Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Volume
    • AJEV and Catalyst Archive
    • Best Papers
    • ASEV National Conference Technical Abstracts
    • Collections
    • Free Sample Issue
  • Information For
    • Authors
    • Open Access and Subscription Publishing
    • Submission
    • Subscribers
      • Proprietary Rights Notice for AJEV Online
    • Permissions and Reproductions
    • Advertisers
  • About Us
  • Feedback
  • Alerts
  • Help
  • Login
  • ASEV MEMBER LOGIN

User menu

  • Log in

Search

  • Advanced search
American Journal of Enology and Viticulture
  • Log in
  • Follow ajev on Twitter
  • Follow ajev on Linkedin
American Journal of Enology and Viticulture

Advanced Search

  • Home
  • Content
    • Current Volume
    • AJEV and Catalyst Archive
    • Best Papers
    • ASEV National Conference Technical Abstracts
    • Collections
    • Free Sample Issue
  • Information For
    • Authors
    • Open Access and Subscription Publishing
    • Submission
    • Subscribers
    • Permissions and Reproductions
    • Advertisers
  • About Us
  • Feedback
  • Alerts
  • Help
  • Login
  • ASEV MEMBER LOGIN
Article

ASEV Honorary Research Lecture 2007

Beyond the Genome, Opportunities for a Modern Viticulture: A Research Overview
Michela Troggio, Silvia Vezzulli, Massimo Pindo, Giulia Malacarne, Paolo Fontana, Flavia Maia Moreira, Laura Costantini, M. Stella Grando, Roberto Viola, Riccardo Velasco
Am J Enol Vitic. June 2008 59: 117-127; published ahead of print June 02, 2008 ; DOI: 10.5344/ajev.2008.59.2.117
Michela Troggio
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Silvia Vezzulli
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Massimo Pindo
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Giulia Malacarne
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Paolo Fontana
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Flavia Maia Moreira
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Laura Costantini
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
M. Stella Grando
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Roberto Viola
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
Riccardo Velasco
1Istituto Agrario di San Michele all’Adige Research Center, via E. Mach 1, 38010 San Michele a/Adige (TN), Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Find this author on ADS search
  • Find this author on Agricola
  • Search for this author on this site
  • For correspondence: riccardo.velasco@iasma.it
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Grapevine is one of the world’s most important fruit crops. All 60 2n = 38 Vitis species worldwide are diploids that cross easily; hybrids are fertile and advanced generation pedigrees are available. The cultivated grape species Vitis vinifera has the potential to become a model for fruit tree genetics. Given its cultural and economic importance, grapevine has received much attention from the scientific community in the last few years, resulting in considerable progress in genetic and genomic research. A consensus sequence of the grapevine genome was generated, providing information on overall organization, gene content, and structural components of the DNA in the 19 chromosomes of V. vinifera. Extensive genetic mapping has been conducted in Vitis ssp. based on SSR markers, including the identification of quantitative trait loci for a variety of traits. A large set of single-nucleotide polymorphisms was developed from expressed sequence tags, bacterial artificial chromosome-end sequences, and unique regions of the assembled genome of Pinot noir, providing a comprehensive grapevine genetic map. Single-nucleotide polymorphism markers represent a substantial resource for molecular breeding programs, providing a new basis for map-based gene isolation and fine-mapping quantitative trait loci by identifying candidate genes.

  • Vitis vinifera
  • genetic map
  • whole genome sequencing
  • candidate gene

Grapevine is one of the world’s most important fruit crops. Europe grows the highest percentage of the world’s grapes (50%), followed by Asia (23%), the Americas (20%), Africa (5%), and Oceania (2%). The majority of grapes produced worldwide are from cultivars of Vitis vinifera L. ssp. sativa, while the rest are other Vitis spp. and interspecific hybrids. Of total grape production, 70% is used for wine, 22% for table grapes, and 8% for raisins; other transformed products such as juices, jams, and jellies are only of local interest. Several commodities are by-products or derivatives of the wine industry, such as must, marc distillates, marc pulp, tartaric acid, seed oil, and vinegar. The earliest evidence of winemaking has been found in Iran and dates back to ~7400–7000 bp (before present); since that time this beverage has been present throughout the development of human culture. A traditional icon of the Mediterranean diet along with olive oil and wheat (Panagiotakos et al. 2004), winemaking has spread to the New World during recent centuries. Wine is a nutraceutical product; when the fruit is consumed as table grapes or as a transformed product (juice, wine, etc.), it helps reduce cardiovascular disease and has anticarcinogenic and extrogenic properties (Burns et al. 2000). These nutraceutical properties are due to the high concentration of resveratrol in the berry, which prevents blood platelet aggregation and elevates beneficial HDL (high density lipoprotein), the antioxidant quercitin, and ellagic acid, which scavenges carcinogens and moves them out of the body (Kharb and Singh 2004). The medicinal value of grapes and wine was known by the ancient Egyptians and by Hippocrates (2467–2384 bp) (Masquelier 1992). Grapes and wine today are part of the Ayurvedic medicine of East India (Paul et al. 1999) and the traditional medicines of South Africa, the Middle East, and China (Kalt 2001). Vitis vinifera is mentioned in several pharmacopeias. Novel uses of grapes and wine are becoming popular: fasting on grapes is called “ampelotherapy” (the grape cure) and is alleged to be powerfully detoxifying and alkalinizing, and cosmetic treatments with wine and its derivates are referred to as “winetherapy.”

Vitis vinifera ssp. sativa is hermaphroditic, except for rare cases. Although most fruit and nut trees, woody ornamental trees and shrubs, herbaceous perennials, and many annuals are incompatible, cultivated grapevine is self-fertile and thus is not an obligate outcrosser. Wild grapevines are dioecious, and, due to their prevailing wind- and insect-pollinated habit, outbreeding is ensured with high gene flow. Plant breeding through controlled pollination between selected genotypes produces cultivars that are highly heterozygous and carry a heavy load of deleterious recessives: inbreeding depression is severe enough that, by the second or third generation, sterility often ensues. All 60 2n = 38 Vitis species worldwide are diploids that can be easily crossed; hybrids are fertile and advanced generation pedigrees are available (Olmo 1979).

Grapevine is a potential model organism for fruit trees, as poplar is for forest trees (Tuskan et al. 2006). Although genotype is a major determinant for transformation (Wang et al. 2005), Vitis spp. can be transformed, regenerated, and micropropagated via somatic embryogenesis of anthers (Kikkert et al. 2005). The relatively small haploid genome size of V. vinifera (475 Mbp; Lodhi and Reisch 1995) compared to many other perennial plant species (Arumuganathan and Earle 1991) facilitates molecular genetic study of Vitis. Unfortunately, grapevine breeding is a time-consuming process because of the long reproductive cycle, the large size of plants, and the fact that productivity and quality can be evaluated at best only after five years. Molecular tools may overcome these difficulties and open the way to new strategies for more efficient breeding (Morgante and Salamini 2003).

Given these features and applications, it is not surprising that grape has received much attention from the scientific community, resulting in considerable progress in genetic and genomic research and complementing advances made with the sequenced Arabidopsis, rice, and poplar.

