5.6 udv032 0.0 vvin52 28.1 vviv17 6.5 chr16_2398048_A_G 28.6 chr16_14236898_A_C 7.9 chr16_2777528_A_C 4.5 chr16_564726_A_G 29.1 chr16_11883624_A_G 9.8 chr16_3076923_A_C 5.6 chr16_390838_A_G 29.4 chr16_13924855_A_C 15.4 chr16_6156372_A_G chr16_14941632_C_T 18.0 chr16_8312253_C_T 30.3 12.8 udv032 chr16_13176674_A_C 18.8 vviv17 15.1 chr16_2071561_A_G 31.3 vmc3g11 19.8 chr16_11883624_A_G 16.6 chr16_2628110_A_G 31.5 chr16_13454897_G_T 21.0 chr16_13176674_A_C 18.4 chr16_5831871_C_T 31.7 chr16_14904922_C_T 21.9 vmc3g11 19.4 chr16_6635670_A_G 33.0 chr16_14012643_A_C 22.2 chr16_13454897_G_T 33.2 chr16_15839130_C_T 23.6 chr16_14012643_A_C 33.9 chr16_14031523_A_G 24.6 chr16_14031523_A_G 35.1 chr16_16062134_C_T 27.2 chr16_14621383_A_C 33.8 chr16_14236898_A_C 36.4 chr16_16755485_C_T 28.5 chr16_14962132_A_G 34.6 chr16_13924855_A_C 36.6 chr16_14621383_A_C 30.7 chr16_15919400_G_T 35.6 chr16_14941632_C_T 37.8 chr16_16811470_A_G 30.8 vchr16b 37.1 chr16_14904922_C_T 38.0 chr16_14962132_A_G 31.4 chr16_16828360_A_C 38.6 chr16_15839130_C_T 39.0 chr16_16977773_G_T 32.8 chr16_17364047_G_T 40.7 chr16_16062134_C_T 40.2 chr16_15919400_G_T 35.0 chr16_17727730_G_T 42.0 chr16_16755485_C_T 40.4 vchr16b 37.8 chr16_18613606_A_G 43.5 chr16_16811470_A_G 40.9 chr16_16828360_A_C 41.8 chr16_19543957_A_C 44.8 chr16_16977773_G_T 42.4 chr16_17364047_G_T 43.2 chr16_19685105_G_T 42.7 chr16_17636753_C_T vvmd37 48.6 chr16_17636753_C_T 44.8 52.6 chr16_18123606_A_G 44.6 chr16_17727730_G_T vmc5f5 54.0 chr16_18611653_A_G 46.4 chr16_18123606_A_G 48.2 chr16_21063951_G_T 56.8 chr16_19317589_A_C 47.6 chr16_18613606_A_G 49.6 chr16_21175234_C_T 57.5 chr16_19332808_A_G 47.8 chr16_18611653_A_G 51.0 chr16_21452310_A_G 62.0 chr16_19684096_A_G 50.5 chr16_19317589_A_C 51.8 vmc5a1 63.1 chr16_20650586_A_G 51.2 chr16_19332808_A_G 52.9 chr16_21816296_C_T 64.6 vmc5f5 51.6 chr16_19543957_A_C 54.5 vmc4b7.2 65.6 chr16_21143114_A_G 53.0 chr16_19685105_G_T 66.7 chr16_20984180_A_G 54.7 vvmd37 68.1 chr16_21637262_G_T 55.4 chr16_19684096_A_G 69.2 chr16_21684113_C_T 56.0 vmc5f5 70.2 chr16_21774875_G_T 57.0 chr16_20650586_A_G 71.1 vmc5a1 58.1 chr16_21063951_G_T 74.5 vvmd5 59.0 chr16_21143114_A_G 59.5 chr16_21175234_C_T 60.0 chr16_20984180_A_G 60.9 chr16_21452310_A_G 61.5 chr16_21637262_G_T 61.9 chr16_21816296_C_T 62.7 vmc5a1 63.2 chr16_21684113_C_T 64.3 chr16_21774875_G_T 64.5 vmc4b7.2 67.7 vvmd5

166

6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR17 LG17GRTE TE17

0.0 chr17_12149478_A_C 1.4 chr17_11155358_A_G 3.0 vviv08 5.2 vvin68 6.2 vmc9g4 0.0 chr17_2458289_A_G 7.7 chr17_8394730_A_C 0.9 chr17_2545670_A_G 24.0 chr17_5927601_C_T 0.0 chr17_126505_A_G 1.7 chr17_2738511_A_G 29.5 vvin73 1.2 chr17_411270_A_C 5.8 chr17_3740954_C_T 31.6 chr17_5317010_A_G 2.4 chr17_663737_A_G 6.7 chr17_3918881_C_T 32.9 chr17_5098307_A_C 3.7 chr17_846245_C_T 7.7 chr17_4566595_C_T 33.5 chr17_4794234_C_T 5.8 chr17_1487449_A_G 9.1 vviq22b 34.3 vviq22b 8.7 chr17_1589362_A_G 10.6 chr17_5209230_C_T 34.9 chr17_4530106_C_T 10.1 chr17_1772634_C_T 12.2 vchr17c 35.4 vchr17c 11.6 chr17_2362414_A_C 12.6 chr17_5541199_A_G 36.2 chr17_5209230_C_T 12.3 vmc3c11.1 13.7 chr17_5675510_C_T 36.4 chr17_5541199_A_G 13.4 chr17_3023784_G_T 14.8 chr17_6144365_C_T 36.8 chr17_6144365_C_T 14.8 vmc2h3 15.4 vmc2h3 37.7 chr17_5675510_C_T 15.3 chr17_3709859_G_T 20.2 chr17_6892227_A_C 39.7 chr17_4566595_C_T 20.5 chr17_3803904_A_G 21.8 vrzag15 40.3 chr17_4243383_A_G 23.0 chr17_4243383_A_G 23.2 chr17_7049764_A_G 40.5 chr17_3918881_C_T 27.8 chr17_4530106_C_T 23.8 chr17_7538286_A_G 41.5 chr17_3740954_C_T 28.5 vviq22b 29.8 chr17_9758858_A_G 42.7 chr17_3803904_A_G 29.2 chr17_4794234_C_T 30.5 chr17_9828632_C_T 44.8 chr17_2738511_A_G 29.9 chr17_5098307_A_C 32.2 chr17_10738550_A_C 45.8 chr17_2458289_A_G 31.2 chr17_5317010_A_G 32.8 chr17_10152197_C_T 46.3 chr17_2545670_A_G 33.1 vchr17c 33.8 chr17_11736527_A_G 47.8 vmc2h3 34.0 vvin73 34.7 chr17_11764553_A_G 48.4 chr17_3709859_G_T 39.5 chr17_5927601_C_T 36.8 chr17_11643601_C_T 49.3 vrzag15 49.8 chr17_3023784_G_T 50.6 chr17_6892227_A_C 50.9 vmc3c11.1 51.6 chr17_2362414_A_C 53.1 chr17_1772634_C_T 53.5 chr17_7049764_A_G 54.2 chr17_7538286_A_G 56.0 chr17_8394730_A_C 54.6 chr17_1589362_A_G 57.5 vmc9g4 57.4 chr17_1487449_A_G 58.5 vvin68 59.5 chr17_846245_C_T 60.7 vviv08 59.9 chr17_9758858_A_G 62.3 chr17_11155358_A_G 60.6 chr17_9828632_C_T 63.7 chr17_12149478_A_C 60.8 chr17_663737_A_G 62.0 chr17_411270_A_C 62.3 chr17_10738550_A_C 62.9 chr17_10152197_C_T 63.2 chr17_126505_A_G 63.9 chr17_11736527_A_G 64.8 chr17_11764553_A_G 66.9 chr17_11643601_C_T

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6 A Genetic Linkage Map of a Graciano x Tempranillo Wine Grape Population

GR18 LG18GRTE TE18

0.0 vmc2a3 2.9 chr18_882766_A_G 7.1 chr18_1413920_C_T 9.9 chr18_2682028_G_T 12.7 vmc3e5 12.8 chr18_3182141_A_C 14.0 chr18_350514_C_T 14.6 chr18_657173_C_T 17.2 chr18_949408_C_T 18.6 chr18_1216061_A_G 20.0 chr18_1581001_C_T 21.4 chr18_1752360_G_T 22.8 chr18_2049874_C_T 24.2 chr18_2989358_A_G 26.3 chr18_3349358_C_T 0.0 vmc3e5 27.4 chr18_6483117_C_T 1.2 chr18_350514_C_T 29.7 chr18_6647705_A_G 1.9 chr18_657173_C_T 31.4 chr18_4047334_C_T 4.5 chr18_949408_C_T 5.9 chr18_1216061_A_G 31.8 chr18_6812296_C_T 7.3 chr18_1581001_C_T 32.7 chr18_4279998_A_G 32.9 chr18_6981288_C_T 0.0 vmc2a3 8.7 chr18_1752360_G_T 34.8 chr18_4347089_A_G 10.0 chr18_2049874_C_T 38.0 chr18_7878415_C_T 2.9 chr18_882766_A_G 11.5 chr18_2989358_A_G 38.2 chr18_4702061_A_G 13.6 chr18_3349358_C_T 38.9 chr18_8106640_G_T 7.1 chr18_1413920_C_T 18.6 chr18_4047334_C_T 20.0 chr18_4279998_A_G 40.1 vvim93 9.9 chr18_2682028_G_T 22.1 chr18_4347089_A_G 42.5 chr18_5280694_A_G 12.8 chr18_3182141_A_C 25.5 chr18_4702061_A_G 43.4 chr18_9410687_A_G 27.3 vmcng1b9 44.4 vmcng1b9 29.5 vvim93 45.4 chr18_9454209_C_T 30.9 chr18_5280694_A_G 45.9 chr18_5481124_A_G 33.7 chr18_5481124_A_G 47.3 chr18_10060673_C_T 34.8 vvim72 47.6 vvim72 36.1 chr18_SNP453_375 48.9 chr18_SNP453_375 27.4 chr18_6483117_C_T 38.2 chr18_7047685_G_T 50.1 chr18_10233056_C_T 29.7 chr18_6647705_A_G 38.9 chr18_7446110_A_C 51.0 chr18_7047685_G_T 31.9 chr18_6812296_C_T 51.7 chr18_7446110_A_C 33.1 chr18_6981288_C_T 41.6 chr18_9340550_A_G 54.8 chr18_9340550_A_G 43.9 chr18_10156555_C_T 56.5 chr18_10156555_C_T 35.0 vvim93 44.6 udv005 45.5 vvim10 56.6 chr18_11214982_A_G 39.0 chr18_7878415_C_T 57.5 udv005 40.9 chr18_8106640_G_T 46.0 chr18_11046746_C_T 58.4 vvim10 47.7 chr18_11977415_G_T 58.6 chr18_11468483_C_T 44.2 chr18_9410687_A_G 49.6 chr18_12077018_C_T 58.9 chr18_11046746_C_T 46.3 chr18_9454209_C_T 51.4 vvip08 60.0 chr18_11544918_C_T 48.3 chr18_10060673_C_T 53.1 udv117 60.6 chr18_11977415_G_T 51.2 chr18_10233056_C_T 53.4 chr18_13410273_A_G 61.1 chr18_12012282_G_T 54.4 vviu04 57.9 chr18_11214982_A_G 54.9 vmc2a7 62.5 chr18_12077018_C_T 60.0 chr18_11468483_C_T 55.2 chr18_17449052_C_T 63.8 chr18_13176714_A_G 61.4 chr18_11544918_C_T 64.4 vvip08 62.5 chr18_12012282_G_T 56.0 udv134 64.8 chr18_13442809_C_T 56.1 chr18_18040316_A_G 64.9 chr18_13176714_A_G 57.0 chr18_r_578035_A_G 65.5 udv117 65.7 udv117 58.2 chr18_r_2677945_C_T 66.5 chr18_13410273_A_G 67.0 chr18_13442809_C_T 66.9 chr18_13477594_A_G 68.5 chr18_13477594_A_G 59.6 chr18_19179094_G_T 67.4 chr18_17449052_C_T 62.2 chr18_19472444_C_T 69.4 chr18_13699106_A_G 63.5 chr18_19574196_A_C 67.8 chr18_13699106_A_G 69.9 vviu04 68.1 vmc2a7 70.9 chr18_14167184_A_G 63.8 vmcng2f12 68.2 vviu04 67.7 chr18_24102114_G_T 72.0 chr18_14131357_A_G 69.1 chr18_18040316_A_G 73.6 chr18_14305845_A_G 68.3 vvin16 69.4 chr18_14167184_A_G 70.5 vmc7f2 74.5 udv134 69.6 chr18_r_578035_A_G 75.1 chr18_r_2413513_A_G 71.5 chr18_26702868_C_T 70.1 udv134 73.0 chr18_27469511_A_G 77.8 chr18_19866092_C_T 73.6 chr18_r_4506475_A_G 71.1 chr18_r_2677945_C_T 78.2 vmcng2f12 71.2 chr18_14131357_A_G 79.0 chr18_20006640_A_C 75.0 chr18_27445707_A_G 72.5 chr18_19179094_G_T 76.6 chr18_r_3430364_C_T 80.3 chr18_23605680_C_T 79.6 chr18_28798739_C_T 72.8 chr18_14305845_A_G 81.7 vvin16 73.8 chr18_r_2413513_A_G 84.2 chr18_r_3085413_A_G 84.7 chr18_29067693_A_G 75.4 chr18_19472444_C_T 85.9 chr18_29021394_A_G 85.9 chr18_24467985_G_T 75.8 chr18_19866092_C_T 87.4 chr18_25040954_A_G 76.2 chr18_19574196_A_C 89.9 chr18_25931148_G_T 76.6 vmcng2f12 99.5 chr18_28134820_G_T 77.4 chr18_20006640_A_C 101.7 chr18_29262315_C_T 78.7 chr18_23605680_C_T 80.5 vvin16 81.1 chr18_24102114_G_T 82.6 chr18_r_3085413_A_G 83.4 vmc7f2 84.3 chr18_24467985_G_T 84.4 chr18_26702868_C_T 85.8 chr18_25040954_A_G 85.9 chr18_27469511_A_G 86.6 chr18_r_4506475_A_G 87.9 chr18_27445707_A_G 88.3 chr18_25931148_G_T 89.6 chr18_r_3430364_C_T 92.6 chr18_28798739_C_T 97.6 chr18_29067693_A_G 97.9 chr18_28134820_G_T 98.8 chr18_29021394_A_G 100.1 chr18_29262315_C_T