Since a white paper concerning the grapevine genomics initiative (IGGP, International Grapevine Genome Project, www.vitaceae.org) was written in Davis, California, in June 2001, researchers on grapevine have had access to most of the tools commonly used in this field, from published microsatellite (or simple sequence repeat, SSR) markers developed as international consortiums (Vitis Microsatellite Consortium) or as national projects. Thus, a second-generation set of markers based on single-nucleotide polymorphisms (SNPs), which along with insertion/deletion (in/del) events provide reliable PCR-based genetic markers, has been developed, exploiting the most frequent genetic differences within a species (Rafalski 2002). Hundreds SNP-based markers have been developed in grapevine and uploaded to public databases (NCBI, http://www.ncbi.nlm.nih.gov). Extensive genetic maps have been constructed in Vitis spp. based on these markers (Troggio et al. 2007), including the identification of quantitative trait loci (QTLs) for a variety of traits (Xu et al. 2008). Moreover, given the robust physical mapping information of Cabernet Sauvignon and Pinot noir (Adam-Blondon et al. 2005; http://genomics.research.iasma.it), large collections of expressed sequence tags (ESTs) (Moser et al. 2005, da Silva et al. 2005, Peng et al. 2007), and associated proteomics and metabolic profiling (Sarry et al. 2004, Pereira et al. 2005, Castro et al. 2005, Mattivi et al. 2006, Deluc et al. 2007, Deytieux et al. 2007), grapevine genome sequencing is a timely and important undertaking. To date, grapevine is the first fruit tree to have its genome deciphered (Jaillon et al. 2007, Velasco et al. 2007).

This article is an overview of the genetic and genomic tools cited and their use in molecular-based approaches aimed at gene isolation and characterization, biodiversity exploitation, and breeding applications.

Molecular marker development and genetic mapping.

Molecular marker development helps clarify the genetic basis for complex traits and facilitates construction of genetic linkage maps. Linkage maps are a prerequisite for study of both qualitative and quantitative trait inheritance and for integration of the molecular information necessary for marker-assisted selection (MAS; Mazur and Tingey 1995), map-based cloning (Tanksley et al. 1992), and anchoring to physical maps (Meyers et al. 2005) and genome sequences. Thus, a key resource forming the basis of classical genetics and genomics of V. vinifera is the construction of a dense genetic map based on well-characterized, gene-specific molecular markers.

Most first-generation linkage maps of V. vinifera were based on anonymous genetic loci such as SSR and amplified fragment length polymorphism (AFLP) markers (Lodhi et al. 1995, Dalbò et al. 2000, Doligez et al. 2002, Grando et al. 2003, Adam-Blondon et al. 2004, Doucleff et al. 2004, Fischer et al. 2004, Riaz et al. 2004, Fanizza et al. 2005, Doligez et al. 2006, Lowe and Walker 2006, Riaz et al. 2006, Xu et al. 2008). Recently, resistance gene analog (RGA)-derived markers have been mapped (Di Gaspero et al. 2007, Welter et al. 2007). SNP-based markers were an improved resource, providing a new basis for map-based gene isolation, and for fine-mapping QTLs by identifying candidate genes (Troggio et al. 2007, Salmaso et al. 2008, S. Vezzulli, unpublished data, 2007). In particular, the development of integrated reference linkage maps (515 loci, Doligez et al. 2006; 1134 loci, S. Vezzulli, unpublished data, 2007) covering most of the 475 Mbp grapevine genome (Lodhi and Reisch 1995) has allowed cross-talk between maps and crossing populations developed in different parts of the world.

For several years, the development of SNP markers has been a priority at the Istituto Agrario di San Michele all’Adige (IASMA) research center and considerable investment has been made in high-tech instruments and laboratory equipment. We started with gel-based techniques such as single-strand conformational polymorphism analysis (SSCP; Orita et al. 1989) and considered fluorescence-based techniques such as minisequencing (Syvanen 2005, Troggio et al. 2008). Essentially, low-throughput genotyping systems have been replaced with mid- and high-throughput genotyping systems, culminating in the successful application of the SNPlex assay (Applied Biosystems Inc.) in grapevine (Lijavetzky et al. 2007, Pindo et al. 2008). High efficiency coupled with full automation of SNP detection and screening can generate over 200,000 data points per week.

In addition to the available IGGP SSR reference set, the development at IASMA of hundreds of SNP markers—mainly targeting genes—laid the foundations for construction of high resolution and functional linkage maps in grapevine (Troggio et al. 2007, Salmaso et al. 2008, S. Vezzulli, unpublished data, 2007). The first haplotype analysis in Vitis spp. was also performed (Salmaso et al. 2004).

Recently, a large set of SNPs was mapped in a mapping population of 94 F1 individuals derived from a V. vinifera cross of the cultivars Syrah and Pinot noir. SNP-based markers were developed from both gene sequences derived by cDNA libraries (dbEST at NCBI) and genomic sequences derived by sequencing of bacterial artificial chromosome (BAC)-ends (BESs; Cinzia Segala 2005, unpublished data) with similar efficiency rates of 38.3% of polymorphic markers from 454 selected EST sequences and 35% from 903 selected BESs, respectively. Spanning 1,245 cM over 19 linkage groups (LGs), the map was generated from the segregation of 483 SNP-based markers, 132 SSRs, and 379 AFLP markers (994 loci) and represents the first fine grapevine genetic map produced with transferable markers (Troggio et al. 2007). To build the two parental and the consensus maps, a recently developed mapping program TMAP (Cartwright et al. 2007) that considers genotyping errors and reduces the inflationary effect of increasing the number of markers was used. Errors inflate the number of recombinations and considerably expand map intervals (Harald et al. 2000). In this respect, the Syrah x Pinot noir map is more reliable in marker order and marker distance estimation (Cartwright et al. 2007). The accuracy of marker order estimated by meiotic methods was also verified using physical distance information for genetically mapped markers contained in the anchored BAC contigs (http://genomics.research.iasma.it) and using anchoring on the genome sequence of Pinot noir (accession numbers AM423240-AM489403 at the EMBL/GenBank/ DDBJ databases; Velasco et al. 2007). Based on this recent achievement, the Pinot noir map has been improved with an additional 800 SNPs (dbSNP accession numbers at the NCBI SNP database from 76900200 to 76900755) on specific regions of the grapevine genome poorly covered by previous markers (Pindo et al. 2008) and an additional 94 individual progeny screened with the SNPlex genotyping system (Michela Troggio 2007, unpublished data). Since it is much easier to generate a high resolution genetic map in the presence of high recombination values, in regions of suppressed recombination more progeny size are needed to recover the number of crossovers necessary for constructing detailed genetic maps (Tanksley et al. 1992).