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GR19 LG19GRTE TE19

0.0 chr19_556638_A_C 0.9 chr19_847340_C_T 1.9 chr19_1003246_A_G 2.4 chr19_1332963_A_G 3.9 vchr19a 5.1 chr19_152078_A_G chr19_669060_C_T 5.9 chr19_1512110_A_C 7.6 chr19_1742936_C_T 7.8 chr19_892666_A_G 0.0 chr19_152078_A_G 9.3 chr19_1626024_A_G 0.9 chr19_669060_C_T 11.1 chr19_1671883_A_G 2.6 vchr19a 11.9 chr19_1306780_A_G 3.6 chr19_892666_A_G 12.6 chr19_2096143_C_T 5.0 chr19_1626024_A_G 13.6 chr19_2424974_A_C 0.0 vchr19a 6.8 chr19_1671883_A_G 13.9 chr19_1943319_G_T 1.2 chr19_556638_A_C 7.6 chr19_1306780_A_G 14.8 chr19_2534560_C_T 2.2 chr19_847340_C_T 9.5 chr19_1943319_G_T 15.2 chr19_3068545_C_T 3.0 chr19_1003246_A_G 10.9 chr19_3068545_C_T 17.7 vmc5e9 3.7 chr19_1332963_A_G 13.0 vmc5e9 19.3 chr19_2824959_A_G 6.7 chr19_1512110_A_C 14.9 chr19_3646758_A_C 19.7 chr19_3646758_A_C 8.3 chr19_1742936_C_T 17.7 chr19_4181812_A_G chr19_4122038_A_G 21.2 13.5 chr19_2096143_C_T 19.0 chr19_4667576_A_G chr19_4181812_A_G 14.6 chr19_2424974_A_C 22.0 chr19_5200598_A_G 23.5 chr19_4667576_A_G 15.8 chr19_2534560_C_T 23.4 chr19_6075953_G_T 24.8 chr19_5491595_C_T 19.7 chr19_2824959_A_G 24.2 chr19_5698330_A_G 26.5 chr19_5200598_A_G 25.2 chr19_6049989_G_T 21.7 chr19_4122038_A_G 27.5 chr19_6324619_A_G 23.0 vmc5e9 25.8 chr19_6167847_C_T 28.0 chr19_6075953_G_T 26.4 vviv70 26.1 chr19_5491595_C_T 29.1 chr19_5698330_A_G 29.0 chr19_6324619_A_G 27.1 chr19_6536852_A_G 29.3 chr19_6343693_C_T 28.0 vmc3b7.2 30.8 chr19_6343693_C_T 29.5 chr19_6049989_G_T 31.6 vviv70 28.8 chr19_6962291_C_T 30.5 vviv70 30.2 chr19_7308475_C_T 33.3 chr19_6490387_C_T 30.7 chr19_6167847_C_T 34.0 chr19_6746737_A_C 30.9 chr19_7441817_C_T chr19_6536852_A_G 32.3 chr19_7805128_A_G 31.8 34.8 vmc3b7.2 chr19_6490387_C_T 37.2 chr19_6987852_C_T 32.9 chr19_7842294_A_G 32.2 chr19_6746737_A_C 34.2 vmc6c7 40.7 chr19_7750416_A_G 32.9 vmc3b7.2 42.6 vmc6c7 36.4 chr19_8605080_A_G 33.6 chr19_6962291_C_T 37.0 vchr19b 44.2 vchr19b 34.7 chr19_7308475_C_T 46.7 chr19_9007778_A_G 38.0 chr19_9149189_C_T 35.5 chr19_6987852_C_T 39.8 chr19_9705567_A_G 49.5 chr19_9659960_A_C 35.7 chr19_7441817_C_T 51.7 chr19_10502194_G_T 43.3 chr19_13987684_C_T 36.8 chr19_7805128_A_G 43.9 chr19_17850588_C_T 53.0 chr19_17731671_A_G 37.6 chr19_7842294_A_G 54.1 chr19_16894398_C_T 45.1 chr19_17515853_A_G 38.8 chr19_7750416_A_G 46.7 chr19_19011413_A_G 56.5 chr19_22216217_C_T 39.6 vmc6c7 56.8 udv062 59.5 chr19_23090424_C_T 41.3 chr19_8605080_A_G 61.1 chr19_23742119_C_T 42.0 vchr19b 61.0 chr19_22332335_A_G 42.8 chr19_9149189_C_T 61.9 chr19_23460766_C_T 44.5 chr19_9705567_A_G 44.7 chr19_9007778_A_G 47.5 chr19_9659960_A_C 48.0 chr19_13987684_C_T 48.6 chr19_17850588_C_T 49.7 chr19_10502194_G_T 49.9 chr19_17515853_A_G 51.0 chr19_17731671_A_G 51.4 chr19_19011413_A_G 52.1 chr19_16894398_C_T 54.5 chr19_22216217_C_T 54.8 udv062 59.0 chr19_22332335_A_G 59.9 chr19_23460766_C_T 64.3 chr19_23090424_C_T 65.8 chr19_23742119_C_T

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6.6 Reference

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Appl Genet 114:1249-1263 Di Gaspero G, Cipriani G, Marrazzo MT, Andreetta D, Prado Castro MJ, Peterlunger E, Testolin R (2005) Isolation of (AC)n-microsatellites in Vitis vinifera L. and analysis of genetic background in grapevines under marker assisted selection. Mol Breed 15:11-20 Di Gaspero G, Peterluner E, Testolin R, Edwards KJ, Cipriani G (2000) Conservation of microsatellite loci within the genus Vitis. Theor Appl Genet 101:301-308 Doligez A, Adam-Blondon AF, Cipriani G, Di Gaspero G, Laucou V, Merdinoglu D, Meredith CP, Riaz S, Roux C, This P (2006) An integrated SSR map of grapevine based on five different populations. Theor Appl Genet 113:369-382 Doligez A, Bouquet A, Danglot Y, Lahogue F, Riaz S, Meredith CP, Edwards KJ, This P (2002) Genetic mapping of grapevine (Vitis vinifera L.) applied to the detection of QTLs for seedlessness and berry weight. Theor Appl Genet 105:780-795 Don RH, Cox PT, Wainwright BJ, Baker K, Mattick JS (1991) ‘Touchdown’ PCR to circumvent spurious priming during gene amplification. Nucleic Acids Res 19:4008. Doucleff M, Jin Y, Gao F, Riaz S, Krivanek AF (2004) A genetic linkage map of grape, utilizing Vitis rupestris and Vitis arizonica. Theor Appl Genet 109:1178-1187 Eibach R and Töpfer R (2003) Success in resistance breeding: ‘Regent’ and its steps into the market. Acta hort 603:687-691 Elshire RJ, Glaubitz JC, Sun Q, Poland JA, KawamotoK, BucklerES, MitchellSE (2011) A robust, simple genotyping-by-sequencing (GBS) approach forhigh diversity species. PLoS One 6:e19379 Emanuelli F, Battilana J, Costantini L, Le Cunff L, Boursiquot JM, This P, Grando MS (2010) A candidate gene association study on muscat flavor in grapevine (Vitis vinifera L.). BMC Plant Biol 10: 241 Fernandez-Gonzalez M, Mena A, Izquierdo P, Martinez J (2007) Genetic characterization of grapevine (V. vinifera L.) cultivars from Castilla La Mancha (Spain) using microsatellite markers. Vitis 46:126-130 Fischer BM, Salakhutdinov I, Akkurt M, Eibach R, Edwards KJ, Töpfer R, Zyprian EM (2004) Quantitative trait locus analysis of fungal disease resistance factors on a molecular map of grapevine. Theor Appl Genet 108:501-515 Gerber S and Rodolphe F (1994) An estimation of the genome length of maritime pine (Pinus pinaster Ati). Theor Appl Genet 88:289-292 Grando MS, Bellin D, Edwards KJ, Pozzi C, Stefanini M, Velasco R (2003) Molecular linkage maps of Vitis vinifera L. and Vitis riparia Mchx. Theor Appl Genet 106:1213-1224 Grattapaglia D and Sederoff R (1994) Genetic linkage maps of Eucalyptus grandis and Eucalyptus urophylla using a pseudo-testcross: mapping strategy and RAPD markers. Genetics 137:1121-1137 Hulbert SH, Ilott TW, Legg EJ, Lincoln SE, Lander ES, Michelmore RW (1998) Genetic analysis of the fungus Bremia lactucae, using restriction fragment length polymorphisms. Genetics 120:947-958. Ibañez J, Velez MD, de Andres MT, Borrego J (2009) Molecular markers for establishing distinctness in vegetatively propagated crops: a case study in grapevine. Theor Appl Genet 119:1213-1222 Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N, Aubourg S, Vitulo N, Jubin C, Vezzi A, Legeai F, Hugueney P, Dasilva C, Horner D, Mica E, Jublot D, Poulain J, Bruyère C, Billault A, Segurens B, Gouyvenoux M, Ugarte E, Cattonaro F, Anthouard V, Vico V, Del Fabbro C, Alaux M, Di Gaspero G, Dumas V, Felice N, Paillard S, Juman I, Moroldo M, Scalabrin S, Canaguier A, Le Clainche I, Malacrida G, Durand E, Pesole G, Laucou V, Chatelet P, Merdinoglu D, Delledonne M, Pezzotti M, Lecharny A, Scarpelli C, Artiguenave F, Pè ME, Valle G, Morgante M, Caboche M, Adam-Blondon A-F, Weissenbach J, Quétier F, Wincker P (2007) The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449:463-467. Jones GV, White MA, Cooper OR, StorchmannK (2005) Climate Change and Global Wine Quality. Climatic Change 73(3): 319-343 Kosambi DD (1944) The estimation of map distance from recombination values. Annals of

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Eugenics12:172-175 Lander ES and Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics121(1):185-99. Lande R and Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124(3):743-756. Lijavetzky D, Cabezas JA, Ibáñez A, Rodriguez V, Martínez-Zapater JM (2007) High throughput SNP discovery and genotyping in grapevine (Vitis vinifera L.) by combining a re-sequencing approach and SNPlex technology. BMC Genomics 8:424 Lin H and Walker MA (1998) Identifying grape rootstocks with simple sequence repeat (SSR) DNA markers. Am J Enol Vitic 49:403-407 LiuX, Guo L, You J, Liu X, He Y, Yuan J, Liu G, Feng Z (2010) Progress of Segregation Distortion in Genetic Mapping of Plants. Res J Agro 4(4): 78-83 Lodhi MA, Daly MJ, Ye GN, Weeden NF, Reisch BI (1995) A molecular marker based linkage map of Vitis. Genome 38:786-794 Lodhi MA and Reisch BI (1995) Nuclear DNA content of Vitis species, cultivars and other genera of the Vitaceae. Theor Appl Genet 90:11-16 Lowe KM and Walker MA (2006) Genetic linkage map of the interspecific grape rootstock cross Ramsey (Vitis champinii) × Riparia Gloire (Vitis riparia). Theor Appl Genet 112:1582-1592 Maliepaard C, Jansen J, Ooijen JW (1997) Linkage analysis in a full-sib family of an outbreeding plant species: overview and consequences for applications. Genet Res 70: 237-250 Merdinoglu D, Butterlin G, Bevilacqua L, Chiquet V, Adam-Blondon AF, Decroocq S (2005) Development and characterization of a large set of microsatellite markers in grapevine (Vitis vinifera L.) suitable for multiplex PCR. Mol Breed 15:349-366 Moroldo M, Paillard S, Marconi R, Fabrice L, Canaguier A, Cruaud C, De Berardinis V, Guichard C, Brunaud V, Le Clainche I, Scalabrin S, Testolin R, Di Gaspero G, Morgante M, Adam-Blondon AF (2008) A physical map of the heterozygous grapevine ‘Cabernet Sauvignon’ allows mapping candidate genes for disease resistance. BMC Plant Biol 8:66 Myles S, Chia JM, Hurwitz B, Simon C, Zhong GY, Buckler E, Ware D (2010) Rapid Genomic Characterization of the Genus Vitis. PLoS One 5(1):e8219 Oetting WS, Lee HK, Flanders DJ, Wiesner GL, Sellers TA, King RA (1995) Linkage analysis with multiplexed short tandem repeat polymorphisms using infrared fluorescence and M13 tailed primers. Genomics 30: 450-458, Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincoln SE, Tanksley SD (1988) Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 335(6192):721-726. Riaz S, Dangl GS, Edwards KJ, Meredith CJ (2004) A microsatellite marker based framework linkage map of Vitis vinifera L. Theor Appl Genet 108:864-872 Riaz S, Tenscher AC, Ramming DW, Walker MA (2011) Using a limited mapping strategy to identify major QTLs for resistance to grapevine powdery mildew (Erysiphe necator) and their use in marker-assisted breeding. Theor Appl Genet 122: 1059-1073 Riaz S, Tenscher AC, Rubin J, Graziani R, Pao SS, Walker MA (2008) Fine-scale genetic mapping of two Pierce’s disease resistance loci and a major segregation distortion region on chromosome 14 of grape. TheorAppl Genet 117:671-681 Salmaso M, Malacarne G, Troggio M, Faes G, Stefanini M, Grando MS, Velasco R (2008) A grapevine (Vitis vinifera L.) genetic map integrating the position of 139 expressed genes. Theor Appl Genet 116:1129-1143 Schlterer C (2004) The evolution of molecular markers - just a matter of fashion? Nat Rev Genet 5:63-69