An integrated genetic map of the five elite V. vinifera L. cultivars Syrah, Pinot noir, Grenache, Cabernet Sauvignon, and Riesling, parents of 275 individuals from three crosses, was thus developed with 33.3% common markers per pair of crosses. Spanning 1,443 cM over 19 linkage groups, the complete integrated map (also built by TMAP) comprises 1134 markers (350 AFLPs, 332 BESs, 169 ESTs, and 283 SSRs) and shows a mean distance between neighbor markers of 1.27 cM. This is therefore the densest genetic map developed so far in grapevine. Marker order was mainly conserved between the integrated map and the very dense Syrah x Pinot noir consensus map, except for a few inversions. Moreover, marker order was proved reliable since this integrated linkage map partially anchors the genome sequence of Pinot noir through 671 markers (Velasco et al. 2007). These markers anchor 623 contigs assembled into 178 metacontigs for a total genome coverage of 360.4 Mb (S. Vezzulli, unpublished data, 2007). This “species consensus map” will serve as a fundamental tool for molecular breeding in V. vinifera and related species.

Physical mapping and marker integration.

Development of physical maps and their integration with genetic maps is needed to isolate and clone genes of interest efficiently (Barker et al. 2005, Troggio et al. 2007). In grapevine, a growing resource of BAC libraries is available (Adam-Blondon et al. 2005, Lamoureux et al. 2006; http://genomics.research.iasma.it; www.vitaceae.com), mainly for well-known V. vinifera cultivars but also for V. vinifera genotypes introgressed with disease resistance genes. A further step will be to develop BAC libraries on other Vitis species that are used as sources for resistance genes for grapevine breeding (Pauquet et al. 2001). These BAC libraries can be used for development of local physical maps or whole genome physical maps.

Physical maps consist of the assembly into contigs of overlapping large insert clones based on fingerprint similarities and the presence of common markers. These assemblies are made using the software FPC (Soderlund et al. 2000) and the distance is in base pairs. Most physical maps developed for plants are now based on assembly of BAC clones (Meyers et al. 2004).

The first BAC-based physical map of the grape genome and its integration with the genetic map has been reported for the cultivar Pinot noir, which is highly heterozygous at the sequence level (Velasco et al. 2007), using FPC (http://genomics.research.iasma.it) and high information content fingerprinting of 49,536 BAC clones from Pinot noir. The FPC program that assembles contigs obtained by BAC fingerprinting is designed to assemble highly homozygous genomes. Assembly could be improved using new map assembly algorithms that explicitly deal with the presence of two haplotypes regardless of the frequency and patterns of heterozygosity (Dustin Cartwright 2006, unpublished data).

Two complementary strategies have been adopted to help integrate the Syrah x Pinot genetic map. The first strategy involved construction of BAC pools (Barillot et al. 1991) as described (Klein et al. 2000). 24,576 BAC clones with a mean insert size of 100 kb (five genome equivalents) contained in 64 384-well microtiter plates were arranged in a stack and sampled in six distinct ways. Three of these were according to the Cartesian coordinates (first configuration): plate pool (PP), side pool (SP), and front pool (FP). The three remaining pool types were sections taken at an angle through the stack (second configuration): row pool (RP), column pool (CP), and diagonal pool (DP). In total, the six pool types resulted in 184 BAC pools. Five of the six configurations (PP, FP, RP, CP, and DP) were composed of 32 pools, each containing 768 BACs. The sixth configuration (SP) was composed of 24 pools each containing 1024 BACs. Thus, each clone in the stack was present in exactly one pool of each configuration.

The BAC pools were then screened with EST, SSR, and AFLP primers to identify mapped fragments. SSR and EST amplification products from BAC pools were run on agarose gel. BAC clones hosting SSR and EST markers were identified by a Unix-based application with a web interface. AFLP amplification products from BAC pools were analyzed on acrylamide gels along with amplification products from the two parents and the mapping population as a control (AFLP Quant-Pro, Keygene, Wageningen, Netherlands). BACs containing AFLPs were identified in the same way as the other markers.

To improve integration between genetic and physical maps, a second strategy used markers derived from collections of BESs (Cinzia Segala 2005, unpublished data; Troggio et al. 2007) with subsequent determination of their genetic position on the linkage map. 30,832 Pinot noir BESs were characterized and used to integrate the Syrah x Pinot noir genetic map with the Pinot noir physical map; an additional 68,000 BESs from the Pinot noir sequencing project were used to assemble the Pinot noir genome (Velasco et al. 2007).

A total of 623 markers from a Syrah x Pinot noir genetic map were anchored on 367 BAC contigs covering 352 Mb (Troggio et al. 2007). Based on contigs with two or more genetically mapped markers, regions of both increased and reduced recombination were identified within the grape genome, although the results are not conclusive because of significant uncertainties in both genetic and physical distances. The genome sequencing data can help resolve the problem (Figure 1⇓). Variations in the correspondence between physical and genetic distance along chromosomes have already been well documented in several species (Tanksley et al. 1992, Chen et al. 2002, King et al. 2002). When available, such variations add important information to map-based cloning projects. The BAC pooling strategy was also used as a high-throughput method to identify positive clones for a corresponding target region within the thousands of BAC clones of a genomic library (Pindo et al. 2006).

Figure 1
  • Download figure
  • Open in new tab
Figure 1

The image displayed by the comparative map tool CMap of LG 7 (29–34 cM interval), the BAC contigs 4487 and 4734, and the anchored metacontig 16506 of the assembled V. vinifera genome. Correspondences between marker loci in the genetic map, the BAC contig, and the assembled metacontig are shown with solid lines.

A specific primer pair was designed on the MybA1 sequence (AB111100) to develop a new EST marker to position on the genetic map of the hybrid Merzling (the complex genotype ‘Freiburg 993-60’ derived from multiple crosses also involving wild species such as V. rupestris and V. lincecumii) x V. vinifera cv. Teroldego cross (FxT). MybA1 co-segregated with the phenotypic trait “color” on LG 2 (Salmaso et al. 2008). The BAC pools were screened with the MybA1 primer combination and two positive clones, 1044_B05 and 1085_L05, were found. Based on SNPs found within the corresponding BESs, a new SNP-based marker (1044B05) was developed. The polymorphisms in the FxT mapping population and in the V. vinifera cv. Syrah x Pinot noir cross (SxP) were assessed using the minisequencing technique (Troggio et al. 2008). As the SxP population does not segregate for the “color trait,” its position was indirectly located on the map (Figure 2⇓).

Figure 2
  • Download figure
  • Open in new tab
Figure 2

(A) Mapping of “color trait” and MybA1 markers on LG2 on the genetic map Merzling (the complex genotype ‘Freiburg 993-60’ derived from multiple crosses also involving wild species such as V. rupestris and V. lincecumii) x V. vinifera cv. Teroldego (FxT) cross. (B) Identification of BAC clones 1044_B05 and 1085_L05 by MybA1 primers combination. (A and C) Mapping of 1044B05F marker on LG2 on the genetic maps FxT and Syrah x Pinot noir (SxP) crosses.

Whole genome sequencing.

Given its cultural and economic importance, winegrape was an obvious first candidate among woody fruit crops to have its genome deciphered. Two projects were aimed at sequencing the grapevine genome. One was a collaboration between IASMA and two private companies, Myriad Genetics, Incorporated, and the 454 Life Science (Velasco et al. 2007). The other was a consortium of French and Italian public laboratories (Jaillon et al. 2007). The latter project selected a near-homozygous line, originally derived from Pinot noir, that has been bred close to full homozygosity (estimated at ~ 93%) by successive selfings to facilitate assembly of 12X shotgun sequences.