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Scott KD, Eggler P, Seaton G, Rosetto M, Ablett EM, Lee LS, Henry RJ (2000) Analysis of SSRs derived from grape ESTs. Theor Appl Genet 100:723-726 Sefc KM, Regner F, Turetschek E, Glossl J, Steinkellner H (1999) Identification of microsatellite sequences in Vitis riparia and their applicability for genotyping of different Vitis species. Genome 42:367-373 Tessier C, DavidJ, ThisP, BoursiquotJM, CharrierA (1999) Optimization of the choice of molecular markers for varietal identification in Vitis vinifera L. Theor Appl Genet 98:171-177 Thomas MR and Scott NS (1993) Microsatellite repeats in grapevine reveal DNA polymorphisms when analysed as sequence-tagged sites (STSs). Theor Appl Genet 86:985-990 Thomas MR, Matsumoto S, Cain P, Scott N (1993) Repetitive DNA of grapevine: classes present and sequences suitable for cultivar identification. Theor Appl Genet 86:173-180 Troggio M, Malacarne G, Coppola G, Segala C, Cartwright DA, Pindo M, Stefanini M, Mank R, Moroldo M, Morgante M, Grando MS, Velasco R (2007) A dense singlenucleotidepolymorphismbased genetic linkage map of grapevine (Vitis vinifera L.) anchoring Pinot Noir bacterial artificial chromosome contigs. Genetics 176:2637-2650 Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77-78 Van Ooijen JW and Voorrips RE (2001) JoinMap® version 3.0: software for the calculation of genetic linkage maps. Plant Research International, Wageningen Velasco R, Zharkikh A, Troggio M, Cartwright DA, Cestaro A, Pruss D, Pindo M, FitzGerald LM, Vezzulli S, Reid J, Malacarne G, Iliev D, Coppola G, Wardell B, Micheletti D, Macalma TM, Facci M, Mitchell JT, Perazzolli M, Eldredge G, Gatto P, Oyzerski R, Moretto M, Gutin N, Stefanini M, Chen Y, Segala C, Davenport C, Demattè L, Mraz A, Battilana J, Stormo K, Costa F, Tao Q, Si-Ammour A, Harkins T, Lackey A, Perbost C, Taillon B, Stella A, Solovyev V, Fawcett JA, Sterck L, Vandepoele K, Grando MS, Toppo S, Moser C, Lanchbury J, Bogden R, Skolnick M, Sgaramella V, Bhatnagar SK, Fontana P, Gutin A, Van de Peer Y, Salamini F, Viola R (2007) High quality draft consensus sequence of the genome of a heterozygous grapevine variety. PLoS ONE 2:e1326. Vezzulli S, Micheletti D, Riaz S, Pindo M, Viola R, This P, Walker A, Troggio M, Velasco R (2008) A SNP transferability survey within the genus Vitis. BMC Plant Biol 8:128 Walker A, Lee E, Bogs J, Mc David DJ, Thomas MR, Robinson SP (2007) White grapes arose through the mutation of two similar and adjacent regulatory genes. Plant J 49:772-785. Wang N, Fang L, Xin H, Wang L, Li S (2012) Construction of a high-density genetic map for grape using next generation restriction-site associated DNA sequencing. BMC Plant Biol 12: 148

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7 QTL ANALYSIS OF AGRONOMIC, ENOLOGICAL AND SEED TRAITS

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7 QTL analysis of agronomic,enologicaland seedtraits

Abstract

Thirty traits including agronomic, enological and seed parameterswere evaluated in a segregating population of 151 plants for three consecutive years. AQTL (Quantitative Trait Loci) analysis was carried out using MapQTL 6.0 software with the simple interval mapping(SIM) combined with permutations analysis and the non-parametric Kruskal-Wallis (KW) test.

For agronomical traits, 25 QTLs were detected overall. Three QTLs for cluster number were identified on LG5, LG11 and LG16 and two QTLs for cluster weight on LG2 and LG18. Fertility index effects were locatedon LG2, LG9 and LG11 and four QTLs for berry weight were detected on LG3, LG5, LG8 and LG18. Among phenology-related traits two QTLs for sprouting were detected on LG8 and LG17. QTLs forflowering were detected on LG1 and LG19; and veraisontime showed significant associations with LG3, LG5 and LG9. Furthermore, QTLs were detected for ripening time, flowering period and veraison period.

For enological traits, 16 QTLs were identified for total acidity(LG12, LG14 and LG15); berry skin anthocyanins(LG1, LG2 and LG18); colour intensity(LG2 and LG3); total and extractable anthocyaninscontent(LG2, LG 3 and LG18); and tanninsand total polyphenol index (LG1, LG2 and LG9).

Fiifteen QTL were identified for seed traits where seed number and seed weight effects were mainly located on LG2, LG3, LG5 and LG11 and seed tannins, TPI and catechin contents were identified on LG7, LG15 and LG18.

Co-localization of QTL for several traits was observed on LG2, LG5 and LG18, suggesting close linkage or pleiotropic effects. Variance explained for most QTL was higher than 10% and reproducibility across years was present for some QTL.

These results reveal novel insight into the genetic control of relevant enologicaltraits for wine grape, and provide an important basis for carrying out research at the gene level facilitating MAS strategies.

Key words: Vitisvinifera, grapevine breeding, phenolic maturity, phenology

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7.1 Introductions

Grapevine (VitisVinifera L.), one of the oldest cultivated plants, is almost the most widespread fruit cropcultivated with the production of wine, juice, fresh fruit and raisins, making an important contribution to the economy of many countries in the world (Perl and Eshdat 2007; Soneji and Nagewara-Rao 2011).

Since domestication, grape breeding has combined hybridization and selection of somatic mutations leading to the existence of 5,000-10,000 cultivars of Vitisvinifera L. (Olmo 1996; Alleweldt and Possingham 1988). Though grape have tremendous genetic diversity and an extremely wild range of variant have been selected over the millennia, the production of locally adapted, high yielding quality cultivars adapted to abiotic and biotic stress are the common objectives of most grape breeding programs (Soneji and Nagewara-Rao 2011). As grapes are produced for different purpose such as table grape, raisin, and wine production, achieving these objectives is difficult (Riaz et al. 2007).

The first artificial crosses in Vitisspecies were performed in the eastern USA during the early nineteenth century (Di Gaspero et al. 2012). From the late nineteenth century onwards, thousands of Vitis interspecific crosses were performed to create cultivars including rootstocks resistant to phylloxera and pathogens introduced in Europe (This et al. 2006; Di Gaspero et al. 2012).

InVitisvinifera L., the first crossbreds documented (choosing both father and mother plants) seem to be those created by LuisBouschet de Bernard in 1828 in southern France, with the objective of reinforcing the colour of red wines (Paul 1996). From this time onwards, several private grape breeders undertook hybridization works to improve different traits of agronomical interest such as precocity, berry size, yield, muscat flavour or resistance. After World War II, most breeding programs were undertaken by public agronomical institutes, using mating designs based on a deeper genetic knowledge (Lacombe et al. 2013).

But, there are several main constraints to grape breeding and genetic improvement. Grape is a long-lived perennial with a long juvenile period and requires time and space for adequate evaluation. Hermaphroditism, self-fertility and easy out-crossing appeared preponderant in cultivated V. vinifera(This et al. 2006) which was generally affected by

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inbreeding depression when self-pollinated (Alleweldt and Possingham 1988; Olmo 1996), in relation to its high heterozygosity (This et al. 2006; Laucou et al. 2011).Moreover, characters that make a good cultivar are usually polygenic in their inheritance, with only few traits being controlled by single genes with dominant alleles.

Agronomic traits and berry quality traits of interest for breeding may be qualitative but are predominantly quantitative in nature. To develop robust trait-linked markers and to understand the responsible genes their genetic mapping is required.

Over the past 10 years a tremendous amount of molecular genetic information and genetic maps based on readily transferable molecular markers are available from a range of Vitis species background enabling the identification of genomic regions associated with interesting traits.The availability of grapevine whole-genome sequences and several physical mapping also offers new opportunities to search for genes encoding for proteins containing both nucleotide binding sites (NBS) and leucine rich repeats (LRR) domains, to identify candidate genes and to better understand the molecular and physiological basis of traits of interest (Jaillon et al. 2007; Velasco et al. 2007; Moroldo et al. 2008; Riaz et al. 2011).

Qualitative traits can be directly integrated in such maps or analyzed with the BSA strategy. Quantitative traits have to be analyzed using marker information in combination with the statistical approach of QTL analysis.Current breeding programmes based on marker-assisted selection (MAS) (Di Gaspero and Cattonaro 2010) are increasingly efficient, as studies have identified quantitative trait loci (QTLs) for numerous traits.

Many QTL of agronomical trait has been detected, such as axillary shoot growth (Fisher et al. 2004),number of cluster per vine (Fanizza et al. 2005), cluster weight (Fanizza et al. 2005), number of berry per cluster (Fanizza et al. 2005), fertility (Doligez et al. 2010; Grzeskowiak et al. 2013), flower sex (Dalbó et al. 2000; Riaz et al. 2006;Marguerit et al. 2009; Fechter et al. 2012), inflorescence morphology (Marguerit et al. 2009);leaf morphology (Welter et al. 2007), phenology related traits (Cabezas et al. 2006; Costantini et al. 2008 ; Duchêne et al. 2012 ; Grzeskowiak et al. 2013).

Moreover, QTL for seed traits were identified for seedlessness (Striem et al. 1996; Lahogue et al. 1998; Doligez et al. 2002; Mejía et al. 2011), seed number and seed

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weight (Doligez et al 2002; Cabezas et al. 2006; Costantini et al. 2008).

In addition, QTL for berry quality traits were studied, such as berry weight (Doligez et al. 2002; Fanizza et al. 2005; Cabezas et al. 2006; Costantini et al. 2008), berry size (Fisher et al. 2004), aroma content of the berry (Battilana et al. 2009; Duchêne et al. 2009) , muscatflavor (Emanuelli et al. 2010),and proanthocyanidin composition ofberry and seed (Huang et al. 2012).

Furthermore, QTL analysis for resistance traits were studied especially with interspecific crosses, such as resistance to fungal diseases (Dalbó et al. 2001; Fisher et al. 2004; Welter et al. 2007; Bellin et al. 2009; Marguerit et al. 2009; Moreira et al. 2011; Riaz et al. 2011; Barba et al. 2014),phylloxera root resistance(Zhang et al. 2009); Pierce´s disease resistance (Riaz et al. 2006; 2008), and resistance to Xiphinema index (Xu et al. 2008).

In this study, we conducted ananalysis of the quantitative distribution of agronomic, enologic, phenological traits, as well as seed traits in aGraciano x Tempranillo population. Our goal was to locate QTL on the genetic map built with microsatellite and SNPs markers,propose candidate genes underlying these QTLand improve knowledge on the genetic determination of these traits in grapevine which could facilitate MASstrategies.

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7.2 Material and methods

7.2.1Plants material

An intraspecific hybrid population (163 genotypes) derived from controlled crosses between two Spanish wine grape varieties, Graciano and Tempranillo (Vitisvinifera L), was used for this study. The individual hybrids (one plant per genotype) have been grown on their own roots since 2004inVarea (Logroño, La Rioja, Spain) on a sandy-loam soil, in East-West orientation with 3 m spacing between rows and 1 m between plants and trained to double Royat cordon. Standard irrigation, fertilization and plant protection practices for La Rioja region were performed. The plants first flowered and fruited in 2007.