The IASMA project focused on the elite cultivar Pinot noir, with the multiple goals of genome assembly, gene identification and annotation, and identification of the most possible polymorphisms. Of special interest to biologists and breeders are polymorphisms in and around coding regions. Pinot noir is highly polymorphic, with two clearly distinguishable haplotypes revealing several million SNPs and small indels. This represents a substantial resource for molecular breeding programs and trait and QTL marker association.

Three main strategies are currently used for sequencing complex genomes: a clone-by-clone approach, a whole-genome shotgun approach, and a combination of both (Green 2001). The first and last strategies need a very precise positioning of BAC clones on a physical map. The strategy for whole-genome sequencing was suggested by the large set of markers available for Pinot noir and the troubleshooting developed during physical mapping experiments.

Two sequencing techniques were adopted. The Sanger 6.5X sequencing approach was integrated with a scalable, highly parallel sequencing by synthesis (SBS) system with throughput significantly greater than that of capillary electrophoresis instruments. The 4.2X coverage of SBS was crucial for identifying polymorphic sites and closing most gaps between DNA contigs, both by providing increased coverage and by using a method free of biases introduced by cloning before sequencing. This is the first project which used both Sanger and SBS shotgun sequencings for a large eukaryotic genome.

Existing software and strategies were not adequate to assemble this highly heterozygous genome. A novel approach to genomic alignment was developed to generate a single final sequence representing both chromosomes of Pinot noir. The result is a mosaic of two Pinot noir haplotypes which includes haplotype-specific gaps (sequences inserted in one haplotype but not in the other) and in which all identified SNP-type polymorphisms are represented in the final consensus by IUPAC ambiguous base notations. Around two million highly validated SNPs and more than a million in/dels were identified in the assembled sequences.

Two BAC libraries and a fosmid library were end-sequenced to assemble large meta-contigs. Contigs were oriented and ordered on appropriate chromosomes by high-throughput marker development and genotyping in an F1 cross of Syrah x Pinot noir (Figure 3⇓). The SNP-based markers were essential to improve the metacontig assembly. Many adjacent metacontigs that were not initially merged because of nonsignificant links between them were associated with neighboring genetic markers and could therefore be safely merged into a single larger metacontig. On the other hand, if a metacontig was associated with several markers from different LGs or with distant markers from the same LG, it was considered chimeric and was split into two separate metacontigs by a semi-automated procedure.

Figure 3
  • Download figure
  • Open in new tab
Figure 3

Image produced by the comparative map program tool CMap available at http://genomics.research.iasma.it, showing LG4 of the V. vinifera genetic map (on the left) and anchored metacontigs of the assembled V. vinifera genome.

The assembled contigs were used to predict gene content. Various gene prediction methods were used: FgenesH homology-based FgenesH+ (Solovyev et al. 2006), Twin-scan (Korf et al. 2001), GlimmerHMM (Majoros et al. 2004), and Tentative Consensus (TC) transcripts derived from 320,000 ESTs deposited in public databases. Gene annotation followed a consensus approach. BLAST searches were performed against UniProt and plant protein databases annotated with GO terms of various domain libraries (Prints, HMMPIR Pfam, and SMART).

The sequence provides information on overall organization, gene content, and structural components of the DNA in the 19 chromosomes of V. vinifera. 44,179 contigs merged into 2,093 meta-contigs covering 477.1 Mb of genomic DNA. Of these, 435.1 Mb were anchored to the 19 LGs using 1,356 markers. More than 80% of the anchored metacontigs were oriented by two or more markers. The number of predicted genes is 29,585, of which 96.1% were assigned to LGs. Of around 2,000,000 SNPs, 1,751,176 were mapped to chromosomes and one or more of them were identified in 86.7% of anchored genes.

The enormous set of data generated by the two projects will constitute a useful resource for marker-assisted selection and breeding, once QTLs and monogenic traits are assigned to well-defined regions.

Candidate gene isolation for modern breeding.

Although more than 5,000 V. vinifera cultivars exist, the global market is dominated by a few wine and table grape cultivars. Ancient cultivars and germplasm are disappearing as a consequence, even though they represent an important source of genetic variability and traits of interest (This et al. 2006). The need for a new approach to breeding that exploits the great diversity within the Vitis genus is widely recognized. Dense integrated genetic and physical maps are a key step in map-based cloning projects for genes of interest. The long generation time and the space needed to grow large progenies means this approach is more problematic in perennial species than in annual species, although some success has already been obtained (Patocchi et al. 1999, Claverie et al. 2004). Integrated genetic and physical maps are very efficient in accelerating gene mapping, as no polymorphism is required for anchoring them on BAC clones. Such integrated maps are thus invaluable resources for the quick development of new markers in targeted regions using BESs (Barker et al. 2005, Castellarin et al. 2006, Troggio et al. 2007), for candidate gene approaches by establishing links between genetic maps—where QTLs for traits of interest have been located—and gene-containing BACs, and for preparing and accelerating map-based cloning projects.

Three different studies illustrate integrated use of genetics and genomic tools. The first (Barker et al. 2005) is a recently published local fine map in grapevine for a region containing a major gene for resistance to powdery mildew, Run1 (Pauquet et al. 2001, Donald et al. 2002). A BAC library developed from a resistant genotype was screened with two of the closest markers and contigs were built, mainly for the Run1-carrying haplotype, amplifying a band in the introgressed chromosome and nothing in the susceptible haplotype. The BESs generated from the BACs in this region allowed development of new markers and the extension of the contigs. Large progeny screening for refinement allowed a family of candidate genes to be localized in this region (Barker et al. 2005).

The second study is the linkage map from the hybrid Merzling x V. vinifera cv. Teroldego cross based on localization of expressed genes and unique genomic sequences (Salmaso et al. 2008). This mapping experiment was based on an F1 population selected for many segregating traits including tolerance to fungal pathogens, color and quality of anthocyanins, resistance to Daktulosphaira vitifoliae, bunch shape and compactness, and high- versus low-quality berry metabolic profiles. T he aims of this study were to map berry color genes participating in anthocyanin metabolism, such as the last five enzymes of the anthocyanin pathway—chalcone isomerase (CHI), flavanone 3-hy-droxylase (F3H), dihydro-flavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), and UDP glucose-flavonoid 3-o-glucosyl transferase (UFGT) —and some myb transcription factors proposed as regulators of the phenylpropanoid pathway (Kobayashi et al. 2002) and to study their correlation with berry color. In the Merzling x Teroldego segregating population, berry color co-segregated with a myb gene mapping to LG2, in accordance with other recently published papers (Lijavetzky et al. 2006, This et al. 2007). The study described above, however, is the first demonstration of co-localization of Myb with color, starting from a cross- population segregating for berry color.

The third study characterized QTLs for berry and phenology-related traits (Costantini et al. 2008). Controlling the timing of ripening initiation, length of maturation period, berry size and color, acidity, and the relative assortment of volatile and nonvolatile compounds that contribute to aroma in grapes are major concerns to viticulturists and wine makers. In addition, there is an increasing demand for seedless varieties in the table grape market. A better understanding of grapevine phenology could allow wider control of ripening time, thus offering the possibility of staggering the harvest over the growing season, expanding production into periods when the fruit has a higher market value, and ensuring optimal adaptation to climatic and geographic conditions (Jones 2006).