7.2.2 Phenotypicevaluation

Overall thirty agronomic,enological and seed traits were evaluated in the hybrid population during three growing seasons (2008-2010) as described in Chapter 3 Materials and Methods.

For agronomic traits

Dates of sprouting (S), flowering time (F), veraison time (V), and ripening time (R) were investigated in field. Flowering period (FP), veraison period (VP), interval from sprouting to flowering (S-F), interval from flowering to veraison (F-V), and interval from veraison to ripening (V-R) were calculated as described by Duchêne and Schneider (2005) and Costantini et al. (2008).

Yield per vineand number of clusters per vine (CN), average weight of clusters (CW),fertility index (FI), mean berry weight (BW) were calculated.

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For enological traits

Berry qualitytraits such as sugar content (expressed as degree Baumé), pH, total acidity (expressed asg/L tartaric acid) and berry skin anthocyanin content (mg/g) were evaluated.

The phenolic maturity indices were determined following Saint-Criq et al. (1998) as described in Nadal (2010)and include:total anthocyanin content (TAn), extractable anthocyanin (EAn), colour intensity (CI), total polyphenols index (TPI) and tannin contents (TC). Then, extractability index (%EI) and seed maturity (%SM) werealso calculated (Nadal, 2010).

For seed traits

Five traits:mean seed number per berry (SN),mean seedfresh weight (SW),total polyphenol index (TPI) of seed, seed tannins content (TC) and seed catechin content. were measured as described in Chapter 3 Materials and Methods.

7.2.3 Construction of the genetic map

Genotyping of the individual F1 plants was performed by screening a variety of PCR (Polymerase Chain Reaction) based 271SSRs markers (Thomas et al. 1993), one CAPS (Cleaved Amplified Polymorphic Sequence) marker (Walker et al. 2007), and 18K SNPs (Single Nucleotide Polymorphism) markers (Lijaveztky et al. 2007; Cabezas et al. 2011). The selection, amplification and analysis of markers were described in detail in Chapter 3Materials and Methods.

Genetic maps for Graciano and Tempranillo and a consensus linkage map for the cross were independently generated using 151 F1 individual population and two way pseudo-test cross strategy. Genotypes with more than 10% missing data were not considered for linkage analysis. The mapping software Joinmap® 3.0 (Van Ooijen and Voorrips, 2001) was used with a cross-pollination (CP) population type, excluding bands heterozygous in both parents (hk x hk segregation type). The segregations thatcould not be handled directly by JoinMap (a0 x cd and ab x c0, where 0 represents a null allele) were included in a duplicated form, as described as Doligez et al (2002). They were treated as two separate loci, one segregating only in the one-banded parent and the other one segregating only in the two banded parent.

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Logarithm of the odds (LOD) and recombination frequency thresholds (REC) were fixed at 4.0 and 0.45, respectively, to assign markers to linkage groups and establish marker order. Kosambi mapping function (Kosambi 1944) wasused for the estimation of map distances.Three rounds of mapping were performed and the third-round map was chosen when applicable.

The χ2 test was applied to test the segregation ratio of the F1 population. All the statistical analyses were performed with software SPSS V. 14.0 and STATGRAPHICS 16.0.

7.2.4 QTL detection

Many traits of agronomic and berry quality are complex, but are scored as discrete classes or categories. They are treated as quantitative traits, though some of them do not show continuous variation (Falconer 1989). Simple interval mapping handles the analysis of ordinal or discrete phenotypic categories (Lander and Botstein 1989). In this study, QTL analysis was carried out using MapQTL 6.0 software (Van Ooijen et al. 2009) with the simple interval mapping (SIM).

QTLs were declared signiﬁcant, if the max imum LOD exceeded the linkage group and/or genome-wide LOD threshold (calculated using 1,000 permutations) and mean error rate was lower than 0.05.

First, the non-parametric Kruskal–Wallis (KW) rank sum test, designed for categorical data, was applied to the global segregation of each locus, and then, simple interval mapping (SIM) was used. A stringency significance level of P = 0.05 was used for the KW test. Maximum LOD values were used to estimate QTL peak position, the conﬁdence intervals were estimated in cM and corresponded to an LOD score drop of one on either side of the likelihood peak. A QTL was considered significant only when it was detected by both methods.

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7.3 Results

All QTLs were detected at p=0.05 genome wide and chromosome wide with interval mapping method. Percentage of explained variance in Table 7-1, Table7-2 and Table 7-3 took into account the presence of the other QTL, i.e. values are additive.

7.3.1 QTL of productivity traits

QTL detection of Cluster Number (CN)

For cluster number (CN), three QTLs were located on LG5, LG11 and LG16 that were significant only in one year.The QTL on LG5 explained up to 14.8% and 14.3% of phenotypic variance on consensus map and Graciano map in 2008, respectively. The QTL found on LG11 explained up to 15.6% and 11.2%of variancein the consensus map and maternal map in 2010, respectively.The minor QTL on LG16 only explains up to 10.9% and 8.5%of variance on the consensus mapand Graciano map, respectively,in 2009(Table 7-1).

QTL detection of Cluster Weight (CW)

Two QTLfor cluster weight (CW) were detected in LG2 and LG18 separately (Table 7-1). The QTL on LG2 explained up to 15.1-20.5% and11.6-14.1% of phenotypic variance on the integrated mapand Tempranillo map in 2009 and 2010 respectively.A minor QTL on LG18 explained up to 15.7% and7.2% of variance on consensus map and maternal map in 2008, respectively.

QTL detection of Fertility Index (FI)

For fertility index (FI), three QTLs were detected in LG2, LG 9 and LG11 (Table 7-1). Only the QTL on LG11 that explains up to 8.8-15.0% and 7.5-11.5% of phenotypic variancein consensus and paternal mapwas reproducible over two different years. The QTL on LG2 and LG9 explained between 6% and 13.5% of the total variance and were significant in only year.

QTL detection of Berry Weight (BW)

Four QTLs forberry weight (BW) were detected in LG3, LG5, LG8 and LG18 (Table 7-1).All QTLwere significant in two different years.The QTLs on LG3 and LG5 could be found only on Graciano map, and the QTLs on LG8 and LG18 could only be

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detected on Tempranillo map.

QTL detection of Sprouting

For sprouting (S), two QTLs were detected on LG8 and LG17 (Table 7-1). The QTL on LG8 explained 16.3-19.5% and 12.0% of phenotypic variance on consensus map and Gracianomap, respectively.The QTL on LG17explained 22.2% and 5.5-8.3%of phenotypic variance on the consensus map and Tempranillo map, respectively.

QTL detection of Flowering

Two QTLs for flowering (F) were detected on LG1 and LG19 (Table 7-1). The QTLon LG1 explained 7.8-11.9%, 6.9%and 5.9% of phenotypic variance in consensus map, Graciano and Tempranillo map, respectively.The QTL on LG19explained9.7% and 5.1-5.6% of phenotypic variance in consensus map and Gracianomap, respectively.

QTL detection of Veraison time

Forveraison (V), three QTLs were detected on LG3, LG5 and LG9 (Table 7-1). The QTL onLG3 explained 9.6-14.5% and 7.7-11.4% of phenotypic variance with a max LOD value of 4.6on the consensus map andGraciano map, respectively.The QTL onLG5explained 10.5-18.4%, 6.8-7.1% and 6.0% of phenotypic variance on consensus map, Graciano and Tempranillo map, respectively. The QTL on LG9 explained 8.6-12.0% and 6.2-9.9% of phenotypic variance on consensus map and Tempranillo map, respectively.

Besidesthe QTL of three main phenological stagesduringgrapevine development cycle: sprouting, flowering and veraison were detected,the QTLs were detected for flowering period(FP,2QTLs), veraison period (VP, 3 QTLs), interval of sprouting-flowering (SF, 1 QTL), interval of flowering-veraison (FV, 4 QTLs), interval of veraison-ripening (VR, 3 QTLs) and interval of sprouting-flowering (SR, 2 QTLs) (Table 7-1).

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Table 7-1 Characteristics of main QTLs detected for agronomic traits LOD Pos. LOD 1-LOD threshold % Traits LG map Year Mark KW (cM) peak interval 0.95 Expl. CW GW CN 5 Cons 2008 9.2 chr5_2406415 4.0 3.0-14.7 3.3 4.9 14.8 ******* GR 2008 8.7 chr5_2406415 3.9 6.7-14.1 1.8 2.9 14.3 *******

11 Cons 2010 20.4 chr11_3661147 4.9 19.7-28.1 3.1 4.7 15.6 ****** TE 2010 19.7 chr11_3661147 3.4 15.9-19.7 1.5 2.8 11.2 ******

16 Cons 2009 4.2 chr16_390838 3.1 1.2-23.7 2.9 4.6 10.9 **** GR 2009 6.6 chr16_390838 2.4 6.6-12.8 1.7 2.8 8.5 ****

CW 2 Cons 2009 44.0 vmc6b11 4.4 37.8-44.0 3.5 3.0 15.1 ****** Cons 2010 44.0 vmc6b11 6.6 34.6-45.0 2.9 4.7 20.5 ******* TE 2009 50.6 vmc6b11 3.3 15.0-50.6 1.6 2.9 11.6 ****** TE 2010 50.6 vmc6b11 4.4 36.0-50.6 1.6 3.0 14.1 *******

18 Cons 2008 17.2 chr18_949408 4.3 8.1-21.4 3.2 5.1 15.7 ** GR 2008 3.9 chr18_949408 1.9 3.9-19.6 1.7 3.0 7.2 **

FI 2 Cons 2009 39.1 chr2_6790442 4.2 39.1-48.7 3.1 4.6 13.5 ****** GR 2009 45.2 chr2_6790442 3.6 42.4-53.1 1.7 3.0 11.9 ******

9 Cons 2008 19.0 chr9_3297341 3.7 16.9-19.0 2.9 4.8 11.4 **** TE 2008 19.3 chr9_3297341 1.9 11.2-19.7 1.6 2.8 6.2 ****

11 Cons 2009 23.7 chr11_3693100 2.6 13.0-37.8 3.0 4.6 8.8 **** Cons 2010 23.7 chr11_3693100 4.8 15.7-29.5 2.9 4.6 15.0 ******* TE 2009 22.2 chr11_3693100 2.3 8.9-29.6 1.5 2.9 7.5 **** TE 2010 22.2 chr11_3693100 3.6 17.9-23.6 1.4 3.0 11.5 *******

BW 3 Cons 2008 55.5 chr3_r_294211 3.1 21.4-55.5 3.1 4.8 11.5 *** Cons 2009 55.5 chr3_r_294211 4.3 51.8-65.9 3.1 4.6 14.7 ****** GR 2009 56.6 chr3_r_294211 2.5 47.3-56.6 1.5 2.9 8.8 ****** GR 2010 56.6 chr3_r_294211 2.0 48.8-56.6 1.7 2.9 6.9 ****

5 Cons 2009 61.1 vmc3c7 4.5 59.8-69.1 3.4 4.6 15.6 ****** Cons 2010 61.1 vmc3c7 3.9 26.8-61.1 3.2 4.5 12.7 ** GR 2010 24.5 chr5_5042229 3.5 16.3-29.3 1.7 2.9 11.5 ******

8 Cons 2008 46.9 chr8_SNP865_80 3.0 38.9-52.1 3.3 4.8 11.2 **** Cons 2009 46.9 chr8_SNP865_80 4.9 45.5-46.9 3.1 4.6 16.9 ****** TE 2008 37.9 chr8_SNP865_80 1.8 21.0-37.9 1.7 2.8 6.8 **** TE 2009 37.9 chr8_SNP865_80 2.4 37.9-61.7 1.7 2.9 8.7 ******

18 Cons 2009 88.3 chr18_25931148 2.7 81.5-92.6 3.2 4.6 9.5 *** Cons 2010 88.3 chr18_25931148 3.3 88.3-90.6 3.3 4.5 10.8 **** TE 2009 90.2 chr18_25931148 1.9 79.7-90.2 1.7 2.9 6.7 *** TE 2010 90.2 chr18_25931148 2.3 86.7-90.8 1.7 2.8 7.8 ****

S 8 Cons 2009 48.8 vmc5h2 5.8 45.5-48.8 3.3 4.6 16.3 ******* Cons 2010 48.8 vmc5h2 7.1 48.8-78.8 3.2 5.2 19.5 ***** GR 2009 57.5 vmc5h2 4.2 55.6-64.2 1.6 2.8 12.0 *******

17 Cons 2010 59.2 chr17_8394730 8.2 59.2-61.7 3.4 5.2 22.2 **** TE 2009 56.0 chr17_8394730 1.9 29.5-33.0 1.8 2.9 5.5 **** TE 2010 56.0 chr17_8394730 2.8 33.0-44.4 1.5 2.6 8.3 ****

F 1 Cons 2008 18.6 chr1_2145603 2.5 18.6-40.9 3.0 4.6 7.8 **** Cons 2009 18.6 chr1_2145603 3.6 18.6-30.8 2.8 8.4 11.9 * GR 2008 5.9 chr1_2145603 2.2 5.9-12.2 1.6 2.8 6.9 **** TE 2009 18.5 chr1_4542269 1.7 3.1-27.7 1.4 2.3 5.9 *

19 Cons 2010 65.8 chr19_23742119 3.0 57.8-65.8 3.0 4.4 9.7 **** GR 2008 61.0 chr19_23742119 1.7 20.8-61.0 1.5 2.8 5.6 ** GR 2010 61.0 chr19_23742119 1.5 37.7-61.0 1.5 2.9 5.1 ****

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Continued LOD Pos. LOD 1-LOD threshold % Traits LG map Year Mark KW (cM) peak interval 0.95 Expl. CW GW V 3 Cons 2009 24.6 chr3_3366191 2.9 24.6-47.7 2.9 4.5 9.6 *** Cons 2010 24.6 chr3_3366191 4.6 11.3-24.6 3.1 4.6 14.5 *** GR 2009 24.4 chr3_3366191 2.3 24.4-43.7 1.6 2.9 7.7 *** GR 2010 24.4 chr3_3366191 3.5 24.4-30.4 1.7 2.9 11.4 ***

5 Cons 2008 95.3 chr5_24297333 3.3 82.7-98.7 3.5 4.6 10.5 ****** Cons 2009 95.3 chr5_24297333 5.8 67.4-98.7 3.3 4.5 18.4 **** GR 2008 98.3 chr5_24297333 2.1 84.1-98.3 1.7 2.8 6.8 ****** GR 2009 98.3 chr5_24297333 2.1 89.0-98.4 1.7 2.9 7.1 ****

9 Cons 2009 10.1 chr9_1233128 3.6 0.4-10.1 3.0 4.5 12.0 ****** Cons 2010 10.1 chr9_1233128 2.6 3.2-10.1 3.0 4.6 8.6 *** TE 2009 10.2 chr9_1233128 3.0 10.2-19.7 1.7 2.8 9.9 ****** TE 2010 10.2 chr9_1233128 1.9 10.2-20.2 1.5 2.8 6.2 ***

R 5 Cons 2008 61.1 vmc3c7 4.6 63.2-64.1 3.4 4.5 14.3 **** Cons 2009 61.1 vmc3c7 4.6 64.1-69.4 3.2 4.5 15.1 ****** TE 2009 42.3 vmc3c7 3.1 18.8-44.8 1.6 2.9 10.4 ******

10 Cons 2008 45.2 vvin78 3.3 38.0-48.1 3.1 4.5 10.6 ***** GR 2008 49.2 vvin78 2.0 49.2-58.0 1.6 2.9 6.6 *****

FP 6 Cons 2009 56.9 chr6_19623776 2.5 16.6-56.1 2.9 4.5 8.5 **** GR 2009 54.5 chr6_19623776 1.9 44.4-54.5 1.5 2.9 6.3 ****

7 Cons 2008 35.6 vmc1a2 3.8 35.6-37.7 3.2 4.6 11.7 ****** TE 2008 36.9 vmc1a2 2.5 22.8-51.7 1.8 2.8 8.0 *****

VP 8 Cons 2009 41.6 chr8_13954154 3.8 37.2-41.6 3.3 4.5 12.4 ****** GR 2009 49.7 chr8_13954154 3.2 43.7-49.7 1.7 2.8 10.8 ******

11 Cons 2008 59.5 chr11_19692807 3.2 45.9-59.5 3.0 4.5 10.3 **** Cons 2010 59.5 chr11_19692807 3.0 59.5-62.5 3.1 4.5 9.7 ** TE 2008 58.0 chr11_19692807 2.5 25.1-58.0 1.5 2.9 8.0 **** TE 2010 58.0 chr11_19692807 1.7 23.6-58.0 1.6 2.8 5.5 **

17 Cons 2008 24.0 chr17_5927601 2.6 11.7-24.0 3.1 4.5 8.3 **** Cons 2009 24.0 chr17_5927601 4.9 14.7-29.0 3.3 4.5 15.8 *** TE 2008 39.5 chr17_5927601 2.3 23.0-50.5 1.5 2.9 7.3 **** TE 2009 39.5 chr17_5927601 2.2 39.5-63.3 1.5 2.8 7.4 ***

SF 8 Cons 2009 48.8 vmc5h2 7.1 45.5-49.8 3.2 4.5 21.9 ******* Cons 2010 48.8 vmc5h2 2.9 40.3-48.8 3.2 5.4 9.4 ****** GR 2009 57.5 vmc5h2 4.9 50.8-63.2 1.5 2.9 15.8 ******* GR 2010 57.5 vmc5h2 2.1 35.7-57.5 1.6 2.7 6.8 ******

FV 1 Cons 2008 68.7 chr1_21505077 3.0 16.0-68.7 3.0 4.6 9.7 **** Cons 2009 68.7 chr1_21505077 3.6 68.7-71.5 3.1 4.7 12.0 *** Cons 2010 68.7 chr1_21505077 4.0 66.5-68.7 3.2 4.6 12.8 *** GR 2009 60.7 chr1_21505077 2.4 36.7-60.7 1.5 2.9 8.2 *** GR 2010 60.7 chr1_21505077 2.3 49.7-60.7 1.5 2.8 7.7 ***

3 Cons 2009 18.0 chr3_3006386 3.1 18.0-22.8 3.0 4.7 10.2 ** Cons 2010 18.0 chr3_3006386 4.3 4.9-18.0 3.1 4.6 13.8 ****** GR 2010 18.1 chr3_3006386 3.6 18.1-30.5 1.6 2.8 11.5 ****** GR 2009 18.1 chr3_3006386 2.8 18.1-19.0 1.6 2.9 9.2 **

5 Cons 2008 95.3 chr5_24297333 3.9 95.3-98.0 3.5 4.6 12.4 ****** Cons 2009 95.3 chr5_24297333 5.7 83.8-95.3 3.2 4.7 18.3 ***** GR 2008 98.3 chr5_24297333 2.9 89.0-98.4 1.9 2.8 9.2 ****** GR 2009 98.3 chr5_24297333 2.7 89.0-98.4 1.9 2.9 9.1 *****

15 Cons 2009 44.5 chr15_18774361 3.8 43.9-44.5 3.1 4.7 12.4 ****** Cons 2010 44.5 chr15_18774361 3.5 41.5-44.5 3.0 4.6 11.4 ****** TE 2009 45.0 chr15_18774361 2.4 18.9-45.0 1.6 2.9 8.0 ****** TE 2010 45.0 chr15_18774361 2.1 30.2-45.0 1.6 2.9 7.0 ******

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Continued LOD Pos. LOD 1-LOD threshold % Traits LG map Year Mark KW (cM) peak interval 0.95 Expl. CW GW VR 4 Cons 2009 47.9 chr4_16835940 2.6 40.2-47.9 3.0 4.4 8.9 ** Cons 2010 47.9 chr4_16835940 3.3 47.2-50.6 2.9 4.7 10.9 ** GR 2009 39.5 chr4_16835940 1.9 24.2-57.5 1.5 2.9 6.4 **

5 Cons 2009 61.4 chr5_14739326 4.7 56.5-61.4 3.1 4.4 15.6 ******* Cons 2010 61.4 chr5_14739326 2.9 56.5-61.6 3.2 4.7 9.7 **** TE 2009 43.8 chr5_14739326 4.6 38.7-44.8 1.5 2.9 15.2 ******* TE 2010 43.8 chr5_14739326 2.1 27.4-56.2 1.7 2.9 7.1 ****

10 Cons 2008 49.1 chr10_8198035 3.1 37.6-49.1 3.1 4.4 9.9 **** GR 2008 54.0 chr10_8198035 1.9 52.3-58.0 1.6 2.8 6.2 **** GR 2009 54.0 chr10_8198035 1.9 41.9-54.0 1.6 2.9 6.4 **

SR 1 Cons 2009 66.5 chr1_20944785 3.8 52.2-71.9 3.1 4.4 12.8 **** GR 2009 58.4 chr1_20944785 2.9 58.4-70.1 1.6 2.9 9.8 **** TE 2009 61.0 chr1_21007656 1.8 61.0-70.0 1.6 2.9 6.1 ***

5 Cons 2009 57.6 chr5_r_249870 4.2 49.3-57.6 3.1 4.4 13.9 ******* TE 2009 40.2 chr5_r_249870 4.1 31.0-44.8 1.7 2.9 13.7 *******

LG linkage group, Markermarker nearest to the QTL position, Pos. QTL position on LG, LOD peak LOD value at QTL position, LOD threshold chromosome-wide (CW) and genome-wide (GW) LOD threshold (P < 0.05), % Explproportion of the total phenotypic variance explained by the QTL, KW = Kruskal–Wallis significance level, given by the P value (* 0.1, ** 0.05, *** 0.01; **** 0.005; ***** 0.001; ****** 0.0005; ******* 0.0001)

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7 QTL Analysis of Agronomic, Enological and Seed Traits

7.3.2 QTL for enological traits

QTL detection of Total Acidity (TA)

For total acidity (TA), three QTLs were detected on LG12, LG14 and LG15 (Table 7-2). The main QTL on LG14 explained 19.6-21.8% and 7.2-14% of phenotypic varianceon consensus map and Graciano map, respectively. The second QTL found on LG15 explained up to 13.9-15.3%, 13.8% and 6.9-8.2%of variancein consensus map, Graciano and Tempranillo map, respectively. The minor QTL on LG12 explained 10.8- 14.7%, 6.5-9.4% and 6.4% of variance on consensus map, Graciano and Tempranillo map, respectively.

QTL detection of Berry skin Anthocyanins, Colour Intensity, Total anthocyaninscontent, extractable anthocyanins content.

The main QTL for berry skin anthocyanins (BSAn), colour intensity (CI), total anthocyanin content (TAn), and extractable anthocyanins content (EAn) were detected in LG2 where one gene colour located, explained maximum 67.5%, 58.2%, 65.1% and 62.4% of phenotypic variance in consensus map, respectively(Table 7-2). Furthermore, other QTLs were detected for these traits in different LG.

For berry skin anthocyanins (BSAn), two more QTLs were detected in LG1 and LG18. The QTL on LG18 explainedup to 19,5%of the variance.Moreover, the QTL on LG18 was detected with two regions in consensus map, which combined with two QTLs from Graciano map and Tempranillo map respectively. The minor QTL detected in LG1 explainedbetween 10 and 12% of variance (Table 7-2).

For colourintensity (CI), one more QTLwas detected in LG3. The QTL explained between 16.1% and 12.0% ofvariance (Table 7-2).

For total anthocyaninscontent (TAn), one more QTLwas detected in LG18. The QTL explained 12.6-18.0%, 6.7-8.9% and 9.3-12.2% of variance on consensus map,Graciano and Tempranillo map, respectively (Table 7-2).

Forextractable anthocyanins content (EAn), one more QTL was detected in LG3. The QTL explained 12.9-19.1% and 12.2-13.5% of variance in consensus map andGraciano map, respectively (Table 7-2).

Furthermore, QTLs were detected for two important traits for vinification: tannins

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7 QTL Analysis of Agronomic, Enological and Seed Traits

content (TC) and total polyphenol index (TPI) .

QTL detection of Tannins Content (TC) and Total Polyphenol Index (TPI)

Two QTLs fortannins content (TC) were detected on LG1 and LG9 (Table 7-2). Both QTL were reproducible in at least two different years.

For total polyphenol index (TPI), two QTLs were detected on LG2 and LG9 (Table 7-2) explaining about 20% of the total variance.