In the above work, the genetic determinism of grapevine flowering, fruit maturation timing, berry size, and seed content was investigated by performing a quantitative analysis in combination with phenotypic data collected over three years (Costantini et al. 2008). Complete linkage maps containing microsatellites, AFLPs, and candidate genes were developed from Italia x Big Perlon, a table grape segregating F1 progeny ( Fanizza et al. 2005), and used to detect QTLs.

Phenotypic data distributions (Figure 4⇓) were very similar over three years. A continuous variation, typical of quantitative traits, and a transgressive segregation were observed for all traits. Several associations between traits within each year were found using the Spearman rank-order correlation test. Many concerned component variables of the same character, but correlations between different traits were also detected.

Figure 4
  • Download figure
  • Open in new tab
Figure 4

Distribution of veraison time and mean seed dry weight in the Italia x Big perlon progeny in 2004.

QTL analysis revealed the existence of several regions regulating the variation of phenological traits, namely on LG1 (flowering time), LG2 (flowering time, veraison time, veraison period, flowering-veraison interval, and veraison-ripening interval), LG6 (flowering time, veraison time, ripening date, flowering-veraison interval, and flowering-ripening interval), LG12 (veraison-ripening interval), and finally LG16 (veraison time, veraison period, and flowering-veraison interval). No QTL could be identified for flowering period. The existence of a major QTL on LG18 regulating berry size and seed content was confirmed.

QTL analysis indicates the regions of a genome which contribute to trait variation. The following step is to narrow down these regions so effects can be ascribed to specific genes. To this purpose, the candidate gene approach was adopted on two levels (Costantini et al. 2008). First, “functional candidate genes” selected according to their hypothetical biological role were mapped and tested for linkage with QTLs. Second, the genomic sequence of Pinot noir was used to identify “positional candidate genes” in proximity to molecular markers underlying QTLs. Gene prediction and protein similarity searches suggested some interesting proteins with known roles in flower and fruit development in other plant species may be involved in the studied phenotypes.

Conclusion

The grape genome sequence is fully deciphered. Decoding the full sequence information provides candidate genes implicated in traits relevant to grape cultivation, such those influencing wine quality via secondary metabolites and those connected with the extreme susceptibility of grape to pathogens. A new era in grapevine breeding is now opened. The huge number of SNPs revealed by sequencing the Pinot noir genome will allow screening of thousands of new genotypes obtained by appropriate breeding programs. Mapping of fruit quality and disease-resistance genes enables genomewide association studies. In the near future, this information will make possible a new viticulture.

Footnotes

  • Dr. Velasco presented this lecture at the 2007 ASEV Annual Meeting, Reno, Nevada. The author’s project has been funded by the Foundation Cassa di Risparmio di Trento e Rovereto and the Province of Trento, Italy.