Table 7-2Characteristics of main QTLs detected for Enological traits LOD Pos. LOD 1-LOD threshold % Traits LG Map Year Markers KW (cM) peak interval 0.95 Expl CW GW TA 12 Cons 2009 57.3 vmcng2d.11 4.3 43.0-76.5 3.2 4.5 14.7 **** Cons 2010 57.3 vmcng2d.11 3.3 54.5-60.9 3.0 4.7 10.8 **** GR 2009 57.3 vmcng2d.11 2.6 26.7-57.3 1.7 2.9 9.4 **** GR 2010 57.3 vmcng2d.11 1.9 53.6-57.6 1.7 2.9 6.5 ***** TE 2008 66.8 chr12_22601976 1.7 57.2-68.9 1.2 2.9 6.4 ** TE 2010 66.8 chr12_22601976 1.9 65.1-68.9 1.1 2.9 6.4 **

14 Cons 2008 40.4 chr14_23045539 6.2 39.5-42.9 3.0 4.6 21.8 ******* Cons 2010 40.4 chr14_23045539 6.3 40.4-50.3 3.0 4.7 19.6 ******* GR 2008 35.6 chr14_23045539 3.8 23.6-42.4 1.6 2.9 14.0 ******* GR 2009 35.6 chr14_23045539 2.0 20.7-47.6 1.6 2.9 7.2 **** GR 2010 35.6 chr14_23045539 3.4 35.6-41.4 1.5 2.9 11.0 *******

15 Cons 2008 37.8 chr15_17275357 4.2 32.0-44.5 3.0 4.6 15.3 ******* Cons 2009 2.9 chr15_356487 4.0 2.9-10.9 2.9 4.5 13.9 **** GR 2008 37.5 chr15_17275357 3.7 37.6-43.9 1.7 2.9 13.8 ******* TE 2009 4.2 chr15_356487 2.3 2.0-8.5 1.6 2.9 8.2 **** TE 2010 4.2 chr15_356487 2.0 4.2-22.6 1.9 2.9 6.9 ****

BSAn 1 Cons 2009 19.5 chr1_4542269 3.0 8.4-19.5 3.2 4.7 10.5 ***** Cons 2010 19.5 chr1_4542269 3.7 16.8-24.5 3.1 4.7 12.2 ***** TE 2009 19.5 chr1_4542269 2.8 7.6-19.5 1.6 3.0 9.8 ***** TE 2010 19.5 chr1_4542269 3.4 11.7-19.5 1.7 3.0 11.2 ******

2 Cons 2008 37.5 20D18CB9 3.1 37.5-40.4 3.1 4.7 13.8 **** Cons 2009 37.5 20D18CB9 30.0 35.9-37.8 3.1 4.7 67.5 ******* Cons 2010 37.5 20D18CB9 22.8 36.9-37.8 3.1 4.7 54.8 ******* GR 2009 62.2 chr12_18523796 3.5 54.8-64.1 1.7 2.9 12.3 ******* GR 2010 62.2 chr12_18523796 2.8 51.0-62.2 1.6 2.8 9.4 ****** TE 2008 53.4 20D18CB9 2.6 48.3-53.4 1.5 2.9 12.0 **** TE 2009 53.4 20D18CB9 24.7 48.3-53.4 1.6 3.0 60.4 ******* TE 2010 53.4 20D18CB9 20.9 48.3-53.4 1.5 3.0 51.7 *******

18 Cons 2008 66.5 chr18_13410273 4.5 66.5-67.8 3.2 4.7 19.5 ****** Cons 2009 2.9 chr18_882766 4.3 2.9-4.8 3.4 4.7 14.7 ****** GR 2008 53.4 chr18_13410273 2.9 21.0-53.4 1.6 2.9 13.1 ****** TE 2009 3.2 chr18_882766 2.4 3.2-6.4 1.7 3.0 8.7 ******

CI 2 Cons 2008 38.5 chr2_14057713 12.9 37.8-40.9 3.2 5.0 58.2 ******* Cons 2009 38.5 chr2_14057713 20.7 35.9-45.0 3.2 4.6 57.4 ******* Cons 2010 38.5 chr2_14057713 12.0 35.9-46.0 3.6 5.0 36.2 ******* GR 2008 56.8 chr2_10345624 2.7 51.0-56.8 1.5 2.8 16.8 **** GR 2009 56.8 chr2_10345624 2.9 53.1-56.8 1.6 2.9 11.3 ****** TE 2008 52.3 chr2_14057713 9.4 50.3-52.2 1.5 3.0 47.2 ******* TE 2009 52.3 chr2_14057713 19.0 50.3-52.2 1.6 3.0 54.2 ******* TE 2010 52.3 chr2_14057713 10.2 50.3-52.2 1.6 2.9 31.8 ******* Continued

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7 QTL Analysis of Agronomic, Enological and Seed Traits

LOD Pos. LOD 1-LOD threshold % Traits LG Map Year Markers KW (cM) peak interval 0.95 Expl CW GW CI 3 Cons 2008 33.1 chr3_3869027 2.9 33.1-42.0 3.2 5.0 16.6 **** Cons 2009 33.1 chr3_3869027 4.3 29.9-33.1 3.1 4.6 16.1 ****** GR 2008 33.2 chr3_3869027 1.9 33.2-45.5 1.8 2.8 12.0 **** GR 2009 33.2 chr3_3869027 3.9 33.2-39.5 1.6 2.9 14.8 *******

TAn 2 Cons 2008 37.5 20D18CB9 14.2 37.5-40.1 3.2 4.8 61.7 ******* Cons 2009 37.5 20D18CB9 25.2 37.5-38.5 3.1 4.8 65.1 ******* Cons 2010 37.5 20D18CB9 21.3 37.5-45.0 3.1 4.7 55.0 ******* GR 2008 56.8 chr2_10345624 2.2 56.8-65.9 1.6 2.9 13.8 **** GR 2009 56.8 chr2_10345624 2.4 46.5-56.8 1.5 3.0 9.5 ***** GR 2010 56.8 chr2_10345624 2.1 56.8-64.1 1.6 3.0 7.7 ***** TE 2008 53.4 20D18CB9 11.3 48.3-53.4 1.5 2.8 53.3 ******* TE 2009 53.4 20D18CB9 19.3 44.2-53.4 1.5 2.9 55.4 ******* TE 2010 53.4 20D18CB9 18.5 47.3-53.4 1.6 3.1 50.0 *******

TAn 18 Cons 2008 2.8 chr18_882766 2.9 2.8-4.8 3.3 4.8 18.0 **** Cons 2009 2.8 chr18_882766 4.2 2.8-22.8 3.4 4.8 16.0 ***** Cons 2010 2.8 chr18_882766 3.6 2.8-41.1 3.3 4.7 12.6 **** GR 2009 56.1 chr18_18040316 2.2 56.1-74.6 1.6 3.0 8.9 *** GR 2010 56.1 chr18_18040316 1.9 43.6-56.1 1.7 3.0 6.7 *** TE 2008 3.2 chr18_882766 1.9 3.2-14.7 1.8 2.8 12.2 **** TE 2009 3.2 chr18_882766 2.3 3.2-8.2 1.8 2.9 9.3 *****

EAn 2 Cons 2008 37.5 20D18CB9 12.9 37.5-41.9 3.2 4.8 58.2 ******* Cons 2009 37.5 20D18CB9 23.4 35.9-37.8 3.0 4.7 62.4 ******* Cons 2010 37.5 20D18CB9 16.0 35.9-46.0 3.2 4.6 45.0 ******* GR 2008 56.8 chr2_10345624 2.8 50.0-56.8 1.5 4.6 17.0 ****** GR 2009 56.8 chr2_10345624 2.7 53.1-56.8 1.5 3.0 10.7 ***** GR 2010 56.8 chr2_10345624 2.4 53.1-56.8 1.6 2.8 8.6 **** TE 2008 53.4 20D18CB9 8.8 48.3-53.4 1.6 2.9 44.9 ******* TE 2009 53.4 20D18CB9 17.9 44.2-53.4 1.6 2.9 52.7 ******* TE 2010 53.4 20D18CB9 13.3 47.3-53.4 1.5 3.0 39.2 *******

3 Cons 2008 31.8 chr3_4354008 3.1 31.8-53.1 3.2 4.8 19.1 ***** Cons 2009 31.8 chr3_4354008 3.3 31.8-63.9 3.1 4.7 12.9 ***** GR 2008 31.9 chr3_4354008 2.1 31.9-47.3 1.6 4.6 13.5 **** GR 2009 31.9 chr3_4354008 3.1 31.9-39.6 1.6 3.0 12.2 *****

TC 1 Cons 2008 28.8 chr1_15605027 3.1 21.9-28.8 3.2 4.6 19.0 * Cons 2010 28.8 chr1_15605027 4.7 28.8-52.5 3.1 4.7 16.2 **** GR 2009 32.0 chr1_15605027 1.4 32.0-46.7 1.6 4.7 5.7 **** GR 2010 32.0 chr1_15605027 2.0 32.0-39.7 1.7 2.8 7.1 ****

9 Cons 2010 10.1 chr9_1233128 4.3 4.2-10.1 2.9 4.7 14.9 ***** TE 2008 10.1 chr9_1233128 1.8 6.3-10.1 1.6 2.7 11.4 **** TE 2009 10.1 chr9_1233128 2.2 10.1-20.7 1.6 2.9 8.5 * TE 2010 10.1 chr9_1233128 1.6 10.1-32.2 1.5 2.9 5.9 *****

TPI 2 Cons 2009 43.7 vmc5g7 5.7 34.9-46.0 3.2 4.7 21.0 ******* Cons 2010 43.7 vmc5g7 3.8 36.8-43.7 3.2 4.7 13.3 **** TE 2009 50.3 vmc5g7 5.3 43.2-52.4 1.6 2.9 19.7 ****** TE 2010 50.3 vmc5g7 3.2 48.3-52.4 1.6 2.9 11.4 ******

9 Cons 2008 22.0 chr9_3405399 3.5 22.0-24.2 3.0 4.7 21.1 ***** TE 2008 23.7 chr9_3405399 2.0 23.7-43.5 1.6 2.9 12.8 *****

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7 QTL Analysis of Agronomic, Enological and Seed Traits

7.3.3 QTLs of Seed traits

Seed number and seed weight influence berry weight and berry maturation. Besides, the phenolic content of grape seeds is of critical interest as these compounds form a large proportion of wine tannins. They are involved in quality aspects as they affect both colour and flavour of wines. Flavan-3-ols mainly contribute to the structure of wines. Flavanol polymers (condensed tannins) play a particularly important role in the astringency of wines. They may react with anthocyanins through an intermolecular copigmentation process, leading to the definition and stability of colour in red wines.

QTL detection of Seed Number (SN)

For seed number(SN), two major QTLs were detected in LG3 and LG5 (Table 7-3).The QTL on LG3 explained more than 35% of the variance. The closest marker was chr3_10713706. The QTL on LG5 had a similar effect being the closest marker was VMC3C7.

Furthermore, one minor QTL for seed number was detected in LG2 significant for Tempranillo which explained 11.5% of trait variance (Table 7-3).

QTL detection of Seed Weight (SW)

For seed weight (SW), four QTLs were detected in LG2, LG3, LG5 and LG11 (Table 7-3). In LG2 of, the QTL on LG2 explained 25.1-31.2% and 15.8-16.5% of variance on consensus map and Graciano map, respectively. The closest marker was chr2_5236271.The QTL on LG5 explained 13.4-14.0% and 9.7-11.9% of variance on consensus map and Graciano map, respectively. The closest marker was chr5_9640285. The QTL on LG11 explained 12.9- 15.4% and 10.1% of variance on consensus map and Tempranillo map, respectively. The QTLs on LG3 can only detected in Graciano map, explained 7.8% of variance.

QTL detection of Total Polyphenol Index (TPI) of seed

For total polyphenol index of seed(S_TPI),three QTLs were detected in LG7, LG15 and LG18 (Table 7-3). The QTL on LG7 explained 13.4-14.2% and 10.8-11.3% of variance in consensus map and Graciano map, respectively. The closest marker was chr7_15331303. The QTL on LG15 explained 12.4-21.1%, 7.0-15.4% and 6.0-6.1% of variance in consensus map, Graciano and Tempranillo map, respectively. The closest

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marker was vmc5g8. The QTL on LG18 explained 16.6 and 6.5-11.5% of variance on consensus map and Tempranillo map, respectively. The closest marker was chr18_19866092.

QTL detection of Tannin contents of Seed

Three QTLs fortannin contents of seed (S_TC) were detected in LG7, LG15 and LG18 (Table 7-3). The QTL on LG7 explained13.0-16.8%, 8.0-8.1% and 9.3% of variance in consensus map, Graciano and Tempranillo map, respectively. The closest marker was chr7_3316137. The QTL on LG15 explained 11.6-16.0% and 6.1-10.6% of variance inconsensus map and Graciano map, respectively. The closest marker wasVMC5G8. The QTL on LG18 explained 15.7-21.1% and 6.9-18.3% of variance in consensus map and Tempranillo map, respectively. The closest marker was chr18_20006640.

QTL detection of Catechin contents of seed

For catechin contents of seed (S_CAT), two major QTLs were detected in LG15 and LG18(Table7-3). The QTL on LG15 explained 19.4-29.6% and 17.1-26.4% of variance in consensus map and Graciano map, respectively. The closest marker was chr15_17275357. The QTL on LG18 explained 10.7-16.6% and 6.3-13.3% of variance in consensus map and Tempranillo map, respectively. The closest marker was chr18_25931148.

The identification of QTLs for seed traits will be useful in the selection for new wine grape genotypes with good enological features.

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Table 7-3Characteristics of main QTLs detected forseed traits LOD Pos. 1-LOD threshold % Traits LG Map Year Marker LOD KW (cM) interval 0.95 Expl. CW GW SN 3 Cons 2008 54.9 chr3_10713706 10.6 48.7-60.0 3.2 4.7 35.5 ******* Cons 2009 54.9 chr3_10713706 19.7 38.8-54.9 3.1 4.7 52.2 ******* Cons 2010 54.9 chr3_10713706 19.4 50.9-54.9 3.1 4.6 49.2 ******* GR 2008 54.2 chr3_10713706 10.5 47.3-54.2 1.7 2.9 35.2 ******* GR 2009 54.2 chr3_10713706 19.3 50.4-54.2 1.7 2.9 51.4 ******* GR 2010 54.2 chr3_10713706 19.2 48.8-54.4 1.6 2.9 48.8 *******

5 Cons 2008 60.8 vmc3c7 12.8 58.6-60.8 3.2 4.7 41.3 ******* Cons 2009 60.8 vmc3c7 20.2 59.9-62.0 3.2 4.7 53.0 ******* Cons 2010 60.8 vmc3c7 19.1 60.8-64.1 3.1 4.6 48.6 ******* GR 2008 65.0 vmc3c7 9.4 51.4-70.1 1.9 2.9 32.3 ******* GR 2009 65.0 vmc3c7 18.2 62.4-67.2 1.7 2.9 49.5 ******* GR 2010 65.0 vmc3c7 16.2 49.5-65.0 1.8 2.9 43.1 *******

2 TE 2009 13.4 chr2_5662969 2.5 13.4-18.4 1.6 2.9 8.9 **** TE 2010 13.4 chr2_5662969 3.5 8.1-16.4 1.5 2.9 11.5 ******

SW 2 Cons 2009 23.6 chr2_5236271 7.7 16.2-26.7 3.3 4.7 25.1 ******* Cons 2010 23.6 chr2_5236271 10.7 18.1-24.6 3.2 4.6 31.2 ******* GR 2009 26.6 chr2_5236271 4.6 14.7-36.1 1.6 2.9 15.8 ******* GR 2010 26.6 chr2_5236271 5.2 26.6-28.6 1.5 2.9 16.5 *******

3 GR 2009 53.0 chr3_11238850 2.1 14.8-53.0 1.6 2.9 7.7 **** GR 2010 53.0 chr3_11238850 2.3 40.6-53.0 1.7 2.9 7.8 ******

5 Cons 2009 56.5 chr5_9640285 3.9 56.5-60.2 3.3 4.7 13.4 ****** Cons 2010 56.5 chr5_9640285 4.3 45.3-64.2 3.2 4.6 14.0 ****** GR 2009 58.5 chr5_9640285 3.4 58.5-63.3 1.7 2.9 11.9 ****** GR 2010 58.5 chr5_9640285 3.3 49.3-58.5 1.9 2.9 9.7 ******

11 Cons 2009 37.4 chr11_8613276 4.5 37.4-50.1 3.0 4.7 15.4 **** Cons 2010 37.4 chr11_8613276 4.0 37.4-41.4 3.1 4.6 12.9 ** TE 2009 35.9 chr11_8613276 2.9 25.1-35.9 1.5 2.9 10.1 ****

S_TPI 7 Cons 2009 70.4 chr7_15331303 4.0 63.5-70.4 3.2 4.6 14.2 **** Cons 2010 70.4 chr7_15331303 4.1 70.4-74.1 3.2 4.6 13.4 ****** GR 2008 55.8 chr7_15331303 1.7 22.8-55.8 1.6 3.0 10.8 ** GR 2009 55.8 chr7_15331303 3.1 19.1-55.8 1.7 2.9 11.3 **** GR 2010 55.8 chr7_15331303 3.5 38.9-55.8 1.6 3.0 11.5 ******

15 Cons 2009 25.5 vmc5g8 6.2 25.5-27.0 3.1 4.6 21.1 ****** Cons 2010 25.5 vmc5g8 3.8 25.5-36.2 3.1 4.6 12.4 ** GR 2009 22.2 vmc5g8 4.4 22.2-38.5 1.6 2.9 15.4 ******* GR 2010 22.2 vmc5g8 2.1 22.2-24.7 1.6 3.0 7.0 *** TE 2009 25.8 chr15_15102375 1.7 25.8-32.4 1.6 2.9 6.1 *** TE 2010 25.8 chr15_15102375 1.8 2.0-25.8 1.6 2.9 6.0 *

18 Cons 2010 75.8 chr18_19866092 5.2 68.1-78.7 3.4 4.6 16.6 ****** TE 2009 77.8 chr18_19866092 1.8 72.0-80.7 1.8 2.9 6.5 ** TE 2010 77.8 chr18_19866092 3.5 77.8-80.7 1.8 2.9 11.5 ******

S_TC 7 Cons 2009 20.4 chr7_3316137 4.8 20.4-53.5 3.1 4.8 16.8 **** Cons 2010 20.4 chr7_3316137 4.0 20.4-70.1 3.2 4.5 13.0 ** GR 2009 13.6 vvmd6 2.2 13.6-48.6 1.6 2.8 8.0 **** GR 2010 13.6 vvmd6 2.4 13.6-29.9 1.7 2.8 8.1 ***** TE 2009 20.2 chr7_3316137 2.6 20.2-25.7 1.7 2.8 9.3 ****

15 Cons 2008 25.5 vmc5g8 2.7 25.5-27.0 3.1 4.9 17.3 ** Cons 2009 25.5 vmc5g8 4.6 24.7-37.7 3.0 4.8 16.0 ****** Cons 2010 25.5 vmc5g8 3.6 14.4-25.5 2.9 4.5 11.6 ** GR 2008 22.2 vmc5g8 1.6 15.2-54.6 1.6 2.9 10.6 **** GR 2009 22.2 vmc5g8 2.9 22.2-44.9 1.6 2.8 10.3 ****** GR 2010 22.2 vmc5g8 1.8 22.2-60.7 1.5 2.8 6.1 **

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Continued LOD Pos. 1-LOD threshold % Traits LG Map Year Marker LOD KW (cM) interval 0.95 Expl. CW GW S_TC 18 Cons 2009 77.4 chr18_20006640 4.5 73.8-77.4 3.3 4.8 15.7 **** Cons 2010 77.4 chr18_20006640 6.9 72.8-93.6 3.2 4.5 21.5 ******* TE 2008 79.3 chr18_20006640 1.8 66.3-79.3 1.8 2.9 12.0 ** TE 2009 79.3 chr18_20006640 1.9 64.2-79.3 1.6 2.8 6.9 **** TE 2010 79.3 chr18_20006640 5.8 79.3-81.0 1.8 2.8 18.3 *******

S_ C AT 15 Cons 2008 37.8 chr15_17275357 4.6 21.1-47.8 3.0 4.8 27.3 ******* Cons 2009 37.8 chr15_17275357 9.2 36.2-40.4 3.1 4.6 29.6 ******* Cons 2010 37.8 chr15_17275357 6.2 33.9-41.8 3.0 4.7 19.4 ******* GR 2008 37.5 chr15_17275357 4.3 19.8-51.1 1.7 2.9 25.9 ******* GR 2009 37.5 chr15_17275357 8.1 21.0-45.4 1.6 2.9 26.4 ******* GR 2010 37.5 chr15_17275357 5.4 24.7-41.0 1.5 2.9 17.1 *******

18 Cons 2009 88.3 chr18_25931148 3.0 77.4-88.3 3.2 4.6 10.7 ** Cons 2010 88.3 chr18_25931148 5.2 71.2-95.6 3.3 4.7 16.6 ******* TE 2009 90.2 chr18_25931148 1.7 64.2-90.2 1.8 2.8 6.3 ** TE 2010 90.2 chr18_25931148 4.1 64.2-90.2 1.6 2.8 13.3 *******

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7.4 Discussion

7.4.1 Agronomical traits

QTLs foryield were detected but not reproducible across years. Yield mainly correlated with cluster number, berry weight, and cluster weight (Table 4-4) as described Fanizza et al (2005) and Song et al. (2014). Yield components show the same behaviour in our research, and they remain of interest when breeding for yield.

Several QTLs for fertility index in grapevine were previously detected on LG8 and LG19 (Fanizza et al. 2005); LG5 and LG14 (Doligez et al. 2010); LG3 and LG18 (Grzeskowiak et al. 2013). Here, we identified 3 QTLs for Fertility index, only the QTL on LG11 was consistent in two years.

Our results differ from the QTLs previously published above, this discrepancy could partly result from genotype x year interactions and/or from segregation difference between crosses. Part of this divergence might be attributed to the difference in trait measurement. We recorded the number of inflorescences per shoot at flowering period as Doligez et al, whereas Fanizza et al. (2005) measured the number of cluster per vine at harvest, Grezkowiak et al. (2013) obtained fertility index by dividing the total number of fruit clusters by the total number of shoots per plant. Which is a composite trait integrating not only the number of inflorescences per shoot at flowering, but also the number of shoots per vine and the rate of full development of inflorescences into cluster (Doligez et al. 2010). Furthermore, the variation in cultivars, environmental factors (especially temperature), as well as grafting and training methods would affect the difference in fruitfulness (Sommer et al. 2000).

The higher phenotypic correlation observed between years in our research was consistent with the stability over years of the QTL on LG11, but could also result from environment correlation between years and/or limited genotype x year interactions. The low phenotypic correlation observed between years for fertility was consistent with QTL instability among years, as for Fanizza et al. (2005) and Doligez et al.(2010).The QTL of consensus map explained only a slightly higher percentage of total phenotypic variance than that of parental maps, suggesting little dominance effect.

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7.4.2 Phenology traits

The QTL mapping methods typically rely on the assumption that the phenotype follows a normal distribution for each QTL genotype. In our case, all phonological datasets displayed a non-normal distribution (as mentioned in chapter 4, Song et al. 2014). In general, the interval mapping procedure (including the multiple QTL model and cofactor selection) is quite robust against deviations from normality (Van Ooijen 2009). Therefore, we performed this method together with a maximum likelihood mixture model and the permutation test based on the actual data, rather than assuming normality.

The QTL for flowering time on chromosome 7 has already been found by Duchêne et al. (2012) and Grzeskowiak et al. (2013). This locus contains several genes implicated in the flowering process, such as VvFT (FLOWERING LOCUS T) and VvSVP1 (SHORT VEGETATIVE PHASE 1) (Carmona et al. 2007; Diaz-Riquelme et al. 2009). Other QTLs for this trait were found in progenies from different biparental crosses on chromosomes 1, 2, 6, 14, 15 and 18 (Costantini et al. 2008; Carreño Ruiz 2012; Duchêneet al. 2012). Likewise, the QTLs for veraison beginning and veraison end on chromosome 2 have been discovered previously in different studies, along with other QTLs on chromosomes 1, 3, 5, 6, 16 and 18 (Costantini et al. 2008; Carreño Ruiz 2012; Duchêneet al. 2012; Grzeskowiak et al. 2013).

The QTL on chromosome 17 has also been detected in the progeny of Ruby Seedless xMoscatel as related to veraisonand berry colour (Carreño Ruiz 2012, Grzeskowiak et al. 2013).

Whatever the extent of the correlation between years, the percentage of variance explained by each QTL was quite low (no more than 15%). This could be due to a large dependency of variation on environmental factors in each year, but also to a possible large number of QTLs of small effects involved in the genetic determinism of these complex traits. Most of which could have remained undetected given the limited progeny size available in our study.

7.4.3Enological traits

To our knowledge, it is first QTL analysis of wine grape for enological traits, such as total acidity, tannins contents, total polyphenol index and colour related traits (berry

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skin anthocyanins content, colour intensity, total anthocyanins content and extractable anthocyains content).

One major QTL of colour related traits was detected on LG2 with a high variance explainedin consensus map and Tempranillo map, the closest marker was caps/chr2_14057713. The same QTL was detected in Graciano map too, but with a lower variance explained, the marker being chr2_18523769/chr2_10345624. This corresponds to the locus identified on chromosome 2 responsible for berry colour, associated withVvMybA1 and VvMybA2 genes and involved in the regulation of anthocyanin biosynthesis (Kobayashi et al. 2004; Fournier-Level et al. 2010).

The colour of berry skin is determined by the quantity and composition of anthocyainis. Myb-related transcription factor genes such as, VlmybA1-1, VlmybA1-2, VlmybA2 and VvMYBA2r regulate anthocyanin biosynthesis (Azuma et al. 2008).

Recent investigations on the genetic bases of the trait provided evidence that a single gene cluster, located on chromosome 2, is responsible for most of this variation in colour, and that colour phenotype is due to the combined additive effect of the VvMybA gene alleles (Fournier-Level et al. 2009).

There are more QTL were located for berry color traits. Two QTL on LG1 and LG18 for berry skin anthocyanins content, one QTL on LG3 for colour intensity, one QTL on LG18 for total anthocyaninscontent and one QTL on LG3 for extractable anthocyanins content.

7.4.4 Seed traits

Several authors studied seed traits with many parameters, such as seed number, seed fresh weight, seed dry weight. About seed number and seed weight compared withprevious research, there are QTLs in the same chromosome and similar positions. Doligez et al.(2002) detected the QTLs for SN and SW on LG18 with an 11-51% variance explained; using the population MTP3140, a F1 progeny obtained by crossing two partially seedless genotypes. Cabezas et al. (2006) detected QTLs for SN on LG14,LG18, and LG4; the QTLsfor SW on LG1, LG3, LG10 and LG18 using a populationDominga x Autumn Seedless. Costantini et al. (2008) detected one QTL forSN on LG2 and QTLs for SW on LG10, LG13, LG15, and LG18 using Italia x Big Perlon population.

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The predominant genetic model on the inheritance of seedlessness in grapes assumes that the seedlessness trait was under the control of three independent recessive genes regulated by a dominate geneSDI (Seed Development inhibitor)located on LG18 (Doligez et al. 2002; Cabezas et al. 2006). The QTL reported on LG18 were only detected on the seedless progenitor map, thiscould explain that QTLs for seed number weredetected on LG2, LG3 and LG5 but not in LG18 in our study. Besides the progenitors of the populations are wine grapes (Graciano and Tempranillo) not table grapes as in most referenced studies.