  • Copyright © 2008 by the American Society for Enology and Viticulture

Literature Cited

  1. ↵
    Adam-Blondon, A.F., A. Bernole, G. Faes, D. Lamoureux, S. Pateyron, M.S. Grando, M. Caboche, R. Velasco, and B. Chalhoub. 2005. Construction and characterization of BAC libraries from major grapevine cultivars. Theor. Appl. Genet. 110:1363–1371.
    OpenUrlCrossRefPubMed
  2. ↵
    Adam-Blondon, A.F., C. Roux, D. Claux, G. Butterlin, D. Merdinoglu, and P. This. 2004. Mapping 245 SSR markers on the Vitis vinifera genome: A tool for grape genetics. Theor. Appl. Genet. 109:1017–1027.
    OpenUrlCrossRefPubMed
  3. ↵
    Arumuganathan, K., and E. Earle. 1991. Nuclear DNA content of some important plant species. Plant Mol. Biol. 9:208–218.
    OpenUrl
  4. ↵
    Barillot, E., B. Lacroix, and D. Cohen. 1991. Theoretical analysis of library screening using a N-dimensional pooling strategy. Nucleic Acids Res. 19:6241–6247.
    OpenUrlCrossRefPubMed
  5. ↵
    Barker, C.L., T. Donald, J. Pauquet, M.B. Ratnaparkhe, A. Bouquet, A.F. Adam-Blondon, M.R. Thomas, and I. Dry. 2005. Genetic and physical mapping of the grapevine powdery mildew resistance gene, Run1, using a bacterial artificial chromosome library. Theor. Appl. Genet. 111:370–377.
    OpenUrlCrossRefPubMed
  6. ↵
    Burns, J., P.T. Gardner, J. O’Neil, S. Crawford, I. Morecroft, D.B. McPhail, C. Lister, D. Matthews, M.R. MacLean, M.E. Lean, G.G. Duthie, and A. Crozier. 2000. Relationship among antioxidant activity, vasodilation capacity, and phenolic content of red wines. J. Agric. Food Chem. 48:220–230.
    OpenUrlCrossRefPubMed
  7. ↵
    Cartwright, D.A., M. Troggio, R. Velasco, and A. Gutin. 2007. Genetic mapping in the presence of genotyping errors. Genetics 176:2521–2527.
    OpenUrlAbstract/FREE Full Text
  8. ↵
    Castellarin, S.D., G. Di Gaspero, R. Marconi, A. Nonis, E. Peter-lunger, S. Paillard, A.F. Adam-Blondon, and R. Testolin. 2006. Color variation in red grapevines (Vitis vinifera L.): Genomic organization, expression of flavonoid 3’-hydroxylase, flavonoid 3’,5’-hydroxylase genes and related metabolite profiling of red cyanidin-/blue delphinidin-based anthocyanins in berry skin. BMC Genomics 7:12.
    OpenUrlCrossRefPubMed
  9. ↵
    Castro, A.J., C. Carapito, N. Zorn, C. Magne, E. Leize, A. Van Dorsselaer, and C. Clement. 2005. Proteomic analysis of grapevine (Vitis vinifera L.) tissues subjected to herbicide stress. J. Exp. Bot. 56:2783–2795.
    OpenUrlCrossRefPubMed
  10. ↵
    Chen, M., et al. 2002. An integrated physical and genetic map of the rice genome. Plant Cell 14:537–545.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    Claverie, M., E. Dirlewanger, P. Cosson, N. Bosselut, A.C. Lecouls, R. Voisin, M. Kleinhentz, B. Lafargue, M. Caboche, B. Chalhoub, and D. Esmenjaud. 2004. High-resolution mapping and chromosome landing at the root-knot nematode resistance locus Ma from Myrobalan plum using a large-insert BAC DNA library. Theor. Appl. Genet. 109:1318–1327.
    OpenUrlCrossRefPubMed
  12. ↵
    Costantini, L., J. Battilana, F. Lamaj, G. Fanizza, and M.S. Grando. 2008. Berry and phenology-related traits in grapevine (Vitis vinifera L.): From quantitative trait loci to underlying genes. BMC Plant Biol. 8:38.
    OpenUrlCrossRefPubMed
  13. ↵
    da Silva, F.G., et al. 2005. Characterizing the grape transcriptome. Analysis of expressed sequence tags from multiple Vitis species and development of a compendium of gene expression during berry development. Plant Physiol. 139:574–597.
    OpenUrlAbstract/FREE Full Text
  14. ↵
    Dalbò, M.A., G.N. Ye, N.F. Weeden, H. Steinkellner, K.M. Sefc, and B.I. Reisch. 2000. Gene controlling sex in grapevines placed on a molecular marker-based genetic map. Genome 43:333–340.
    OpenUrlCrossRefPubMed
  15. ↵
    Deluc, L.G., J. Grimplet, M.D. Wheatley, R.L. Tillett, D.R. Quilici, C. Osborne, D.A. Schooley, K.A. Schlauch, J.C. Cushman, and G.R. Cramer. 2007. Transcriptomic and metabolite analyses of Cabernet Sauvignon grape berry development. BMC Genomics 8:429.
    OpenUrlCrossRefPubMed
  16. ↵
    Deytieux, C., L. Geny, D. Lapaillerie, S. Claverol, M. Bonneu, and B. Doneche. 2007. Proteome analysis of grape skins during ripening. J. Exp. Bot. 58:1851–1862.
    OpenUrlCrossRefPubMed
  17. ↵
    Di Gaspero, G., G. Cipriani, A.F. Adam-Blondon, and R. Testolin. 2007. Linkage maps of grapevine displaying the chromosomal locations of 420 microsatellite markers and 82 markers for R-gene candidates. Theor. Appl. Genet. 114:1249–1263.
    OpenUrlCrossRefPubMed
  18. ↵
    Doligez, A., A.F. Adam-Blondon, G. Cipriani, G. Di Gaspero, V. Laucou, D. Merdinoglu, C. Meredith, S. Riaz, C. Roux, and P. This. 2006. An integrated SSR map of grapevine based on five mapping populations. Theor. Appl. Genet. 113:369–382.
    OpenUrlCrossRefPubMed
  19. ↵
    Doligez, A., A. Bouquet, Y. Danglot, F. Lahogue, S. Riaz, C.P. Meredith, K.J. Edwards, and P. This. 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.
    OpenUrlCrossRefPubMed
  20. ↵
    Donald, T.M., F. Pellerone, A.F. Adam-Blondon, A. Bouquet, M.R. Thomas, and I.B. Dry. 2002. Identification of resistance gene analogs linked to a powdery mildew resistance locus in grapevine. Theor. Appl. Genet. 104:610–618.
    OpenUrlCrossRefPubMed
  21. ↵
    Doucleff, M., Y. Jin, F. Gao, S. Riaz, A.F. Krivanek, and M.A. Walker. 2004. A genetic linkage map of grape, utilizing Vitis rupestris and Vitis arizonica. Theor. Appl. Genet. 109:1178–1187.
    OpenUrlCrossRefPubMed
  22. ↵
    Fanizza, G., F. Lamaj, L. Costantini, R. Chaabane, and M.S. Grando. 2005. QTL analysis for fruit yield components in table grapes (Vitis vinifera). Theor. Appl. Genet. 111:658–664.
    OpenUrlCrossRefPubMed
  23. ↵
    Fischer, B.M., I. Salakhutdinov, M. Akkurt, R. Eibach, K.J. Edwards, R. Toepfer, and E. Zyprian. 2004. Quantitative trait locus analysis of fungal disease resistance factors on a molecular map of grapevine. Theor. Appl. Genet. 108:501–515.
    OpenUrlCrossRefPubMed
  24. ↵
    Grando, M.S., D. Bellin, K.J. Edwards, C. Pozzi, M. Stefanini, and R. Velasco. 2003. Molecular linkage maps of Vitis vinifera L. and Vitis riparia Michx. Theor. Appl. Genet. 106:1213–1224.
    OpenUrlCrossRefPubMed
  25. ↵
    Green, E.D. 2001. Strategies for the systematic sequencing of complex genomes. Nat. Rev. Genet. 2:573–583.
    OpenUrlCrossRefPubMed
  26. ↵
    Harald, H., H. Goring, and J. Terwilliger. 2000. Linkage analysis in the presence of errors II: Marker-locus genotyping errors modelled with hypercomplex recombination fractions. Am. J. Hum. Genet. 66:1107–1118.
    OpenUrlCrossRefPubMed
  27. ↵
    Jaillon, O., et al. 2007. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449:463–467.
    OpenUrlCrossRefPubMed
  28. ↵
    Jones, G. 2006. Climate change and wine: Observations, impacts and future implications. Wine Ind. J. 21:21–26.
    OpenUrl
  29. ↵
    Kalt, W. 2001. Health functional phytochemicals of fruit. Hortic. Rev. 27:269–315.
    OpenUrl
  30. ↵
    Kharb, S., and V. Singh. 2004. Nutriceuticals in health and disease prevention. Indian J. Clin. Biochem. 19:50–53.
    OpenUrl
  31. ↵
    Kikkert, J.R., M.J. Striem, J.R. Vidal, P.G. Wallace, J. Barnard, and B.I. Reisch. 2005. Long-term study of somatic embryogenesis from anthers and ovaries of 12 grapevine (Vitis spp.) genotypes. In Vitro Cell. Develop. Biol. Plant. 41:232–239.
    OpenUrl
  32. ↵
    King, J., I.P. Armstead, I.S. Donnison, H.M. Thomas, R.N. Jones, M.J. Kearsey, L.A. Roberts, A. Thomas, W.G. Morgan, and I.P. King. 2002. Physical and genetic mapping in the grasses Lolium perenne and Festuca pratensis. Genetics 161:315–324.
    OpenUrlAbstract/FREE Full Text
  33. ↵
    Klein, P.E., R.R. Klein, S.W. Cartinhour, P.E. Ulanch, J. Dong, J.A. Obert, D.T. Morishige, S.D. Schlueter, K.L. Childs, M. Ale, and J.E. Mullet. 2000. A high-throughput AFLP-based method for constructing integrated genetic and physical maps: progress toward a sorghum genome map. Genome Res. 10:789–807.
    OpenUrlAbstract/FREE Full Text
  34. ↵
    Kobayashi, S., M. Ishimaru, K. Hiraoka, and C. Honda. 2002. Myb-related genes of Kyoho grape (Vitis labruscana) regulate anthocyanin biosynthesis. Planta 215:924–933.
    OpenUrlCrossRefPubMed
  35. ↵
    Korf, I., P. Flicek, D. Duan, and M.R. Brent. 2001. Integrating genomic homology into gene structure prediction. Bioinformatics 17:S140–148.
    OpenUrlCrossRefPubMed
  36. ↵
    Lamoureux, D., et al. 2006. Anchoring of a large set of markers onto a BAC library for the development of a draft physical map of the grapevine genome. Theor. Appl. Genet. 113:344–356.
    OpenUrlCrossRefPubMed
  37. ↵
    Lijavetzky, D., L. Ruiz-Garcia, J.A. Cabezas, M.T. De Andres, G. Bravo, A. Ibanez, J. Carreno, F. Cabello, J. Ibanez, and J.M. Martinez-Zapater. 2006. Molecular genetics of berry color variation in table grape. Mol. Gen. Genet. 276:427–435.
    OpenUrl
  38. ↵