QTLs for seed weight detected on LG2 explaininga high percentage of the variance may be correlated with the Sex traits, which affect seed traits (Fechter et al. 2012).

7.4.5 Pleiotropic effects of the QTL and correlation with traits

The distribution of QTL showed a regionalization trend, either due to pleiotropic effects or close linkage. For example, in our research, most QTLwere detected in LG2, LG5 and LG18, and no QTL were detected for any traits on LG13.

QTL for some traits such as cluster weight, with BSAn, CI, TAn, EAn, TPI, SN, SW which showed significant correlations (chapter 4, Song et al. 2014), were detected on the same chromosome LG2 at similar regions, as expected.

Pleiotropic effects of genes or close linkageamong multiple genes are common genetic basis for related traits.

7.4.6 The consistency of QTL mapping

In recent years, the rapid growth of research papersingrapevine QTL mapping indicatesthat QTL mapping can be reliable and meaningful tools for molecular marker-assisted breeding.

The QTLs detected in different mapping populations have greater differences, may be affected by parental, population size, genotype x environment interaction and the types of genetic maps, etc (Beavis et al. 1994). QTL mapping and genetic analysis is based on the phenotypic differences and distribution among populations, therefore the results of QTL mapping is true reflectionof one gene expression in a specific genetic background and environmental conditions, which means the result of QTL mapping varied in different experiments. The accuracy and reliability of the QTL mapping

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depends on the density of molecular marker on linkage map, population size, field trial management, accuracy of measurement and appropriate and correct analysis method. If the mapping population is not sensitive to environmental variation, and field data is very accurate, then the QTL mapping results have a high repeatability.

The QTL position in cM differs between the consensus and the parental maps. This is due to the differences in the total length of the LGs. In case of LG7, where the maternal map is missing the first seven markers of the integrated map, the positions differ strongly. Nevertheless, that QTL is flanked by the same marker.

7.5 Conclusions

As a conclusion, our research reached its objective of finding stable QTLs of agronomical traits, enological traits and seed traits, even though expected genetic progress would be limited given the low fraction of genetic variation explained by some QTLs.

Our results would be usefully completed in future studies by looking for more favorable alleles at these QTLs in other genetic resources. The availability of the two grapevine genome sequences (Jaillon et al. 2007; Velasco et al. 2007) will conveniently provide positional candidate genes close to QTLs LOD peaks, facilitating the finding of sequence polymorphisms responsible for phenotype variation.

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Breeding Resources, Temperate Fruits. Springer, Berlin, pp: 223-240 Striem MJ, Ben-Hayyim G, Spiegel-Roy P (1996) Identifying molecular genetic markers associated with seedlessness in grape. J Am Soc Hortic Sci 121: 758-763 This P, Lacombe T, Thomas MR (2006) Historical origins and genetic diversity of wine grapes. Trends Genet 22: 511-19 This P (2011) High throughput analysis of grape genetic diversity as a tool for germplasm collection management. Theor Appl Genet 122(6): 1233-1245. Van Ooijen JW (2009) MapQTL® 6. Software for the mapping of quantitative traits loci in experimental populations of diploid species. Kyazma BV, Wageningen, the Netherlands Velasco R, Zharkikh A, Troggio M, Cartwright DA, Cestaro A, Pruss A, Pruss D, Pindo M, FitzGerald LM, Vezulli S, Reid J, Malacarne G, Iliev D, Coppola G, Wardell B, Micheletti D, Macalma T, Facci M, Mitchell JT, Perrazzolli M, Eldredge G, Gatto P, Oyzerski R, Moretto M, Gutin N, Stefanini M, Chen Y, Segala C, Davenport C, Dematte L, Mraz A, Battilana J, Stormo K, Costa F, Tao Q, Si-Ammour A, Harkins T, Lackey A, Perbost C, Taillon B, Stella A, Solovyev V, Fawcett JA, Sterck L, Vandepoele K, Grando SM, Toppo S, Moser C, Lanchbury J, Bogden R, Skolnick M, Sgaramella V, Bhatnagar SK, Fontana P, Gutin A, Van de Peer Y, Salamini F, Viola R (2007) A high quality draft consensus sequence of the genome of a heterozygous grapevine variety. PLoSOne 2(12): e1326. Vivas N, Glories Y, Lagune L, Saucier C, Augustin M (1994)Estimation du degré de polymérisation des procyanidines du raisin et du vinpar la méthodeaup-dimethylaminocinnamaldéhyde. J Int Sci Vigne et du Vin 28(4): 319-336 Welter L, Gokturk-Baydar N, Akkurt M, Maul E, Eibach R, Töpfer R, Zyprian EM (2007) Genetic mapping and localization of quantitative trait loci affecting fungal disease resistance and leaf morphology in grapevine (Vitis vinifera L). Mol Breed 20:359– 374 Xu K, Riaz S, Roncoroni NC, Jin Y, Hu R, Zhou R, Walker MA (2008) Genetic and QTL analysis of resistance to Xiphinema index in a grapevine cross. Theor Appl Genet 116:305-311 Zhang J, Hausmann L, Eibach R, Welter LJ, Töpfer R, Zyprian EM (2009) A framework map from grapvine V3125 (Vitisvinifera ´Schiavagrossa´ x ´Riesling´) x rootstock cultivar ´Borner´ (Vitis riparia x Vitis cinerea) to localize genetic determinants of phylloxea root resistance. Theor Appl Genet 119:1039-1051

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General Conclusions

1. The phenotypic segregation of 16 agronomic traits and 11 enological traits was studied using 151 progeny derived from two Spanish wine grapes Graciano x Tempranillo (Vitisvinifera L.) for three consecutive years. All traits presented transgressive segregation and continuous variation. Year effect was significant for all traits except total, extractable and skin anthocyanins content. However, a high level of genotype consistency for enological traits was revealed by repeatabilities and correlations between years. Significant correlations among traits were observed but most associations were weak.Fourteen genotypes were pre-selected for further researchbased on ripening time, cluster weight, berry weight and anthocyanin content by cluster analysis.

2. The CAPS (Cleaved Amplified Polymorphic Sequence)markerfor the VvmybA genotype showed that the number of homozygous and heterozygous genotypes for the functional colour allele adjusted to a 1:1 segregation ratio, and that homozygous genotypes had significantly higher anthocyanins contentsuggesting that it would be a useful marker in indirect selection for anthocyanins content.

3. Anthocyanincompositionof F1 population and parents was studied during 2 growing seasons (2009 and 2010) and resulted in consistent profiles of 15 compounds. The concentration of 13 identified anthocyanins and the percentage of non acylated, acetyl, and coumarylanthocyaninsshowed transgressive segregation towards lower values except for peonidin content. Year effectwas observed for individual anthocyanin contents but not for the ratios betweenanthocyanins. Moreover, the ratio of Pn/Mv can be considered as a potential varietal marker in the population.

4. A genetic linkage map was constructed using 151 F1 hybrids.A consensus map with a total of 1210 markers (183SSRs, 1 CAPs and 1026 SNPs) was assembled covering 1385.8 cM distributed into 19 linkage groups, with an average interval length of 1.2cM between markers.The maternal map consisted of 147 SSR markers and 535 SNP markers assembled into 19 linkage groups spanning 1264.4 cM. The average distance between markers was 1.9 cM. The paternal map consisted of 136 SSR

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markers and 491 SNP markers aligned into 19 linkage groups (LOD 3) covering 1220.5 cM with an average distance between markers of 2.0 cM.

5. Strong segregation distortion was observed in LG17 of Graciano for all SSR markers and SNP markers (p < 0.001). No statistically significant difference in recombination rate was observed between Graciano and Tempranillo based on 64 common strong linked marker intervals. The expected genome coverage of maternal, paternal and consensus map were around 86%, but differences were observed between the maps for estimated genome length and observed genome map coverage.

6. QTL (Quantitative Traits Loci) analysis was carried out for agronomic, enological and seed traits evaluated in the population. For agronomical traits, 25 QTLs were detected for cluster number, cluster weight, fertility index, berry weight, sprouting, flowering, veraison time, ripening time, flowering period and veraison period.For enological traits, 16 QTLs were detected overall for total acidity, berry skin anthocyanins, colour intensity, total anthocyanin content, extractable anthocyanins, tannin contents and total polyphenol index.For seed traits, 15 QTLs were detected for seed number, seed weight, total polyphenol index, tannin content, and catechin content of seeds.

7. QTL for different components of the same trait or for correlated traits detected in our study co-localize in the same regions as expected. Clusters of QTL for different traits were detected on LG2, LG5 and LG 18.

8. This is the first genetic study involving two relevant Spanish wine grape varieties. Results of this research reveal novel insights into the genetic control of relevant traits for wine grape, and would be useful for breeding new genotypes with better quality features and adaptation to future environmental conditions.

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Conclusiones Generales

1. Se ha estudiado la segregación fenotípica de 16 caracteres agronómicos y 11 caractres enológicos utilizando una progenie de 151 individuos obtenidos de dos variadades españolas de vinificación Graciano x Tempranillo (Vitis vinifera L.) en tres años consecutivos. Todos los caracteres presentaron segregación transgresiva y variación continua. El factor año fue significativo para todos los caracteres excepto contenido total de antocianos, antocianos extraíbles y antocianos del hollejo. Sin embargo para los caracteres enológicos se observó un alto grado de consistencia en el comportamiento de los genotipos medido por las repetibilidades y correlaciones entre años. Se observaron correlaciones significativas entre los caracteres pero la mayoría de las asociaciones fueron débiles. Catorce genotipos fueron pre-seleccionados para futuras investigaciones en base a la fecha de maduración, peso del racimo, peso de la baya y contenido en antocianos.

2. El análisis de marcadores CAPs (Cleaved Amplified Polymorphic Sequence) para el genotipo VvmybA mostró que la segregación para el alelo funcional del color se ajustaba a la proporción 1:1 para homocigotos y heterocigotos y que los genotipos homocigotos presentaban concentraciones significativamente mayores de antocianos lo que indica que este marcador puede ser utilizado para selección indirecta de contenido en antocianos.

3. La composición antociánica de la población y los parentales se estudió durante 2 años (2009 y 2010) y resultó en perfiles reproducibles de 15 compuestos. La concentración de 13 antocianos identificados y el porcentaje de antocianos no acilados, acetilados y cumarilados mostró segregación transgresiva hacia valores mas bajos que los parentales excepto para el contenido en peonidina. Se detectó efecto significativo del año para el contenido en antocianos individuales pero no para los ratios entre antocianos. Además el ratio Pn/Mv puede ser considerado como un marcador varietal en esta población.

4. Se construyó un mapa genético de ligamiento usando 151 híbridos. El mapa consenso se ensambló con un total de 1210 marcadores (183 SSRs, 1 CAPs y 1026 SNPs) que cubren 1385.8 cM distribuidos en 19 grupos de ligamiento, con una

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distancia media entre marcadores de 1,2 cM. El mapa del parental femenino incluyó 147 marcadores microsatélites y 535 marcadores SNP combinados en 19 grupos de ligamiento que cubren 1264.4 cM. La distancia media entre marcadores fue de 1,9 cM. El mapa del parental masculino contiene 136 marcadores microsatélites y 491 SNP alineados en 19 grupos de ligamiento (LOD 3) que abarcan 1220.5 cM con un intervalo medio de 2.0 cM.

5. Se observó distorsion de la segregación muy significativa (p < 0.001) para todos los marcadores SSR y SNP del grupo de ligamiento 17 en el parental Graciano. La tasa de recombinación en Graciano y Tempranillo, basada en 64 intervalos de ligamiento comunes, no presentó diferencias significativas entre ellos. La cobertura del genoma esperada para los mapas materno, paterno y consenso fue del 86% de media, pero se observaron diferencias entre los mapas en la longitud estimada y la cobertura observada del genoma.

6. Se llevó a cabo un análisis QTL (Quantitative Traits Loci) para los caracteres agronómicos, enológicos y de semilla evaluados en la población. Se detectaron 25 QTL para caracteres agronómicos asociados a número de racimos, peso del racimo, índice de fertilidad, peso de la baya, fecha de brotación, floración, envero y madurez; y período de floración y de envero. Para los caracteres enológicos se detectaron 16 QTL significativos para acidez total, antocianos del hollejo, intensidad de color, contenido en antocianos totales y extraíbles y contenido en taninos e índice de polifenoles totales. Quince QTL fueron identificados para carcateres de semilla, entre ellos número de semilla, peso de semilla, índice de polifenoles y contenido en taninos y catequinas de la semilla.

7. Los QTL para diferentes componentes de un carácter complejo o para caracteres correlacionados en nuestro estudio se localizaron en las mismas regiones cromosómicas como se esperaba. Grupos de QTL para varios caracteres se identificaron en los grupos de ligamiento 2, 5 y 18.

8. Este es el primer estudio genético de variedades españolas de relevancia para la vinificación. Los resultados de esta investigación serán de utilidad para la obtención de nuevos genotipos de mejor calidad y con mayor adaptación a condiciones climáticas futuras.

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