    Lijavetzky, D., J.A. Cabezas, A. Ibanez, V. Rodriguez, and J.M. Martinez-Zapater. 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.
    OpenUrlCrossRefPubMed
  39. ↵
    Lodhi, M.A., M.J. Daly, G.N. Ye, N.F. Weeden, and B.I. Reisch. 1995. A molecular marker based linkage map of Vitis. Genome 38:786–794.
    OpenUrlCrossRefPubMed
  40. ↵
    Lodhi, M.A., and B.I. Reisch. 1995. Nuclear DNA content of Vitis species, cultivars, and other genera of the Vitaceae. Theor. Appl. Genet. 90:11–16.
    OpenUrlCrossRef
  41. ↵
    Lowe, K.M., and M.A. Walker. 2006. Genetic linkage map of the interspecific grape rootstock cross Ramsey (Vitis champinii) x Riparia Gloire (Vitis riparia). Theor. Appl. Genet. 112: 1582–1592.
    OpenUrlCrossRefPubMed
  42. ↵
    Majoros, W.H., M. Pertea, and S.L. Salzberg. 2004. TigrScan and GlimmerHMM: Two open source ab initio eukaryotic gene-finders. Bioinformatics 20:2878–2879.
    OpenUrlCrossRefPubMed
  43. ↵
    Masquelier, J. 1992. La vigne, plante médicinale. Naissance et essor d’une thérapeutique. Bull. OIV 65:177–196.
    OpenUrl
  44. ↵
    Mattivi, F., R. Guzzon, U. Vrhovsek, M. Stefanini, and R. Velasco. 2006. Metabolite profiling of grape: Flavonols and anthocyanins. J. Agric. Food Chem. 54:7692–7702.
    OpenUrlCrossRefPubMed
  45. ↵
    Mazur, B., and S. Tingey. 1995. Genetic mapping and introgression of genes of agronomic importance. Curr. Opin. Biotechnol. 6:175–182.
    OpenUrlCrossRef
  46. ↵
    Meyers, B.C., S. Kaushik, and R.S. Nandety. 2005. Evolving disease resistance genes. Curr. Opin. Plant Biol. 8:129–134.
    OpenUrlCrossRefPubMed
  47. ↵
    Meyers, B.C., S. Scalabrin, and M. Morgante. 2004. Mapping and sequencing complex genomes: Let’s get physical! Nat. Rev. Genet. 5:578–588.
    OpenUrl
  48. ↵
    Morgante, M., and F. Salamini. 2003. From plant genomics to breeding practice. Curr. Opin. Biotechnol. 14:214–219.
    OpenUrlCrossRefPubMed
  49. ↵
    Moser, C., C. Segala, P. Fontana, I. Salakhudtinov, P. Gatto, M. Pindo, E. Zyprian, R. Toepfer, M.S. Grando, and R. Velasco. 2005. Comparative analysis of expressed sequence tags from different organs of Vitis vinifera L. Func. Integr. Genomics 5:208–217.
    OpenUrl
  50. ↵
    Olmo, H.P. 1979. Grapes. In Evolution of Crop Plants. Simmonds, London.
  51. ↵
    Orita, M., H. Iwahana, H. Kanazawa, K. Hayashi, and T. Sekiya. 1989. Detection of polymorphisms of human DNA by gel electrophoresis as single-strand conformation polymorphisms. Proc. Nat. Acad. Sci. U.S.A. 86:2766–2770.
    OpenUrlAbstract/FREE Full Text
  52. ↵
    Panagiotakos, D.B., C. Pitsavos, E. Polychronopoulos, C. Chryso-hoou, A. Zampelas, and A. Trichopoulou. 2004. Can a Mediterranean diet moderate the development and clinical progression of coronary heart disease? A systematic review. Med. Sci. Monit. 10:RA193–198.
    OpenUrlPubMed
  53. ↵
    Patocchi, A., B.A. Vinatzer, L. Gianfranceschi, S. Tartarini, H.B. Zhang, S. Sansavini, and C. Gessler. 1999. Construction of a 550 kb BAC contig spanning the genomic region containing the apple scab resistance gene Vf. Mol. Gen. Genet. 262:884–891.
    OpenUrlCrossRefPubMed
  54. ↵
    Paul, B., I. Masih, J. Deopujari, and C. Charpentier. 1999. Occurrence of resveratrol and pterostilbene in age-old darakchasava, an ayurvedic medicine from India. J. Ethnopharmacol. 68:71–76.
    OpenUrl
  55. ↵
    Pauquet, J., A. Bouquet, P. This, and A.F. Adam-Blondon. 2001. Establishment of a local map of AFLP markers around the powdery mildew resistance gene Run1 in grapevine and assessment of their usefulness for marker assisted selection. Theor. Appl. Genet. 103:1201–1210.
    OpenUrlCrossRef
  56. ↵
    Peng, F.Y., K.E. Reid, N. Liao, J. Schlosser, D. Lijavetzky, R. Holt, J.M. Martinez Zapater, S. Jones, M. Marra, J. Bohlmann, and S.T. Lund. 2007. Generation of ESTs in Vitis vinifera wine grape (Cabernet Sauvignon) and table grape (Muscat Hamburg) and discovery of new candidate genes with potential roles in berry development. Gene 402:40–50.
    OpenUrlCrossRefPubMed
  57. ↵
    Pereira, G.E., J.P. Gaudillere, C. Van Leeuwen, G. Hilbert, O. Lavialle, M. Maucourt, C. Deborde, A. Moing, and D. Rolin. 2005. 1H-NMR and chemometrics to characterize mature grape berries in four wine-growing areas in Bordeaux, France. J. Agric. Food. Chem. 53:6382–6389.
    OpenUrlCrossRefPubMed
  58. ↵
    Pindo, M., G. Malacarne, G. Faes, G. Coppola, C. Segala, M.S. Grando, M. Troggio, and R.Velasco. 2006. BAC pooling strategy to identify target regions of the grapevine genome. Abstr. 5. Plant Genomics and European Meeting, p. 151.
  59. ↵
    Pindo, M., S. Vezzulli, G. Coppola, D. Cartwright, A. Zharkikh, R. Velasco, and M. Troggio. 2008. SNP high-throughput screening in grapevine using the SNPlexTM genotyping system. BMC Plant Biol. 8:12.
    OpenUrlCrossRefPubMed
  60. ↵
    Rafalski, A. 2002. Applications of single nucleotide polymorphisms in crop genetics. Curr. Opin. Plant Biol. 5:94–100.
    OpenUrlCrossRefPubMed
  61. ↵
    Riaz, S., G.S. Dangl, K.J. Edwards, and C.P. Meredith. 2004. A microsatellite marker based framework linkage map of Vitis vinifera L. Theor. Appl. Genet. 108:864–872.
    OpenUrlCrossRefPubMed
  62. ↵
    Riaz, S., A. Krivanek, K. Xu, and M. Walker. 2006. Refined mapping of the Pierce’s disease resistance locus, PdR1, and Sex on an extended genetic map of Vitis rupestris × V. arizonica. Theor. Appl. Genet. 113:1317–1329.
    OpenUrlCrossRefPubMed
  63. ↵
    Salmaso, M., G. Faes, C. Segala, M. Stefanini, I. Salakhutdinov, E. Zyprian, R. Toepfer, M.S. Grando, and R. Velasco. 2004. Genome diversity and gene haplotypes in the grapevine (Vitis vinifera L.), as revealed by single nucleotide polymorphisms. Mol. Breed. 14:385–395.
    OpenUrlCrossRef
  64. ↵
    Salmaso, M., G. Malacarne, M. Troggio, G. Faes, M. Stefanini, M.S. Grando, and R. Velasco. 2008. A grapevine (Vitis vinifera L.) genetic map integrating the position of 139 expressed genes. Theor. Appl. Genet. DOI 10.1007/s00122-008-0741-3.
  65. ↵
    Sarry, J.E., N. Sommerer, F.X. Sauvage, A. Bergoin, M. Rossignol, G. Albagnac, and C. Romieu. 2004. Grape berry biochemistry revisited upon proteomic analysis of the mesocarp. Proteomics 4:201–215.
    OpenUrlCrossRefPubMed
  66. ↵
    Soderlund, C., S. Humphray, A. Dunham, and L. French. 2000. Contigs built with fingerprints, markers, and FPC V4.7. Genome Res. 10:1772–1787.
    OpenUrlAbstract/FREE Full Text
  67. ↵
    Solovyev, V., P. Kosarev, I. Seledsov, and D. Vorobyev. 2006. Automatic annotation of eukaryotic genes, pseudogenes, and promoters. Genome Biol. 7 Suppl 1:S10.1–12.
    OpenUrl
  68. ↵
    Syvanen, A.C. 2005. Toward genome-wide SNP genotyping. Nat. Genet. 37:s5–s10.
    OpenUrlCrossRefPubMed
  69. ↵
    Tanksley, S.D., et al. 1992. High density molecular linkage maps of the tomato and potato genomes. Genetics 132:1141–1160.
    OpenUrlAbstract/FREE Full Text
  70. ↵
    This, P., T. Lacombe, and M.R. Thomas. 2006. Historical origins and genetic diversity of wine grapes. Trends Genet. 22:511–519.
    OpenUrlCrossRefPubMed
  71. ↵
    This, P., T. Lacombe, M. Cadle-Davidson, and C.L. Owens. 2007. Wine grape (Vitis vinifera L.) color associates with allelic variation in the domestication gene VvmybA1. Theor. Appl. Genet. 114:723–730.
    OpenUrlCrossRefPubMed
  72. ↵
    Troggio, M., G. Malacarne, G. Coppola, C. Segala, D.A. Cartwright, M. Pindo, M. Stefanini, R. Mank, M. Moroldo, M. Morgante, M.S. Grando, and R. Velasco. 2007. A dense single-nucleotide polymorphism-based genetic linkage map of grapevine (Vitis vinifera L.) anchoring Pinot noir bacterial artificial chromosome contigs. Genetics 176:2637–2650.
    OpenUrlAbstract/FREE Full Text
  73. ↵
    Troggio, M., G. Malacarne, S. Vezzulli, G. Faes, M. Salmaso, and R. Velasco. 2008. Comparison of different methods for SNP detection in grapevine. Vitis 47:21–30.
    OpenUrl
  74. ↵
    Tuskan, G.A., et al. 2006. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313:1596–1604.
    OpenUrlAbstract/FREE Full Text
  75. ↵
    Velasco, R., et al. 2007. High quality draft consensus sequence of the genome of a heterozygous grapevine variety. PLoS ONE 2:e1326.
    OpenUrlCrossRef
  76. ↵
    Wang, Q., P. Li, U. Hanania, N. Sahar, M. Mawassi, R. Gafny, I. Sela, E. Tanne, and A. Perl. 2005. Improvement of aAgro-bacterium-mediated transformation efficiency and transgenic plant regeneration of Vitis vinifera L. by optimizing selection regimes and utilizing cryopreserved cell suspensions. Plant Sci. 168:565–571.
    OpenUrl
  77. ↵
    Welter, L., N. Göktürk-Baydar, M. Akkurt, E. Maul, R. Eibach, R. Toepfer, and E. Zyprian. 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.
    OpenUrlCrossRef
  78. ↵
    Xu, K., S. Riaz, N.C. Roncoroni, Y. Jin, R. Hu, R. Zhou, and M.A. Walker. 2008. Genetic and QTL analysis of resistance to Xiphinema index in a grapevine cross. Theor. Appl. Genet. 116:305–311.
    OpenUrlCrossRefPubMed
PreviousNext
Back to top

Vol 59 Issue 2

  • Table of Contents
  • Table of Contents (PDF)
  • Index by author
Print
View full PDF
Email Article

Thank you for your interest in spreading the word on AJEV.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
ASEV Honorary Research Lecture 2007
(Your Name) has forwarded a page to you from AJEV
(Your Name) thought you would like to read this article from the American Journal of Enology and Viticulture.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
You have accessRestricted access
ASEV Honorary Research Lecture 2007
Michela Troggio, Silvia Vezzulli, Massimo Pindo, Giulia Malacarne, Paolo Fontana, Flavia Maia Moreira, Laura Costantini, M. Stella Grando, Roberto Viola, Riccardo Velasco
Am J Enol Vitic.  June 2008  59: 117-127;  published ahead of print June 02, 2008 ; DOI: 10.5344/ajev.2008.59.2.117

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
You have accessRestricted access
ASEV Honorary Research Lecture 2007
Michela Troggio, Silvia Vezzulli, Massimo Pindo, Giulia Malacarne, Paolo Fontana, Flavia Maia Moreira, Laura Costantini, M. Stella Grando, Roberto Viola, Riccardo Velasco
Am J Enol Vitic.  June 2008  59: 117-127;  published ahead of print June 02, 2008 ; DOI: 10.5344/ajev.2008.59.2.117
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Save to my folders

Jump to section

  • Article
    • Abstract
    • Molecular marker development and genetic mapping.
    • Physical mapping and marker integration.
    • Whole genome sequencing.
    • Candidate gene isolation for modern breeding.
    • Conclusion
    • Footnotes
    • Literature Cited
  • Figures & Data
  • Info & Metrics
  • PDF

Related Articles

Cited By...

More from this TOC section

  • Predicting Berry Quality Attributes in cv. Xarel·lo Rain-Fed Vineyards Using Narrow-Band Reflectance-Based Indices
  • Grapevine Crown Gall Suppression Using Biological Control and Genetic Engineering: A Review of Recent Research
  • Effect of Winery Yeast Lees on Touriga Nacional Red Wine Color and Tannin Evolution
Show more Articles

Similar Articles

AJEV Content

  • Current Volume
  • Archive
  • Best Papers
  • ASEV National Conference Technical Abstracts
  • Collections
  • Free Sample Issue

Information For

  • Authors
  • Open Access/Subscription Publishing
  • Submission
  • Subscribers
  • Permissions and Reproductions
  • Advertisers

Other

  • Home
  • About Us
  • Feedback
  • Help
  • Alerts
  • Catalyst
  • ASEV
asev.org

© 2023 American Society for Enology and Viticulture.  ISSN 0002-9254.

Powered by HighWire